2022
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Nemet, Gregory; Greene, Jenna: Innovation in low-energy demand and its implications for policy. In: Oxford Open Energy, vol. 1, 2022, ISSN: 2752-5082, (oiac003). @article{10.1093/ooenergy/oiac003,
title = {Innovation in low-energy demand and its implications for policy},
author = {Gregory Nemet and Jenna Greene},
url = {https://doi.org/10.1093/ooenergy/oiac003},
doi = {10.1093/ooenergy/oiac003},
issn = {2752-5082},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Oxford Open Energy},
volume = {1},
abstract = {Scenarios featuring low-energy demand have the potential to meet climate stabilization targets while also pursuing inclusive well-being and multiple Sustainable Development Goals. Recent papers have assembled detailed bottom-up estimates to inform integrated assessment modeling and show that LED outcomes are feasible and have beneficial effects across multiple goals. But realizing the full potential in LED depends on innovation -- i.e. improvement in LED technology and widespread adoption of both LED technology and behavior -- as well as policies supporting innovation. We review the literature to better understand the role of technological innovation in enabling LED scenarios and how policy interventions can stimulate those innovations. We structure our review using a policy analysis framework that includes specifying multiple LED policy goals, describing the distinct characteristics of LED technology to understand policy needs. The distinct characteristics of LED innovation include multiple attributes and new services, many heterogeneous adopters, small granular scale, many iterations, local system integration, and rebound effects, among others. We also consider five important drivers of change in LED innovation: higher living standards and preferences for clean environments, urbanization, digitalization, demand for novel services and the emergence of prosumers. The analysis in this review of the literature leads to nine LED policy design guidelines.},
note = {oiac003},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Scenarios featuring low-energy demand have the potential to meet climate stabilization targets while also pursuing inclusive well-being and multiple Sustainable Development Goals. Recent papers have assembled detailed bottom-up estimates to inform integrated assessment modeling and show that LED outcomes are feasible and have beneficial effects across multiple goals. But realizing the full potential in LED depends on innovation -- i.e. improvement in LED technology and widespread adoption of both LED technology and behavior -- as well as policies supporting innovation. We review the literature to better understand the role of technological innovation in enabling LED scenarios and how policy interventions can stimulate those innovations. We structure our review using a policy analysis framework that includes specifying multiple LED policy goals, describing the distinct characteristics of LED technology to understand policy needs. The distinct characteristics of LED innovation include multiple attributes and new services, many heterogeneous adopters, small granular scale, many iterations, local system integration, and rebound effects, among others. We also consider five important drivers of change in LED innovation: higher living standards and preferences for clean environments, urbanization, digitalization, demand for novel services and the emergence of prosumers. The analysis in this review of the literature leads to nine LED policy design guidelines. |
Li, Shihua; Wang, Yingping; Ciais, Philippe; Sitch, Stephen; Sato, Hisashi; Shen, Miaogen; Chen, Xiuzhi; Ito, Akihiko; Wu, Chaoyang; Kucharik, Christopher J.; Yuan, Wenping: Deficiencies of Phenology Models in Simulating Spatial and Temporal Variations in Temperate Spring Leaf Phenology. In: Journal of Geophysical Research: Biogeosciences, vol. 127, no. 3, pp. e2021JG006421, 2022, (e2021JG006421 2021JG006421). @article{https://doi.org/10.1029/2021JG006421,
title = {Deficiencies of Phenology Models in Simulating Spatial and Temporal Variations in Temperate Spring Leaf Phenology},
author = {Shihua Li and Yingping Wang and Philippe Ciais and Stephen Sitch and Hisashi Sato and Miaogen Shen and Xiuzhi Chen and Akihiko Ito and Chaoyang Wu and Christopher J. Kucharik and Wenping Yuan},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021JG006421},
doi = {https://doi.org/10.1029/2021JG006421},
year = {2022},
date = {2022-01-01},
journal = {Journal of Geophysical Research: Biogeosciences},
volume = {127},
number = {3},
pages = {e2021JG006421},
abstract = {Abstract Spring leaf phenology and its response to climate change have crucial effects on surface albedo, carbon balance, and the water cycle of terrestrial ecosystems. Based on long-term (period 1963–2014) in situ observations of budburst date and leaf unfolding date of more than 300 deciduous woody species from 32 sites across the temperate zone in China, we conducted model-data comparison of spatial and temporal variations for spring leaf phenology calculated using the phenology modules that were embed into 10 existing terrestrial ecosystem models. Our results suggested that ORganizing Carbon and Hydrology in Dynamic EcosystEms and Spatially Explicit Individual-Based performed the best in reproducing the spatial patterns of spring leaf phenology, but tended to underestimate the temporal variations in responding to temperature warming, showing low interannual variability (IAV) and temperature sensitivity (ST). In contrast, the performances of Vegetation Integrated SImulator for Trace Gases were the best in modeling IAV and ST. BIOME3, Lund-Potsdam-Jena model, Joint UK Land Environment Simulator, BioGeochemical Cycles, Community Land Model, Integrated Biosphere Simulator, and Commonwealth Scientific and Industrial Research Organisation Atmosphere Biosphere Land Exchange Model failed to reproduce both the spatial and temporal patterns. Using temperature series (1960–2100) form Coupled Model Intercomparison Project Number 6 scenarios to force the 10 phenology modules, our results highlighted large uncertainties in predicting spring leaf phenology changes with the warming climate, and more work is required to deal with the deficiencies of phenology model parameters and algorithms.},
note = {e2021JG006421 2021JG006421},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Spring leaf phenology and its response to climate change have crucial effects on surface albedo, carbon balance, and the water cycle of terrestrial ecosystems. Based on long-term (period 1963–2014) in situ observations of budburst date and leaf unfolding date of more than 300 deciduous woody species from 32 sites across the temperate zone in China, we conducted model-data comparison of spatial and temporal variations for spring leaf phenology calculated using the phenology modules that were embed into 10 existing terrestrial ecosystem models. Our results suggested that ORganizing Carbon and Hydrology in Dynamic EcosystEms and Spatially Explicit Individual-Based performed the best in reproducing the spatial patterns of spring leaf phenology, but tended to underestimate the temporal variations in responding to temperature warming, showing low interannual variability (IAV) and temperature sensitivity (ST). In contrast, the performances of Vegetation Integrated SImulator for Trace Gases were the best in modeling IAV and ST. BIOME3, Lund-Potsdam-Jena model, Joint UK Land Environment Simulator, BioGeochemical Cycles, Community Land Model, Integrated Biosphere Simulator, and Commonwealth Scientific and Industrial Research Organisation Atmosphere Biosphere Land Exchange Model failed to reproduce both the spatial and temporal patterns. Using temperature series (1960–2100) form Coupled Model Intercomparison Project Number 6 scenarios to force the 10 phenology modules, our results highlighted large uncertainties in predicting spring leaf phenology changes with the warming climate, and more work is required to deal with the deficiencies of phenology model parameters and algorithms. |
Sanford, Gregg R.; Jackson, Randall D.; Rui, Yichao; Kucharik, Christopher J.: Land use-land cover gradient demonstrates the importance of perennial grasslands with intact soils for building soil carbon in the fertile Mollisols of the North Central US. In: Geoderma, vol. 418, pp. 115854, 2022, ISSN: 0016-7061. @article{SANFORD2022115854,
