Macro Roundup

Benjamin Zycher on lack of clarity in climate trends, "...For one thing,there is no observable upward trend in the number of "hot" days between 1895 and 2017; 11 of the 12 years with the highest number of such days occurred before 1960. Since 2005, NOAA has maintained the U.S. Climate Reference Network, comprising 114 meticulously maintained temperature stations spaced more or less uniformly across the lower 48 states, along with 21 stations in Alaska and two stations in Hawaii. They are placed to avoid heat-island effects and other such distortions as much as possible. The reported data show no increase in average temperatures over the available 2005-2020 period. In addition, a recent reconstruction of global temperatures over the past 1 million years — created using data from ice-sheet formations — shows that there is nothing unusual about the current warm period. Rising sea levels are another frequently cited example of impending climate crisis. And yet sea levels have been rising since at least the mid-19th century. This rise is tied closely with the end of the Little Ice Age that occurred not long before, which led to a rise in global temperatures, some melting of sea ice, and a thermal expansion of sea water. There is some evidence showing an acceleration in sea-level rise beginning in the early 1990s: Satellite measurements of sea levels began in 1992 and show a sea-level rise of about 3.2 millimeters per year between 1993 and 2010. Before 1992, when sea levels were measured with tidal gauges, the data showed an increase of about 1.7 millimeters per year on average from 1901 to 1990.... A study of changes in Arctic and Antarctic sea ice yields very different inferences. Since 1979, Arctic sea ice has declined relative to the 30-year average (again, the degree to which this is the result of anthropogenic factors is not known). Meanwhile, Antarctic sea ice has been growing relative to the 30-year average, and the global sea-ice total has remained roughly constant since 1979....During all periods from 10 years (2006-2015) to 65 (1951-2015) years in length, the observed temperature trend lies in the lower half of the collection of climate model simulations, and for several periods it lies very close (or even below) the 2.5th percentile of all the model runs. Over shorter periods, such as the last two decades, a plethora of mechanisms have been put forth to explain the observed/modeled divergence, but none do so completely and many of the explanations are inconsistent with each other...."

Benjamin Zycher, "The Case for Climate-Change Realism,"National Affairs, Summer 2021, https://www.nationalaffairs.com/publications/detail/the-case-for-climate-change-realism

The Case for Climate-Change Realism

There is a long and infamous history of world leaders marking humanity's "last chance" to avoid the ravages of man-made climate change.

In 1989, for instance, the director of the New York office of the U.N. Environment Program warned that rising sea levels would cause entire nations to disappear if the global-warming trend were not reversed by the year 2000. "Ecological refugees will become a major concern," he cautioned, "and what's worse is you may find that people can move to drier ground, but the soils and the natural resources may not support life. Africa doesn't have to worry about land, but would you want to live in the Sahara?"

In 2007, the chairman of the Intergovernmental Panel on Climate Change (IPCC) gave the world just a few years to act: "If there's no action before 2012, that's too late. What we do in the next two to three years will determine our future. This is the defining moment."

Two years later, Prince Charles warned that we had just 96 months to avert "irretrievable climate and ecosystem collapse, and all that goes with it." "Our consumerist society comes at an enormous cost to the Earth," professed the man with multiple palaces, fleets of automobiles, a large and permanent entourage, endless opportunities for world travel, and all the other trappings of royalty. "We must face up to the fact that the Earth cannot afford to support it."

More recently, President Joe Biden echoed the existentialist concerns of his environmentalist predecessors in his inaugural address: "A cry for survival comes from the planet itself. A cry that can't be any more desperate or any more clear." This past April, while unveiling new climate-change legislation, the president reiterated the absolute imperative to act quickly and decisively to curb greenhouse-gas emissions: "We really have no choice. We have to get this done."

Not only were many of these predictions unfounded, their tone and optics were also often ripe for ridicule. The problem, however, is that Republicans rarely go beyond dismissive mockery in responding to climate alarmists. When then-president Donald Trump insisted global warming was a "hoax" or a concept "created by and for the Chinese," he took this dismissive attitude too far. Though he later insisted that his comments were meant as jokes, it is clear that such rhetoric is hardly a serious response to an empirical, scientific claim about potentially worrying changes in Earth's climate.

More concerning than the lack of seriousness in these remarks is the way such rhetoric plays into the hands of climate alarmists. The truth is that the available climate science gives serious reason to doubt, and even dispute, the claims of climate catastrophists and their allies in Washington. But when the best that Republicans can muster in defense of a more nuanced, realistic approach to understanding and responding to climate change is to refer to it as a "hoax," they empower Democrats to publicly — and, quite frankly, credibly — accuse them of "denialism," thereby establishing themselves as the undisputed "party of science."

A better understanding of climate science is not merely worthwhile for improving policymaking, but for improving political strategy. If Republican leaders took the time to acquaint themselves with climate science, they would find that Democrats' apocalyptic forecasts and radical policy proposals are largely and increasingly untethered from reality. From a purely tactical perspective, Republicans interested in fighting the Green New Deal and other extremist climate policies would be wise to cease ignoring environmental science and instead become its staunchest public advocates.

Such a re-orientation would require significant changes in the way those on the right discuss climate change. But before they can even begin implementing those changes, conservatives will need to re-engage with climate science. After having been dismissive of climate alarmism for years, it's time for conservatives to recommit themselves to understanding the scientific basics and to arguing the compelling case for climate realism.

CAUSES OF CLIMATE CHANGE

The basic theory of man-made ("anthropogenic") climate change is straightforward and undisputed: Atmospheric concentrations of greenhouse gases, emitted in part through the burning of fossil fuels, absorb some heat that would otherwise escape from the Earth's atmosphere into space, raising temperatures in the troposphere (the lowest atmospheric layer) and at the Earth's surface. This warming effect is called "radiative forcing." As greenhouse-gas concentrations rise, this warming increases too, but with diminishing impacts. In other words, the effect of an increase in greenhouse gases grows smaller as more greenhouse gas accumulates in the atmosphere.

