Tag Archives: Tradeoffs

AI Won’t Repeal Scarcity, Tradeoffs, Or Jobs

04 Monday Aug 2025

Posted by in Artificial Intelligence, Labor Markets

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Every now and then I grind my axe against the proposition that AI will put humans out of work. It’s a very fashionable view, along with the presumed need for government to impose “robot taxes” and provide everyone with a universal basic income for life. The thing is, I sense that my explanations for rejecting this kind of narrative have been a little abstruse, so I’m taking another crack at it now.

Will Human Workers Be Obsolete?

The popular account envisions a world in which AI replaces not just white-collar technocrats, but by pairing AI with advanced robotics, it replaces workers in the trades as well as manual laborers. We’ll have machines that cure, litigate, calculate, forecast, design, build, fight wars, make art, fix your plumbing, prune your roses, and replicate. They’ll be highly dextrous, strong, and smart, capable of solving problems both practical and abstract. In short, AI capital will be able to do everything better and faster than humans! The obvious fear is that we’ll all be out of work.

I’m here to tell you it will not happen that way. There will be disruptions to the labor market, extended periods of joblessness for some individuals, and ultimately different patterns of employment. However, the chief problem with the popular narrative is that AI capital will require massive quantities of resources to produce, train, and operate.

Even without robotics, today’s AIs require vast flows of energy and other resources, and that includes a tremendous amount of expensive compute. The needed resources are scarce and highly valued in a variety of other uses. We’ll face tradeoffs as a society and as individuals in allocating resources both to AI and across various AI applications. Those applications will have to compete broadly and amongst themselves for priority.

AI Use Cases

There are many high-value opportunities for AI and robotics, such as industrial automation, customer service, data processing, and supply chain optimization, to name a few. These are already underway to a significant extent. To that, however, we can add medical research, materials research, development of better power technologies and energy storage, and broad deployment in delivering services to consumers and businesses.

In the future, with advanced robotics, AI capital could be deployed in domains that carry high risks for human labor, such as construction of high rise buildings, underwater structures, and rescue operations. This might include such things as construction of solar platforms and large transports in space, or the preparation of space habitats for humans on other worlds.

Scarcity

There is no end to the list of potential applications of AI, but neither is there an end to the list of potential wants and aspirations of humanity. Human wants are insatiable, which sometimes provokes ham-fisted efforts by many governments to curtail growth. We have a long way to go before everyone on the planet lives comfortably. But even then, peoples’ needs and desires will evolve once previous needs are satisfied, or as technology changes lifestyles and practices. New approaches and styles drive fashions and aesthetics generally. There are always individuals who will compete for resources to experiment and to try new things. And the insatiability of human wants extends beyond the strictly private level. Everyone has an opinion about unsatisfied needs in the public sphere, such as infrastructure, maintenance, the environment, defense, space travel, and other dimensions of public activity.

Futurists have predicted that the human race will seek to become a so-called Type I civilization, capable of harnessing all of the energy on our planet. Then there will be the quest to harness all the energy within our solar system (a Type II civilization). Ultimately, we’ll seek to go beyond that by attempting to exploit all the energy in the Milky Way galaxy. Such an expansion of our energy demands would demonstrate how our wants always exceed the resources we have the ability to exploit.

In other words, scarcity will always be with us. The necessity of facing tradeoffs won’t ever be obviated, and prices will always remain positive. The question of dedicating resources to any particular application of AI will bring tradeoffs into sharper relief. The opportunity cost of many “lesser” AI and robotics applications will be quite high relative to their value to investors. Simply put, many of those applications will be rejected because there will be better uses for the requisite energy and other resources.

Tradeoffs

Again, it will be impossible for humans to accomplish many of the tasks that AI’s will perform, or to match the sheer productivity of AIs in doing so. Therefore, AI will have an absolute advantage over humans in all of those tasks.

