FYI: Estimating economic damage from climate change in the United States

[usmbguest5318]

On June 30, 2017, Solomon Hsiang, et al published in Science "Estimating economic damage from climate change in the United States."

Hsiang et al. collected national data documenting the responses in six economic sectors to short-term weather fluctuations. These data were integrated with probabilistic distributions from a set of global climate models and used to estimate future costs during the remainder of this century across a range of scenarios (see the Perspective by Pizer). In terms of overall effects on gross domestic product, the authors predict negative impacts in the southern United States and positive impacts in some parts of the Pacific Northwest and New England.​

The abstract from the study is as follows:

Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).​

Some of the key findings are pictorially shown below.

Spatial distributions of projected damages

County-level median values for average 2080 to 2099 RCP8.5 impacts. Impacts are changes relative to counterfactual “no additional climate change” trajectories. Color indicates magnitude of impact in median projection; outline color indicates level of agreement across projections (thin white outline, inner 66% of projections disagree in sign; no outline, ≥83% of projections agree in sign; black outline, ≥95% agree in sign; thick white outline, state borders; maps without outlines shown in fig. S2). Negative damages indicate economic gains.

(A) Percent change in yields, area-weighted average for maize, wheat, soybeans, and cotton.
(B) Change in all-cause mortality rates, across all age groups.
(C) Change in electricity demand.
(D) Change in labor supply of full-time-equivalent workers for low-risk jobs where workers are minimally exposed to outdoor temperature.
(E) Same as (D), except for high-risk jobs where workers are heavily exposed to outdoor temperatures.
(F) Change in damages from coastal storms. (
G) Change in property-crime rates.
(H) Change in violent-crime rates.
(I) Median total direct economic damage across all sectors [(A) to (H)].​

Probabilistic national aggregate damage functions by sector.

Dot-whiskers indicate the distribution of direct damages in 2080 to 2099 (averaged) for multiple realizations of each combination of climate models and scenario projection (dot, median; dark line, inner 66% credible interval; medium line, inner 90%; light line, inner 95%). Green are from RCP2.6, yellow from RCP4.5, red from RCP8.5. Distributions are located on the horizontal axis according to GMST change realized in each model-scenario combination (blue axis is change relative to preindustrial). Black lines are restricted cubic spline regressions through median values, and gray shaded regions are bounded (above and below) by restricted cubic spline regressions through the 5th and 95th quantiles of each distribution, all of which are restricted to intercept the origin.

(A) Total agricultural impact accounting for temperature, rainfall, and CO2 fertilization (CO2 concentration is uniform within each RCP, causing discontinuities across scenarios).
(B) Without CO2effect.
(C) All-cause mortality for all ages.
(D) Electricity demand used in process model, which does not resample statistical uncertainty (SM section G).
(E and F) Labor supply for (E) low-risk and (F) high-risk worker groups.
(G) Property-crime rates.
(H) Violent-crime rates.​

 

[Iceweasel]

LOL, thanks for the morning chuckle. Yeah, we would all be better off if government confiscated even more of our money and spent it on their wet dreams across the globe.

 

[Toddsterpatriot]

On June 30, 2017, Solomon Hsiang, et al published in Science "Estimating economic damage from climate change in the United States."

Hsiang et al. collected national data documenting the responses in six economic sectors to short-term weather fluctuations. These data were integrated with probabilistic distributions from a set of global climate models and used to estimate future costs during the remainder of this century across a range of scenarios (see the Perspective by Pizer). In terms of overall effects on gross domestic product, the authors predict negative impacts in the southern United States and positive impacts in some parts of the Pacific Northwest and New England.​

The abstract from the study is as follows:

Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).​

Some of the key findings are pictorially shown below.

Spatial distributions of projected damages

County-level median values for average 2080 to 2099 RCP8.5 impacts. Impacts are changes relative to counterfactual “no additional climate change” trajectories. Color indicates magnitude of impact in median projection; outline color indicates level of agreement across projections (thin white outline, inner 66% of projections disagree in sign; no outline, ≥83% of projections agree in sign; black outline, ≥95% agree in sign; thick white outline, state borders; maps without outlines shown in fig. S2). Negative damages indicate economic gains.

(A) Percent change in yields, area-weighted average for maize, wheat, soybeans, and cotton.
(B) Change in all-cause mortality rates, across all age groups.
(C) Change in electricity demand.
(D) Change in labor supply of full-time-equivalent workers for low-risk jobs where workers are minimally exposed to outdoor temperature.
(E) Same as (D), except for high-risk jobs where workers are heavily exposed to outdoor temperatures.
(F) Change in damages from coastal storms. (
G) Change in property-crime rates.
(H) Change in violent-crime rates.
(I) Median total direct economic damage across all sectors [(A) to (H)].​

Probabilistic national aggregate damage functions by sector.

Dot-whiskers indicate the distribution of direct damages in 2080 to 2099 (averaged) for multiple realizations of each combination of climate models and scenario projection (dot, median; dark line, inner 66% credible interval; medium line, inner 90%; light line, inner 95%). Green are from RCP2.6, yellow from RCP4.5, red from RCP8.5. Distributions are located on the horizontal axis according to GMST change realized in each model-scenario combination (blue axis is change relative to preindustrial). Black lines are restricted cubic spline regressions through median values, and gray shaded regions are bounded (above and below) by restricted cubic spline regressions through the 5th and 95th quantiles of each distribution, all of which are restricted to intercept the origin.

(A) Total agricultural impact accounting for temperature, rainfall, and CO2 fertilization (CO2 concentration is uniform within each RCP, causing discontinuities across scenarios).
(B) Without CO2effect.
(C) All-cause mortality for all ages.
(D) Electricity demand used in process model, which does not resample statistical uncertainty (SM section G).
(E and F) Labor supply for (E) low-risk and (F) high-risk worker groups.
(G) Property-crime rates.
(H) Violent-crime rates.​

I don't see anything here about higher crop yields.

 

[Muhammed]

I wonder how much money was wasted on these ridiculous soothsayers.

 

[usmbguest5318]

On June 30, 2017, Solomon Hsiang, et al published in Science "Estimating economic damage from climate change in the United States."