title = {Land use-land cover gradient demonstrates the importance of perennial grasslands with intact soils for building soil carbon in the fertile Mollisols of the North Central US},
author = {Gregg R. Sanford and Randall D. Jackson and Yichao Rui and Christopher J. Kucharik},
url = {https://www.sciencedirect.com/science/article/pii/S0016706122001616},
doi = {https://doi.org/10.1016/j.geoderma.2022.115854},
issn = {0016-7061},
year = {2022},
date = {2022-01-01},
journal = {Geoderma},
volume = {418},
pages = {115854},
abstract = {The impact of land use change and agricultural management on the cycling of soil organic carbon (SOC) is not well understood, limiting our ability to manage for, and accurately model, soil carbon changes at both local and regional scales. To address this issue, we combined long-term soil incubations with acid-hydrolysis and dry combustion to parse total SOC (Ct) into three operationally defined SOC pools (active, slow, and recalcitrant) from 9 long-term sites with varying land uses on current and former tallgrass prairie soil. Land uses represented a gradient of soil disturbance histories including remnant prairie, restored prairie, grazed pasture, annual crop rotations, and continuous maize. Dry combustion was used to estimate total carbon (Ct, physical), while acid hydrolysis of both the active (Ca) and slow (Cs) pools was used to estimate a recalcitrant carbon pool (Cr, chemical). Non-linear modeling of CO2 efflux data from the long-term incubations was then used to estimate Ca, and the decomposition rates of both Ca and Cs (ka and kr, biological). The size of the slow pools Cs was then defined mathematically as Ct-(Ca + Cr). Remnant prairie had the highest Ct, while cool-season pasture and a 35-y-old restored prairie had higher Ct than the other agricultural systems. All agricultural systems, including pasture, had the highest fraction of Ct as Cr (∼50%), whose mean residence time (MRT) in these soils is ≥500 years (Paul et al., 2001a) demonstrating that this fraction persists, while the more labile fractions were lost over the course of a few months (Ca) to a few decades (Cs) as a result of tillage-intensive agriculture. The two- to four-decade MRT time of Cs indicated a pool likely to be more responsive to the 20 to 40 years of land-use practices used at some of the sites. The Cs pool was largest in the remnant- and 35-y-old prairies indicating significant C accrual and stabilization compared to the agricultural ecosystems. Interestingly, the remnant prairie maintained the highest Ca pool as well, demonstrating the strong connection between the quantity of fresh C inputs and the potential for long-term C stabilization and accrual. The accumulation of C in active (≈labile) pools as a first step toward long-term stabilization highlights the tenuous nature of early carbon gains, which can be quickly lost in response to climate change or poor management.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The impact of land use change and agricultural management on the cycling of soil organic carbon (SOC) is not well understood, limiting our ability to manage for, and accurately model, soil carbon changes at both local and regional scales. To address this issue, we combined long-term soil incubations with acid-hydrolysis and dry combustion to parse total SOC (Ct) into three operationally defined SOC pools (active, slow, and recalcitrant) from 9 long-term sites with varying land uses on current and former tallgrass prairie soil. Land uses represented a gradient of soil disturbance histories including remnant prairie, restored prairie, grazed pasture, annual crop rotations, and continuous maize. Dry combustion was used to estimate total carbon (Ct, physical), while acid hydrolysis of both the active (Ca) and slow (Cs) pools was used to estimate a recalcitrant carbon pool (Cr, chemical). Non-linear modeling of CO2 efflux data from the long-term incubations was then used to estimate Ca, and the decomposition rates of both Ca and Cs (ka and kr, biological). The size of the slow pools Cs was then defined mathematically as Ct-(Ca + Cr). Remnant prairie had the highest Ct, while cool-season pasture and a 35-y-old restored prairie had higher Ct than the other agricultural systems. All agricultural systems, including pasture, had the highest fraction of Ct as Cr (∼50%), whose mean residence time (MRT) in these soils is ≥500 years (Paul et al., 2001a) demonstrating that this fraction persists, while the more labile fractions were lost over the course of a few months (Ca) to a few decades (Cs) as a result of tillage-intensive agriculture. The two- to four-decade MRT time of Cs indicated a pool likely to be more responsive to the 20 to 40 years of land-use practices used at some of the sites. The Cs pool was largest in the remnant- and 35-y-old prairies indicating significant C accrual and stabilization compared to the agricultural ecosystems. Interestingly, the remnant prairie maintained the highest Ca pool as well, demonstrating the strong connection between the quantity of fresh C inputs and the potential for long-term C stabilization and accrual. The accumulation of C in active (≈labile) pools as a first step toward long-term stabilization highlights the tenuous nature of early carbon gains, which can be quickly lost in response to climate change or poor management. |
Vavrus, Stephen J; Kucharik, Christopher J; He, Feng; Kutzbach, John E; Ruddiman, William F: Did agriculture beget agriculture during the past several millennia?. In: The Holocene, vol. 32, no. 7, pp. 680-689, 2022. @article{doi:10.1177/09596836221088231,
title = {Did agriculture beget agriculture during the past several millennia?},
author = {Stephen J Vavrus and Christopher J Kucharik and Feng He and John E Kutzbach and William F Ruddiman},
url = {https://doi.org/10.1177/09596836221088231},
doi = {10.1177/09596836221088231},
year = {2022},
date = {2022-01-01},
journal = {The Holocene},
volume = {32},
number = {7},
pages = {680-689},
abstract = {The Early Anthropogenic Hypothesis posits that carbon emissions from ancient farming caused global warming by raising greenhouse gas concentrations (GHG) during the late-Holocene, in contrast to declining GHG during prior interglacials. Here, we explore whether this hypothesized pre-industrial anthropogenic climate change also fostered agriculture by creating more favorable growing conditions. We investigate this question using transient GCM experiments and the Cultivation Suitability Index, CSI, which quantifies farming potential based on climatic and soil factors. The Community Earth System Model (CESM) simulated the climate of the last 6000 years under two alternative forcing scenarios: (1) ACTUAL: orbital variations, historical land cover change, and observed GHG increase; and (2) NATURAL: orbital variations, fixed (natural) land cover, and expected natural GHG decline. The CSI was computed using CESM model output and observed soil properties. Ancient land clearance affected the simulated climate both biogeochemically (via carbon emissions) and biogeophysically (altered surface albedo and land-atmosphere energy fluxes). Biogeochemical effects generally dominated, as evidenced by a warmer (and slightly wetter) global climate in ACTUAL versus NATURAL by year 1850. But a few regions were cooler in ACTUAL, especially interior Eurasia during winter-spring, due to a higher surface albedo from cropland. The expansion of agriculture generally mitigated the orbitally induced decline in cultivation potential in boreal extratropics but worsened it in low latitudes. Our results suggest that ancient farming may have thus promoted a “push-pull” migration during the late-Holocene by inducing climate changes that encouraged a northward spread of agriculture.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The Early Anthropogenic Hypothesis posits that carbon emissions from ancient farming caused global warming by raising greenhouse gas concentrations (GHG) during the late-Holocene, in contrast to declining GHG during prior interglacials. Here, we explore whether this hypothesized pre-industrial anthropogenic climate change also fostered agriculture by creating more favorable growing conditions. We investigate this question using transient GCM experiments and the Cultivation Suitability Index, CSI, which quantifies farming potential based on climatic and soil factors. The Community Earth System Model (CESM) simulated the climate of the last 6000 years under two alternative forcing scenarios: (1) ACTUAL: orbital variations, historical land cover change, and observed GHG increase; and (2) NATURAL: orbital variations, fixed (natural) land cover, and expected natural GHG decline. The CSI was computed using CESM model output and observed soil properties. Ancient land clearance affected the simulated climate both biogeochemically (via carbon emissions) and biogeophysically (altered surface albedo and land-atmosphere energy fluxes). Biogeochemical effects generally dominated, as evidenced by a warmer (and slightly wetter) global climate in ACTUAL versus NATURAL by year 1850. But a few regions were cooler in ACTUAL, especially interior Eurasia during winter-spring, due to a higher surface albedo from cropland. The expansion of agriculture generally mitigated the orbitally induced decline in cultivation potential in boreal extratropics but worsened it in low latitudes. Our results suggest that ancient farming may have thus promoted a “push-pull” migration during the late-Holocene by inducing climate changes that encouraged a northward spread of agriculture. |