However, there are many potential applications of AI that are of comparatively low value. These include a variety of low-skill tasks, but also tasks that require some dexterity or continuous judgement and adjustment. Operationalizing AI and robots to perform all these tasks, and diverting the necessary capital and energy away from other uses, would have a tremendously high opportunity cost. Human opportunity costs will not be so high. Thus, people will have a comparative advantage in performing the bulk if not all of these tasks.

Sure, there will be novelty efforts and test cases to train robots to do plumbing or install burglar alarm systems, and at some point buyers might wish to have robots prune their roses. Some people are already amenable to having humanoid robots perform sex work. Nevertheless, humans will remain competitive at these tasks due to the comparatively high opportunity costs faced by AI capital.

There will be many other domains in which humans will remain competitive. Once more, that’s because the opportunity costs for AI capital and other resources will be high. This includes many of the skilled trades, caregivers, and a great many management functions, especially at small companies. Their productivity will be enhanced by AI tools, but those jobs will not be decimated.

The key here is understanding that 1) capital and resources generally are scarce; 2) high value opportunities for AI are plentiful; and 3) the opportunity cost of funding AI in many applications will be very high. Humans will still have a comparative advantage in many areas.

Who’s the Boss?

There are still other ways in which human labor will always be required. One in particular involves the often complementary nature of AI and human inputs. People will have roles in instructing and supervising AIs, especially in tasks requiring customization and feedback. A key to assuring AI alignment with the objectives of almost any pursuit is human review. These kinds of roles are likely to be compensated in line with the complexity of the task. This extends to the necessity of human leadership of any organization.

That brings me to the subject of agentic and fully autonomous AI. No matter how sophisticated they get, AIs will always be the product of machines. They’ll be a kind of capital for which ownership should be confined to humans or organizations representing humans. We must be their masters. Disclaiming ownership and control of AIs, and granting agentic AIs the same rights and freedoms as people (as many have imagined) is unnecessary and possibly dangerous. AIs will do much productive work, but that work should be on behalf of human owners, and human labor will be deployed to direct and assess that work.

AIs (and People) Needing People

The collaboration between AIs and humans described above will manifest more broadly than anything task-specific, or anything we can imagine today. This is typical of technological advance. First-order effects often include job losses as new innovations enhance productivity or replace workers outright, but typically new jobs are created as innovations generate new opportunities for complementary products and services both upstream in production or downstream among ultimate users. In the case of AI, while much of this work might be performed by other AIs, at a minimum these changes will require guidance and supervision by humans.

In addition, consumers tend to have an aesthetic preference for goods and services produced by humans: craftsmen, artists, and entertainers. For example, if you’ve ever shopped for an oriental rug, you know that hand-knotted rugs are more expensive than machine-weaved rugs. Durability is a factor as well as uniqueness, the latter being a hallmark of human craftspeople. AI might narrow these differences over time, but the “human touch” will always have value relative to “comparable” AI output, even at a significant disadvantage in terms of speed and uncertainty regarding performance. The same is true of many other forms, such as sports, dance, music, and the visual arts. People prefer to be entertained by talented people, rather than highly-engineered machines. The “human touch” also has advantages in customer-facing transactions, including most forms of service and high-level sales/financial negotiations.

Owning the Machines

Finally, another word about AI ownership. An extension of the fashionable narrative that AIs will wholly replace human workers is that government will be called upon to tax AI and provide individuals with a universal basic income (UBI). Even if human labor were to be replaced by AIs, I believe that a “classic” UBI would be the wrong approach. Instead, all humans should have an ownership stake in the capital stock. This is wealth that yields compound growth over time and produces returns that make humans less reliant on streams of labor income.

