The Council's Socio-Economic Panel is in the midst of an interesting discussion about #wreckfish in the South Atlantic – listen in until 5 PM and then join us online again tomorrow beginning at 8 AM: https://t.co/dwPU1JelpN pic.twitter.com/a36dhZ5qMR
— SAFMC (@SAFMC) February 6, 2018
Environmental Economics
The cromulent economics blog
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I'm here:
February 6 & 7, 2018
Socio-Economic Panel
Crowne Plaza Hotel
4381 Tanger Outlet Boulevard
North Charleston, SC 29418Webinar Registration
February 6, 2017, 1:00 PM – 5:00 PM
February 7, 2017, 8:00 AM – 12:30 PM
Public Comment
Note: I listened to Spotify's "This is R.E.M." playlist for about 4 hours. Once that ran out of songs I listened to Radio Free Europe and, my favorite album, Reckoning (1984) to further remind me of my college and grad school days.
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I'm on a one year boycott of the Transdisciplinary Journal of the @ISEEORG https://t.co/AgQINNEpOz
— John C. Whitehead (@johnwhitehead81) February 5, 2018
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Hi! I am glad to have gotten an invitation from John to guest-blog here. I am an Associate Professor of economics at the Andrew Young School at Georgia State University. My research on environmental economics is in solar geoengineering, health effects of air pollution and climate change, behavioral economics, and some other stuff. I teach environmental economics at the undergraduate and PhD level.
So let me start by highlighting some recent research that I've done with co-authors Nolan Miller and David Molitor of the University of Illinois. In this article in The Conversation, we describe the results from a recent working paper in which we use claims data from Medicare combined with weather monitor data to estimate 1) the effect of temperature extremes on elderly mortality, 2) how these effects differ across different regions of the country, and 3) the extent to which this can inform us about the effects of climate change and the potential to adapt to climate change.
We summarize our findings on the first question:
Our key finding is that both heat waves and cold snaps increase mortality rates. For example, the mortality rate from a day with average temperatures between 90 and 95 degrees Fahrenheit is higher by about 1 death per 100,000 individuals than a day with an average temperature between 65 and 70 degrees. Deaths also increase, by about one-half per 100,000 individuals, on days when the average temperature is less than 20 degrees.
There is also substantial heterogeneity in these effects across the country (FYI the editors of The Conversation didn't want us to use fancy words like "heterogeneity," but I trust that readers of this blog will be OK with them):
In hot places like Miami, cold days have a very large impact on mortality, while the impact of hot days is smaller. In contrast, hot days in Fargo have a very large impact on mortality, but an additional cold day has little effect. In fact, the effect of the hottest days (90 degrees or higher) in the coldest places is about two to three times larger than the effect of the coldest days (less than 20 degrees) in the hottest places.
Finally, we use the cross-sectional heterogeneity across regions to predict the scope for future climate change adaptation. Basically, as currently-cold places like Chicago warm up and start to look more like currently-warm places like Miami, climate-wise, those currently-cold places will begin to exhibit the temperature-mortality relationship that currently-warm places currently have. Make sense?
This graph summarizes our predictions on the effects of climate change, depending on whether we allow for regional heterogeneity or not and whether we allow for future adaptation or not. Ignoring heterogeneity (blue bars) makes it look like climate change will be bad for hot places but actually good for cold places. (This incidentally is what is presented in this recent study by Solomon Hsiang and co-authors, which got quite a bit of press.) But, once you account for regional heterogeneity (green bars), that's not true anymore – climate change is bad everywhere and even worse for cold places than for hot places. Lastly, when allowing for adaptation (gray bars), climate change isn't as bad.
As we emphasize in the working paper and in the Conversation article, this doesn't mean that adaptation is a silver bullet that solves all climate change problems. For one thing, we don't model the costs of adaptation, which could be substantial. Second, we don't consider other responses to climate change like abatement or geoengineering.
The two main takeaways are that 1) we need to carefully consider regional heterogeneity when modeling climate change impacts, and 2) we need to carefully consider adaptation when modeling climate change impacts.
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Question 1: In the figure below, identify the 'highest' peak
Question 2: In the graph below, identify the 'highest' number
Question 3: Reconcile your answers to Question 1, Question 2, and the following Tweet:
Thank you for all of the nice compliments and reviews on the State of the Union speech. 45.6 million people watched, the highest number in history.
@FoxNews beat every other Network, for the first time ever, with 11.7 million people tuning in. Delivered from the heart! [emphasis added]Sometimes a visual comparison helps:
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My hard copy just now arrived in my box. The website says it was "First Published October 16, 2015." Abstract:
American households’ willingness to pay (WTP) for soccer player development is measured using the contingent valuation method. Data are drawn from two national surveys administered before and after the 2014 World Cup event. Individuals are asked whether they perceive that additional funding for player development will improve the chances of the national team’s performance at the 2018 World Cup and whether they are willing to pay an annual household tax to fund the program. A bivariate probit model accounts for correlation between the two decisions. WTP estimates indicate that the intangible benefits of player development are roughly twice the cost.
