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Archive for September 2019







  1. Emissions trading was first used in the Acid Rain Program of the 1970s based on very different ways that SO2 emission reduction could be achieved. The two primary methods of lowering SO2 emissions from power plants are fuel switching, which increases variable cost with minimal capital investment requirements, and the installation of scrubbers and sulfur plants, which requires significant capital investment with a minimal effect on operating costs.
  2. In general, the optimal combination of these methods would vary among utility firms according to size, location, availability of fuel and technological options, future plans, and management or investor priorities. Also, the cost of cutting emissions in general is likely to vary among power plants according to plant size, level of technological sophistication, and access to technology. Therefore, the cost of meeting command and control regulations varies from firm to firm.
  3. It was in this context that John Dales first proposed that to discover and minimize the marginal cost of aggregate pollution abatement the affected firms should cooperate and work together as a group to cut aggregate emissions of the portfolio of firms and that therefore environmental regulation should address aggregate emissions instead of firm by firm emissions on a command and control basis (Dales, 1968).
  4. This idea was first tried by the EPA with the 1977 Amendment to the CAA (EPA, 2001) (Halbert, 1977) and refined into a cap-and-trade emissions trading system called the Acid Rain Program described in Title IV of the 1990 Amendments to the CAA (Popp, 2003) (Waxman, 1991) (Ellerman, 2000). This innovation is recognized as a milestone in environmental regulation.
  5. In the cap-and-trade market of the Acid Rain Program, the EPA issues allowances, or permits to pollute, in units of one million tons of SO2 per year. The sum of the allowances issued for each emission reduction period (ERP) is set to the limit or cap on aggregate emissions from all power generation units in the plan. The aggregate cap is gradually reduced in each subsequent ERP in accordance with a fixed emission reduction schedule for the duration of the plan.
  6. The allowances are distributed to the individual units in accordance with unit size measured as the total annual heat production in a defined historical reference period for which both heat production and emissions were measured and are known with some degree of certainty. Emissions at each unit are accurately measured during the ERP. At the end of the ERP each unit pays for its emissions with the allowances it had received at the beginning of the year. Units that do not have enough allowances to pay for their emissions are penalized. This mechanism is the cap component of cap-and-trade.
  7. The trade component of cap-and-trade is that during the ERP the participating units may trade allowances among themselves or with third parties in a market where clearing prices are determined by bids and asks as in commodities markets with the exception that with a limited number of traders, it is a thin and illiquid market lacking in the power of price discovery enjoyed by deep and liquid commodities markets. Holders of excess allowances, that is, those units that were able to cut emissions more deeply than required, can put their excess allowances up for sale in the emissions trading market at their ask price. Likewise, units that are unable to meet the cap can place buy orders in the emissions market at their bid price. When bids and asks cross the market clears, trades occur, and the marginal price of aggregate emission reduction is thus discovered (Chan, 2012) (Conniff, 2009) (Dales, 1968) (Ellerman A. , 2002).
  8. In this way, emission allowances are traded among the regulated entities and the aggregate emission target is met without forcing each and every unit to cut emissions at the same rate or with the same technology as in command and control regulation. Thereby the overall cost of compliance is lowered to the aggregate marginal cost in accordance with the mechanism described by John Dales (Dales, 1968).
  9. There are certain positive features of the market for SO2 emissions that are relevant in its comparison with emerging markets for trading CO2 emissions (Jenkins, 2009). The most important of these is that the regulatory regime of the Acid Rain Program is well defined in terms of geography and legal infrastructure. The regulatory authority of the US Government and the rights and obligations of the regulated utilities are well defined by the constitution and the laws of the United States of America, the powers of the Federal Government, and the provisions of the Clean Air Act and its Amendments in 1970, 1977, and 1990, and Congressional authority that requires the EPA to limit SO2 emissions across state lines. At the same time the rights of the regulated utilities are protected by law and by a well-functioning judiciary. These necessary conditions and assumptions for a functioning emissions trading program does not exist in the AGW carbon trading scheme where the United Nations is in charge but with no legal vested authority or means of enforcement and with the additional complexity created by the UNFCCC that differentiate nations into those with emission reduction obligations and those with no emission reduction obligations but with both classes of nations asked to submit INDCs in the so called Paris “Agreement”.



  1. What Refinitiv does: “This report is our assessment of the major global carbon markets in 2018, the aim being to show the main trends in global emission trading systems and areas where such systems are emerging. We collect data from official sources – most notably carbon trading platforms such as ICE, EEX, KRX, and the Chinese carbon exchanges – and, where relevant, estimate the size of non-market bilateral over-the-counter transactions to estimate the total volume traded.
  2. Carbon Credit Trading is Booming: World emission markets grew strongly in 2018, both in volume and in value. Strong growth in traded volumes and price rallies in Europe and North America led to a boom year in emission trading in 2018. Volume increased 45% to 9.1 gigatonnes worth of CO2 equivalents, the highest level since 2013. Thanks largely to the stellar rise in European allowance unit (EUA) prices in 2018, more than tripling from €8 to €25/t, the overall market value increased 250%, to €144 bn, and by far the highest level since the European Union Emission Trading System (EU ETS) was launched in 2005. Since then the EU ETS has represented the lion’s share of global carbon trading. The carbon team at Refinitiv attributes the European price rally mainly to anticipation of the Market Stability Reserve (MSR) that came into effect in January 2019. This instrument will significantly tighten the supply of emission allowances.



  1. Carbon credits are created by the combination of permits, offsets, and tradability. The permit is permission granted to a country, company or organization to produce a certain amount of emissions any portion of which can then be sold in the carbon credits market if not used. A complexity in the carbon trading scheme is the offset provision. It provides an incentive to firms or countries with no emission reduction obligation to invest in climate action the net effect of which may be sold to countries, firms, or individuals to cancel out a portion of their emissions. This provision is commonly seen in air travel where airlines buy offsets that cancel out the emissions from a flight and then sell the offset to passengers who wish to be carbon neutral.
  2. However, unlike the emission trading in the acid rain program, the climate change implementation of what appears to be the same provision is less well defined and vastly more complicated. First, there is no well defined legal superstructure for its regulation and implementation such that the structure and procedures are poorly defined and poorly regulated. Secondly, the emission problem to be solved by emission trading is poorly defined.
  3. A specific issue pointed out in [Sovacool, “Four Problems with Global Carbon Markets, Energy & Environment, Vol. 22, No. 6 (2011), pp. 681-694] is non-linearity. As described in related posts on this site, a complexity with the carbon budget is that the remaining carbon budget cannot be computed by subtraction or by linear proportionality but must be recomputed because of the non-linearity of the progression of the carbon budget through the time span of its implementation [LINK] [LINK] . Yet carbon credit trading and carbon offset markets necessarily assume a linear relationship. Therefore the basis of the pricing changes over the time span of the credit but the pricing does not.
  4. In “Why are carbon markets failing? The Guardian, Fri 12 Apr 2013, Steffen Böhm, Professor of management and sustainability at Essex Business School points out the absence of government and regulatory oversight with well defined rules and definitions and their enforcement in the emission trading system of the carbon credit market. As pointed out above in the comparison with the Acid Rain Program, although the carbon credit market is derived from a comparison with the Acid Rain Program, the parallel is lacking the the well defined legal and governance superstructure that oversaw and ensured the success of the acid rain program. Dr. Böhm thus describes the carbon credit and offset market as inefficient and corrupt and says that the carbon trading system has failed citing these structural deficiencies as reasons for its failure.
  5. The essential problem here, not just in the carbon credits market, but in the entire enterprise for saving the planet with climate action, is that the government, regulatory, legal, and management superstructure is the United Nations which sees itself as the EPA of the world in the comparison with the Acid Rain Program but it is not the EPA and has none of the EPA’s governance and regulatory powers, skills, and ability that made the acid rain program a success. This is the fundamental flaw in the assumed parallel between the acid rain program and the carbon credits market.
  6. It is precisely this absence of governance and regulatory oversight that things like the Shell offset story can happen [Shell will spend $300 million to offset carbon emissions. Here’s the catch, By Akshat Rathi, Quartz, April 10, 2019]. Here Mr. Rathi reports that Shell sells carbon offsets to its customers in the Netherlands and uses those proceeds to buy carbon credits at the carbon credits market. If the carbon credits were truly a reduction that could be checked and verified and overseen by a professional body such as the EPA, it may have some validity but what we have is a dysfunctional bureaucracy at the UN as the sole governing and regulatory body of the carbon credits market. This regulatory vacuum also explains the ability of logging companies who plant and harvest trees anyway to sell carbon credits every time they plant. And in terms of climate action carbon budgets, the emission reduction in the books contains carbon credits purchased by Annex1 countries from dubious projects in nonAnnex countries such as the alleged “preservation” of forests that probably would have been there anyway.