Heineman, Emily Marrs; Kucharik, Christopher J.: Characterizing Dominant Field-Scale Cropping Sequences for a Potato and Vegetable Growing Region in Central Wisconsin. In: Land, vol. 11, no. 2, 2022, ISSN: 2073-445X. @article{land11020273,
title = {Characterizing Dominant Field-Scale Cropping Sequences for a Potato and Vegetable Growing Region in Central Wisconsin},
author = {Emily Marrs Heineman and Christopher J. Kucharik},
url = {https://www.mdpi.com/2073-445X/11/2/273},
doi = {10.3390/land11020273},
issn = {2073-445X},
year = {2022},
date = {2022-01-01},
journal = {Land},
volume = {11},
number = {2},
abstract = {Crop rotations are known to improve soil health by replenishing lost nutrients, increasing organic matter, improving microbial activity, and reducing disease risk and weed pressure. We characterized the spatial distribution of crops and dominant field-scale cropping sequences from 2008 to 2019 for the Wisconsin Central Sands (WCS) region, a major producer of potato and vegetables in the U.S. The dominant two- and three-year rotations were determined, with an additional focus on assessing regional potato rotation management. Our results suggest corn and soybean are the two most widely planted crops, occurring on 67% and 36% of all agricultural land at least once during the study period. The most frequent two- and three-year crop rotations include corn, soybean, alfalfa, sweet corn, potato, and beans, with continuous corn being the most dominant two- and three-year rotations (13.2% and 8.5% of agricultural land, respectively). While four- and five-year rotations for potato are recommended to combat pest and disease pressure, 23.2% and 65.9% of potato fields returned to that crop in rotation after two and three years, respectively. Furthermore, 5.6% of potato fields were planted continuously with that crop. Given potato’s high nitrogen (N) fertilizer requirements, the prevalence of sandy soils, and ongoing water quality issues, adopting more widespread use of four- or five-year rotations of potato with crops that require zero or less N fertilizer could reduce groundwater nitrate concentrations and improve water quality.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Crop rotations are known to improve soil health by replenishing lost nutrients, increasing organic matter, improving microbial activity, and reducing disease risk and weed pressure. We characterized the spatial distribution of crops and dominant field-scale cropping sequences from 2008 to 2019 for the Wisconsin Central Sands (WCS) region, a major producer of potato and vegetables in the U.S. The dominant two- and three-year rotations were determined, with an additional focus on assessing regional potato rotation management. Our results suggest corn and soybean are the two most widely planted crops, occurring on 67% and 36% of all agricultural land at least once during the study period. The most frequent two- and three-year crop rotations include corn, soybean, alfalfa, sweet corn, potato, and beans, with continuous corn being the most dominant two- and three-year rotations (13.2% and 8.5% of agricultural land, respectively). While four- and five-year rotations for potato are recommended to combat pest and disease pressure, 23.2% and 65.9% of potato fields returned to that crop in rotation after two and three years, respectively. Furthermore, 5.6% of potato fields were planted continuously with that crop. Given potato’s high nitrogen (N) fertilizer requirements, the prevalence of sandy soils, and ongoing water quality issues, adopting more widespread use of four- or five-year rotations of potato with crops that require zero or less N fertilizer could reduce groundwater nitrate concentrations and improve water quality. |
Berg, Elizabeth; Kucharik, Christopher: The Dynamic Relationship between Air and Land Surface Temperature within the Madison, Wisconsin Urban Heat Island. In: Remote Sensing, vol. 14, no. 1, 2022, ISSN: 2072-4292. @article{rs14010165,
title = {The Dynamic Relationship between Air and Land Surface Temperature within the Madison, Wisconsin Urban Heat Island},
author = {Elizabeth Berg and Christopher Kucharik},
url = {https://www.mdpi.com/2072-4292/14/1/165},
doi = {10.3390/rs14010165},
issn = {2072-4292},
year = {2022},
date = {2022-01-01},
journal = {Remote Sensing},
volume = {14},
number = {1},
abstract = {The urban heat island (UHI) effect, the phenomenon by which cities are warmer than rural surroundings, is increasingly important in a rapidly urbanizing and warming world, but fine-scale differences in temperature within cities are difficult to observe accurately. Networks of air temperature (Tair) sensors rarely offer the spatial density needed to capture neighborhood-level disparities in warming, while satellite measures of land surface temperature (LST) do not reflect the air temperatures that people physically experience. This analysis combines both Tair measurements recorded by a spatially-dense stationary sensor network in Dane County, Wisconsin, and remotely-sensed measurements of LST over the same area—to improve the use and interpretation of LST in UHI studies. The data analyzed span three summer months (June, July, and August) and eight years (2012-2019). Overall, Tair and LST displayed greater agreement in spatial distribution than in magnitude. The relationship between day of the year and correlation was fit to a parabolic curve (R2 = 0.76},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The urban heat island (UHI) effect, the phenomenon by which cities are warmer than rural surroundings, is increasingly important in a rapidly urbanizing and warming world, but fine-scale differences in temperature within cities are difficult to observe accurately. Networks of air temperature (Tair) sensors rarely offer the spatial density needed to capture neighborhood-level disparities in warming, while satellite measures of land surface temperature (LST) do not reflect the air temperatures that people physically experience. This analysis combines both Tair measurements recorded by a spatially-dense stationary sensor network in Dane County, Wisconsin, and remotely-sensed measurements of LST over the same area—to improve the use and interpretation of LST in UHI studies. The data analyzed span three summer months (June, July, and August) and eight years (2012–2019). Overall, Tair and LST displayed greater agreement in spatial distribution than in magnitude. The relationship between day of the year and correlation was fit to a parabolic curve (R2 = 0.76 |
Turlej, Contributors: Konrad; Ozdogan, Mutlu; Radeloff, Volker C.: Mapping forest types over large areas with Landsat imagery partially affected by clouds and SLC gaps. In: International Journal of Applied Earth Observation and Geoinformation, vol. 107, pp. 102689, 2022, ISSN: 1569-8432. @article{KONRADTURLEJ2022102689,
title = {Mapping forest types over large areas with Landsat imagery partially affected by clouds and SLC gaps},
author = {Contributors: Konrad Turlej and Mutlu Ozdogan and Volker C. Radeloff},
url = {https://www.sciencedirect.com/science/article/pii/S0303243422000150},
doi = {https://doi.org/10.1016/j.jag.2022.102689},
issn = {1569-8432},
year = {2022},
date = {2022-01-01},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {107},
pages = {102689},