Hsiang et al. collected national data documenting the responses in six economic sectors to short-term weather fluctuations. These data were integrated with probabilistic distributions from a set of global climate models and used to estimate future costs during the remainder of this century across a range of scenarios (see the Perspective by Pizer). In terms of overall effects on gross domestic product, the authors predict negative impacts in the southern United States and positive impacts in some parts of the Pacific Northwest and New England.​

The abstract from the study is as follows:

Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).​

Some of the key findings are pictorially shown below.

Spatial distributions of projected damages

County-level median values for average 2080 to 2099 RCP8.5 impacts. Impacts are changes relative to counterfactual “no additional climate change” trajectories. Color indicates magnitude of impact in median projection; outline color indicates level of agreement across projections (thin white outline, inner 66% of projections disagree in sign; no outline, ≥83% of projections agree in sign; black outline, ≥95% agree in sign; thick white outline, state borders; maps without outlines shown in fig. S2). Negative damages indicate economic gains.

(A) Percent change in yields, area-weighted average for maize, wheat, soybeans, and cotton.
(B) Change in all-cause mortality rates, across all age groups.
(C) Change in electricity demand.
(D) Change in labor supply of full-time-equivalent workers for low-risk jobs where workers are minimally exposed to outdoor temperature.
(E) Same as (D), except for high-risk jobs where workers are heavily exposed to outdoor temperatures.
(F) Change in damages from coastal storms. (
G) Change in property-crime rates.
(H) Change in violent-crime rates.
(I) Median total direct economic damage across all sectors [(A) to (H)].​

Probabilistic national aggregate damage functions by sector.

Dot-whiskers indicate the distribution of direct damages in 2080 to 2099 (averaged) for multiple realizations of each combination of climate models and scenario projection (dot, median; dark line, inner 66% credible interval; medium line, inner 90%; light line, inner 95%). Green are from RCP2.6, yellow from RCP4.5, red from RCP8.5. Distributions are located on the horizontal axis according to GMST change realized in each model-scenario combination (blue axis is change relative to preindustrial). Black lines are restricted cubic spline regressions through median values, and gray shaded regions are bounded (above and below) by restricted cubic spline regressions through the 5th and 95th quantiles of each distribution, all of which are restricted to intercept the origin.

(A) Total agricultural impact accounting for temperature, rainfall, and CO2 fertilization (CO2 concentration is uniform within each RCP, causing discontinuities across scenarios).
(B) Without CO2effect.
(C) All-cause mortality for all ages.
(D) Electricity demand used in process model, which does not resample statistical uncertainty (SM section G).
(E and F) Labor supply for (E) low-risk and (F) high-risk worker groups.
(G) Property-crime rates.
(H) Violent-crime rates.​

I don't see anything here about higher crop yields.

Okay.....I'm not sure what to make of your observation (more accurately, what you state you haven't observed) in that regard. Do you mean you read the paper and didn't see anything about higher crop yields, or do you mean that you didn't see any coverage of them in my posted comments?

If the latter, I didn't attempt to replicate all the content of the paper. If the former and you desire specific information about agricultural phenomena and projections/impacts, you'll need to read the reference papers from which Hsiang et al drew information to develop their econometric models. Remember, the paper I cited in the OP an economic one, not an agricultural and/or earth science one. (Perhaps I should have put the thread in the Economy subforum rather than the Environment one?)

For the details on crop yield data the economists modeled see the following:

• Supplementary Materials for Estimating economic damage from climate change in the United States -- This document is where one finds the details about how specifically agricultural information gathered from the papers noted below was used in the economic modeling.
  
  • Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change
  • Regional disparities in the CO2 fertilization effect and implications for crop yields
  • Attached paper: "Crop planting dates: an analysis of global patterns"

I realize now that the "feeling" I had of having overlooked something in the composition of my OP was inserting the hyperlink to the paper.

 

[Billy_Bob]

The paper is based on flawed modeling of weather patterns and their predicted outcome on every day life.

Given that the whole premises is based on AGW models with no predictive power, the whole paper is based on laughable bull shit of the highest order.. Its CRAP..

Someone gave $500,000 dollars for a pure bull shit propaganda piece.

Our tax dollars at work...

 

[Billy_Bob]

We are assured of a coming ice age... but no one is preparing for that inevitable occurrence.. Everyone is hyped up for a nonexistent warming threat...

 

[Toddsterpatriot]

On June 30, 2017, Solomon Hsiang, et al published in Science "Estimating economic damage from climate change in the United States."

Hsiang et al. collected national data documenting the responses in six economic sectors to short-term weather fluctuations. These data were integrated with probabilistic distributions from a set of global climate models and used to estimate future costs during the remainder of this century across a range of scenarios (see the Perspective by Pizer). In terms of overall effects on gross domestic product, the authors predict negative impacts in the southern United States and positive impacts in some parts of the Pacific Northwest and New England.​

The abstract from the study is as follows:

Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).​

Some of the key findings are pictorially shown below.

Spatial distributions of projected damages

County-level median values for average 2080 to 2099 RCP8.5 impacts. Impacts are changes relative to counterfactual “no additional climate change” trajectories. Color indicates magnitude of impact in median projection; outline color indicates level of agreement across projections (thin white outline, inner 66% of projections disagree in sign; no outline, ≥83% of projections agree in sign; black outline, ≥95% agree in sign; thick white outline, state borders; maps without outlines shown in fig. S2). Negative damages indicate economic gains.

(A) Percent change in yields, area-weighted average for maize, wheat, soybeans, and cotton.
(B) Change in all-cause mortality rates, across all age groups.
(C) Change in electricity demand.
(D) Change in labor supply of full-time-equivalent workers for low-risk jobs where workers are minimally exposed to outdoor temperature.
(E) Same as (D), except for high-risk jobs where workers are heavily exposed to outdoor temperatures.
(F) Change in damages from coastal storms. (
G) Change in property-crime rates.
(H) Change in violent-crime rates.
(I) Median total direct economic damage across all sectors [(A) to (H)].​

Probabilistic national aggregate damage functions by sector.