  1. Sedjo, Roger A., and Gregg Marland. “Inter-trading permanent emissions credits and rented temporary carbon emissions offsets: some issues and alternatives.” Climate Policy 3.4 (2003): 435-444.  Permit trading among polluting parties is now firmly established as a policy tool in a range of environmental policy areas. The Kyoto Protocol accepts the principle that sequestration of carbon in the terrestrial biosphere can be used to offset emissions of carbon from fossil fuel combustion and outlines mechanisms. Although the lack of guaranteed permanence of biological offsets is often viewed as a defect, this paper argues that the absence of guaranteed permanence need not be a fundamental problem. We view carbon emissions as a liability issue. One purpose of an emissions credit system is to provide the emitter with a means to satisfy the carbon liability associated with her firm’s (or country’s) release of carbon into the atmosphere. We have developed and here expand on a rental approach, in which sequestered carbon is explicitly treated as temporary: the emitter temporarily satisfies his liability by temporarily “parking” his liability, for a fee, in a terrestrial carbon reservoir, or “sink,” such as a forest or agricultural soil. Finally, the paper relates the value of permanent and temporary sequestration and argues that both instruments are tradable and have a high degree of substitutability that allows them to interact in markets.
  2. Streetman, Foy. “Carbon credit marketing system.” U.S. Patent Application No. 10/753,291. 2005:  A carbon credit system includes a server computer operably connected to an Internet and having an operating system and a memory operably associated therewith, carbon credit software operably disposed in the memory and accessible through said operating system, wherein a carbon credit product or carbon credit service can be purchased through said carbon credit software and which carries a predetermined number of carbon credits and said purchase causes one of a good and service certificate bearing a carbon credit consumer symbol (“CCCP”) to be sent to said purchaser. A method of promoting carbon reduction is provided.
  3. Benson, Sally M. “Monitoring carbon dioxide sequestration in deep geological formations for inventory verification and carbon credits.” SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 2006.  Large scale implementation of CO2 Capture and Storage is under serious consideration by governments and industry around the world. The pressing need to find solutions to the CO2 problem has spurred significant research and development in both CO2 capture and storage technologies. Early technical success with the three existing CO2 storage  projects and over 30 years experience with CO2-EOR have provided confidence that long term storage is possible in appropriately selected geological storage reservoirs.  Monitoring is one of the key enabling technologies for CO2 storage. It is expected to serve a number of purposes – from providing information about safety and environmental concerns, to inventory verification for national accounting of greenhouse gas emissions and carbon credit trading. This paper addresses a number of issues related specifically to monitoring for the purpose of inventory accounting and trading carbon credits. First, what information would be needed for the purpose of inventory verification and carbon trading credits? With what precision and detection levels should this information be provided? Second, what monitoring methods and approaches are available? Third, do the instruments and monitoring approaches available today have sufficient resolution and detection levels to meet these needs? Theoretical calculations and field measurements of CO2 in both the subsurface and atmosphere are used to support the discussions presented here. Finally, outstanding issues and opportunities for improvement are identified.
  4. McHale, Melissa R., E. Gregory McPherson, and Ingrid C. Burke. “The potential of urban tree plantings to be cost effective in carbon credit markets.” Urban Forestry & Urban Greening 6.1 (2007): 49-60.  Emission trading is considered to be an economically sensitive method for reducing the concentrations of greenhouse gases, particularly carbon dioxide, in the atmosphere. There has been debate about the viability of using urban tree plantings in these markets. The main concern is whether or not urban planting projects can be cost effective options for investors. We compared the cost efficiency of four case studies located in Colorado, and used a model sensitivity analysis to determine what variables most influence cost effectiveness. We believe that some urban tree planting projects in specific locations may be cost effective investments. Our modeling results suggest that carbon assimilation rate, which is mainly a function of growing season length, has the largest influence on cost effectiveness, however resource managers can create more effective projects by minimizing costs, planting large-stature trees, and manipulating a host of other variables that affect energy usage.
  5. Laurance, William F. “A new initiative to use carbon trading for tropical forest conservation.” Biotropica 39.1 (2007): 20-24.  I describe a new initiative, led by a coalition of developing nations, to devise a viable mechanism for using carbon trading to protect old‐growth tropical forests. I highlight some of the practical and political hurdles involved in forest‐carbon trading, and explain why this initiative is rapidly gaining broad‐based political support.
  6. Laurance, William F. “Can carbon trading save vanishing forests?.” BioScience 58.4 (2008): 286-287. Among the many nasty things that humans are doing to the environment, few rank worse than destroying tropical forests. Rainforests sustain an astonishing diversity of species, and they are vital for keeping our planet livable—they limit soil erosion, reduce floods, maintain natural hydrological cycles, and help to stabilize the climate. Yet around 13 million hectares of tropical forest are destroyed every year—the equivalent of 50 football fields a minute.If we hope to rein in global warming, the last thing we should do is raze tropical forests. Destroying these forests dumps vast quantities of greenhouse gases into the atmosphere—roughly one-fifth of all human carbon emissions, more than the entire global transportation sector. Further, tropical forests, which copiously transpire water vapor into the atmosphere as they photosynthesize, are major drivers of cloud formation. Clouds cool the planet by reflecting solar energy back into space, and they also sustain regional rainfall, which limits destructive forest fires. Undisturbed tropical forests may even be a major carbon sink, according to some studies, with Amazonia alone absorbing perhaps two billion tons of carbon dioxide each year. Hence, saving a hectare of tropical forest does far more to reduce global warming than does saving a hectare of temperate or boreal forest (Bala et al. 2007). In recent years, many scientists have advocated carbon trading as a way to slow tropical deforestation. The idea, known as “REDD” (reducing emissions from deforestation and degradation), is simple in concept. Under international agreements such as the Kyoto Protocol, participating nations agree to reduce their carbon emissions below a certain level. Nations that struggle to meet their emissions target can buy carbon credits from other countries that either have no target (as is currently the case for developing nations) or that produce fewer emissions than allowed. Like any tradable commodity, the price of carbon credits is largely determined by supply and demand. In theory, everyone should win with REDD. Wealthy nations could pay to help slow deforestation as part of an overall effort to meet their emissions target. Protecting an imperiled forest in Peru, for instance, might lead to the same net reduction of carbon emissions—and be considerably cheaper—than retrofitting a coal-fired generating plant in Ohio. In a transaction like this, dangerous carbon emissions are reduced, a biologically rich forest is protected, and Peru gains direly needed foreign revenues. For such reasons several influential studies, such as the widely heralded Stern Report in the United Kingdom, have advocated REDD as a vital and cost-effective strategy for slowing global warming. In any effort to slow harmful climate change, tropical forests are the low-hanging fruit