Radiative forcing also creates a series of highly complex feedback effects in terms of ocean evaporation, cloud formation, precipitation, and other phenomena over which there exists far less scientific consensus. Because of that substantial uncertainty, the net effects of both radiative forcing and its associated feedback effects on global temperatures are the subject of sharp debate.

Despite political rhetoric suggesting the contrary, there is little dispute in the scientific literature that the amount of warming caused by radiative forcing alone is small: A doubling of atmospheric greenhouse-gas concentrations in the absence of feedback effects would increase surface temperatures by about 1 degree Celsius after the climate system fully adjusts. But that slight warming creates feedback effects that would affect the initial forcing impact. Thus, the determinative question in scientists' attempts to predict the impact of anthropogenic climate change is how "sensitive" the Earth's climate system is to radiative forcing. A low sensitivity would mean warming caused by man will be very small or even undetectable, while a high sensitivity would mean warming caused by man could be catastrophic.

Anthropogenic warming is "real" in the sense that increasing atmospheric concentrations of greenhouse gases are having detectable effects. The difficult issues are whether those effects are large or small, whether the resulting changes are adverse or beneficial on net (only extremists deny that there are some benefits to warming), and the degree to which natural variations are also contributing to climate change.

This last question is especially crucial in determining the scale of anthropogenic warming. After all, climate phenomena are driven by much more than atmospheric concentrations of greenhouse gases. One example among many is the Pacific Decadal Oscillation, a shift in circulation patterns in the northern Pacific Ocean that occurs approximately every 30 years. It has a warm phase and a cool phase, affecting temperatures in the northern hemisphere. Another example is the Southern Oscillation, which involves cycles of temperature fluctuation — known commonly as "El Niño" during the warm phase and "La Niña" during the cool phase — in the eastern and central equatorial Pacific Ocean. A third is the Atlantic Multi-Decadal Oscillation, a natural variability in sea-surface temperatures in the North Atlantic Ocean with cycles estimated at 60 to 80 years. There is also a substantial body of evidence indicating that changes in solar activity affect climate patterns on Earth as well. These examples are far from exhaustive.

In short, the complexity of the climate system is not to be understated. Moreover, the concept of a global surface temperature — quite apart from whether there have been changes in it — is more problematic than is commonly recognized. This figure is not the result of a single, centralized measurement; instead, it is the average of large positive and negative data reported by widely dispersed measurement stations subject to often-uncorrected changes in surrounding conditions. Notwithstanding those data issues, it is useful to briefly review the available global surface (land and ocean) temperature record since the "Little Ice Age," which ended no later than 1850.

The Earth is warming: The data reported by the National Oceanic and Atmospheric Administration (NOAA) show that temperatures increased after 1850 until the early 1880s, and then fell — particularly after the eruption of Krakatoa in 1883 — until 1910. They then increased substantially (by about 0.7 degrees Celsius) through 1945. A mild cooling period followed through the mid-1970s, after which temperatures increased through the late 1990s and early 2000s. Temperatures were more-or-less constant through about 2015, and have increased since then. Beginning in 1979, the satellite measurements for the entire troposphere show warming of about 0.17 degrees Celsius per decade, and for the troposphere over the tropics, of about 0.13 degrees Celsius per decade. These measurements are consistent with those from weather satellites, the data from which extend back to the 1950s.

The 1910-1945 warming — which was very roughly the same magnitude as that observed from the mid-1970s through about 2000 — is of particular interest in that it cannot be explained by higher greenhouse-gas concentrations, which increased from 300 parts per million to 310 parts per million over that period. This reinforces the commonsense observation that temperatures result from some combination of natural and anthropogenic influences. But the former have received far less attention than one might assume. As climatologist Roy Spencer of the University of Alabama in Huntsville and NASA notes:

Very little research has ever been funded to search for natural mechanisms of warming....it has simply been assumed that global warming is manmade. This assumption is rather easy for scientists since we do not have enough accurate global data for a long enough period of time to see whether there are natural warming mechanisms at work. The United Nation's Intergovernmental Panel on Climate Change (IPCC) claims that the only way they can get their computerized climate models to produce the observed warming is with anthropogenic. But they're not going to find something if they don't search for it. More than one scientist has asked me, "What else COULD it be?" Well, the answer to that takes a little digging.

The central point here is not that we can say for certain that climate change is driven primarily by natural phenomena acting independent of anthropogenic factors. But it is important to recognize that the assumption of many politicians, environmental groups, and no small number of scientist-activists — that humans are the single most significant cause of climate change — is simply unsupported by the available science. Such an absence of evidence cautions humility; it should make Americans think twice before embracing radical solutions to a problem that we are only beginning to comprehend.

CLIMATE TRENDS

Beyond exhibiting extreme overconfidence in a cherry-picked analysis of climate-change causes, politicians and activists frequently ground their alarmism in frightening predictions about consequences that are likewise far from certain. This is not only true within the very new (and still quite unreliable) field of predictive climate science; it is true even in the context of ongoing climate phenomena. Indeed, politicians and journalists frequently characterize dramatic or unusual climate phenomena as the product of anthropogenic climate change, yet there is little evidence to support those claims.

For one thing, there is no observable upward trend in the number of "hot" days between 1895 and 2017; 11 of the 12 years with the highest number of such days occurred before 1960. Since 2005, NOAA has maintained the U.S. Climate Reference Network, comprising 114 meticulously maintained temperature stations spaced more or less uniformly across the lower 48 states, along with 21 stations in Alaska and two stations in Hawaii. They are placed to avoid heat-island effects and other such distortions as much as possible. The reported data show no increase in average temperatures over the available 2005-2020 period. In addition, a recent reconstruction of global temperatures over the past 1 million years — created using data from ice-sheet formations — shows that there is nothing unusual about the current warm period.