Dot-whiskers indicate the distribution of direct damages in 2080 to 2099 (averaged) for multiple realizations of each combination of climate models and scenario projection (dot, median; dark line, inner 66% credible interval; medium line, inner 90%; light line, inner 95%). Green are from RCP2.6, yellow from RCP4.5, red from RCP8.5. Distributions are located on the horizontal axis according to GMST change realized in each model-scenario combination (blue axis is change relative to preindustrial). Black lines are restricted cubic spline regressions through median values, and gray shaded regions are bounded (above and below) by restricted cubic spline regressions through the 5th and 95th quantiles of each distribution, all of which are restricted to intercept the origin.

(A) Total agricultural impact accounting for temperature, rainfall, and CO2 fertilization (CO2 concentration is uniform within each RCP, causing discontinuities across scenarios).
(B) Without CO2effect.
(C) All-cause mortality for all ages.
(D) Electricity demand used in process model, which does not resample statistical uncertainty (SM section G).
(E and F) Labor supply for (E) low-risk and (F) high-risk worker groups.
(G) Property-crime rates.
(H) Violent-crime rates.​

I don't see anything here about higher crop yields.

Okay.....I'm not sure what to make of your observation (more accurately, what you state you haven't observed) in that regard. Do you mean you read the paper and didn't see anything about higher crop yields, or do you mean that you didn't see any coverage of them in my posted comments?

If the latter, I didn't attempt to replicate all the content of the paper. If the former and you desire specific information about agricultural phenomena and projections/impacts, you'll need to read the reference papers from which Hsiang et al drew information to develop their econometric models. Remember, the paper I cited in the OP an economic one, not an agricultural and/or earth science one. (Perhaps I should have put the thread in the Economy subforum rather than the Environment one?)

For the details on crop yield data the economists modeled see the following:

• Supplementary Materials for Estimating economic damage from climate change in the United States -- This document is where one finds the details about how specifically agricultural information gathered from the papers noted below was used in the economic modeling.
  • Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change
  • Regional disparities in the CO2 fertilization effect and implications for crop yields
  • Attached paper: "Crop planting dates: an analysis of global patterns"

I realize now that the "feeling" I had of having overlooked something in the composition of my OP was inserting the hyperlink to the paper.

Okay.....I'm not sure what to make of your observation (more accurately, what you state you haven't observed) in that regard.

Longer growing seasons, milder winters, higher yields.
If you look at the Little Ice Age, you see short growing seasons, early frost and famine.

 

[usmbguest5318]

On June 30, 2017, Solomon Hsiang, et al published in Science "Estimating economic damage from climate change in the United States."

Hsiang et al. collected national data documenting the responses in six economic sectors to short-term weather fluctuations. These data were integrated with probabilistic distributions from a set of global climate models and used to estimate future costs during the remainder of this century across a range of scenarios (see the Perspective by Pizer). In terms of overall effects on gross domestic product, the authors predict negative impacts in the southern United States and positive impacts in some parts of the Pacific Northwest and New England.​

The abstract from the study is as follows:

Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).​

Some of the key findings are pictorially shown below.

Spatial distributions of projected damages

County-level median values for average 2080 to 2099 RCP8.5 impacts. Impacts are changes relative to counterfactual “no additional climate change” trajectories. Color indicates magnitude of impact in median projection; outline color indicates level of agreement across projections (thin white outline, inner 66% of projections disagree in sign; no outline, ≥83% of projections agree in sign; black outline, ≥95% agree in sign; thick white outline, state borders; maps without outlines shown in fig. S2). Negative damages indicate economic gains.

(A) Percent change in yields, area-weighted average for maize, wheat, soybeans, and cotton.
(B) Change in all-cause mortality rates, across all age groups.
(C) Change in electricity demand.
(D) Change in labor supply of full-time-equivalent workers for low-risk jobs where workers are minimally exposed to outdoor temperature.
(E) Same as (D), except for high-risk jobs where workers are heavily exposed to outdoor temperatures.
(F) Change in damages from coastal storms. (
G) Change in property-crime rates.
(H) Change in violent-crime rates.
(I) Median total direct economic damage across all sectors [(A) to (H)].​

Probabilistic national aggregate damage functions by sector.

Dot-whiskers indicate the distribution of direct damages in 2080 to 2099 (averaged) for multiple realizations of each combination of climate models and scenario projection (dot, median; dark line, inner 66% credible interval; medium line, inner 90%; light line, inner 95%). Green are from RCP2.6, yellow from RCP4.5, red from RCP8.5. Distributions are located on the horizontal axis according to GMST change realized in each model-scenario combination (blue axis is change relative to preindustrial). Black lines are restricted cubic spline regressions through median values, and gray shaded regions are bounded (above and below) by restricted cubic spline regressions through the 5th and 95th quantiles of each distribution, all of which are restricted to intercept the origin.

(A) Total agricultural impact accounting for temperature, rainfall, and CO2 fertilization (CO2 concentration is uniform within each RCP, causing discontinuities across scenarios).
(B) Without CO2effect.
(C) All-cause mortality for all ages.
(D) Electricity demand used in process model, which does not resample statistical uncertainty (SM section G).
(E and F) Labor supply for (E) low-risk and (F) high-risk worker groups.
(G) Property-crime rates.
(H) Violent-crime rates.​

I don't see anything here about higher crop yields.

Okay.....I'm not sure what to make of your observation (more accurately, what you state you haven't observed) in that regard. Do you mean you read the paper and didn't see anything about higher crop yields, or do you mean that you didn't see any coverage of them in my posted comments?

If the latter, I didn't attempt to replicate all the content of the paper. If the former and you desire specific information about agricultural phenomena and projections/impacts, you'll need to read the reference papers from which Hsiang et al drew information to develop their econometric models. Remember, the paper I cited in the OP an economic one, not an agricultural and/or earth science one. (Perhaps I should have put the thread in the Economy subforum rather than the Environment one?)

For the details on crop yield data the economists modeled see the following:

• Supplementary Materials for Estimating economic damage from climate change in the United States -- This document is where one finds the details about how specifically agricultural information gathered from the papers noted below was used in the economic modeling.
  • Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change
  • Regional disparities in the CO2 fertilization effect and implications for crop yields
  • Attached paper: "Crop planting dates: an analysis of global patterns"

I realize now that the "feeling" I had of having overlooked something in the composition of my OP was inserting the hyperlink to the paper.

Okay.....I'm not sure what to make of your observation (more accurately, what you state you haven't observed) in that regard.