  7. Hurteau, Matthew D., George W. Koch, and Bruce A. Hungate. “Carbon protection and fire risk reduction: toward a full accounting of forest carbon offsets.” Frontiers in Ecology and the Environment 6.9 (2008): 493-498.  Management of forests for carbon uptake is an important tool in the effort to slow the increase in atmospheric CO2 and global warming. However, some current policies governing forest carbon credits actually promote avoidable CO2 release and punish actions that would increase long‐term carbon storage. In fire‐prone forests, management that reduces the risk of catastrophic carbon release resulting from stand‐replacing wild‐fire is considered to be a CO2 source, according to current accounting practices, even though such management may actually increase long‐term carbon storage. Examining four of the largest wildfires in the US in 2002, we found that, for forest land that experienced catastrophic stand‐replacing fire, prior thinning would have reduced CO2 release from live tree biomass by as much as 98%. Altering carbon accounting practices for forests that have historically experienced frequent, low‐severity fire could provide an incentive for forest managers to reduce the risk of catastrophic fire and associated large carbon release events. (long term versus short term forest management dilemma).
  8. Wara, Michael W., and David G. Victor. “A realistic policy on international carbon offsets.” Program on Energy and Sustainable Development Working Paper 74 (2008): 1-24. As the United States designs its strategy for regulating emissions of greenhouse gases, two central issues have emerged. One is how to limit the cost of compliance while still maintaining environmental integrity. The other is how to “engage” developing countries in serious efforts to limit emissions. Industry and economists are rightly concerned about cost control yet have found it difficult to mobilize adequate political support for control mechanisms such as a “safety valve;” they also rightly caution that currently popular ideas such as a Fed-like Carbon Board are not sufficiently fleshed out to reliably play a role akin to a safety valve. Many environmental groups have understandably feared that a safety valve would undercut the environmental effectiveness of any program to limit emissions of greenhouse gases. These politics are, logically, drawing attention to the possibility of international offsets as a possible cost control mechanism. Indeed, the design of the emission trading system in the northeastern U.S. states (RGGI) and in California (the recommendations of California’s AB32 Market Advisory Committee) point in this direction, and the debate in Congress is exploring designs for a cap and trade system that would allow a prominent role for international offsets. This article reviews the actual experience in the world’s largest offset market—the Kyoto Protocol Clean Development Mechanism (CDM)—and finds an urgent need for reform. Welldesigned offsets markets can play a role in engaging developing countries and encouraging sound investment in low-cost strategies for controlling emissions. However, in practice, much of the current CDM market does not reflect actual reductions in emissions, and that trend is poised to get worse. Nor are CDM-like offsets likely to be effective cost control mechanisms. The demand for these credits in emission trading systems is likely to be out of phase with the CDM supply. Also, the rate at which CDM credits are being issued today—at a time when demand for such offsets from the European ETS is extremely high—is only one-twentieth to one-fortieth the rate needed just for the current CDM system to keep pace with the projects it has already registered. If the CDM system is reformed so that it does a much better job of ensuring that emission credits represent genuine reductions then its ability to dampen reliably the price of emission permits will be even further diminished. We argue that the U.S., which is in the midst of designing a national regulatory system, should not to rely on offsets to provide a reliable ceiling on compliance costs. More explicit cost control mechanisms, such as “safety valves,” would be much more effective. We also counsel against many of the popular “solutions” to problems with offsets such as imposing caps on their use. Offset caps as envisioned in the Lieberman-Warner draft legislation, for example, do little to fix the underlying problem of poor quality emission offsets because the cap will simply fill first with the lowest quality offsets and with offsets laundered through other trading systems such as the European scheme. Finally, 1 We thank Kyle Danish, Michael Levi, Chris Mottershead, Billy Pizer, and Tauna Szymanski for their valuable comments on early versions of this manuscript; errors and opinions are fully our own. We suggest that the actual experience under the CDM has had perverse effects in developing countries—rather than draw them into substantial limits on emissions it has, by contrast, rewarded them for avoiding exactly those commitments. Offsets can play a role in engaging developing countries, but only as one small element in a portfolio of strategies. We lay out two additional elements that should be included in an overall strategy for engaging developing countries on the problem of climate change. First, the U.S., in collaboration with other developed countries, should invest in a Climate Fund intended to finance critical changes in developing country policies that will lead to near-term reductions. Second, the U.S. should actively pursue a series of infrastructure deals with key developing countries with the aim of shifting their longer-term development trajectories in directions that are both consistent with their own interests but also produce large greenhouse gas emissions reductions.
  9. Mathews, John A. “Carbon-negative biofuels.” Energy policy 36.3 (2008): 940-945.  Current Kyoto-based approaches to reducing the earth’s greenhouse gas problem involve looking for ways to reduce emissions. But these are palliative at best, and at worst will allow the problem to get out of hand. It is only through sequestration of atmospheric carbon that the problem can be solved. Carbon-negative biofuels represent the first potentially huge assault on the problem, in ways that are already technically feasible and practicable. The key to carbon negativity is to see it not as technically determined but as an issue of strategic choice, whereby farmers and fuel producers can decide how much carbon to return to the soil. Biochar amendment to the soil not only sequesters carbon but also enhances the fertility and vitality of the soil. The time is approaching when biofuels will be carbon negative by definition, and, as such, they will sweep away existing debates over their contribution to the solution of global warming.