Rising sea levels are another frequently cited example of impending climate crisis. And yet sea levels have been rising since at least the mid-19th century. This rise is tied closely with the end of the Little Ice Age that occurred not long before, which led to a rise in global temperatures, some melting of sea ice, and a thermal expansion of sea water. There is some evidence showing an acceleration in sea-level rise beginning in the early 1990s: Satellite measurements of sea levels began in 1992 and show a sea-level rise of about 3.2 millimeters per year between 1993 and 2010. Before 1992, when sea levels were measured with tidal gauges, the data showed an increase of about 1.7 millimeters per year on average from 1901 to 1990.

But because the datasets are from two different sources — satellite measurements versus tidal gauges — they are not directly comparable, and therefore they cannot be interpreted as showing an acceleration in sea-level rises. Moreover, the period beginning in 1993 is short in terms of global climate phenomena. Since sea levels have risen at a constant rate, remained constant, or even fallen during similar relatively short periods, inferences drawn from them are problematic. It is of course possible there has been an acceleration in sea-level rise, but even still, it would not be clear whether such a development stemmed primarily from anthropogenic or natural causes; clearly, both processes are relevant.

A study of changes in Arctic and Antarctic sea ice yields very different inferences. Since 1979, Arctic sea ice has declined relative to the 30-year average (again, the degree to which this is the result of anthropogenic factors is not known). Meanwhile, Antarctic sea ice has been growing relative to the 30-year average, and the global sea-ice total has remained roughly constant since 1979.

Extreme weather occurrences are likewise used as evidence of an ongoing climate crisis, but again, a study of the available data undercuts that assessment. U.S. tornado activity shows either no increase or a downward trend since 1954. Data on tropical storms, hurricanes, and accumulated cyclone energy (a wind-speed index measuring the overall strength of a given hurricane season) reveal little change since satellite measurements of the phenomena began in the early 1970s. The number of wildfires in the United States shows no upward trend since 1985, and global acreage burned has declined over past decades. The Palmer Drought Severity Index shows no trend since 1895. And the IPCC's Fifth Assessment Report, published in 2014, displays substantial divergence between its discussion of the historical evidence on droughts and the projections on future droughts yielded by its climate models. Simply put, the available data do not support the ubiquitous assertions about the causal link between greenhouse-gas accumulation, temperature change, and extreme weather events and conditions.

Unable to demonstrate that observed climate trends are due to anthropogenic climate change — or even that these events are particularly unusual or concerning — climate catastrophists will often turn to dire predictions about prospective climate phenomena. The problem with such predictions is that they are almost always generated by climate models driven by highly complex sets of assumptions about which there is significant dispute. Worse, these models are notorious for failing to accurately predict already documented changes in climate. As climatologist Patrick Michaels of the Competitive Enterprise Institute notes:

During all periods from 10 years (2006-2015) to 65 (1951-2015) years in length, the observed temperature trend lies in the lower half of the collection of climate model simulations, and for several periods it lies very close (or even below) the 2.5th percentile of all the model runs. Over shorter periods, such as the last two decades, a plethora of mechanisms have been put forth to explain the observed/modeled divergence, but none do so completely and many of the explanations are inconsistent with each other.

Similarly, climatologist John Christy of the University of Alabama in Huntsville observes that almost all of the 102 climate models incorporated into the Coupled Model Intercomparison Project (CMIP) — a tracking effort conducted by the Lawrence Livermore National Laboratory — overstate past and current temperature trends by a factor of two to three, and at times even more. It seems axiomatic to say we should not rely on climate models that are unable to predict the past or the present to make predictions about the distant future.

The overall temperature trend is not the only parameter the models predict poorly. As an example, every CMIP climate model predicts that increases in atmospheric concentrations of greenhouse gas should create an enhanced heating effect in the mid-troposphere over the tropics — that is, at an altitude over the tropics of about 30,000-40,000 feet. The underlying climatology is simple: Most of the tropics is ocean, and as increases in greenhouse-gas concentrations warm the Earth slightly, there should be an increase in the evaporation of ocean water in this region. When the water vapor rises into the mid-troposphere, it condenses, releasing heat. And yet the satellites cannot find this heating effect — a reality suggesting that our understanding of climate and atmospheric phenomena is not as robust as many seem to assume.

The poor predictive record of mainstream climate models is exacerbated by the tendency of the IPCC and U.S. government agencies to assume highly unrealistic future increases in greenhouse-gas concentrations. The IPCC's 2014 Fifth Assessment Report, for example, uses four alternative "representative concentration pathways" to outline scenarios of increased greenhouse-gas concentrations yielding anthropogenic warming. These scenarios are known as RCP2.6, RCP4.5, RCP6, and RCP8.5. Since 1950, the average annual increase in greenhouse-gas concentrations has been about 1.6 parts per million. The average annual increase from 1985 to 2019 was about 1.9 parts per million, and from 2000 to 2019, it was about 2.2 parts per million. The largest increase that occurred was about 3.4 parts per million in 2016. But the assumed average annual increases in greenhouse-gas concentrations through 2100 under the four RCPs are 1.1, 3.0, 5.5, and an astounding 11.9 parts per million, respectively.

The studies generating the most alarmist predictions are the IPCC's Special Report on Global Warming of 1.5°C and the U.S. government's Fourth National Climate Assessment, both of which were published in 2018. Both assume RCP8.5 as the scenario most relevant for policy planning. The average annual greenhouse-gas increase under RCP8.5 is over five times the annual average for 2000-2019 and almost four times the single biggest increase on record. Climatologist Judith Curry, formerly of the Georgia Institute of Technology, describes such a scenario as "borderline impossible."