Longer growing seasons, milder winters, higher yields.
If you look at the Little Ice Age, you see short growing seasons, early frost and famine.

• Global Water Resources: Vulnerability from Climate Change and Population Growth
  • [I]mpending global-scale changes in population and economic development over the next 25 years will dictate the future relation between water supply and demand to a much greater degree than will changes in mean climate.
• The Impact of Global Warming on Agriculture: A Ricardian Analysis

Do you even understand what the point of Hsiang's et al paper is? Given the nature of your comments, I don't think you do for I suspected you'd honed in on something that wasn't germane to the paper's purpose and that even after providing you with links to the content that addressed your question, you wouldn't read them and figure it out. TY for your confirming as much.

If you read the supporting materials to which I linked, you'd see that though Hsiang et al have factored in the impact on agricultural yields, those factors don't have a discriminating place in the overall impact model; however, the gains due to increasing crop yields during the warming period are included in the economic impact. Indeed, the models account too for yield, growing season variations and climatic disparities east and west of the 100th meridian. Additionally, you'll find included in the modeling equations provision for storage of food crops as well as changes in consumption of them. (The supplemental materials even explain -- presumably for the benefit of non-economists -- why they incorporated storage in the model.)

Please read and bother to fully comprehend the information made available to you before you make inane comments in the thread. Barring that, at least ask intelligent questions, ones that show you've made a rigorous effort to comprehend the topic of discussion.

 

[Toddsterpatriot]

On June 30, 2017, Solomon Hsiang, et al published in Science "Estimating economic damage from climate change in the United States."

Hsiang et al. collected national data documenting the responses in six economic sectors to short-term weather fluctuations. These data were integrated with probabilistic distributions from a set of global climate models and used to estimate future costs during the remainder of this century across a range of scenarios (see the Perspective by Pizer). In terms of overall effects on gross domestic product, the authors predict negative impacts in the southern United States and positive impacts in some parts of the Pacific Northwest and New England.​

The abstract from the study is as follows:

Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).​

Some of the key findings are pictorially shown below.

Spatial distributions of projected damages

County-level median values for average 2080 to 2099 RCP8.5 impacts. Impacts are changes relative to counterfactual “no additional climate change” trajectories. Color indicates magnitude of impact in median projection; outline color indicates level of agreement across projections (thin white outline, inner 66% of projections disagree in sign; no outline, ≥83% of projections agree in sign; black outline, ≥95% agree in sign; thick white outline, state borders; maps without outlines shown in fig. S2). Negative damages indicate economic gains.

(A) Percent change in yields, area-weighted average for maize, wheat, soybeans, and cotton.
(B) Change in all-cause mortality rates, across all age groups.
(C) Change in electricity demand.
(D) Change in labor supply of full-time-equivalent workers for low-risk jobs where workers are minimally exposed to outdoor temperature.
(E) Same as (D), except for high-risk jobs where workers are heavily exposed to outdoor temperatures.
(F) Change in damages from coastal storms. (
G) Change in property-crime rates.
(H) Change in violent-crime rates.
(I) Median total direct economic damage across all sectors [(A) to (H)].​

Probabilistic national aggregate damage functions by sector.

Dot-whiskers indicate the distribution of direct damages in 2080 to 2099 (averaged) for multiple realizations of each combination of climate models and scenario projection (dot, median; dark line, inner 66% credible interval; medium line, inner 90%; light line, inner 95%). Green are from RCP2.6, yellow from RCP4.5, red from RCP8.5. Distributions are located on the horizontal axis according to GMST change realized in each model-scenario combination (blue axis is change relative to preindustrial). Black lines are restricted cubic spline regressions through median values, and gray shaded regions are bounded (above and below) by restricted cubic spline regressions through the 5th and 95th quantiles of each distribution, all of which are restricted to intercept the origin.

(A) Total agricultural impact accounting for temperature, rainfall, and CO2 fertilization (CO2 concentration is uniform within each RCP, causing discontinuities across scenarios).
(B) Without CO2effect.
(C) All-cause mortality for all ages.
(D) Electricity demand used in process model, which does not resample statistical uncertainty (SM section G).
(E and F) Labor supply for (E) low-risk and (F) high-risk worker groups.
(G) Property-crime rates.
(H) Violent-crime rates.​

I don't see anything here about higher crop yields.

Okay.....I'm not sure what to make of your observation (more accurately, what you state you haven't observed) in that regard. Do you mean you read the paper and didn't see anything about higher crop yields, or do you mean that you didn't see any coverage of them in my posted comments?

If the latter, I didn't attempt to replicate all the content of the paper. If the former and you desire specific information about agricultural phenomena and projections/impacts, you'll need to read the reference papers from which Hsiang et al drew information to develop their econometric models. Remember, the paper I cited in the OP an economic one, not an agricultural and/or earth science one. (Perhaps I should have put the thread in the Economy subforum rather than the Environment one?)

For the details on crop yield data the economists modeled see the following:

• Supplementary Materials for Estimating economic damage from climate change in the United States -- This document is where one finds the details about how specifically agricultural information gathered from the papers noted below was used in the economic modeling.
  • Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change
  • Regional disparities in the CO2 fertilization effect and implications for crop yields
  • Attached paper: "Crop planting dates: an analysis of global patterns"

I realize now that the "feeling" I had of having overlooked something in the composition of my OP was inserting the hyperlink to the paper.

Okay.....I'm not sure what to make of your observation (more accurately, what you state you haven't observed) in that regard.

Longer growing seasons, milder winters, higher yields.
If you look at the Little Ice Age, you see short growing seasons, early frost and famine.

• Global Water Resources: Vulnerability from Climate Change and Population Growth
  • [I]mpending global-scale changes in population and economic development over the next 25 years will dictate the future relation between water supply and demand to a much greater degree than will changes in mean climate.
• The Impact of Global Warming on Agriculture: A Ricardian Analysis

Do you even understand what the point of Hsiang's et al paper is? Given the nature of your comments, I don't think you do for I suspected you'd honed in on something that wasn't germane to the paper's purpose and that even after providing you with links to the content that addressed your question, you wouldn't read them and figure it out. TY for your confirming as much.