  10. Lohmann, Larry. “Neoliberalism and the calculable world: The rise of carbon trading.” Upsetting the offset: the political economy of carbon markets (2009): 25-40.First proposed in the 1960s, pollution trading was developed by US economists and derivatives traders in the 1970s and 1980s and underwent a series of failed policy experiments in that country before becoming the centrepiece of the US Acid Rain Programme in the 1990s at a time of deregulatory fervour. In 1997, the Bill Clinton regime successfully pressed for the Kyoto Protocol to become a set of carbon trading instruments (Al Gore, who carried the US ultimatum to Kyoto, later became a carbon market actor himself). In the 2000s Europe picked up the initiative to become the host of what is today the world’s largest carbon market, the EU Emissions Trading Scheme (EU ETS) – although under Barack Obama the US may soon take over that position. Carbon markets now trade over US$100 billion yearly, and are projected to rival the financial derivatives market, currently the world’s largest, within a decade. Pioneered by figures such as Richard Sandor of the Chicago Board of Trade and Ken Newcombe, who relinquished leadership of the World Bank’s carbon funds to become a carbon trader at firms such as Goldman Sachs, carbon markets have recently become a magnet for hedge funds, banks, energy traders and other speculators. Carbon trading treats the safeguarding of climatic stability, or the earth’s capacity to regulate its climate, as a measurable commodity. After being granted or auctioned off to private firms or other polluters, the commodity can then be allocated ‘cost-effectively’ via market mechanisms. Obviously, the commoditized capacity in question was never produced for sale. Rather than being consumed, it is continually reused. Although difficult to define or even locate, the capacity forms part of the background ‘infrastructure’ for human survival. Framing it as a commodity, moreover, involves complex contradictions and blowbacks (Lohmann, 2009). Current efforts to assemble carbon markets are likely, when carried beyond a certain point, to engender systemic crises. The earth’s climate-regulating capacity is thus a quintessential Polanyian ‘fictitious commodity’. Accordingly, illuminating comparisons and contrasts can be drawn with Polanyi’s original ‘fictitious commodities’ of land, labour and money, as well as with other candidates for ‘fictitious commodity’ status that have been proposed since, including knowledge, health, genes and uncertainty. The attempt to build a climate commodity proceeds in several steps. First, the goal of maintaining the earth’s capacity to regulate its climate is conceptualized in terms of numerical greenhouse gas emissions reduction targets. Governments determine – although currently more on explicitly political than on climatological grounds – how much of the world’s physical, chemical and biological ability to regulate its own climate should be enclosed, ‘propertized’, privatised and made scarce. They then give it out (or, sometimes, sell it) to large polluters, before ‘letting the market decide’ on its final distribution (Lohmann, 2005; Lohmann, 2006). Making climate benefits and dis-benefits into quantifiable ‘things’ opens them up to the possibility of exchange. For example, once climate benefit is identified with emissions reductions, an emissions cut in one place becomes climatically ‘equivalent’ to, and thus exchangeable with, a cut of the same magnitude elsewhere. An emissions cut owing to one technology becomes climatically equivalent to an emissions cut that relies on another. An emissions cut that is part of a package that brings about one set of social effects becomes climatically equivalent to a cut associated with another set of  social effects. Where emissions permit banking is allowed, an emissions cut at one time becomes climatically equivalent to a cut achieved at another. Once all these identities are established, it becomes possible for a market to select for the emissions reductions (and, ipso facto, the climate benefits) that can be achieved most cheaply.   [FULL TEXT DOWNLOAD]
  11. Fairbairn, Eduardo MR, et al. “Cement replacement by sugar cane bagasse ash: CO2 emissions reduction and potential for carbon credits.” Journal of environmental management 91.9 (2010): 1864-1871.  This paper presents a study of cement replacement by sugar cane bagasse ash (SCBA) in industrial scale aiming to reduce the CO2 emissions into the atmosphere. SCBA is a by-product of the sugar/ethanol agro-industry abundantly available in some regions of the world and has cementitious properties indicating that it can be used together with cement. Recent comprehensive research developed at the Federal University of Rio de Janeiro/Brazil has demonstrated that SCBA maintains, or even improves, the mechanical and durability properties of cement-based materials such as mortars and concretes. Brazil is the world’s largest sugar cane producer and being a developing country can claim carbon credits. A simulation was carried out to estimate the potential of CO2 emission reductions and the viability to issue certified emission reduction (CER) credits. The simulation was developed within the framework of the methodology established by the United Nations Framework Convention on Climate Change (UNFCCC) for the Clean Development Mechanism (CDM). The State of São Paulo (Brazil) was chosen for this case study because it concentrates about 60% of the national sugar cane and ash production together with an important concentration of cement factories. Since one of the key variables to estimate the CO2 emissions is the average distance between sugar cane/ethanol factories and the cement plants, a genetic algorithm was developed to solve this optimization problem. The results indicated that SCBA blended cement reduces CO2 emissions, which qualifies this product for CDM projects.
  12. Hua, Guowei, T. C. E. Cheng, and Shouyang Wang. “Managing carbon footprints in inventory management.” International Journal of Production Economics 132.2 (2011): 178-185. There is a broad consensus that mankind must reduce carbon emissions to mitigate global warming. It is generally accepted that carbon emission trading is one of the most effective market-based mechanisms to curb the amount of carbon emissions. This paper investigates how firms manage carbon footprints in inventory management under the carbon emission trading mechanism. We derive the optimal order quantity, and analytically and numerically examine the impacts of carbon trade, carbon price, and carbon cap on order decisions, carbon emissions, and total cost. We make interesting observations from the numerical examples and provide managerial insights from the analytical results.
    • Bumpus, Adam G. “The matter of carbon: understanding the materiality of tCO2e in carbon offsets.” Antipode 43.3 (2011): 612-638.  This paper examines the socio‐natural relations inherent in the commodification of carbon reductions as they are generated in energy‐based carbon offset project activities, and abstracted to wider market systems. The ability to commodify carbon reductions takes place through a socionatural–technical complex that is defined by the material nature of technology’s interaction with the atmosphere, local social processes and the evolving governing systems of carbon markets. Carbon is not unproblematically commodified: some projects and technologies allow a more cooperative commodification than others. The examples of a hydroelectricity plant and an improved cookstove project in Honduras are used as empirical case studies to illustrate the difficulties and opportunities associated with the relational aspects of carbon commodification. Drawing upon select literatures from post‐structural thought to complement the principal lens of a more structural, materiality of nature analysis, the paper also outlines the reasons why carbon offset reform is needed if offsets are to more progressively engage debates about climate mitigation and North–South development.
    • Diabat, Ali, et al. “Strategic closed-loop facility location problem with carbon market trading.” IEEE Transactions on engineering Management 60.2 (2012): 398-408.  The burgeoning environmental regulations are forcing companies to green their supply chains by integrating all of their business value-adding operations so as to minimize the impact on the environment. One dimension of greening the supply chain is extending the forward supply chain to collection and recovery of products in a closed-loop configuration. Re-manufacturing is the basis of profit-oriented reverse logistics in which recovered products are restored to a marketable condition in order to be resold to the primary or secondary market. In this paper, we introduce a multiechelon multicommodity facility location problem with a trading price of carbon emissions and a cost of procurement. The company might either incur costs if the carbon cap, normally assigned by regulatory agencies, is lower than the total emissions, or gain profit if the carbon cap is higher than the total emissions. A numerical study is presented which studies the impact of different carbon prices on cost and configuration of supply chains.