RCP6 is certainly more realistic. It predicts a temperature increase of 3 degrees Celsius by 2100 in the average of the CMIP models. But on average, those CMIP models overstate the documented temperature record by a factor of at least two. Ultimately, models with a poor record of successfully accounting for past data and highly unrealistic future greenhouse-gas concentrations should not be considered a reasonable basis for future policy formulation.

It is worth noting that the public discussion of the climate crisis consistently ignores the very real possibility that the small amount of warming that will likely occur might yield noteworthy benefits. The peer-reviewed literature reports that over the last 35 years, a substantial greening of a quarter to half the Earth has occurred, of which over two-thirds is due to carbon-dioxide fertilization. The peer-reviewed literature also reports that this fertilization effect has been accompanied by a sharp increase in plants' water-use efficiency. Moreover, a well-known multinational study of the mortality effects of heat and cold finds that the latter results in far more premature deaths. Though there will likely be some negative consequences of a warming planet, these early reports remind us that there will likely be positive effects as well.

Finally, we cannot ignore the potentially adverse effects of government policies designed to fight climate change. Before we rush toward hasty assessments of a climate disaster, we need to take full stock of these potential benefits and costs.

COSTS OF COMBATING CLIMATE CHANGE

The implicit assertion of various climate proposals is that they would, upon implementation, yield significant beneficial effects. Yet curiously, their proponents almost never specify the future effects in terms of temperatures and other important climate measurements. The reality is that even policies that would most aggressively reduce greenhouse-gas emissions would likely have only very small effects on climate.

To estimate the impact of such policies, we must use a climate model. This model would make predictions about temperatures that will occur at some distant future year — say, the year 2100 — both with and without the policies under consideration. For years, the U.S. government has used the climate model developed with funding from the Environmental Protection Agency. This model suits our purposes here as well as any.

The predicted effects of the various proposals put forth may surprise many readers. Even if we were to incorporate assumptions that exaggerate the impact of reduced greenhouse-gas emissions, full implementation of the "net-zero" emissions goals of the Biden administration would reduce global temperatures by 0.17 degrees Celsius by 2100. That effect would be barely detectable given normal variation in the global temperature record. The much-discussed Green New Deal — also a net-zero proposal — would have about the same effect. The Paris agreement, if implemented immediately and enforced strictly, would have a similar impact of about 0.17 degrees Celsius. A 50% emissions cut by China would yield an impact of 0.18 degrees Celsius. A net reduction to zero greenhouse-gas emissions by all 37 member states of the Organization for Economic Cooperation and Development would increase that figure to about 0.35 degrees Celsius. An immediate global emissions cut of 75% would yield an impact of 0.54 degrees Celsius.

Despite the lackluster effects of these climate proposals, they would be exceedingly costly. Sharp reductions in greenhouse-gas emissions can be accomplished only by substituting expensive energy for cheaper energy, notwithstanding ubiquitous assertions to the contrary by proponents of climate policies. My estimate of the cost of the electricity portion of the Green New Deal alone is $500 billion per year, or about $4,000 annually per U.S. household. Setting aside the reality that such policies are not even remotely viable politically, there is serious reason to doubt such extraordinary costs are worth whatever minor reductions in temperature they might achieve.

A similar analysis can be applied to the recommendations of the IPCC's Global Warming of 1.5°C report referenced above. Buried in its supporting documentation is a discussion of the taxes on greenhouse-gas emissions that would be needed to limit warming in this century to 1.5 degrees Celsius. In 2019 dollars, the midpoint of the range for the imagined 2030 tax level comes out to $3,213 per ton. That works out to a tax per gallon of gasoline of about $30.

The way to understand the true costs of proposed climate policies is to consider the reductions in greenhouse-gas emissions that would be needed to achieve the older, "safe" levels of emissions sought by climate activists. Under the average of the CMIP models, the temperature-reduction goal for RCP6 would be 1 degree Celsius (from 3 degrees Celsius down to a goal of 2 degrees Celsius), which would require greenhouse-gas emissions cuts almost 600% greater than those promised under the Paris accord. In the RCP8.5 scenario, the average temperature increase predicted is 4.9 degrees Celsius by 2100; a reduction of 2.9 degrees Celsius would require greenhouse-gas cuts over 1700% of those promised in the agreement.

WEIGHING THE INTERESTS OF FUTURE GENERATIONS

These figures are staggering, and utterly impossible to achieve. This forces us to recognize that any policy we implement will involve significant costs and trade-offs. Rather than determining the policy solutions for us, as some politicians pretend it can do, the available climate science demands that our leaders weigh these trade-offs in consideration of our manifold interests, commitments, and values.

One important metric to consider in navigating the fraught cost-benefit analysis of a particular course of action is the concept of a "discount rate." When applied to proposed policies, a discount rate is a weighting of the relative values of effects experienced soon (or during the current period) and those experienced further out in time. A low discount rate means that the future is not being discounted heavily relative to the present; a discount rate of zero would mean that any future period, regardless of how distant in time, has the same weight as the current one. A high discount rate means that the future is discounted more heavily relative to the present.

Any investment of scarce resources must compete, at least in principle, with alternative investments. Those alternatives, on average, can be expected to yield some economic rate of return to the economy in the aggregate, even if that expected return is difficult to measure or even to define. Accordingly, the costs and benefits of alternative investments must be compared so as to yield choices among those alternatives that produce the highest total economic return to society. Conceptually, the most straightforward way to evaluate a given investment or government policy is to apply that economic rate of return as the discount rate, and to see if the stream of costs and benefits has a "net present value" greater than zero. If so, the project is justified economically, and undertaking it will increase the aggregate value of all investments considered together.

Among the chief arguments proponents of aggressive climate policies promote is the contention that climate change is a dangerous phenomenon that will impose massive harms on future generations. Accordingly, the argument goes, a low or even zero discount rate applied to the analysis of climate policies is appropriate as a means by which the interests of future generations should be taken into account.