If you read the supporting materials to which I linked, you'd see that though Hsiang et al have factored in the impact on agricultural yields, those factors don't have a discriminating place in the overall impact model; however, the gains due to increasing crop yields during the warming period are included in the economic impact. Indeed, the models account too for yield, growing season variations and climatic disparities east and west of the 100th meridian. Additionally, you'll find included in the modeling equations provision for storage of food crops as well as changes in consumption of them. (The supplemental materials even explain -- presumably for the benefit of non-economists -- why they incorporated storage in the model.)

Please read and bother to fully comprehend the information made available to you before you make inane comments in the thread. Barring that, at least ask intelligent questions, ones that show you've made a rigorous effort to comprehend the topic of discussion.

Do you even understand what the point of Hsiang's et al paper is?

Yes, I understand highlighting negatives and ignoring positives.
Did they mention fewer winter deaths if the climate warms?

I suspected you'd honed in on something that wasn't germane to the paper's purpose

Obviously. Benefits aren't germane to panic mongering.

 

[usmbguest5318]

On June 30, 2017, Solomon Hsiang, et al published in Science "Estimating economic damage from climate change in the United States."

Hsiang et al. collected national data documenting the responses in six economic sectors to short-term weather fluctuations. These data were integrated with probabilistic distributions from a set of global climate models and used to estimate future costs during the remainder of this century across a range of scenarios (see the Perspective by Pizer). In terms of overall effects on gross domestic product, the authors predict negative impacts in the southern United States and positive impacts in some parts of the Pacific Northwest and New England.​

The abstract from the study is as follows:

Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).​

Some of the key findings are pictorially shown below.

Spatial distributions of projected damages

County-level median values for average 2080 to 2099 RCP8.5 impacts. Impacts are changes relative to counterfactual “no additional climate change” trajectories. Color indicates magnitude of impact in median projection; outline color indicates level of agreement across projections (thin white outline, inner 66% of projections disagree in sign; no outline, ≥83% of projections agree in sign; black outline, ≥95% agree in sign; thick white outline, state borders; maps without outlines shown in fig. S2). Negative damages indicate economic gains.

(A) Percent change in yields, area-weighted average for maize, wheat, soybeans, and cotton.
(B) Change in all-cause mortality rates, across all age groups.
(C) Change in electricity demand.
(D) Change in labor supply of full-time-equivalent workers for low-risk jobs where workers are minimally exposed to outdoor temperature.
(E) Same as (D), except for high-risk jobs where workers are heavily exposed to outdoor temperatures.
(F) Change in damages from coastal storms. (
G) Change in property-crime rates.
(H) Change in violent-crime rates.
(I) Median total direct economic damage across all sectors [(A) to (H)].​

Probabilistic national aggregate damage functions by sector.

Dot-whiskers indicate the distribution of direct damages in 2080 to 2099 (averaged) for multiple realizations of each combination of climate models and scenario projection (dot, median; dark line, inner 66% credible interval; medium line, inner 90%; light line, inner 95%). Green are from RCP2.6, yellow from RCP4.5, red from RCP8.5. Distributions are located on the horizontal axis according to GMST change realized in each model-scenario combination (blue axis is change relative to preindustrial). Black lines are restricted cubic spline regressions through median values, and gray shaded regions are bounded (above and below) by restricted cubic spline regressions through the 5th and 95th quantiles of each distribution, all of which are restricted to intercept the origin.

(A) Total agricultural impact accounting for temperature, rainfall, and CO2 fertilization (CO2 concentration is uniform within each RCP, causing discontinuities across scenarios).
(B) Without CO2effect.
(C) All-cause mortality for all ages.
(D) Electricity demand used in process model, which does not resample statistical uncertainty (SM section G).
(E and F) Labor supply for (E) low-risk and (F) high-risk worker groups.
(G) Property-crime rates.
(H) Violent-crime rates.​

I don't see anything here about higher crop yields.

Okay.....I'm not sure what to make of your observation (more accurately, what you state you haven't observed) in that regard. Do you mean you read the paper and didn't see anything about higher crop yields, or do you mean that you didn't see any coverage of them in my posted comments?

If the latter, I didn't attempt to replicate all the content of the paper. If the former and you desire specific information about agricultural phenomena and projections/impacts, you'll need to read the reference papers from which Hsiang et al drew information to develop their econometric models. Remember, the paper I cited in the OP an economic one, not an agricultural and/or earth science one. (Perhaps I should have put the thread in the Economy subforum rather than the Environment one?)

For the details on crop yield data the economists modeled see the following:

• Supplementary Materials for Estimating economic damage from climate change in the United States -- This document is where one finds the details about how specifically agricultural information gathered from the papers noted below was used in the economic modeling.
  • Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change
  • Regional disparities in the CO2 fertilization effect and implications for crop yields
  • Attached paper: "Crop planting dates: an analysis of global patterns"

I realize now that the "feeling" I had of having overlooked something in the composition of my OP was inserting the hyperlink to the paper.

Okay.....I'm not sure what to make of your observation (more accurately, what you state you haven't observed) in that regard.

Longer growing seasons, milder winters, higher yields.
If you look at the Little Ice Age, you see short growing seasons, early frost and famine.

• Global Water Resources: Vulnerability from Climate Change and Population Growth
  • [I]mpending global-scale changes in population and economic development over the next 25 years will dictate the future relation between water supply and demand to a much greater degree than will changes in mean climate.
• The Impact of Global Warming on Agriculture: A Ricardian Analysis

Do you even understand what the point of Hsiang's et al paper is? Given the nature of your comments, I don't think you do for I suspected you'd honed in on something that wasn't germane to the paper's purpose and that even after providing you with links to the content that addressed your question, you wouldn't read them and figure it out. TY for your confirming as much.

If you read the supporting materials to which I linked, you'd see that though Hsiang et al have factored in the impact on agricultural yields, those factors don't have a discriminating place in the overall impact model; however, the gains due to increasing crop yields during the warming period are included in the economic impact. Indeed, the models account too for yield, growing season variations and climatic disparities east and west of the 100th meridian. Additionally, you'll find included in the modeling equations provision for storage of food crops as well as changes in consumption of them. (The supplemental materials even explain -- presumably for the benefit of non-economists -- why they incorporated storage in the model.)

Please read and bother to fully comprehend the information made available to you before you make inane comments in the thread. Barring that, at least ask intelligent questions, ones that show you've made a rigorous effort to comprehend the topic of discussion.