    1. Denier: “There’s no evidence of global warming, and computer models are unreliable.”  The Science: Scientists don’t need computer models to tell them global warming is under way. For that, they can look to surface-temperature records .
    2. Denier: “Global temperatures stopped rising in 1998.”   The Science: the 10 hottest years on record have all come since 1998. 
    3. Denier: Glaciers are actually growing?   The Science:  Some glaciers are stable, and a few are even growing, but many that provide key freshwater supplies are melting at an alarming rate.
    4. Denier: The climate has changed before, so we can’t be blamed for changing it now.   The Science:  Earth’s climate has changed lots of times without human help, but does that really mean humans are incapable of changing it? That’s like arguing that humans can’t start bush fires because in the past they’ve happened naturally.
    5. Denier: Global warming is good for humans.   The ScienceCO2 does boost plant growth, and warmer weather can initially benefit crops in northern regions. But this view ignores vast, long-term dangers in favor of scattered short-term benefits. 
    6. CAUTION: It is easy to be insulting and talk down to these uninformed people so please be careful and be kind


    Source: Mother Nature Network: [LINK]



    SUMMARY: We find no evidence for the high profile claim by the WMO [LINK] and climate science in general that AGW is driving down September minimum and March maximum sea ice extent in the Arctic. It is proposed that all sources of heat, including geothermal heat, are important in the study of sea ice dynamics. In particular, the extensive geothermal heat sources of the Arctic region described in related posts on this site [LINK] [LINK] must be considered instead of an attribution of convenience to AGW that derives from an atmosphere bias in climate science in which all surface phenomena are seen in the context of AGW. As for the Antarctic, no evidence is found of sea ice decline either in the September Maximum or in the February Minimum.  In both of these calendar months we find both extent and area measures of sea ice show a rising trend. An oddity of the results is that September maximum sea ice area is growing while cooling while the February minimum is growing while warming. Such anomalous results further emphasize the importance of geothermal heat in the understanding of sea ice dynamics as explained in related posts [LINK]  [LINK] [LINK].















    1. This update was added to the post when 2019 data for the important month of September became available. It is noted that due to the extreme seasonal cycle of sea ice extent, the proposed AGW driven decline of sea ice is measured in terms of its seasonal maximum (March in the North and September in the South) and its seasonal minimum (September in the North and February in the South). These seasonal extremes are highlighted in the Figure 7 above and Figure 8 below. Both the EXTENT and AREA measures of sea ice are presented and analyzed.
    2. The data for the Arctic (NORTH) appear in Figure 7 above. The temperature data in this chart are UAH lower troposphere temperatures for the North Polar Ocean. They show statistically significant warming trends along with statistically significant decline in sea ice extent for all calendar months suggesting that the decline in sea ice may be driven by AGW and this apparent inverse relationship is generally interpreted in terms of the assumption that global warming by way of fossil fuel emissions is causing a decline in sea ice extent.
    3. In the “EXTENT CORR” column of Figure 7 we find the statistically significant negative correlations between temperature and sea ice extent needed to support the causal relationship that AGW drives the decline in sea ice extent. However, since correlation derives from both shared trends and responsiveness, it is necessary to remove the shared trend effect and isolate the effect of responsiveness and these correlations are shown in the detrended correlation column labeled DETCOR.
    4. Here, with a sample size of 40, correlations with absolute value greater than ρ=0.464 can be considered statistically significant at α=0.001 as suggested by Valen Johnson in “Revised standards for statistical significance” [LINK] in which he addresses the state of an unacceptable rate of irreproducible results in published research. At the more commonly used error rate of α=0.01, the lowest correlation for statistical significance in this case is ρ=0.358Using these criteria we find as follows for the Arctic sea ice extent in Figure 7 above:
    5. A decline in March Maximum EXTENT is found along with a statistically significant warming trend but the data do not indicate a corresponding decline in March maximum sea ice AREA. Detrended correlation analysis does not show that the decline in March Maximum sea ice extent can be explained in terms of global warming.
    6. Declines in both September Minimum sea ice EXTENT and sea ice AREA are found. However, detrended correlation analysis does not show that the observed sea ice decline in EXTENT or in AREA can be explained in terms of temperature trends as no responsiveness relationship is found.
    7. The data for the SOUTH (Antarctic) appear in Figure 8 below. No evidence is found of sea ice decline either in the September Maximum or in the February Minimum. The temperature data in this chart are UAH lower troposphere temperatures for the South Polar Ocean. In both of these calendar months we find both extent and area measures of sea ice show a rising trend. An oddity of the detrended correlation of these rising trends of sea ice with ambient temperature shows the odd result that the September maximum area is growing over a period of cooling while February minimum sea ice extent is growing while warming. Such anomalous results point to the importance of geothermal heat in the understanding of sea ice dynamics as explained in related posts [LINK] [LINK] .





    Eisenman, Ian, Norbert Untersteiner, and J. S. Wettlaufer. “On the reliability of simulated Arctic sea ice in global climate models.” Geophysical Research Letters 34.10 (2007).  While most of the global climate models (GCMs) currently being evaluated for the IPCC Fourth Assessment Report simulate present‐day Arctic sea ice in reasonably good agreement with observations, the intermodel differences in simulated Arctic cloud cover are large and produce significant differences in downwelling longwave radiation. Using the standard thermodynamic models of sea ice, we find that the GCM‐generated spread in longwave radiation produces equilibrium ice thicknesses that range from 1 to more than 10 meters. However, equilibrium ice thickness is an extremely sensitive function of the ice albedo, allowing errors in simulated cloud cover to be compensated by tuning of the ice albedo. This analysis suggests that the results of current GCMs cannot be relied upon at face value for credible predictions of future Arctic sea ice.