abstract = {The ecosystem services that forests provide depend on tree species composition. Therefore, it is important to map not only forest extent and its dynamics, but also composition. Open access to Landsat has resulted in considerable improvements in remote sensing methods for mapping tree species, but most approaches fail to perform when there is a shortage of clear observations. Our main goal was to map forest composition with Landsat imagery in various data availability conditions, and to investigate how the missing data, either due to clouds or scan line problems affect classification accuracy. We tested a data driven approach that is based on multi-temporal analysis of the tree species’ spectral characteristics making it applicable to regional-scale mapping even when the gap-free imagery is not available. Our study area consisted of one Landsat footprint (26/28) located in Northern Wisconsin, USA. We selected this area because of numerous tree species (23), heterogenic composition of forests where the majority of stands are mixed, and availability of high-quality reference data. We quantified how classification accuracy at the species level was affected by a) the amount of missing data due to cloud cover and Scanning Line Corrector (SLC) gaps, b) the number of acquisitions, and c) the seasonal availability of images. We applied a decision tree classifier, capable of handling missing data to both single- and a three-year Landsat-7 and Landsat-8 observations. We classified the dominant tree species in each pixel and grouped results to forest stands to match our reference data. Our results show four major findings. First, producer’s and user’s accuracies range from 46.2% to 96.2% and from 59.9% to 93.7%, respectively for the most abundant forest types in the study area (all types covering greater than 2% of the forest area). Second, all tree species were mapped with overall accuracy above 70% even in when we restricted our data set to images having gaps larger than 30% of the study area. Third, the classification accuracy improved with more acquisitions, especially when images were available for the fall, spring, and summer. Finally, producer’s accuracies for pure-stands were higher than those for mixed stands by 10 to 30 percentage points. We conclude that inclusion of Landsat imagery with missing data allows to map forest types with accuracies that previously could be achieved only for those rare years for which several gap-free images were available. The approach presented here is directly applicable to Landsat-like observations and derived products such as seasonal composites and temporal statistics that miss 30% or more of the data for any single date to develop forest composition maps that are important for both forest management and ecology.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The ecosystem services that forests provide depend on tree species composition. Therefore, it is important to map not only forest extent and its dynamics, but also composition. Open access to Landsat has resulted in considerable improvements in remote sensing methods for mapping tree species, but most approaches fail to perform when there is a shortage of clear observations. Our main goal was to map forest composition with Landsat imagery in various data availability conditions, and to investigate how the missing data, either due to clouds or scan line problems affect classification accuracy. We tested a data driven approach that is based on multi-temporal analysis of the tree species’ spectral characteristics making it applicable to regional-scale mapping even when the gap-free imagery is not available. Our study area consisted of one Landsat footprint (26/28) located in Northern Wisconsin, USA. We selected this area because of numerous tree species (23), heterogenic composition of forests where the majority of stands are mixed, and availability of high-quality reference data. We quantified how classification accuracy at the species level was affected by a) the amount of missing data due to cloud cover and Scanning Line Corrector (SLC) gaps, b) the number of acquisitions, and c) the seasonal availability of images. We applied a decision tree classifier, capable of handling missing data to both single- and a three-year Landsat-7 and Landsat-8 observations. We classified the dominant tree species in each pixel and grouped results to forest stands to match our reference data. Our results show four major findings. First, producer’s and user’s accuracies range from 46.2% to 96.2% and from 59.9% to 93.7%, respectively for the most abundant forest types in the study area (all types covering greater than 2% of the forest area). Second, all tree species were mapped with overall accuracy above 70% even in when we restricted our data set to images having gaps larger than 30% of the study area. Third, the classification accuracy improved with more acquisitions, especially when images were available for the fall, spring, and summer. Finally, producer’s accuracies for pure-stands were higher than those for mixed stands by 10 to 30 percentage points. We conclude that inclusion of Landsat imagery with missing data allows to map forest types with accuracies that previously could be achieved only for those rare years for which several gap-free images were available. The approach presented here is directly applicable to Landsat-like observations and derived products such as seasonal composites and temporal statistics that miss 30% or more of the data for any single date to develop forest composition maps that are important for both forest management and ecology. |
Mailloux, Nicholas A.; Abel, David W.; Holloway, Tracey; Patz, Jonathan A.: Nationwide and Regional PM2.5‐Related Air Quality Health Benefits From the Removal of Energy‐Related Emissions in the United States. In: GeoHealth, vol. 6, no. 5, pp. e2022GH000603, 2022. @article{https://doi.org/10.1029/2022GH000603,
title = {Nationwide and Regional PM2.5‐Related Air Quality Health Benefits From the Removal of Energy‐Related Emissions in the United States},
author = {Nicholas A. Mailloux and David W. Abel and Tracey Holloway and Jonathan A. Patz},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022GH000603},
doi = {https://doi.org/10.1029/2022GH000603},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {GeoHealth},
volume = {6},
number = {5},
pages = {e2022GH000603},
abstract = {Abstract Clean energy policy can provide substantial health benefits through improved air quality. As ambitious clean energy proposals are increasingly considered and adopted across the United States (US), quantifying the benefits of removal of such large air pollution emissions sources is crucial to understanding potential societal impacts of such policy. In this study, we estimate health benefits resulting from the elimination of emissions of fine particulate matter (PM2.5), sulfur dioxide, and nitrogen oxides from the electric power, transportation, building, and industrial sectors in the contiguous US. We use EPA's CO-Benefits Risk Assessment screening tool to estimate health benefits resulting from the removal of PM2.5-related emissions from these energy-related sectors. We find that nationwide efforts to eliminate energy-related emissions could prevent 53,200 (95% CI: 46,900–59,400) premature deaths each year and provide $608 billion ($537–$678 billion) in benefits from avoided PM2.5-related illness and death. We also find that an average of 69% (range: 32%–95%) of the health benefits from emissions removal remain in the emitting region. Our study provides an indication of the potential scale and distribution of public health benefits that could result from ambitious regional and nationwide clean energy and climate mitigation policy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Clean energy policy can provide substantial health benefits through improved air quality. As ambitious clean energy proposals are increasingly considered and adopted across the United States (US), quantifying the benefits of removal of such large air pollution emissions sources is crucial to understanding potential societal impacts of such policy. In this study, we estimate health benefits resulting from the elimination of emissions of fine particulate matter (PM2.5), sulfur dioxide, and nitrogen oxides from the electric power, transportation, building, and industrial sectors in the contiguous US. We use EPA's CO-Benefits Risk Assessment screening tool to estimate health benefits resulting from the removal of PM2.5-related emissions from these energy-related sectors. We find that nationwide efforts to eliminate energy-related emissions could prevent 53,200 (95% CI: 46,900–59,400) premature deaths each year and provide $608 billion ($537–$678 billion) in benefits from avoided PM2.5-related illness and death. We also find that an average of 69% (range: 32%–95%) of the health benefits from emissions removal remain in the emitting region. Our study provides an indication of the potential scale and distribution of public health benefits that could result from ambitious regional and nationwide clean energy and climate mitigation policy. |