Do you even understand what the point of Hsiang's et al paper is?

Yes, I understand highlighting negatives and ignoring positives.

Given that's what you think is the point of the paper, I was right. You don't understand its point or what it says.

Did they mention fewer winter deaths if the climate warms?

You should read it and the supplemental materials to find that answer. Perhaps in the process of doing so, you'd discover the paper's point.

I'm not answering your question directly because, quite frankly, I don't really want to engage in discussion with someone who (1) won't read the paper and (2) can't from the OP and my subsequent remarks about the paper correctly tell what the paper is about.

 

[Toddsterpatriot]

On June 30, 2017, Solomon Hsiang, et al published in Science "Estimating economic damage from climate change in the United States."

Hsiang et al. collected national data documenting the responses in six economic sectors to short-term weather fluctuations. These data were integrated with probabilistic distributions from a set of global climate models and used to estimate future costs during the remainder of this century across a range of scenarios (see the Perspective by Pizer). In terms of overall effects on gross domestic product, the authors predict negative impacts in the southern United States and positive impacts in some parts of the Pacific Northwest and New England.​

The abstract from the study is as follows:

Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).​

Some of the key findings are pictorially shown below.

Spatial distributions of projected damages

County-level median values for average 2080 to 2099 RCP8.5 impacts. Impacts are changes relative to counterfactual “no additional climate change” trajectories. Color indicates magnitude of impact in median projection; outline color indicates level of agreement across projections (thin white outline, inner 66% of projections disagree in sign; no outline, ≥83% of projections agree in sign; black outline, ≥95% agree in sign; thick white outline, state borders; maps without outlines shown in fig. S2). Negative damages indicate economic gains.

(A) Percent change in yields, area-weighted average for maize, wheat, soybeans, and cotton.
(B) Change in all-cause mortality rates, across all age groups.
(C) Change in electricity demand.
(D) Change in labor supply of full-time-equivalent workers for low-risk jobs where workers are minimally exposed to outdoor temperature.
(E) Same as (D), except for high-risk jobs where workers are heavily exposed to outdoor temperatures.
(F) Change in damages from coastal storms. (
G) Change in property-crime rates.
(H) Change in violent-crime rates.
(I) Median total direct economic damage across all sectors [(A) to (H)].​

Probabilistic national aggregate damage functions by sector.

Dot-whiskers indicate the distribution of direct damages in 2080 to 2099 (averaged) for multiple realizations of each combination of climate models and scenario projection (dot, median; dark line, inner 66% credible interval; medium line, inner 90%; light line, inner 95%). Green are from RCP2.6, yellow from RCP4.5, red from RCP8.5. Distributions are located on the horizontal axis according to GMST change realized in each model-scenario combination (blue axis is change relative to preindustrial). Black lines are restricted cubic spline regressions through median values, and gray shaded regions are bounded (above and below) by restricted cubic spline regressions through the 5th and 95th quantiles of each distribution, all of which are restricted to intercept the origin.

(A) Total agricultural impact accounting for temperature, rainfall, and CO2 fertilization (CO2 concentration is uniform within each RCP, causing discontinuities across scenarios).
(B) Without CO2effect.
(C) All-cause mortality for all ages.
(D) Electricity demand used in process model, which does not resample statistical uncertainty (SM section G).
(E and F) Labor supply for (E) low-risk and (F) high-risk worker groups.
(G) Property-crime rates.
(H) Violent-crime rates.​

I don't see anything here about higher crop yields.

Okay.....I'm not sure what to make of your observation (more accurately, what you state you haven't observed) in that regard. Do you mean you read the paper and didn't see anything about higher crop yields, or do you mean that you didn't see any coverage of them in my posted comments?

If the latter, I didn't attempt to replicate all the content of the paper. If the former and you desire specific information about agricultural phenomena and projections/impacts, you'll need to read the reference papers from which Hsiang et al drew information to develop their econometric models. Remember, the paper I cited in the OP an economic one, not an agricultural and/or earth science one. (Perhaps I should have put the thread in the Economy subforum rather than the Environment one?)

For the details on crop yield data the economists modeled see the following:

• Supplementary Materials for Estimating economic damage from climate change in the United States -- This document is where one finds the details about how specifically agricultural information gathered from the papers noted below was used in the economic modeling.
  • Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change
  • Regional disparities in the CO2 fertilization effect and implications for crop yields
  • Attached paper: "Crop planting dates: an analysis of global patterns"

I realize now that the "feeling" I had of having overlooked something in the composition of my OP was inserting the hyperlink to the paper.

Okay.....I'm not sure what to make of your observation (more accurately, what you state you haven't observed) in that regard.

Longer growing seasons, milder winters, higher yields.
If you look at the Little Ice Age, you see short growing seasons, early frost and famine.

• Global Water Resources: Vulnerability from Climate Change and Population Growth
  • [I]mpending global-scale changes in population and economic development over the next 25 years will dictate the future relation between water supply and demand to a much greater degree than will changes in mean climate.
• The Impact of Global Warming on Agriculture: A Ricardian Analysis

Do you even understand what the point of Hsiang's et al paper is? Given the nature of your comments, I don't think you do for I suspected you'd honed in on something that wasn't germane to the paper's purpose and that even after providing you with links to the content that addressed your question, you wouldn't read them and figure it out. TY for your confirming as much.

If you read the supporting materials to which I linked, you'd see that though Hsiang et al have factored in the impact on agricultural yields, those factors don't have a discriminating place in the overall impact model; however, the gains due to increasing crop yields during the warming period are included in the economic impact. Indeed, the models account too for yield, growing season variations and climatic disparities east and west of the 100th meridian. Additionally, you'll find included in the modeling equations provision for storage of food crops as well as changes in consumption of them. (The supplemental materials even explain -- presumably for the benefit of non-economists -- why they incorporated storage in the model.)

Please read and bother to fully comprehend the information made available to you before you make inane comments in the thread. Barring that, at least ask intelligent questions, ones that show you've made a rigorous effort to comprehend the topic of discussion.

Do you even understand what the point of Hsiang's et al paper is?

Yes, I understand highlighting negatives and ignoring positives.

Given that's what you think is the point of the paper, I was right. You don't understand its point or what it says.