    1. In September 2019, the WMO released a report with an alarming list of climate change impacts [LINK] . Included in the many alarming claims made in the report is an extensive and startling evaluation of the decline in sea ice extent in the Arctic and the Antarctic attributed to anthropogenic global warming (AGW).
    2. In the report, the WMO lists six concerns about the AGW impact on polar sea ice extent claiming that : (1) From 1979 to 2019, Arctic summer minimum sea ice extent (September) had declined at a rate of 12% per decade; (2) In each of the years 2015, 2016, 2017, 2018, and 2019, the Arctic summer minimum (September) and winter maximum (March) sea ice extents were lower than the 1981-2010 average; (3) The four lowest values for Arctic winter maximum sea ice extent (March) since 1979 are found in the five most recent years 2015 to 2019; (4) Summer sea ice extent in Antarctica (February) reached its lowest and second lowest extents in 2017 and 2018 respectively; (5) The second lowest winter maximum sea ice extent in Antarctica (September) since 1979 was recorded in 2017. (6) Most remarkably, Antarctic summer minimum (February) and winter maximum (September) sea ice extent in the period 2015-2019 are well below the 1981-2010 average. This surprising result is in sharp contrast with the rising trends for both winter and summer seen in the periods 1979-2018 and 2011-2015. Briefly, a catastrophic sea ice decline is claimed for both poles and the decline is attributed to AGW with the implication that these declines can and must be attenuated by following the UN mandated climate action procedures.
    3. In this post we show that the available sea ice data from January 1979 to December 2018 are inconsistent with the claims made in the WMO September 2019 report [LINK] .
    4.  Figure 1 shows that Arctic sea ice extent has been in decline in all twelve calendar months and that these decline rates are statistically significant. A somewhat weaker decline is seen in the sea ice area. The difference between extent and area has to do with how the satellite measurement grids are tallied [LINK] . To avoid controversy on the choice of sea ice measure, both extent and area are presented in this analysis.
    5. The simple fact that sea ice has been in decline during a time when AGW was in process does not in itself imply that AGW is the cause of the decline. Evidence for such causation must be shown to exist in the data. In Figure 2 we present the correlation between UAH lower troposphere temperature over North Polar Ocean against sea ice extent (and area). If rising air temperature is causing a decline in sea ice we would expect to see a negative correlation between temperature and sea ice extent (and area). And that is what we see in Figure 2 where strong and statistically significant negative correlations are found between temperature and sea ice extent (and area).
    6. However, it is known that correlations between time series data arise from both shared trends over the full span as well as from responsiveness of the object variable to changes in the explanatory variable at a given finite time scale. Only the second source of correlation has a causation interpretation. In Figure 3, the correlation derived from responsiveness at an annual time scale is tested by removing the correlation due to shared trends. The detrended correlation analysis presented in Figure 3 paints a very different picture. Although statistically significant detrended correlation is found in six of the twelve calendar months, no correlation is found at an annual time scale between temperature and sea ice extent in the two critical months of seasonal minimum sea ice extent in September (where the strongest decline is seen) and seasonal maximum sea ice extent in March. Therefore, we find no evidence for the high profile claim by the WMO [LINK] and climate science in general that AGW is driving down September minimum and March maximum sea ice extent in the Arctic. It is proposed that all sources of heat must be considered, in particular the extensive geothermal heat sources of the Arctic region described in a related post [LINK] must be considered instead of arbitrary attribution to AGW derived from the bias in climate science in which all surface phenomena are seen in the context of AGW.
    7. The corresponding analysis for Antarctic sea ice in conjunction with lower troposphere temperatures for the South Polar oceans is presented in Figure 4, Figure 5, and Figure 6. Here the attribution of observed changes to AGW is much weaker particularly so in light of the trend  values which show overall gain and not loss in sea ice extent and area; and none of the correlations between changes in sea ice extent (and area) and lower troposphere temperature are statistically significant.
    8. In light of the above, the claim by the WMO of horrific and alarming impacts of AGW on Arctic and Antarctic sea ice extent are not found to have any basis in the data. An additional consideration is the language and peculiarity of the evidence of AGW impact that appear to indicate a circular reasoning effort to find some kind of peculiarity in the data so that an AGW impact can be claimed. A summary of the WMO statement about sea ice is reproduced below.
    9. WMO: Six concerns about AGW impact on polar sea ice extent: (1) From 1979 to 2019, Arctic summer minimum sea ice extent (September) had declined at a rate of 12% per decade; (2) In each of the years 2015, 2016, 2017, 2018, and 2019, the Arctic summer minimum (September) and winter maximum (March) sea ice extents were lower than the 1981-2010 average; (3) The four lowest values for Arctic winter maximum sea ice extent (March) since 1979 are found in the five most recent years 2015 to 2019; (4) Summer sea ice extent in Antarctica (February) reached its lowest and second lowest extents in 2017 and 2018 respectively; (5) The second lowest winter maximum sea ice extent in Antarctica (September) since 1979 was recorded in 2017. (6) Most remarkably, Antarctic summer minimum (February) and winter maximum (September) sea ice extent in the period 2015-2019 are well below the 1981-2010 average. This surprising result is in sharp contrast with the rising trends for both winter and summer seen in the periods 1979-2018 and 2011-2015.


    [RELATED POST: WMO 2019]










    Climate change is the defining challenge of our time.

    By the Science Advisory Group of the UN Climate Action Summit 2019 (SAGUCAS2019). The SAGUCAS2019 have convened the report United in Science to assemble the key scientific findings of recent work undertaken by major partner organizations in the domain of global climate change research, including the:

    1. World Meteorological Organization (WMO)
    2. UN Environment
    3. Global Carbon Project
    4. The Intergovernmental Panel on Climate Change
    5. Future Earth
    6. Earth League
    7. Global Framework for Climate Services.


    1. United in Science is a synthesis of the key findings from several more detailed reports provided by these partners in a transparent envelope. This important document by the United Nations and global partner organizations, prepared under the auspices of the Science Advisory Group of the Climate Action Summit, features the latest critical data and scientific findings on the climate crisis. It shows how our climate is already changing and highlights the far-reaching and dangerous impacts that will unfold for generations to come. Science informs governments in their decision-making and commitments. I urge leaders to heed these facts, unite behind the science and take ambitious, urgent action to halt global heating and set a path towards a safer, more sustainable future for all.
    2. The UN Climate Action Summit 2019 Science Advisory Group called for this High Level Synthesis Report, to assemble the key scientific findings of recent work undertaken by major partner organizations in the domain of global climate change research, including the World Meteorological Organization, UN Environment, Global Carbon Project, the Intergovernmental Panel on Climate Change, Future Earth, Earth League and the Global Framework for Climate Services. The Report provides a unified assessment of the state of our Earth system under the increasing influence of anthropogenic climate change, of humanity’s response thus far and of the far-reaching changes that science projects for our global climate in the future. The scientific data and findings presented in the report represent the very latest authoritative information on these topics. It is provided as a scientific contribution to the UN Climate Action Summit 2019, and highlights the urgent need for the development of concrete actions that halt the worst effects of climate change.
    3. The Synthesis Report is an example of the international scientific community’s commitment to strategic collaboration in order to advance the use of scientific evidence in global policy, discourse and action. The Science Advisory Group will remain committed to providing its expertise to support the global community in tackling climate change on the road to COP 25 in Santiago and beyond.
    4. This report has been compiled by the World Meteorological Organization under the auspices of the SAGUCAS2019, to bring together the latest climate science related updates from a group of key global partner organizations – The World Meteorological Organization (WMO), UN Environment (UNEP), Intergovernmental Panel on Climate Change (IPCC), Global Carbon Project, Future Earth, Earth League and the Global Framework for Climate Services (GFCS). The content of each chapter of this report is attributable to published information from the respective organizations. Overall content compilation of this material has been carried out by the World Meteorological Organization.
    5. Warmest five-year period on record: The average global temperature for 2015–2019 is on track to be the warmest of any equivalent period on record. It is currently estimated to be 1.1°Celsius (± 0.1 °C) above pre-industrial (1850–1900) times and 0.20 ±0.08 °C warmer than the global average temperature for 2011–2015. The 2015-2019 five-year average temperatures were the highest on record for large areas of the United States, including Alaska, eastern parts of South America, most of Europe and the Middle East, northern Eurasia, Australia, and areas of Africa south of the Sahara. July 2019 was the hottest month