Savings incentives (and negative consumption incentives) are a big step in encouraging more widespread ownership of capital. However, if direct intervention is necessary, early endowments of capital would be far preferable to a UBI because they will largely be saved, fostering economic growth, and they would create better incentives than a UBI. Along those lines, President Trump’s Big Beautiful Bill, which is now law, has established “Baby Bonds” for all American children born in 2025 – 2028, initially funded by the federal government with $1,000. Of course, this is another unfunded federal obligation on top of the existing burden of a huge public debt and ongoing deficits. Given my doubts about the persistence of AI-induced job losses, I reject government establishment of both a UBI and universal endowments of capital.

Summary

Capital and energy are scarce, so the tremendous resource requirements of AI and robotics means that the real world opportunity costs of many AI applications will remain impractically high. The tradeoffs will be so steep that they’ll leave humans with comparative advantages in many traditional areas of employment. Partly, these will come down to a difference in perceived quality owing to a preference for human interaction and human performance in a variety of economic interactions, including patronization of the art and athleticism of human beings. In addition, AIs will open up new occupations never before contemplated. We won’t be out of work. Nevertheless, it’s always a good idea to accumulate ownership in productive assets, including AI capital, and public policy should do a better job of supporting the private initiative to do so.

Biden OMB Suggests Minimal Discounts of Future Benefits

28 Wednesday Jun 2023

Posted by in Big Government, Risk, Tradeoffs

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Tweaks to the projected costs and benefits of prospective regulations or programs can be a great way to encourage domination of resources and society by the state. Of course, public policy ideas will never receive serious consideration unless their “expected” benefits exceed costs. It’s therefore critical that the validity of cost and benefit estimates — to say nothing of their objectivity — are always subject to careful review. By no means does that ensure that the projections are reasonable, however.

Traditionally less scrutinized is the rate at which the future costs and benefits of a program or regulation are discounted into present value terms. The discount rate can have a tremendous impact on the comparison of costs and benefits when their timing differs significantly, which is usually the case.

Intertemporal Tradeoffs

People generally aren’t willing to forsake present pleasure without at least a decent prospect of future gain. Thus, we observe that the deferral of $1 of consumption today generally brings a reward of more than $1 of future consumption. That’s made possible by the existence of productive opportunities for the use of resources. These opportunities, and the freedom to exploit them, allow a favorable tradeoff at which we transform resources across time for the benefit of both our older selves and our progeny. The interaction of savers and investors in such opportunities results in an equilibrium interest rate balancing the supply and demand for saving.

We can restate the tradeoff to demonstrate the logic of discounting. That is, the promise of $1 in the future induces the voluntary deferral of less than $1 of consumption today. To arrive at the amount of the deferral, the promised $1 in the future is discounted at the consumer’s rate of time preference. The promised $1 must cover the initial deferral of consumption plus the consumer’s perceived opportunity cost of lost consumption in the present, or else the “trade” won’t happen.

Discounting practices are broadly embedded in the economy. They provide a rational basis of evaluating inter-temporal tradeoffs. The calculation of net present values (NPVs) and internal rates of return (the discount rate at which NPV = 0) are standard practices for capital budgeting decisions in the private sector. Public-sector cost-benefit analysis often makes use of discounting methodology as well, which is unequivocally good as long as the process is not rigged.

Government Discounting

The Office of Management and Budget (OMB) provides guidance to federal agencies on matters like cost-benefit analysis. As part of a recent proposal that was prompted by executive orders on “Modernizing Regulatory Review” from the Biden Administration, the OMB has recommended revisions to a 2003 Circular entitled “Regulatory Analysis”. A major aspect of the proposal is a downward adjustment to recommended discount rates, largely dressed up as an update for “changes in market conditions”.

Since 2003, the OMB’s guidance on discount rates called for use of a historical average rate on 10-year government bonds. Before averaging, the rate was converted to a “real rate” in each period by subtracting the rate of increase in the Consumer Price Index (CPI). The baseline discount rate of 3% was taken from the average of that real rate over the 30 years ending in 2002. There has been an alternative discount rate of 7% under the existing guidance intended as a nod to the private costs of capital, but it’s not clear how seriously agencies took this higher value.