O. Ashton Morgan, John C. Whitehead, Willingness to Pay for Soccer Player Development in the United States, Journal of Sports Economics, Vol 19, Issue 2, pp. 279 – 296
My favorite part is the author bios (one of these guys is from the USA):
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FiveThirtyEight's assessment of President Trump's first year:
The upshot? Few dramatic changes in either direction. Jobs in the manufacturing and coal mining industries ticked up, the trade deficit shrank a bit, and the number of murders in some big cities decreased. The uninsurance rate got worse — the only change in the opposite direction of Trump’s goal. The biggest movement in the direction Trump hoped to see was the increase in oil and natural gas production.
So, according to these measures, Trump has had a reasonably good year. Of course, that doesn’t mean Trump was personally responsible for all of this change — in fact, many of these trends began long before Trump was elected. But presidents often get credited with whatever happens while they’re in office anyway.
[emphasis added, because that's the important part]
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From the NYTimes editorial page:
… New Jersey, under its new governor, Phil Murphy, a Democrat, will join — more precisely, rejoin — the Regional Greenhouse Gas Initiative, a consortium of nine Eastern and New England states that has achieved substantial emissions reductions from large power plants since its start in 2009. …
RGGI (pronounced “Reggie”) has, in fact, a Republican pedigree, dating back to 2003, when Gov. George Pataki of New York invited other Northeast governors to join a regional effort to reduce carbon emissions. What followed was a regionwide system that sets a declining cap on emissions from large power plants — about 170 in total — and requires individual power producers to buy permits from state governments to pollute. As the cap declines, the price of the permits rises, giving utilities an incentive to find cheaper ways to reduce emissions.
According to various studies, power plant emissions have declined 40 percent since 2009, while the sale of the permits has raised $2.7 billion that’s been invested in efficiency measures and renewable energy. Some of these reductions would have occurred anyway as plants shifted from coal to cheaper, cleaner-burning natural gas, and the reductions are a small fraction of the total greenhouse gases generated in the nine-state region. Even so, it’s a well-designed program that will only get stronger; last August, the nine states agreed to reduce emissions a further 30 percent by 2030. A national program along similar lines passed the House in 2009 but never came to a vote in the Senate.
This reminds of that time when John McCain was campaigning for President on cap-and-trade and … oh, nevermind.
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Anyone at EPA care to weigh in on the 'new' methods for benefit cost analysis?
I have spent my career developing (hopefully) objective methods for the valuation of environmental benefits and costs. This is particularly troubling:
The Trump administration’s rollback of environmental regulations has received ample attention in the media. Unfortunately, all the coverage has done little to slow the campaign at the U.S. Environmental Protection Agency (EPA) and the Department of the Interior to relax critical protections and dismantle former President Obama’s environmental legacy. By one recent count, the Trump administration has overturned — or is in the process of overturning — 60 environmental rules.
Less well known is that in its rush to reverse Obama-era policies, Trump’s team is distorting the way that government agencies such as the EPA evaluate and justify regulations. Yet these distortions may end up emerging as an important part of legal strategies to challenge the Trump administration’s assault on environmental regulations and oversight.
To scrap major environmental regulations put in place by prior administrations or enact new rules, the EPA is required to perform cost-benefit analyses. This has been standard government procedure for decades. Under EPA Administrator Scott Pruitt, the response to the requirement has been unwavering: Exaggerate the costs of environmental regulations; downplay the ecological, financial, and health benefits; and come up with numbers that argue for diminishing the rules or abandoning them altogether. The Pruitt approach is now being applied to some of the Obama administration’s signature achievements, including strengthening the Clean Water Act and the imposition of emissions standards under the Clean Power Plan.
As part of the WOTUS rulemaking, Obama’s EPA released its required economic analysis and found that the anticipated benefits of about $450 million per year would exceed the annual compliance costs of nearly $300 million. The vast majority of benefits were those associated with protecting wetlands, including essential services such as flood control, water quality improvements, fish and wildlife habitat, groundwater recharge, and recreational opportunities.
Then in June 2017, the Trump administration’s proposal to rescind WOTUS included its own analysis, which came to the opposite conclusion about the benefits exceeding the costs. In the new analysis, the quantified benefits for the identical policy were estimated at only $50 million per year — a reduction of nearly 90 percent — while the costs remained the same.
What explains the discrepancy? The EPA under Pruitt’s leadership asserted, with surprisingly little justification, that the 10 studies used to estimate the range of benefits associated with wetlands protection were not recent enough to sufficiently reduce uncertainty. All of the studies were conducted between 1986 and 2000, and while there are well-established techniques to update the results of such studies, none were used, nor did there appear to be any effort to search for more recent studies. The result was simply to assign a value of $0 to the largest category of ecological benefits that had been valued by the same agency two years earlier at more $400 million per year. In effect, zeroing out these benefits made it a foregone conclusion that the rule would not pass a benefit-cost test.
Unfortunately we have now entered what I fear is a partisan cycle and politicization of objective economic analysis. Until now, the EPA has done an admirable job of maintaining, at least, some semblance of objectivity. Sure, political appointees change policies and procedures, but with at least some level of continuity across new administrations. Since Executive Order 12291 during the Reagan Administration, both Republican and Democratic Administrations have relied on objective benefit cost analyses as an input to the rule- and decision-making process. Rarely have administrations so blatantly changed the methods to meet a political objective.
Until now.
My fear is that now that this is underway, any attempts to re-establish objectivity in benefit-cost analysis will be viewed as founded in political gamesmanship rather than objective economic analysis.
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