    6. Sea-level rise is accelerating, sea water is becoming more acidic
      The observed rate of global mean sea-level rise increased from 3.04 millimeters per year (mm/yr) during the period 1997–2006 to approximately 4 mm/yr during the period 2007–2016. The accelerated rate in sea level rise as shown by altimeter satellites is attributed to the increased rate of ocean warming and land ice melt from the Greenland and West Antarctica ice sheetsThe ocean absorbs nearly 25% of the annual emissions of anthropogenic CO2 thereby helping to alleviate the impacts of climate change on the planet. The absorbed CO2 reacts with seawater and increases the acidity of the ocean. Time series of altimetry-based global mean sea level from January 1993–May 2019. The thin black line is a quadratic function showing the mean sea-level rise acceleration. Data source: European Space Agency (ESA) Climate Change Initiative (CCI) sea-level data until December 2015, extended by data from the Copernicus Marine Service (CMEMS) as of January 2016 and near realtime Jason-3 as of April 2019. Observations show an overall increase of 26% in ocean acidity since the beginning of the industrial era. The ecological cost to the ocean, however, is high, as the changes in acidity are linked to shifts in other carbonate chemistry parameters, such as the saturation state of aragonite. This process, detrimental to marine life and ocean services, needs to be constantly monitored through sustained ocean observations.
    7. Continued decrease of sea ice 
      The long-term trend over the 1979-2018 period indicates that Arctic summer sea-ice extent has declined at a rate of approximately 12% per decade. In every year from 2015 to 2019, the Arctic average summer minimum and winter maximum sea-ice extent were well below the 1981–2010 average. The four lowest values for winter sea-ice extent occurred in these five years. Summer sea ice in Antarctica reached its lowest and second lowest extent on record in 2017 and 2018, respectively. The second lowest winter extent ever recorded was also experienced in 2017. Most remarkably sea ice extent values for the February minimum (summer) and September maximum (winter) in the period from 2015-2019 have been well below the 1981-2010 average since 2016. This is a sharp contrast with the 2011-2015 period and the long term 1979-2018 values that exhibited increasing trends in both seasons.
    8. Continued decrease land ice mass: Overall, the amount of ice lost annually from the Antarctic ice sheet increased at least six-fold between 1979 and 2017. The total mass loss from the ice sheet increased from 40 Gigatons (Gt) average per year in 1979–1990 to 252 Gt per year in 2009–2017. Sea level rise contribution from Antarctica averaged 3.6 ± 0.5 mm per decade with a cumulative 14.0 ± 2.0 mm since1979. Most of the ice loss takes place by melting the ice shelves from below, due to incursions of relatively warm ocean water, especially in West Antarctica and to a lesser extent along the Peninsula and in East Antarctica.
    9. Analysis of long-term variations in glacier mass often relies on a set of global reference glaciers, defined as sites with continuous high-quality in situ observations of more than 30 years. Results from these time series are, however, only partly representative for glacier mass changes at the global scale as they are biased to well-accessible regions such as the European Alps, Scandinavia and the Rocky Mountains. Nevertheless, they provide direct information on the year-to-year variability in glacier mass balance in these regions. For the period 2015–2018, data from the World Glacier Monitoring Service (WGMS) reference glaciers indicate an average specific mass change of − 908 mm water equivalent per year. This depicts a greater mass loss than in all other five-year periods since 1950, including the 2011-2015 period. Warm air from a heatwave in Europe in July 2019 reached Greenland, sending temperature and surface melting to record levels. Screen_Shot_2019-09-19_at_15.33.28
    10. Intense heatwaves and wild fires: The Fire Radiative Power (Gigawats)– a measure of heat output from wildfires shown in June for 2019 (red) and the 2003–2018 average (grey) (Source: Copernicus Atmospheric Monitoring Services (CAMS)). Number of undernourished people in the world, 2015–2018 (FAO, IFAD, UNICEF and WHO, 2019. Heatwaves were the deadliest meteorological hazard in the 2015–2019 period, affecting all continents and setting many new national temperature records. Summer 2019 saw unprecedented wildfires in the Arctic region. In June alone, these fires emitted 50 megatons (Mt) of carbon dioxide into the atmosphere. This is more than was released by Arctic fires in the same month from 2010 to 2018 put together. There were multiple fires in the Amazon rainforest in 2019, in particular in August.  The Fire Radiative Power (Gigawats)– a measure of heat output from wildfires shown in June for 2019 (red) and the 2003–2018 average (grey) (Source: Copernicus Atmospheric Monitoring Services (CAMS)). Number of undernourished people in the world, 2015–2018 (FAO, IFAD, UNICEF and WHO, 2019 UISTGC4
    11. Costly tropical cyclones. Overall, the largest economic losses were associated with tropical cyclones. The 2018 season was especially active, with the largest number of tropical cyclones of any year in the twenty-first century. All Northern Hemisphere basins experienced above average activity – the Northeast Pacific recorded its largest Accumulated Cyclone Energy (ACE) value ever. The 2017 Atlantic hurricane season was one of the most devastating on record with more than US$ 125 billion in losses associated with Hurricane Harvey alone. Unprecedented back-to-back Indian Ocean tropical cyclones hit Mozambique in March and April 2019.
    12. Food insecurity increasingAccording to the Food and Agriculture Organization of the United Nations (FAO) report on the State of Food Security and Nutrition in the World, climate variability and extremes are among the key drivers behind the recent rises in global hunger after a prolonged decline and one of the leading contributors to severe food crises. Climate variability and extremes are negatively affecting all dimensions of food security – food availability, access, utilization and stability. The frequency of drought conditions from 2015–2017 show the impact of the 2015–2016 El Niño on agricultural vegetation. The following map shows that large areas in Africa, parts of central America, Brazil and the Caribbean, as well as Australia and parts of the Near East, experienced a large increase in frequency of drought conditions in 2015–2017 compared to the 14-year average.  UISTGC5


    Percentage of time (dekad is a 10-day period) with active vegetation when the Anomaly Hot Spots of Agricultural Production (ASAP) was signaling possible agricultural production anomalies according to NDVI (Normalized Difference Vegetation Index) for more than 25% of the crop areas in 2015–2017 (FAO, IFAD, UNICEF, WFP and WHO, 2018) 

    13. Overall risk of climate-related illness or death increasing: Based on data and analysis from the World Health Organisation (WHO), between 2000 and 2016, the number of people exposed to heatwaves was estimated to have increased by around 125 million. The average length of individual heatwave events was 0.37 days longer, compared to the period between 1986 and 2008, contributing to an increased risk of heat-related illness or deathUISTGC7

    14. Gross domestic product is falling in developing countries due to increasing temperatures. The International Monetary Fund found that for a medium and low-income developing country with an annual average temperature of 25 °C, the effect of a 1 °C increase in temperature is a fall in growth by 1.2%. Countries whose economies are projected to be hard hit by an increase in temperature accounted for only about 20% of global Gross Domestic Product (GDP) in 2016. But they are home to nearly 60% of the global population, and this is expected to rise to more than 75% by the end of the century.

    15. Global Fossil CO2 Emissions. CO​Emissions from fossil fuel use continue to grow by over 1% annually and 2% in 2018 reaching a new high. Growth of coal emissions resumed in 2017.









    16. Greenhouse Gas Concentrations: Increases in CO2 concentrations continue to accelerate. Current levels of CO2, CH4 and N2O represent 146%, 257% and 122% respectively of pre-industrial levels (pre-1750)​. 