The new proposal seeks to update the calculation of recommended discount rates by using more recent data on Treasury rates and inflation. One aspect of the proposal is to utilize the rate on 10-year inflation-indexed Treasury bonds (TIPS) for the years in which it is available (2003-2022). The first ten years of the “new” 30-year average would use the previous methodology. However, the proposal gives examples of how other methods would change the resulting discount rate and requests comments on the most appropriate method of updating the calculation of the 30-year average.

The new baseline discount rate proposed by OMB is 1.7%, and it is lower still for very distant flows of benefits. This is intended as a real, after-tax discount rate on Treasury bonds. It represents an average (and ex post) risk-free rate on bonds held to maturity over the historical period in question, calculated as described by OMB. However, like the earlier guidance, it is not prospective in any sense. And of course it is quite low!

Our Poor Little Rich Ancestors

The projected benefits of regulations or other public initiatives can be highly dubious in the first place. Unintended consequences are the rule rather than the exception. Furthermore, even modest economic growth over several generations will leave our ancestors with far more income and wealth than we have at our disposal today. That means their ability to adapt to changes will be far superior, and they will have access to technologies making our current efforts seem quaint.

Now here’s the thing: discounting the presumed benefits of government intervention at a low rate would drastically inflate their present value. John Cochrane uses an extreme case to illustrate the point. Suppose a climate policy is projected to avoid costs equivalent to 5% of GDP 100 years from now. Those avoided costs would represent a gigantic sum! By then, at just 2% growth, real GDP will be over seven times larger than this year’s output. Cochrane calculates that 5% of real GDP in 2123 is equivalent to 37% of 2023 real GDP. And the presumed cost saving goes on forever.

We can calculate the present value of the climate policy’s benefits to determine whether it’s greater than the proposed cost of the policy. Let’s choose a fairly low discount rate like … oh, say zero. In that case, the present value is infinite, and it is infinite at any discount rate below 2% (such as 1.7%). That’s because the benefits grow at 2% (like real GDP) and go on forever! That’s faster than the diminishing effect of discounting on present value. In mathematical terms, the series does not converge. Of course, this is not discounting. It is non-discounting. Cochrane’s point, however, is that if you take these calculations seriously, you’d be crazy not to implement the policy at any finite cost! You shouldn’t mind the new taxes at all! Or the inflation tax induced by more deficit spending! Or higher regulatory costs passed along to you as a consumer! So just stop your bitching!

Formal Comments to OMB

If Cochrane’s example isn’t enough to convince you of the boneheadedness of the OMB proposal, there are several theoretical reasons to balk. Cochrane provides links to a couple of formal comments submitted to OMB. Joshua Rauh of the Stanford Business School details a few fundamental objections. His first point is that a regulatory impact analysis (RIA), or the evaluation of any other initiative, “should be based on market conditions that prevail at the time of the RIA”. In other words, the choice of a discount rate should not rely on an average over a lengthy historical period. Second, it is unrealistic to assume that the benefits and costs of proposed regulations are risk-free. In fact, unlike Treasury securities, these future streams are quite risky, and they are not tradable, and they are not liquid.

Rauh also notes that the OMB’s proposed decline in discount rates to be applied to benefits or cash flows in more distant periods has no reliable empirical basis. He believes that results based on a constant discount rate should at least be reported. Moreover, agencies should be required to offer justification for their choice of a discount rate relative to the risks inherent in the streams of costs and benefits on any new project or rule.

Rauh is skeptical of recommendations that agencies should add a theoretical risk premium to a risk-free rate, however, despite the analytical superiority of that approach. Instead, he endorses the simplicity of the OMB’s previous guidance for discount rates of 3% and 7%. But he also proposes that RIAs should always include “the complete undiscounted streams of both benefits and costs…”. If there are distributions of possible cost and benefit streams, then multiple streams should be included.