Yet this argument is incorrect. The central interest of future generations is a bequest from previous generations of the most valuable possible capital stock (the sum of all investments), of which environmental quality is but one important dimension among many. There are always unavoidable trade-offs among these dimensions. Maximizing that bequest for future generations requires the current generation to allocate resources efficiently. If environmental policies implemented by the current generation yield less wealth currently as well as a smaller total capital stock for future generations — defined broadly to include environmental values — then, from the viewpoint of those future generations, some additional emissions of pollutants and greenhouse gas would be preferable.

Analytically speaking, climate policy is a forced investment of scarce resources in a particular set of activities and a forced disinvestment in others — for example, in the exploration and development of fossil fuels. The use of an artificially low discount rate for the valuation of those investments distorts the allocation of capital among alternative investments in the current period as well as across current and future periods. The inexorable result is a smaller capital stock to be bequeathed to future generations. An artificially low discount rate for the evaluation of climate policies, therefore, is inconsistent with the interests of future generations, which will be concerned about far more than climate phenomena alone.

To think about this in more concrete terms, it helps to imagine an infant born in a cave some tens of thousands of years ago, in a world with environmental quality effectively untouched by mankind. At birth, that child would have had a life expectancy on the order of 10 years. Had it been able to choose, it would almost certainly have willingly given up some environmental quality in exchange for a longer life expectancy engendered by better housing, food, water, medical care, safety, and other advantages ad infinitum.

The general proposition is straightforward: People willingly give up some environmental quality in exchange for lives that are longer, wealthier, and healthier. Few would choose to live on a pristine desert island; most would prefer closer proximity to family, employment, entertainment, and all of the other myriad beneficial dimensions of living in a world with a less-than-pristine environment. Ultimately, then, the most responsible way for policymakers to move forward on climate policy is to weigh all of their obligations to future generations against one another, rather than focusing on one to the detriment of the rest.

Moreover, future generations will be much wealthier than the current one. The IPCC itself estimates that individual incomes by the end of the century will be at least 400% greater than is the case today, and that the damage from anthropogenic climate change unmitigated by policy initiatives will be only 2.6% of global GDP by 2100. This is virtually the same as the projections generated by the mainstream models estimating the economic effects of changing climate phenomena.

ESCAPING THE NARRATIVE

In 2006, former vice president Al Gore made it clear that "unless drastic measures to reduce greenhouse gases are taken within the next 10 years, the world will reach a point of no return." In 2011, the International Energy Agency argued that the opportunity to avoid dangerous climate change would be "lost forever" if serious actions to limit greenhouse-gas emissions were not implemented within the ensuing five years. Somehow, the fact that these deadlines have come and gone has not led to any discrediting or re-evaluation of the original claims or demands.

Luckily for us, the truth is that the ongoing crusade for major reductions in greenhouse-gas emissions — that is, large shifts away from inexpensive energy — is grounded in an alarmism entirely unsupported by the available scientific evidence. To achieve these perpetually apocalyptic conclusions, climate catastrophists have hid behind modeling results that are notoriously unreliable, climate proposals with impacts either small or ambiguous, and a growing unwillingness to engage in actual debate. Meanwhile, those deeply skeptical of the many assertions climate activists and their political allies make have been shunted aside, branded as "denialists," and banished from polite society.

There seems to be little incentive to challenge these alarmists or their false "science versus denial" dichotomy. But the political stakes are too high for those who can see through climate extremism to dismiss such hysterics and leave the substance of alarmists' claims unchallenged. So long as Democrats can get away with presenting theirs as the "party of science," they will continue to operate with a significant strategic advantage when it comes to public debates on climate policies that will have profound impacts on the country for generations to come. Instead of merely dismissing the faux science that lends support to climate alarmism as a "hoax," conservatives must do more to engage with and reclaim the growing body of scientific evidence that supports their climate-change realism.

Benjamin Zycher is a resident scholar at the American Enterprise Institute.

Crouzet/Eberly argue measuring intangible capital and rents together explains ~ 1/3 to 2/3 of the productivity slowdown in the USA, "... Empirically reasonable values of markups and unmeasured capital can account for about half of the measured productivity slowdown in the 2000s..."

One implication follows for TFP, "...In this framework,markups alone generate a downward bias in TFP growth. They imply that the measured labor share is an overestimate of the true elasticity of output with respect to labor. When capital is growing faster than labor, this biases downward measures of TFP growth. However, this bias is not quantitatively large, even in extreme cases. Omitted intangibles alone could also bias measured TFP growth: if the true stock of unmeasured capital is growing more slowly than the measured stock, measured TFP growth will be biased downward. In addition, omitting intangible investment makes the level of GDP too low, relative to true GDP; therefore, the measured labor share is higher than the elasticity of output to labor. This upward bias in the measured labor share can be large and increases with the amount of intangible investment omitted from GDP calculations. However, an upward bias in the labor share seems surprising empirically, since the measured labor share has been declining since at least the late 1990s..."

Two insights 1)"... First, while in the model, the level of output is mismeasured — it is too low —, its growth rate is correctly measured, because omitted and measured investment, in nominal terms, grow at the same rate on the balanced growth path. Thus, in our framework, mismeasurement of GDP growth is not a source of bias in productivity growth by construction, whereas it is the focus of much previous work...."

2)"... Second, and most important, the model helps clarify under what conditions mismeasurement in the growth rate of capital may lead to underestimating TFP growth. Specifically, we show that this will occur when the price of unmeasured investment grows sufficiently quickly relative to the price of measured capital. This mechanism, on its own, can lead to a substantial downward bias in measured TFP growth, which we characterize analytically..."