Simane, Belay; Kumie, Abera; Berhane, Kiros; Samet, Jonathan; Kjellstrom, Tord; Patz, Jonathan: Occupational Heat Stress in the Floriculture Industry of Ethiopia: Health Risks and Productivity Losses. In: Health, vol. 14, pp. 254-271, 2022. @article{articleb,
title = {Occupational Heat Stress in the Floriculture Industry of Ethiopia: Health Risks and Productivity Losses},
author = {Belay Simane and Abera Kumie and Kiros Berhane and Jonathan Samet and Tord Kjellstrom and Jonathan Patz},
doi = {10.4236/health.2022.142020},
year = {2022},
date = {2022-01-01},
journal = {Health},
volume = {14},
pages = {254-271},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Reaser, Jamie K.; Hunt, Brooklin E.; Ruiz-Aravena, Manuel; Tabor, Gary M.; Patz, Jonathan A.; Becker, Daniel J.; Locke, Harvey; Hudson, Peter J.; Plowright, Raina K.: Fostering landscape immunity to protect human health: A science-based rationale for shifting conservation policy paradigms. In: Conservation Letters, vol. 15, no. 3, pp. e12869, 2022. @article{https://doi.org/10.1111/conl.12869,
title = {Fostering landscape immunity to protect human health: A science-based rationale for shifting conservation policy paradigms},
author = {Jamie K. Reaser and Brooklin E. Hunt and Manuel Ruiz-Aravena and Gary M. Tabor and Jonathan A. Patz and Daniel J. Becker and Harvey Locke and Peter J. Hudson and Raina K. Plowright},
url = {https://conbio.onlinelibrary.wiley.com/doi/abs/10.1111/conl.12869},
doi = {https://doi.org/10.1111/conl.12869},
year = {2022},
date = {2022-01-01},
journal = {Conservation Letters},
volume = {15},
number = {3},
pages = {e12869},
abstract = {Abstract Anthropogenic land use change is a major driver of zoonotic pathogen spillover from wildlife to humans. According to the land use-induced spillover model, land use change alters environmental conditions that in turn alter the dynamics between zoonotic pathogens and their wildlife hosts. Thus, in response to the global spread of the SARS-CoV-2 virus (the agent of COVID-19 disease), there have been renewed calls for landscape conservation as a disease preventive measure, including by the G7 Ministers responsible for Climate and the Environment. Landscape immunity, as a new construct, points to four paradigm shifts the world must favor to effectively mitigate pandemic risks. We provide a landscape immunity primer for policy makers and make the case for “world views” that place Homo sapiens within ecological systems, regard human health as an ecological service, prioritize investments in prevention, and apply ecological restoration to human health goals. Crisis is a conversation starter for reimagining and recommitting ourselves to what is most vital and generative. We urge world leaders to make the move to a nature-positive world.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Anthropogenic land use change is a major driver of zoonotic pathogen spillover from wildlife to humans. According to the land use-induced spillover model, land use change alters environmental conditions that in turn alter the dynamics between zoonotic pathogens and their wildlife hosts. Thus, in response to the global spread of the SARS-CoV-2 virus (the agent of COVID-19 disease), there have been renewed calls for landscape conservation as a disease preventive measure, including by the G7 Ministers responsible for Climate and the Environment. Landscape immunity, as a new construct, points to four paradigm shifts the world must favor to effectively mitigate pandemic risks. We provide a landscape immunity primer for policy makers and make the case for “world views” that place Homo sapiens within ecological systems, regard human health as an ecological service, prioritize investments in prevention, and apply ecological restoration to human health goals. Crisis is a conversation starter for reimagining and recommitting ourselves to what is most vital and generative. We urge world leaders to make the move to a nature-positive world. |
Sun, Zhongxiao; Scherer, Laura; Tukker, Arnold; Spawn-Lee, Seth A.; Bruckner, Martin; Gibbs, Holly K.; Behrens, Paul: Dietary change in high-income nations alone can lead to substantial double climate dividend. In: Nature Food, vol. 3, no. 1, pp. 29-37, 2022, ISSN: 2662-1355. @article{Sun2022,
title = {Dietary change in high-income nations alone can lead to substantial double climate dividend},
author = {Zhongxiao Sun and Laura Scherer and Arnold Tukker and Seth A. Spawn-Lee and Martin Bruckner and Holly K. Gibbs and Paul Behrens},
url = {https://doi.org/10.1038/s43016-021-00431-5},
doi = {10.1038/s43016-021-00431-5},
issn = {2662-1355},
year = {2022},
date = {2022-01-01},
journal = {Nature Food},
volume = {3},
number = {1},
pages = {29-37},
abstract = {A dietary shift from animal-based foods to plant-based foods in high-income nations could reduce greenhouse gas emissions from direct agricultural production and increase carbon sequestration if resulting spared land was restored to its antecedent natural vegetation. We estimate this double effect by simulating the adoption of the EAT--Lancet planetary health diet by 54 high-income nations representing 68% of global gross domestic product and 17% of population. Our results show that such dietary change could reduce annual agricultural production emissions of high-income nations' diets by 61% while sequestering as much as 98.3 (55.6--143.7)thinspaceGtCO2 equivalent, equal to approximately 14 years of current global agricultural emissions until natural vegetation matures. This amount could potentially fulfil high-income nations' future sum of carbon dioxide removal (CDR) obligations under the principle of equal per capita CDR responsibilities. Linking land, food, climate and public health policy will be vital to harnessing the opportunities of a double climate dividend.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A dietary shift from animal-based foods to plant-based foods in high-income nations could reduce greenhouse gas emissions from direct agricultural production and increase carbon sequestration if resulting spared land was restored to its antecedent natural vegetation. We estimate this double effect by simulating the adoption of the EAT--Lancet planetary health diet by 54 high-income nations representing 68% of global gross domestic product and 17% of population. Our results show that such dietary change could reduce annual agricultural production emissions of high-income nations' diets by 61% while sequestering as much as 98.3 (55.6--143.7)thinspaceGtCO2 equivalent, equal to approximately 14 years of current global agricultural emissions until natural vegetation matures. This amount could potentially fulfil high-income nations' future sum of carbon dioxide removal (CDR) obligations under the principle of equal per capita CDR responsibilities. Linking land, food, climate and public health policy will be vital to harnessing the opportunities of a double climate dividend. |
Noon, Monica L.; Goldstein, Allie; Ledezma, Juan Carlos; Roehrdanz, Patrick R.; Cook-Patton, Susan C.; Spawn-Lee, Seth A.; Wright, Timothy Maxwell; Gonzalez-Roglich, Mariano; Hole, David G.; Rockström, Johan; Turner, Will R.: Mapping the irrecoverable carbon in Earth's ecosystems. In: Nature Sustainability, vol. 5, no. 1, pp. 37-46, 2022, ISSN: 2398-9629. @article{Noon2022,
title = {Mapping the irrecoverable carbon in Earth's ecosystems},
author = {Monica L. Noon and Allie Goldstein and Juan Carlos Ledezma and Patrick R. Roehrdanz and Susan C. Cook-Patton and Seth A. Spawn-Lee and Timothy Maxwell Wright and Mariano Gonzalez-Roglich and David G. Hole and Johan Rockström and Will R. Turner},
url = {https://doi.org/10.1038/s41893-021-00803-6},
doi = {10.1038/s41893-021-00803-6},
issn = {2398-9629},
year = {2022},
date = {2022-01-01},
journal = {Nature Sustainability},
volume = {5},
number = {1},
pages = {37-46},