Furthermore, Rauh says that agencies should not recast streams of benefits in the form of certainty equivalents, which interpose various forms of objective functions in order to calculate a “fair guarantee”, rather than a range of actual outcomes. Instead, Rauh insists that straightforward expected values should be used, This is for the sake of transparency and to enable independent assessment of RIAs.

Another comment on the OMB proposal comes from a group of economists at MIT. They have fewer qualms than Rauh regarding the use of risk-adjusted discount rates by government agencies. In addition, they note that risk in the private sector can often be ameliorated by diversification, whereas risks inherent in public policy must be absorbed by changes in taxes, government spending, or unintended costs inflicted on the private sector. Taxpayers, those having stakes in other programs, and the general public bear these risks. Using Treasury rates for discounting presumes that bad outcomes have no cost to society!

Conclusion

Discounting the costs and benefits of proposed regulations and other government programs should be performed with discount rates that reflect risks. Treasury rates are wholly inappropriate as they are essentially risk-free over time horizons often much shorter than the streams of benefits and costs to be discounted. The OMB proposal might be a case of simple thoughtlessness, but I doubt it. To my mind, it aligns a little too neatly with the often expansive agenda of the administrative state. It would add to what is already a strong bias in favor of regulatory action and government absorption of resources. Champions of government intervention are prone to exaggerate the flow of benefits from their pet projects, and low discount rates exaggerate the political advantages they seek. That bias comes at the expense of the private sector and economic growth, where inter-temporal tradeoffs and risks are exploited only at more rational discounts and then tested by markets.

Chill-Out Advisory: Pandemic to Endemic Means Live Again

13 Sunday Feb 2022

Posted by in Pandemic, Public Health, Uncategorized

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We might be just be done with the coronavirus pandemic. That is, it appears to be transitioning to a more permanent endemic phase. What follows are a few details about the Omicron wave and its current status, an attempt to put the risks of Covid in perspective, and a few public policy lessons that are now gaining broad currency but should have been obvious long ago.

What’s The Status?

The Omicron variant became the dominant U.S. strain of the coronavirus in December. Omicron outcompeted Delta, which was very good news because Omicron is far less severe. The chart below (from the CDC Data Tracker site) shows Omicron’s rapid ascendance and displacement of the Delta variant. The orange bar segments represent the proportion of cases of the Delta strain, while the purple and pink segments are Omicron sub-variants known as BA.1 and BA.2, respectively. BA.2 is even more transmissible than BA.1 and is likely to become dominant over the next month or so. However, the BA.2 sub-variant appears to be far less virulent than Delta, like BA.1.

Despite a record number of infections over a period of a month or so, the Omicron wave is tapering just as rapidly as it ramped up, as the next chart demonstrates. In fact, covidestim.org shows that cases are now receding in all states, DC, and Puerto Rico. Here are new cases per million people from Our World in Data:

Whether BA.2 causes cases to plateau for a while, or even a secondary Omicron “wavelet”, is yet to be seen. That would be consistent with the normal Hope-Simpson seasonal pattern of viral prevalence in the northern hemisphere (hat tip: HOLD2):

Data problems make the Omicron wave difficult to assess, however. We don’t know the share of incidental infections for the U.S. as a whole, but more than half of hospitalized Covid patients in Massachusetts and Rhode Island are classified with incidental infections. The proportion in the UK is estimated to be rising and approaching 30% of total cases, with much higher percentages in many regions of England, as shown below.