Their takeaway"...If the price of omitted intangible capital is rising sufficiently fast, an upward bias in measured capital growth (and therefore, a downward bias in measured TFP growth) can occur. However, such mismeasurement would also imply that the level of measured GDP is biased downward, by an amount equal to the flow of intangible investment. This, in turn, would tend to generate a rising measured labor share, at odds with the data. Rising markups, in tandem with rising intangibles, can offset this force and allow simultaneously for a downward bias in measured TFP growth and a declining labor share. We articulated this argument more precisely in balanced growth model featuring both intangibles and markups, and showed, using the input-output tables, that this mechanism could plausibly account for one to two-thirds of the decline in measured TFP growth. Our results do not imply that the rate of growth of output is mismeasured. Rather, they attribute some of this decline to rising relative prices of certain forms of intangible capital. A difficult but worthwhile question is why these forms of intangible capital have become relatively more expensive. Additionally, outside of the balanced growth path, omitted intangibles may bias the measured growth rate of GDP, further exacerbating TFP growth biases. Finally, our balanced growth analysis assumes Cobb-Douglas substitutability between labor and capital, but deviations from this assumption may accentuate the wedge between the 630 measured labor share and the output elasticity of labor, and amplifying measurement bias. We leave these questions to future research...."

"...In Section 3, we then use data on omitted intangibles, in the balanced growth framework, to quantify the size of the combined biases in measured TFP growth. We use the annual Input-Output tables for the 1997-2018 period to measure annual expenditures on 61 commodities or services that are treated as intermediate purchases in national accounting. We then examine those for which reclassifying intermediate purchases as final expenditure on capital goods would have the largest impact on GDP. Several service groups stand out. Professional, Scientific and Technical services, Administrative and Support services, and Management services, lead to large upward adjustments in measured GDP...."

"...Using these data and our balanced growth model, we then estimate that the combination ofmarkups and omitted intangibles can explain between one-third and two-thirds of the decline in U.S. productivity from the pre-1997 to the post-1997 period. Table 1 shows that productivity growth measured using the standard Solow residual, declined by 49bps, from 1.11% to 0.62% per year. Our corrected measure of TFP growth, which adjusts for both 122 intangibles and markups, instead declines from 1.11% to 0.95%, or only about one-third as much Importantly, we also show that measurement bias was likely much smaller in the 1947-1996 period, a period which pre-dates the rise in both intangibles and in markups...."

"... Table 2 reports the time-series averages of the ratios of unadjusted to adjusted GDP, ˆ b, defined as in Equation (18). The averages reported are computed when intermediate use of a single commodity or service group (among the 61 reported in the Use table) is reclassified as intangible investment in isolation. Among the groups with the 10 largest adjustments, 3 service groups are of particular interest. The largest adjustment is associated with the Professional, Scientific and Technical Services (PSTS) group. Reclassifying intermediate expenditures on these services as intangible investment implies that actual GDP is approximately 6% larger than measured GDP. This group comprises service activities that can be purchased externally by firms, such as accounting, consulting, design, or computer services. The two other service groups of interest are Administrative and Support Services, and Management of Companies and Enterprises. The former group measures the use of outsourced business support services (such as personnel administration and training). The latter group measures the service output of establishments that administer other establishments in a company. Our core argument is that intermediate expenditures on these types of services could in fact represent purchases of investment goods by firms, which would then be misclassified in national accounts.The type of capital created by these purchases is intangible, in that it does not have a physical presence. Indeed, these purchases could lead to the accumulation of various forms of organization capital (through consulting, advertising, design, management and personnel-related services), none of which are embodied in physical assets. These expenditures lead to capital accumulation to the extent that the corresponding inputs are not used up in production entirely within the year of their purchase. Taken together, omitting these forms of investment could have large effects on GDP. The first column of Table 3 shows that reclassifying the three service groups mentioned above leads to a cumulative adjustment in the level of GDP in the order of 11%. Accordingly, investment rates adjusted for these omissions are higher than, and diverging from, measured investment rates. Figure 3 reports the time series for both the ratio of unadjusted to adjusted GDP, and for the implied ratio of nominal investment to GDP after adjusting for omitted intangibles. For instance, adjusting for Professional Services leads to an upward revision of approximately 5% in the ratio of nominal investment to nominal GDP….”

"... Figure 4 reports the implied rate of growth of TFP (as well as a red line indicating the average simple Solow residual ˆgZ the post-1997 sample) when adjusting for the 61 commodity and service groups individually. Adjustments of individual service or commodities groups have a positive, though relatively small overall effect on measured TFP growth. Among the largest adjustments is obtained for the PSTS group; alone, it adds approximately 0.1% to overall TFP growth, or 1/5th of the gap between pre- and post- 1997 TFP growth...."

New NBER presents evidence that demand for skill helps drive the Flynn effect (in terms of rising intelligence). Using Swedish administrative data("...We focus on Sweden, where an administrative data join between standardized test scores (collected for military conscription typically at age 18 or 19) and earnings (collected by the tax agency over the lifecycle) allows us to measure the level of and return to skill in a consistent way across cohorts for the near-population of men....")they find that returns to both logical reasoning and vocabulary knowledge skills have fallen over time. They compare actual changes to a counterfactual where returns stay fixed over time, and find that changing returns can explain ~1/3 of the increase in fluid intelligence (general reasoning ability). and the entire decrease in crystallized intelligence (domain knowledge)

Core findings, "... Across the birth cohorts 1962-1975, we find that performance on a logical reasoning task—our proxy for fluid intelligence—improved by 4.5 percentile points relative to the distribution in the 1967 cohort. The estimated lifetime earnings premium to an additional percentile point of logical reasoning performance fell by 0.07 log points, from a base of 0.48 log points. Turning to performance on a vocabulary knowledge test—our proxy for crystallized intelligence—we find that performance declined by 2.8 percentile points. The estimated lifetime premium to an additional percentile point of vocabulary knowledge also fell by 0.07 log points, but from a much lower base of 0.16 log points..."