abstract = {Avoiding catastrophic climate change requires rapid decarbonization and improved ecosystem stewardship at a planetary scale. The carbon released through the burning of fossil fuels would take millennia to regenerate on Earth. Though the timeframe of carbon recovery for ecosystems such as peatlands, mangroves and old-growth forests is shorter (centuries), this timeframe still exceeds the time we have remaining to avoid the worst impacts of global warming. There are some natural places that we cannot afford to lose due to their irreplaceable carbon reserves. Here we map ‘irrecoverable carbon’ globally to identify ecosystem carbon that remains within human purview to manage and, if lost, could not be recovered by mid-century, by when we need to reach net-zero emissions to avoid the worst climate impacts. Since 2010, agriculture, logging and wildfire have caused emissions of at least 4.0 Gt of irrecoverable carbon. The world’s remaining 139.1 ± 443.6 Gt of irrecoverable carbon faces risks from land-use conversion and climate change. These risks can be reduced through proactive protection and adaptive management. Currently, 23.0% of irrecoverable carbon is within protected areas and 33.6% is managed by Indigenous peoples and local communities. Half of Earth’s irrecoverable carbon is concentrated on just 3.3% of its land, highlighting opportunities for targeted efforts to increase global climate security.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Avoiding catastrophic climate change requires rapid decarbonization and improved ecosystem stewardship at a planetary scale. The carbon released through the burning of fossil fuels would take millennia to regenerate on Earth. Though the timeframe of carbon recovery for ecosystems such as peatlands, mangroves and old-growth forests is shorter (centuries), this timeframe still exceeds the time we have remaining to avoid the worst impacts of global warming. There are some natural places that we cannot afford to lose due to their irreplaceable carbon reserves. Here we map ‘irrecoverable carbon’ globally to identify ecosystem carbon that remains within human purview to manage and, if lost, could not be recovered by mid-century, by when we need to reach net-zero emissions to avoid the worst climate impacts. Since 2010, agriculture, logging and wildfire have caused emissions of at least 4.0 Gt of irrecoverable carbon. The world’s remaining 139.1 ± 443.6 Gt of irrecoverable carbon faces risks from land-use conversion and climate change. These risks can be reduced through proactive protection and adaptive management. Currently, 23.0% of irrecoverable carbon is within protected areas and 33.6% is managed by Indigenous peoples and local communities. Half of Earth’s irrecoverable carbon is concentrated on just 3.3% of its land, highlighting opportunities for targeted efforts to increase global climate security. |
Wang, Peidong; Holloway, Tracey; Bindl, Matilyn; Harkey, Monica; Smedt, Isabelle: Ambient Formaldehyde over the United States from Ground-Based (AQS) and Satellite (OMI) Observations. In: Remote Sensing, vol. 14, pp. 2191, 2022. @article{articlec,
title = {Ambient Formaldehyde over the United States from Ground-Based (AQS) and Satellite (OMI) Observations},
author = {Peidong Wang and Tracey Holloway and Matilyn Bindl and Monica Harkey and Isabelle Smedt},
doi = {10.3390/rs14092191},
year = {2022},
date = {2022-01-01},
journal = {Remote Sensing},
volume = {14},
pages = {2191},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Goldberg, D.; Harkey, M.; Foy, B.; Judd, L.; Johnson, J.; Yarwood, G.; Holloway, T.: Evaluating NOx emissions and their effect on O3 production in Texas using TROPOMI NO2 and HCHO. In: Atmospheric Chemistry and Physics Discussions, vol. 2022, pp. 1-33, 2022. @article{acp-2022-299,
title = {Evaluating NOx emissions and their effect on O3 production in Texas using TROPOMI NO2 and HCHO},
author = {D. Goldberg and M. Harkey and B. Foy and L. Judd and J. Johnson and G. Yarwood and T. Holloway},
url = {https://acp.copernicus.org/preprints/acp-2022-299/},
doi = {10.5194/acp-2022-299},
year = {2022},
date = {2022-01-01},
journal = {Atmospheric Chemistry and Physics Discussions},
volume = {2022},
pages = {1-33},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Booth, Eric G.; Kucharik, Christopher J.: Data inaccessibility at sub-county scale limits implementation of manuresheds. In: Journal of Environmental Quality, vol. 51, no. 4, pp. 614-621, 2022. @article{https://doi.org/10.1002/jeq2.20271,
title = {Data inaccessibility at sub-county scale limits implementation of manuresheds},
author = {Eric G. Booth and Christopher J. Kucharik},
url = {https://acsess.onlinelibrary.wiley.com/doi/abs/10.1002/jeq2.20271},
doi = {https://doi.org/10.1002/jeq2.20271},
year = {2022},
date = {2022-01-01},
journal = {Journal of Environmental Quality},
volume = {51},
number = {4},
pages = {614-621},
abstract = {Abstract The manureshed concept aims to rebalance surplus manure nutrients produced at animal feeding operations (sources) and the demands from nutrient-deficient croplands (sinks) to reduce negative environmental impacts and utilize nutrients more efficiently. Due to water quality implications, studies focused on this rebalancing have typically created domain boundaries that match a particular watershed. However, a majority of agricultural datasets that are used to inform these analyses—specifically, livestock populations—are only available at the county scale, which generally does not match watershed boundaries. The common method used to address this mismatch is to weight the county statistics based on the proportion of watershed area within the county. However, these straightforward assumptions imply that animal density is uniform across a county, which can be highly problematic, especially in an era of increasing concentration of livestock production on a smaller land area. We present a case study of the Lake Mendota watershed in south-central Wisconsin using both a typical county-based downscaled dataset as well as a more spatially explicit dataset of livestock counts from the Census of Agriculture that aggregates a set of zip codes that best matches the watershed boundary. This comparison reveals a substantial difference in estimated livestock numbers and their associated manure production that is due to a concentration of dairy operations within the watershed compared with the rest of the county. We argue that sub-county scale data need to become more available and integrated into nutrient and water quality management efforts so that manuresheds can be more effectively delineated and implemented.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract The manureshed concept aims to rebalance surplus manure nutrients produced at animal feeding operations (sources) and the demands from nutrient-deficient croplands (sinks) to reduce negative environmental impacts and utilize nutrients more efficiently. Due to water quality implications, studies focused on this rebalancing have typically created domain boundaries that match a particular watershed. However, a majority of agricultural datasets that are used to inform these analyses—specifically, livestock populations—are only available at the county scale, which generally does not match watershed boundaries. The common method used to address this mismatch is to weight the county statistics based on the proportion of watershed area within the county. However, these straightforward assumptions imply that animal density is uniform across a county, which can be highly problematic, especially in an era of increasing concentration of livestock production on a smaller land area. We present a case study of the Lake Mendota watershed in south-central Wisconsin using both a typical county-based downscaled dataset as well as a more spatially explicit dataset of livestock counts from the Census of Agriculture that aggregates a set of zip codes that best matches the watershed boundary. This comparison reveals a substantial difference in estimated livestock numbers and their associated manure production that is due to a concentration of dairy operations within the watershed compared with the rest of the county. We argue that sub-county scale data need to become more available and integrated into nutrient and water quality management efforts so that manuresheds can be more effectively delineated and implemented. |
2021
|
Schelly, Ian; Rausch, Lisa L.; Gibbs, Holly K.: Brazil’s Cattle Sector Played Large Role in Fires During 2020 Moratorium. In: Frontiers in Forests and Global Change, pp. 183, 2021. @article{nokey,