As I’ve emphasized in the past, case numbers should not be the primary gauge of the state of the pandemic, especially with a more highly contagious but relatively mild variant like Omicron. Hospitalizations are a better measure, but only if “incidental” infections are removed from the counts. That’s been acknowledged only recently by the public health establishment, and even the Biden Administration is emphasizing it as a matter of sheer political expediency. Another measure that might be more reliable for assessing the pandemic in the community as a whole is the number of emergency room patients presenting Covid-like symptoms. From the CDC Data Tracker:

There is no doubt that incidental infections create complications in caring for patients with other ailments. That has a bearing on the utilization of hospital capacity. Generally, however, strains on hospital capacity during the pandemic have been greatly exaggerated. This is not to diminish the hard work and risks faced by health care workers, and there have been spot shortages of capacity in certain localities. However, in general, staffed beds have been more than adequate to meet needs. This chart, like a few others below, is courtesy of Phil Kerpen:

With the more highly transmissible variants we have now, it’s not at all surprising to see a high proportion of incidental cases among inpatients. Incidental infections are likely to inflate counts of Covid deaths as well, given the exceptional and odd way in which Covid deaths are being recorded. It will be some time until we see full U.S. data on cases and deaths net of incidental infections. Moreover, many of the Covid deaths in December and January were from lingering Delta infections, which might still be a factor in the February counts.

How Are Your Odds?

The mild or asymptomatic nature of most Omicron cases, the large proportion of incidental hospitalizations, and the knowledge that Omicron is not a deep respiratory threat should offer strong reassurance to healthy individuals that the variant does not pose a great risk. According to a recent CDC report, in a sample of almost 700,000 vaccinated individuals aged 65 or less without co-morbidities, there were no Covid fatalities or ICU admissions during the 10 months from December 2020 through October 2021. There was only one fatality in the sample of healthy individuals older than 65. There were just 36 fatalities across the full sample of over 1.2 million vaccinated individuals, so COVID’s fatality risk was only about 0.3%. Of those deaths, 28 were among those with four or more risk factors (including co-morbidities and > 65 years). And this was before the advent of Omicron!

I have a few doubts about the CDC’s sample selection and vagaries around certain definitions used. Nevertheless, the results are striking. However, the study did not address risks to unvaccinated adults. Another more limited CDC study found that vaccinated patients were still less likely than the unvaccinated to require critical care during the Omicron wave.

A separate CDC study found a 91% reduction in the likelihood of death for Omicron relative to Delta. A study from the UK (see summary here) found that Omicron cases were 59% less likely than Delta cases to require hospitalization and 69% less likely to result in death within 28 days of a positive test. Omicron was far less deadly among both the vaccinated and the unvaccinated, and the latter had a larger reduction in the likelihood of death. The study was stratified by age as well, with less severe outcomes for Omicron among older cohorts except in the case of death, for which there was no apparent age gradient.

Another unnecessarily contentious issue has been the risk to children during the pandemic. Based on the data, there should never have been much doubt that these risks are quite low. Apparently, however, it was advantageous for teachers’ unions to insist otherwise. Phil Kerpen soundly debunks that claim with the following chart:

Covid has been less deadly to children from infancy through 17 years than the pre-pandemic flu going back to 2012! Oh yes, but teachers FEAR transmission from the children! That claim is just as silly, since children are known to be inefficient transmitters of the virus (and see here).

Now that Omicron has relegated the Delta variant to the history books, the risks going forward seem much more manageable. Omicron is less severe, especially for the vaccinated. Levels of acquired (natural) immunity from earlier infections are now much higher against older strains, and Omicron infections seem to be protective against Delta.

In commentary about the first CDC study discussed above, John Tierney lends perspective to the odds of death from pre-Omicron Covid:

“Those are roughly the same odds that in the course of a year you will die in a fire, or that you’ll perish by falling down stairs. Going anywhere near automobiles is a bigger risk: you’re three times more likely during a given year to be killed while riding in a car, and also three times more likely to be a pedestrian casualty. The 150,000-to-1 odds of a Covid death are even longer than the odds over your lifetime of dying in an earthquake or being killed by lightning.”