"...Because logical reasoning performance rose while its market return fell, a model in which logical reasoning is the only skill dimension would imply that there must have been an increase in the supply of skill, consistent with the hypothesis of a growth in the endowment of fluid intelligence of the sort emphasized in the cognitive science literature. A richer picture emerges when incorporating the second skill dimension. Vocabulary knowledge performance fell along with its market return, suggesting a decline in the demand for this skill dimension. Moreover, the premium to vocabulary knowledge relative to logical reasoning fell by 40 log points. Seen through the lens of our model, the declining relative premium to crystallized intelligence drove a reallocation of effort towards developing abstract reasoning and away from acquiring knowledge. We use the model to decompose the observed trends in skills into a portion driven by changing labor market returns and a portion driven by other factors. According to the estimated model, if the market returns to different skills had remained constant at their 1962 level, logical reasoning and vocabulary knowledge performance would have increased by 2.9 and 3.0 percentile points, respectively. The estimated model thus implies that trends in labor market returns explain 36 percent of the growth in logical reasoning performance and more than fully explain the decline in vocabulary knowledge...."

Evidence, "... Figure 1 depicts the average skill levels xc and the estimated lifetime skill premia Pc across cohorts in the enlistment data along with their 95 percent pointwise and uniform confidence intervals. For convenience we label cohorts with their birth year, i.e., c−29. Figure 1 also depicts the lines of best fit through the plotted series. The top row of plots in Figure 1 shows that logical reasoning skill rose, on average, by 4.5 percentile points, relative to the 1967 distribution, across the birth cohorts from 1962 to 1975. By contrast, vocabulary knowledge skill fell, on average, by 2.8 percentile points. Appendix Figure 9 depicts the cumulative distribution functions of skills in the 1962 and 1975 cohorts. The bottom row of plots in Figure 1 shows that the lifetime skill premium fell for both logical reasoning and vocabulary knowledge. The line of best fit indicates that the lifetime premium for a percentile point of logical reasoning skill fell from 0.48 to 0.41 log points across the birth cohorts from 1962 to 1975, and the lifetime premium for a percentile point of vocabulary knowledge fell from 0.16 to 0.09 log points. Thus, the lifetime premium for both skill dimensions fell, with a proportionately much greater decline for vocabulary knowledge.27 Appendix Figure 10 depicts estimated lifetime skill premia based on a generalization of equation (1) that allows interactions between the skill dimensions...."

Santiago Hermo, Miika Päällysaho, David Seim and Jesse Shapiro, "Labor Market Returns and the Evolution of Cognitive Skills: Theory and Evidence," National Bureau Of Economic Research, August 2021, https://www.nber.org/papers/w29135

“…Figure 4 shows the evolution of logical reasoning and vocabulary knowledge skill in the data and in the counterfactual scenario in which the lifetime skill premia Pc remain constant at their initial level Pc. In the counterfactual scenario, logical reasoning skill increases by 2.9 percentile points instead of 4.5 as in the actual data. Vocabulary knowledge skill increases by 3.0 percentile points rather than falling by 2.8 percentile points. In this sense, according to the model, changes in the lifetime skill premia Pc account for 36.1 percent of the increase in logical reasoning skill (with a standard error of 1.5 percent), and for more than the entire decline in vocabulary knowledge skill….”

Counterfactual

Using evidence from Chile Chad Syverson finds that when workers change firms they generally move from lower to higher productivity employers raising aggregate productivity, but that mean hides a great deal of churn in both directions. They use earnings as a proxy for skill level.

Three core findings, First, "...while on average individual job transitions move workers from lower-productivity to higher-productivity firms, the fraction of all job changes that flow up the productivity ladder is only marginally higher than those in the opposite direction. Almost half (49% in our preferred specification) of all job changes move workers towards firms that have lower productivity levels than their prior employer. Thus net labor reallocation towards higher-productivity firms hides a very large degree of job churning whose productivity implications mostly cancel...."

Second, "....we show that the fraction of up-the-productivity-ladder transitions varies significantly across job flow types and across firm productivity levels. They are more likely for direct job-to-job transitions than for those passing thorough non-employment spells. This is consistent with standard job-search theory, in the sense that movements resulting from on-the-job search should lead to better quality jobs more likely to be available at higher productivity firms. They are also more likely to occur between firms at the high end of the productivity distribution, with up-the-ladder transitions originating from low productivity firms happening less frequently than implied by a model of completely random movements...."

Third, "... the job transitions of younger workers, workers with high skills, and female workers are more likely to be to higher-productivity firms and correspond to larger productivity gains. In fact, young skilled workers provide the plurality of net labor reallocation to higher productivity firms. The net contributions of other large groupings are modest or even negative. Workers with the highest turnover rates contribute proportionally little to aggregate productivity growth...."

They note that their results imply that labor market churn for lower skilled workers likely isn't productivity enhancing, "... Overall, our findings sound a note of caution, or at least modesty in interpretation, about the connections between labor market fluidity and aggregate productivity growth. While fluidity enables reallocation of resources to more productive firms, many of the observed job transitions appear to happen for reasons that are orthogonal to industry or aggregate productivity growth. The flip side of this coin is that there may be a substantial amount of untapped potential for labor reallocations to enhance productivity growth...."