title = {Brazil’s Cattle Sector Played Large Role in Fires During 2020 Moratorium},
author = {Ian Schelly and Lisa L. Rausch and Holly K. Gibbs},
url = {https://www.frontiersin.org/articles/10.3389/ffgc.2021.760853/full},
year = {2021},
date = {2021-11-25},
journal = {Frontiers in Forests and Global Change},
pages = {183},
abstract = {In response to global concern about recent fires, Brazil placed a 120-day moratorium on burning in the Amazon in 2020. We assessed how the cattle sector was linked to these fires by estimating the number of cattle properties involved with fires despite the moratorium, and their roles in cattle supply chains. We examined the land cover prior to the fire to identify instances of fires associated with recent deforestation. Our results show that the cattle sector contributed disproportionately to the fires in 2020, in terms of both the number of properties involved and the area burned. Improvements in both supply chain policies and overall environmental governance in the Amazon are likely needed to avoid recurrences of the catastrophic scale of fires during the 2020 season.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In response to global concern about recent fires, Brazil placed a 120-day moratorium on burning in the Amazon in 2020. We assessed how the cattle sector was linked to these fires by estimating the number of cattle properties involved with fires despite the moratorium, and their roles in cattle supply chains. We examined the land cover prior to the fire to identify instances of fires associated with recent deforestation. Our results show that the cattle sector contributed disproportionately to the fires in 2020, in terms of both the number of properties involved and the area burned. Improvements in both supply chain policies and overall environmental governance in the Amazon are likely needed to avoid recurrences of the catastrophic scale of fires during the 2020 season. |
Spawn-Lee, Seth A; Lark, Tyler J; Gibbs, Holly K; Houghton, Richard A; Kucharik, Christopher J; Malins, Chris; Pelton, Rylie E O; Robertson, G Philip: Comment on `Carbon Intensity of corn ethanol in the United States: state of the science'. In: Environmental Research Letters, vol. 16, no. 11, pp. 118001, 2021. @article{Spawn_Lee_2021,
title = {Comment on `Carbon Intensity of corn ethanol in the United States: state of the science'},
author = {Seth A Spawn-Lee and Tyler J Lark and Holly K Gibbs and Richard A Houghton and Christopher J Kucharik and Chris Malins and Rylie E O Pelton and G Philip Robertson},
url = {https://doi.org/10.1088/1748-9326/ac2e35},
doi = {10.1088/1748-9326/ac2e35},
year = {2021},
date = {2021-11-01},
journal = {Environmental Research Letters},
volume = {16},
number = {11},
pages = {118001},
publisher = {IOP Publishing},
abstract = {In their recent contribution, Scully et al (2021 Environ. Res. Lett. 16 043001) review and revise past life cycle assessments of corn-grain ethanol’s carbon (C) intensity to suggest that a current ‘central best estimate’ is considerably less than all prior estimates. Their conclusion emerges from selection and recombination of sector-specific greenhouse gas emission predictions from disparate studies in a way that disproportionately favors small values and optimistic assumptions without rigorous justification nor empirical support. Their revisions most profoundly reduce predicted land use change (LUC) emissions, for which they propose a central estimate that is roughly half the smallest comparable value they review (figure 1). This LUC estimate represents the midpoint of (a) values retained after filtering the predictions of past studies based on a set of unfounded criteria; and (b) a new estimate they generate for domestic (i.e. U.S.) LUC emissions. The filter the authors apply endorses a singular means of LUC assessment which they assert as the ‘best practice’ despite a recent unacknowledged review (Malins et al 2020 J. Clean. Prod. 258 120716) that shows this method almost certainly underestimates LUC. Moreover, their domestic C intensity estimate surprisingly suggests that cropland expansion newly sequesters soil C, counter to ecological theory and empirical evidence. These issues, among others, prove to grossly underestimate the C intensity of corn-grain ethanol and mischaracterize the state of our science at the risk of perversely affecting policy outcomes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In their recent contribution, Scully et al (2021 Environ. Res. Lett. 16 043001) review and revise past life cycle assessments of corn-grain ethanol’s carbon (C) intensity to suggest that a current ‘central best estimate’ is considerably less than all prior estimates. Their conclusion emerges from selection and recombination of sector-specific greenhouse gas emission predictions from disparate studies in a way that disproportionately favors small values and optimistic assumptions without rigorous justification nor empirical support. Their revisions most profoundly reduce predicted land use change (LUC) emissions, for which they propose a central estimate that is roughly half the smallest comparable value they review (figure 1). This LUC estimate represents the midpoint of (a) values retained after filtering the predictions of past studies based on a set of unfounded criteria; and (b) a new estimate they generate for domestic (i.e. U.S.) LUC emissions. The filter the authors apply endorses a singular means of LUC assessment which they assert as the ‘best practice’ despite a recent unacknowledged review (Malins et al 2020 J. Clean. Prod. 258 120716) that shows this method almost certainly underestimates LUC. Moreover, their domestic C intensity estimate surprisingly suggests that cropland expansion newly sequesters soil C, counter to ecological theory and empirical evidence. These issues, among others, prove to grossly underestimate the C intensity of corn-grain ethanol and mischaracterize the state of our science at the risk of perversely affecting policy outcomes. |
Pelton, Rylie E O; Spawn-Lee, Seth A; Lark, Tyler J; Kim, Taegon; Springer, Nathaniel; Hawthorne, Peter; Ray, Deepak K; Schmitt, Jennifer: Land use leverage points to reduce GHG emissions in U.S. agricultural supply chains. In: Environmental Research Letters, vol. 16, no. 11, pp. 115002, 2021. @article{Pelton_2021,
title = {Land use leverage points to reduce GHG emissions in U.S. agricultural supply chains},
author = {Rylie E O Pelton and Seth A Spawn-Lee and Tyler J Lark and Taegon Kim and Nathaniel Springer and Peter Hawthorne and Deepak K Ray and Jennifer Schmitt},
url = {https://doi.org/10.1088/1748-9326/ac2775},
doi = {10.1088/1748-9326/ac2775},
year = {2021},
date = {2021-10-01},
journal = {Environmental Research Letters},
volume = {16},
number = {11},
pages = {115002},
publisher = {IOP Publishing},
abstract = {Recognizing the substantial threats climate change poses to agricultural supply chains, companies around the world are committing to reducing greenhouse gas (GHG) emissions. Recent modeling advances have increased the transparency of meat and ethanol industry supply chains, where conventional production practices and associated environmental impacts have been characterized and linked to downstream points of demand. Yet, to date, information and efforts have neglected both the spatial variability of production impacts and land use changes (LUCs) across highly heterogeneous agricultural landscapes. Developing effective mitigation programs and policies requires understanding these spatially-explicit hotspots for targeting GHG mitigation efforts and the links to downstream supply chain actors. Here we integrate, for the first time, spatial estimates of county-scale production practices and observations of direct LUC into company and industry-specific supply chains of beef, pork, chicken, ethanol, soy oil and wheat flour in the U.S., thereby conceptually changing our understanding of the sources, magnitudes and influencers of agricultural GHG emissions. We find that accounting for LUC can increase estimated feedstock emissions per unit of production by a factor of 2- to 5-times that of traditionally used estimates. Substantial variation across companies, sectors, and production regions reveal key opportunities to improve GHG footprints by reducing land conversion within their supply chains.