Yet with all this research confirming the low odds of death induced by Omicron, why have we seen recent deaths at levels approaching previous waves? First, many of those deaths are carried over from Delta infections. That means deaths should begin to taper rapidly as February reports roll in. And remember that daily reports do not show deaths by date of death. Deaths usually occur weeks or even months before they are reported. That also means some of the deaths reported might be “harvested” from much earlier fatalities. Second, given the high levels of incidental Omicron infections, some of those deaths are misattributed to Covid, an issue that is not new by any means. Finally, while Omicron is relatively mild for most people, the high rate of transmission means that a high number of especially vulnerable individuals may be infected with severe outcomes. We have seen much more severe consequences for the unvaccinated, of course, and for those with co-morbidities.

Things We Should Have Known

I’ll try to keep this last section brief, but as an introduction I’ll just say that it’s almost as if we’ve been allowing the lunatics to run the asylum. To paraphrase one comment I saw recently, if you wonder why there is so much dissent, you ought to consider the fact the much of what our governments have done (along with many private organizations) was to prohibit things that were demonstrably safe (e.g., going outside, using swing sets, or attending schools) and to encourage things that were demonstrably harmful (e.g., deferring medical care, or masking small children).

The following facts are only now coming into focus among those who’ve been “following the politics” rather than “the science”, despite pretensions to the latter.

Grow Up and Chill Out!

Life is full of risks, and nothing has changed to alter wisdom gained in earlier pandemics. For example, this pearl from a 2006 publication on disease mitigation measures should be heeded (hat tip: Phil Kerpen):

If there is one simple message everyone needs to hear, it is to stop allowing the virus bogeyman to rule your life. It will never go away completely, and it is likely to present risks that is are comparable to the flu going forward. In fact, it might well compete with the flu, which means we won’t be dealing with endemic Covid plus historical flu averages, but some smaller union of the two case loads.

So get out, go back to work, or go have some fun! Get back truckin’ on!

Cut CO2, But What About The Environment?

20 Thursday Nov 2014

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Reducing CO2 emissions can carry a high cost to the environment, as explained by The Climate Skeptic.  The tradeoff is all too real because the resources available for mitigating environmental damage are scarce. The simple economics of pollution abatement suggest that small reductions in CO2 are the best that can be achieved even as opportunities for large reductions in more dangerous pollutants are foregone. From The Skeptic:

“Coal plants produce a lot of CO2, but without the aid of modern scrubbers and such, they also produce SOx, NOx, particulates matter and all the other crap you see in the Beijing air. The problem is that the CO2 production from a coal plant takes as much as 10-100x more money to eliminate than it takes to eliminate all the other bad stuff. … Thus the same money needed to make an only incremental change in CO2 output would make an enormous change in the breath-ability of air in Chinese cities.”

In the developing world, the reductions  in CO2 emissions might also mean the sacrifice of gains in the standard of living and public health. To make matters worse, the actual benefits of reducing CO2 emissions are highly questionable: a warmer climate, should it come to pass, is unlikely to be any catastrophe, and in fact it could produce substantial net benefits for humanity.

Along the same lines, President Obama’s recent call for reduced CO2 emissions is described by Alan Caruba as a “Cruel and Costly Climate Hoax“. The climate panic has been inflamed by a community of climate researchers who have perpetrated fraud in the management of temperature data and corrupted their field’s peer review process,  and who continue to rely on climate models with terrible track records. After roughly 25 years of warming temperatures had dispelled fears of a new ice age, these researchers have recognized the latest 18-year pause in that trend with reluctance, marshaling a variety of excuses for the poor performance of their models: the ocean has acted as a heat sink (false), a series of small volcanic eruptions have caused solar energy to be reflected back into space (speculative at best, and without data prior to the year 2000 to back up the claim), or my favorite… that Chinese carbon emissions have limited solar radiation! How ironic is that?

Reductions in carbon emissions are resource intensive. Those resources have alternative uses that are too valuable to make a cavalier sacrifice. Opportunities for other kinds of environmental enhancements, improvements in public health, and better living standards should carry the day, not carbon reductions.