Did they mention fewer winter deaths if the climate warms?

You should read it and the supplemental materials to find that answer. Perhaps in the process of doing so, you'd discover the paper's point.

I didn't see longer growing season mentioned in your links. Weird.

 

[usmbguest5318]

On June 30, 2017, Solomon Hsiang, et al published in Science "Estimating economic damage from climate change in the United States."

Hsiang et al. collected national data documenting the responses in six economic sectors to short-term weather fluctuations. These data were integrated with probabilistic distributions from a set of global climate models and used to estimate future costs during the remainder of this century across a range of scenarios (see the Perspective by Pizer). In terms of overall effects on gross domestic product, the authors predict negative impacts in the southern United States and positive impacts in some parts of the Pacific Northwest and New England.​

The abstract from the study is as follows:

Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).​

Some of the key findings are pictorially shown below.

Spatial distributions of projected damages

County-level median values for average 2080 to 2099 RCP8.5 impacts. Impacts are changes relative to counterfactual “no additional climate change” trajectories. Color indicates magnitude of impact in median projection; outline color indicates level of agreement across projections (thin white outline, inner 66% of projections disagree in sign; no outline, ≥83% of projections agree in sign; black outline, ≥95% agree in sign; thick white outline, state borders; maps without outlines shown in fig. S2). Negative damages indicate economic gains.

(A) Percent change in yields, area-weighted average for maize, wheat, soybeans, and cotton.
(B) Change in all-cause mortality rates, across all age groups.
(C) Change in electricity demand.
(D) Change in labor supply of full-time-equivalent workers for low-risk jobs where workers are minimally exposed to outdoor temperature.
(E) Same as (D), except for high-risk jobs where workers are heavily exposed to outdoor temperatures.
(F) Change in damages from coastal storms. (
G) Change in property-crime rates.
(H) Change in violent-crime rates.
(I) Median total direct economic damage across all sectors [(A) to (H)].​

Probabilistic national aggregate damage functions by sector.

Dot-whiskers indicate the distribution of direct damages in 2080 to 2099 (averaged) for multiple realizations of each combination of climate models and scenario projection (dot, median; dark line, inner 66% credible interval; medium line, inner 90%; light line, inner 95%). Green are from RCP2.6, yellow from RCP4.5, red from RCP8.5. Distributions are located on the horizontal axis according to GMST change realized in each model-scenario combination (blue axis is change relative to preindustrial). Black lines are restricted cubic spline regressions through median values, and gray shaded regions are bounded (above and below) by restricted cubic spline regressions through the 5th and 95th quantiles of each distribution, all of which are restricted to intercept the origin.

(A) Total agricultural impact accounting for temperature, rainfall, and CO2 fertilization (CO2 concentration is uniform within each RCP, causing discontinuities across scenarios).
(B) Without CO2effect.
(C) All-cause mortality for all ages.
(D) Electricity demand used in process model, which does not resample statistical uncertainty (SM section G).
(E and F) Labor supply for (E) low-risk and (F) high-risk worker groups.
(G) Property-crime rates.
(H) Violent-crime rates.​

I don't see anything here about higher crop yields.

Okay.....I'm not sure what to make of your observation (more accurately, what you state you haven't observed) in that regard. Do you mean you read the paper and didn't see anything about higher crop yields, or do you mean that you didn't see any coverage of them in my posted comments?

If the latter, I didn't attempt to replicate all the content of the paper. If the former and you desire specific information about agricultural phenomena and projections/impacts, you'll need to read the reference papers from which Hsiang et al drew information to develop their econometric models. Remember, the paper I cited in the OP an economic one, not an agricultural and/or earth science one. (Perhaps I should have put the thread in the Economy subforum rather than the Environment one?)

For the details on crop yield data the economists modeled see the following:

• Supplementary Materials for Estimating economic damage from climate change in the United States -- This document is where one finds the details about how specifically agricultural information gathered from the papers noted below was used in the economic modeling.
  • Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change
  • Regional disparities in the CO2 fertilization effect and implications for crop yields
  • Attached paper: "Crop planting dates: an analysis of global patterns"

I realize now that the "feeling" I had of having overlooked something in the composition of my OP was inserting the hyperlink to the paper.

Okay.....I'm not sure what to make of your observation (more accurately, what you state you haven't observed) in that regard.

Longer growing seasons, milder winters, higher yields.
If you look at the Little Ice Age, you see short growing seasons, early frost and famine.

• Global Water Resources: Vulnerability from Climate Change and Population Growth
  • [I]mpending global-scale changes in population and economic development over the next 25 years will dictate the future relation between water supply and demand to a much greater degree than will changes in mean climate.
• The Impact of Global Warming on Agriculture: A Ricardian Analysis

Do you even understand what the point of Hsiang's et al paper is? Given the nature of your comments, I don't think you do for I suspected you'd honed in on something that wasn't germane to the paper's purpose and that even after providing you with links to the content that addressed your question, you wouldn't read them and figure it out. TY for your confirming as much.

If you read the supporting materials to which I linked, you'd see that though Hsiang et al have factored in the impact on agricultural yields, those factors don't have a discriminating place in the overall impact model; however, the gains due to increasing crop yields during the warming period are included in the economic impact. Indeed, the models account too for yield, growing season variations and climatic disparities east and west of the 100th meridian. Additionally, you'll find included in the modeling equations provision for storage of food crops as well as changes in consumption of them. (The supplemental materials even explain -- presumably for the benefit of non-economists -- why they incorporated storage in the model.)

Please read and bother to fully comprehend the information made available to you before you make inane comments in the thread. Barring that, at least ask intelligent questions, ones that show you've made a rigorous effort to comprehend the topic of discussion.

Do you even understand what the point of Hsiang's et al paper is?

Yes, I understand highlighting negatives and ignoring positives.

Given that's what you think is the point of the paper, I was right. You don't understand its point or what it says.

Did they mention fewer winter deaths if the climate warms?

You should read it and the supplemental materials to find that answer. Perhaps in the process of doing so, you'd discover the paper's point.

I didn't see longer growing season mentioned in your links. Weird.

Yet another remark from you that indicates clearly that you don't understand the paper.

Mind you, I'm less troubled that you don't understand the paper and supplementary materials than I am that you don't and you keep making inane attestations about it and what you don't see in it.