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recognizing the substantial threats climate change poses to agricultural supply chains, companies around the world are committing to reducing greenhouse gas (GHG) emissions. Recent modeling advances have increased the transparency of meat and ethanol industry supply chains, where conventional production practices and associated environmental impacts have been characterized and linked to downstream points of demand. Yet, to date, information and efforts have neglected both the spatial variability of production impacts and land use changes (LUCs) across highly heterogeneous agricultural landscapes. Developing effective mitigation programs and policies requires understanding these spatially-explicit hotspots for targeting GHG mitigation efforts and the links to downstream supply chain actors. Here we integrate, for the first time, spatial estimates of county-scale production practices and observations of direct LUC into company and industry-specific supply chains of beef, pork, chicken, ethanol, soy oil and wheat flour in the U.S., thereby conceptually changing our understanding of the sources, magnitudes and influencers of agricultural GHG emissions. We find that accounting for LUC can increase estimated feedstock emissions per unit of production by a factor of 2- to 5-times that of traditionally used estimates. Substantial variation across companies, sectors, and production regions reveal key opportunities to improve GHG footprints by reducing land conversion within their supply chains. |
Diluiso, Francesca; et al,: Coal transitions—part 1: a systematic map and review of case study learnings from regional, national, and local coal phase-out experiences. In: Environmental Research Letters, vol. 16, no. 11, pp. 113003, 2021, (authors include Nemet). @article{Diluiso_2021,
title = {Coal transitions—part 1: a systematic map and review of case study learnings from regional, national, and local coal phase-out experiences},
author = {Francesca Diluiso and et al},
url = {https://doi.org/10.1088/1748-9326/ac1b58},
doi = {10.1088/1748-9326/ac1b58},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
journal = {Environmental Research Letters},
volume = {16},
number = {11},
pages = {113003},
publisher = {IOP Publishing},
abstract = {A rapid coal phase-out is needed to meet the goals of the Paris Agreement, but is hindered by serious challenges ranging from vested interests to the risks of social disruption. To understand how to organize a global coal phase-out, it is crucial to go beyond cost-effective climate mitigation scenarios and learn from the experience of previous coal transitions. Despite the relevance of the topic, evidence remains fragmented throughout different research fields, and not easily accessible. To address this gap, this paper provides a systematic map and comprehensive review of the literature on historical coal transitions. We use computer-assisted systematic mapping and review methods to chart and evaluate the available evidence on historical declines in coal production and consumption. We extracted a dataset of 278 case studies from 194 publications, covering coal transitions in 44 countries and ranging from the end of the 19th century until 2021. We find a relatively recent and rapidly expanding body of literature reflecting the growing importance of an early coal phase-out in scientific and political debates. Previous evidence has primarily focused on the United Kingdom, the United States, and Germany, while other countries that experienced large coal declines, like those in Eastern Europe, are strongly underrepresented. An increasing number of studies, mostly published in the last 5 years, has been focusing on China. Most of the countries successfully reducing coal dependency have undergone both demand-side and supply-side transitions. This supports the use of policy approaches targeting both demand and supply to achieve a complete coal phase-out. From a political economy perspective, our dataset highlights that most transitions are driven by rising production costs for coal, falling prices for alternative energies, or local environmental concerns, especially regarding air pollution. The main challenges for coal-dependent regions are structural change transformations, in particular for industry and labor. Rising unemployment is the most largely documented outcome in the sample. Policymakers at multiple levels are instrumental in facilitating coal transitions. They rely mainly on regulatory instruments to foster the transitions and compensation schemes or investment plans to deal with their transformative processes. Even though many models suggest that coal phase-outs are among the low-hanging fruits on the way to climate neutrality and meeting the international climate goals, our case studies analysis highlights the intricate political economy at work that needs to be addressed through well-designed and just policies.},
note = {authors include Nemet},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A rapid coal phase-out is needed to meet the goals of the Paris Agreement, but is hindered by serious challenges ranging from vested interests to the risks of social disruption. To understand how to organize a global coal phase-out, it is crucial to go beyond cost-effective climate mitigation scenarios and learn from the experience of previous coal transitions. Despite the relevance of the topic, evidence remains fragmented throughout different research fields, and not easily accessible. To address this gap, this paper provides a systematic map and comprehensive review of the literature on historical coal transitions. We use computer-assisted systematic mapping and review methods to chart and evaluate the available evidence on historical declines in coal production and consumption. We extracted a dataset of 278 case studies from 194 publications, covering coal transitions in 44 countries and ranging from the end of the 19th century until 2021. We find a relatively recent and rapidly expanding body of literature reflecting the growing importance of an early coal phase-out in scientific and political debates. Previous evidence has primarily focused on the United Kingdom, the United States, and Germany, while other countries that experienced large coal declines, like those in Eastern Europe, are strongly underrepresented. An increasing number of studies, mostly published in the last 5 years, has been focusing on China. Most of the countries successfully reducing coal dependency have undergone both demand-side and supply-side transitions. This supports the use of policy approaches targeting both demand and supply to achieve a complete coal phase-out. From a political economy perspective, our dataset highlights that most transitions are driven by rising production costs for coal, falling prices for alternative energies, or local environmental concerns, especially regarding air pollution. The main challenges for coal-dependent regions are structural change transformations, in particular for industry and labor. Rising unemployment is the most largely documented outcome in the sample. Policymakers at multiple levels are instrumental in facilitating coal transitions. They rely mainly on regulatory instruments to foster the transitions and compensation schemes or investment plans to deal with their transformative processes. Even though many models suggest that coal phase-outs are among the low-hanging fruits on the way to climate neutrality and meeting the international climate goals, our case studies analysis highlights the intricate political economy at work that needs to be addressed through well-designed and just policies. |
Nemet, Gregory F.: Improving the crystal ball. In: Nature Energy, vol. 6, no. 9, pp. 860-861, 2021, ISSN: 2058-7546. @article{Nemet2021,
title = {Improving the crystal ball},
author = {Gregory F. Nemet},
url = {https://doi.org/10.1038/s41560-021-00903-9},
doi = {10.1038/s41560-021-00903-9},
issn = {2058-7546},
year = {2021},
date = {2021-09-01},
journal = {Nature Energy},
volume = {6},
number = {9},
pages = {860-861},
abstract = {Understanding future costs of energy technologies is crucial for making good decisions about the energy transition. A new paper shows that some types of forecasts have done better than others.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Understanding future costs of energy technologies is crucial for making good decisions about the energy transition. A new paper shows that some types of forecasts have done better than others. |