The data, "...First, we drop all firms that have only one employee for an entire year, as it seems likely that they represent some form of self-employment rather than an actual firm with workers in the spirit of our exercise. We also drop all workers employed in those firms. This is about 1.7% of the initial set of 48 million monthly worker-job matches. Second, we want to avoid spurious job transitions in which workers move to a “new” firm with a different tax ID that is actually directly linked to the previous firm. This includes cases in which the firm tax ID changes, M&As, or separations of a single firm into several business entities for tax reasons. We address this by excluding from the set of transitions all cases in which a significant share of a firm’s workforce jointly reallocates to another firm, whether existing or new. This removes just under 5000 firms.1 Third, we must assign any workers employed by more than one firm in a given month to a single job and employer. We classify the worker’s main job as the one with the longest current tenure and, if there is a tie, the one with the highest average monthly earnings. About 8% of our worker-job observations are dropped as secondary jobs. Finally, as we want to focus on full-time jobs but have no information on hours, we drop all employment relations in which the worker earns an implied full-time wage that is less than the minimum wage for 80% of her tenure. These jobs, which account for 20% of the initial set of observations, are probably part-time. We calculate average labor productivity at the firm level as a measure of annual sales (from form F22) per annual-equivalent worker. Firms’ annual-equivalent workers are simply the sum of workers in firms’ monthly DJ1887 reports, divided by 12. For example, a firm with one worker who is employed the entire year and two employees who each work in six months of the year (whether overlapping months or not) has two annual-equivalent employees.2 Thus, average labor productivity is defined at an annual level...."

The Evidence, "...Panel A presents the results across all 11 million transitions in our sample. Consistent with reallocation being a factor behind productivity growth, the average labor productivity gap between the destination and origin firm in a worker transition is about 8%. However, this aggregate result averages over enormous heterogeneity. This is clear in the large dispersion of the productivity differentials. The 25th percentile of the distribution involves a worker moving to a firm with a labor productivity level about half the size (e −0.625 = 0.535) of the firm she left. The 75th percentile transition is to a firm with a productivity level more than twice as high (e 0.761 = 2.14). The rough symmetry of this dispersion is reflected in the fact that the share of job transitions that move up the productivity ladder is only a shade above half, with 47.6% of worker transitions being to firms less productive than the origin firm. This is a striking result. While net reallocation flows are directed towards more productive firms, the process involves an enormous degree of churning in both directions. Theoretically, even in a frictionless world in which all job transitions were efficient, some transitions down the firm-productivity ladder might be motivated by better firm-worker matches or from other non-pecuniary motives that might drive worker reallocation across different firms…. Nonetheless, the fact that the frequency of upward flows is only marginally higher than that of a completely random reallocation benchmark, with a 50% unconditional change of moving up or down the firm-productivity ladder, is notable...."

"...Panel B addresses a potential concern regarding the exercise shown in Panel A. As discussed earlier, our data defines firm productivity by calendar years. Transitions that occur between calendar years compare productivity measures across different points in time. Secular aggregate productivity growth implies that the average true productivity gaps for across-year transitions would be overestimated. Additionally, the raw productivity gap does not account for systematic differences in average labor productivity across sectors, which are likely related to differences in capital intensity. We therefore adjust productivity across years by subtracting from the measured destination-origin productivity difference both the average annual productivity growth over the period as well as, for transitions across sectors, the difference in mean sector productivity levels. After this adjustment, the average productivity gaps do become smaller, with the average differential for all transitions falling to about 5%, and 51.3% of transitions being upwards. Most of the basic patterns in Panel A remain, however.The biggest difference is that the average and median productivity gaps of job-N-job transitions are now slightly negative.For the rest of the paper, our attention will focus on this measure of adjusted labor productivity differences across transitions, although all results are qualitatively robust to using the unadjusted gaps summarized in Panel A...."

"...To have a sense of the macroeconomic scale of the reallocation process described by the data, we sum sales per worker gaps across all job transitions that take place during a given year and then divide that sum by firms’ total sales that year. This calculation assumes each transition leads to an output change equal to what would happen if the transitioning worker experienced a labor productivity change equal to that between of the two firms she transitions between. The sum of these output changes relative to total output provides a metric of output growth coming from productivity gains through labor reallocation. Over our sample period, the implied average annual sales growth from reallocation computed in this way is 1.35. For comparison, average annual total sales growth in our data is 5.9%. This suggests that labor reallocation from lower to higher marginal product activities is quantitatively important as a potential source of growth..."

"...To explore these elements further and complement our firm-heterogeneity results in the previous section with worker-heterogeneity results, we place every worker into one of 25 age-by-skill worker groups. These use the five skills quintiles derived as above as well as five age groups (less than 25, 25-34, 35-44, 45-54, 55+). We compute the average productivity gap associated with worker transitions within each group. We take further advantage of our data to introduce one additional dimension by dividing the sample into men and women. This is a particularly interesting dimension for an economy like Chile, in which the gender wage gap is large and female labor participation, while growing, is still low. Figure 2 graphs the results. The differences across groups reflect some remarkable patterns. Consistent with the discussion above, productivity differentials are significantly larger for young, skilled workers. They are the largest for workers younger than 25 and in the top skill quintile. Productivity gaps decline monotonically with age, even conditioning on skill, becoming almost negligible for workers over 45. They also tend to increase with worker skill, conditioning on age. The one group where the average productivity difference between the destination and origin firm is clearly negative is for high-skilled workers over 55. This may reflect that a large share of those transitions are non-voluntary and associated with the destruction of such workers’ valuable job ladders. Comparing across genders, productivity gaps for a given age-skill group are consistently larger for female workers. The sole exception is the youngest, least-skilled group, where long time gaps between jobs are frequent. This indicates systematic gender-related difference in the process of labor reallocation. Overall, these results are further evidence of the heterogeneity in the interaction between job transitions and productivity. Productivity-enhancing reallocation is significantly stronger among young, high-skilled, and female workers. We now use these results to decompose productivity gains from reallocation into group-level contributions..."

Elias Albagli, Mario Canales, Chad Syverson, Matias Tapia and Juan Wlasiuk, "Productivity Growth and Workers’ Job Transitions: Evidence from Censal Microdata," National Bureau Of Economic Research, April 2021, https://www.nber.org/papers/w28657