 

[polarbear]

Typical fake news. The author picked a bush league .org ; he knew full well is a total joke when it comes to peer review.
Follow the link and see who "reviewed" this crap:
Roles who reviewed
Editor:
1
Reviewer:
2
Advisor:
1
And after it got published he tweeted all over the internet (553 times!!) that it was "peer reviewed and accepted".
That resulted in 48 other papers copying the original fake news without even questioning it because it has been "peer reviewed and published" already.
Altmetric – Estimating economic damage from climate change in the United States
The rest of the other publications who copied this crap did so knowing full well that there is no way to verify the so called "peer review" because it is as anonymous as the so called sources that published all this Russia collusion crap:

Ethical Guidelines for Reviewers
The review process is conducted anonymously; Science never reveals the identity of reviewers to authors. The privacy and anonymity provisions of this process extend to the reviewer, who should not reveal his or her identity to outsiders or members of the press.
Only some of the submitted papers are reviewed in depth. Cross-review is not required. If we do not receive comments we will proceed based on the reviews in hand. The final responsibility for decisions of acceptance or rejection of a submitted manuscript lies with the editor.

And last but not least another full blown idiot posted it here in this forum as a "peer reviewed scientific article"
What makes you think that everybody else is as stupid as you are?

 

[Billy_Bob]

Typical fake news. The author picked a bush league .org ; he knew full well is a total joke when it comes to peer review.
Follow the link and see who "reviewed" this crap:
Roles who reviewed
Editor:
1
Reviewer:
2
Advisor:
1
And after it got published he tweeted all over the internet (553 times!!) that it was "peer reviewed and accepted".
That resulted in 48 other papers copying the original fake news without even questioning it because it has been "peer reviewed and published" already.
Altmetric – Estimating economic damage from climate change in the United States
The rest of the other publications who copied this crap did so knowing full well that there is no way to verify the so called "peer review" because it is as anonymous as the so called sources that published all this Russia collusion crap:

Ethical Guidelines for Reviewers
The review process is conducted anonymously; Science never reveals the identity of reviewers to authors. The privacy and anonymity provisions of this process extend to the reviewer, who should not reveal his or her identity to outsiders or members of the press.
Only some of the submitted papers are reviewed in depth. Cross-review is not required. If we do not receive comments we will proceed based on the reviews in hand. The final responsibility for decisions of acceptance or rejection of a submitted manuscript lies with the editor.

And last but not least another full blown idiot posted it here in this forum as a "peer reviewed scientific article"
What makes you think that everybody else is as stupid as you are?

LOL... thanks for verifying that it was crap... CRAP

 

[skookerasbil]

Welp........the OP is clearly a sucker for the matrix.

ghey

 

[mamooth]

Thread summary:

Data was presented.

Deniers immediately began screaming various conspiracy theories as a shield, to prevent that data from sullying the purity of their cultist minds.

Nobody was surprised.

Let's try more recent papers, and watch them do it again. This recent paper (Gest et al 2017) is another paper contradicting the "but CO2 lagged temperature after the last ice age!" theory.

CPD - Leads and lags between Antarctic temperature and carbon dioxide during the last deglaciation
---
We find that at the onset of the last deglaciation and at the onset of the Antarctic Cold Reversal (ACR) period CO2 and Antarctic temperature are synchronous within a range of 210 years. Then CO2 slightly leads by 165 ± 116 years at the end of the Antarctic Cold Reversal (ACR) period. Finally, Antarctic temperature significantly leads by 406 ± 200 years at the onset of the Holocene period.
---

Let's do another. This is Dr. Mears fixing the RSS satellite model, to remove the cooling bias. It matches the balloon data and surface data more closely now. The UAH satellite model is now the only model that runs as cool as it does, therefore deniers will use it exclusively, solely because it tells them what they want to hear.

http://journals.ametsoc.org/doi/10.1175/JCLI-D-16-0768.1

Dr. Mears of RSS is quite open about why his algorithms do what they do. Dr. Spencer of UAH ... is not. And yet the denier "WE DEMAND OPEN SCIENCE!" advocates fanatically cling to the more secret algorithm. Curious.

 

[skookerasbil]

Thread summary:

Data was presented.

Deniers immediately began screaming various conspiracy theories as a shield, to prevent that data from sullying the purity of their cultist minds.

Nobody was surprised.

Let's try more recent papers, and watch them do it again. This recent paper (Gest et al 2017) is another paper contradicting the "but CO2 lagged temperature after the last ice age!" theory.

CPD - Leads and lags between Antarctic temperature and carbon dioxide during the last deglaciation
---
We find that at the onset of the last deglaciation and at the onset of the Antarctic Cold Reversal (ACR) period CO2 and Antarctic temperature are synchronous within a range of 210 years. Then CO2 slightly leads by 165 ± 116 years at the end of the Antarctic Cold Reversal (ACR) period. Finally, Antarctic temperature significantly leads by 406 ± 200 years at the onset of the Holocene period.
---

Let's do another. This is Dr. Mears fixing the RSS satellite model, to remove the cooling bias. It matches the balloon data and surface data more closely now. The UAH satellite model is now the only model that runs as cool as it does, therefore deniers will use it exclusively, solely because it tells them what they want to hear.

http://journals.ametsoc.org/doi/10.1175/JCLI-D-16-0768.1

Dr. Mears of RSS is quite open about why his algorithms do what they do. Dr. Spencer of UAH ... is not. And yet the denier "WE DEMAND OPEN SCIENCE!" advocates fanatically cling to the more secret algorithm. Curious.

but losing............. EPA's Pruitt moves to roll back over 30 environmental regulations in record time


Youre like the guy whose team is down 64-0 in the game, intercepts and spikes the ball putting the team down by only 8 touchdowns............


ghey

The science isn't mattering for dick in the real world!!

 

[mamooth]

The science isn't mattering for dick in the real world!!

At least Skook is more honest than most deniers. He's upfront about saying that he doesn't care about the actual science, and instead only cares about implementing his authoritarian utopia. Almost all deniers think that way, but most try to not come across as openly fascist.

 

[CrusaderFrank]

Q. How much must we reduce CO2 in order to halt global warming?

A. $15 Trillion