    WMO Logo








    17. Emissions Gap: Global emissions are not estimated to peak by 2030, let alone by 2020. Implementing current unconditional NDCs (INDCs) would lead to a global mean temperature rise between   2.9 °C and 3.4 °C by 2100 relative to pre-industrial levels, and continuing thereafter. The current level of NDC (INDC) ambition needs to be roughly tripled for emission reduction to be in line with the 2 °C goal and increased five-fold for the 1.5 °C goal. Technically it is still possible to bridge the gap​.







    18. IPCC: Intergovernmental Panel on Climate Change 2018 & 2019 Special Reports: Limiting temperature to 1.5 °C above pre-industrial levels would go hand-in-hand with reaching other world goals such as achieving sustainable development and eradicating poverty. Climate change puts additional pressure on land and its ability to support and supply food, water, health and wellbeing. At the same time, agriculture, food production, and deforestation are major drivers of climate change​

    IPCC logo









    19. Climate Insights: Growing climate impacts increase the risk of crossing critical tipping points. There is a growing recognition that climate impacts are hitting harder and sooner than climate assessments indicated even a decade ago. Meeting the Paris Agreement requires immediate and all-inclusive action encompassing deep de-carbonization complemented by ambitious policy measures, protection and enhancement of carbon sinks and biodiversity, and effort to remove CO2 from the atmosphere​

    Future Earth Earth League logo












    COMMENT#1: ITEM#5: WARMEST 5-YEAR PERIOD ON RECORD:  The AGW issue is understood only as the effect of rising atmospheric CO2 on a long term warming trend. In that context, the finding that the years 2015, 2016, 2017, 2018 have set a temperature record in terms of annual mean temperature has no interpretation. Also, the inclusion of the year 2019 in this record temperature claim with temperatures for 4 calendar months of 2019 still in the future is not possible. An additional consideration is that the 2015-2016 El Nino event was one of the strongest on record and the 2017 & 2018 La Ninas were exceptionally weak as seen in the chart below provided by Meteorologist Jan Null. It is precisely because of such anomalous temperature events that are unrelated to long term trends that temperature events do not serve as evidence of long term trends and AGW, that is, CO2 driven warming, relates only to long term temperature trends and not to temperature events. Particularly egregious is the inclusion of these ENSO event years in a an assessment of CO2 driven long term warming of AGW climate change. The “warmest 5-year” argument for AGW is therefore irrelevant and possibly motivated by bias and activism and not by objective and unbiased scientific inquiry.

    bandicam 2019-07-03 11-47-06-708



    Below are three charts depicting altimeter sea level rise data 1993-2018. The first chart plots sea level against time in years along with a first order linear regression line shown in dots. A statistically significant linear trend is seen with an overall average rate of sea level rise estimated at 3.334 mm/year equivalent to a one meter rise in sea level every 300 years. However, some divergences from a purely linear trend are apparent in the regression line both higher and lower. These divergences are made more clear in the second chart that plots the residuals – the difference between the data and the regression line. In terms of the acceleration issue, we would expect to find that the residuals would be mostly negative at the beginning of the series and gradually rise to above the regression line toward the end of the series. But this is not the case. Instead what we find is that there are large positive differences at the beginning and at the end but with negative differences in the middle from 1998-2014. These data do not support sustained acceleration in sea level rise across the time span studied. That conclusion is supported by the third chart that plots 5-year trends in sea level. Here we find that all the trends are positive implying that over this period sea level rises but does not fall. However, the rate of sea level rise declines from 2001 to 2011 and rises thereafter until 2015 but then declines again toward the end from 2015 to 2018. These data do not provide convincing evidence that sea level rise is accelerating. It is also noted that acceleration in sea level rise, though often presented by the WMO and the IPCC as evidence of human cause, does not in itself prove human cause because the additional data relationships needed in that argument are assumed but not provided, possibly because they don’t exist. For example, if acceleration in sea level rise proves human cause by way of fossil fuel emissions, how does one explain rapid acceleration in sea level rise in the Eemian [LINK] ? To prove human cause of sea level rise by way of fossil fuel emissions; and to support the assumption that climate action in the form of reducing fossil fuel emissions will attenuate sea level rise, a relationship between emissions and sea level rise must exist in the data. Such a relationship was presented in Clark, Peter U., et al. “Sea-level commitment as a gauge for climate policy.” Nature Climate Change 8.8 (2018): 653; However, it is shown in a related post [LINK] that the correlation between cumulative emissions and cumulative sea level rise presented by Clark et al contains neither time scale nor degrees of freedom. This correlation is spurious and has no interpretation in terms of human cause of the observed gradual late Holocene sea level rise. In a related post we show that when these statistics errors in Clark 2018 are corrected, the correlation relating sea level rise to emissions disappears. No evidence is found in the data that the slow residual sea level rise of the late Holocene can be attributed to fossil fuel emissions or that climate action in the form of reducing fossil fuel emissions will attenuate the rate of sea level rise [LINK] . It should also be noted that a sustained and pressing issue in climate science has been the firmly held belief, particularly since the dramatic collapse of the Larsen B ice shelf in 2002 that was arbitrarily attributed to AGW, that some kind of Antarctica ice melt event by way of fossil fuel emissions will cause catastrophic sea level rise [LINK] . The catastrophic sea level rise obsession of climate science with Antarctica is puzzling in the context of known sources of geothermal heat and geological activity that control ice melt dynamics of that continent [LINK] . 




    There have been significant and catastrophic ocean acidification events by CO2 in the past as recorded in paleo climatology.  However, in these events the source of the carbon dioxide was not the atmosphere but geological and from the ocean itself. No paleoclimate record exists to establish the ability of the atmosphere to acidify the ocean. In terms of relative mass, the ocean is 99.62% and the atmosphere 0.38% of their combined mass. Ocean acidification events of the past are understood in terms of ocean floor and geological sources of carbon and not in terms of atmospheric effects [LINK] . The theory of atmosphere driven ocean acidification is studied with correlation analysis in a related post. No evidence is found to attribute changes in oceanic inorganic carbon to fossil fuel emissions [LINK] . It is likely that the ocean acidification hypothesis entered the climate change narrative by way of the PETM climate change event when extensive and devastating ocean acidification had occurred as described in a related post [LINK] . However, there is no parallel between PETM and AGW that can be used to relate the characteristics of one to those of the other. In the case of ocean acidification in the PETM event, the source of carbon was a monstrous release of geological carbon from the ocean floor or from the mantle. The event caused the ocean to lose all its elemental oxygen by way of carbon oxidation and undergo significant decline in pH. Much of the carbon dioxide was also vented to the atmosphere and that caused atmospheric CO2 to rise precipitously.  But this correspondence of ocean acidification in the presence of rising atmospheric CO2 does not apply to AGW. Whereas PETM started in the ocean and spread to the atmosphere, the AGW event started in the atmosphere and is thought to have spread to the oceans. The evidence presented in a related post  [LINK]  does not support this hypothesis. The effort by climate change scientists to relate all observed changes on the surface of the planet to fossil fuel emissions likely derives from an activism bias to promote fossil fueled catastrophe that corrupts the process of unbiased scientific inquiry in this field [LINK] . 




    1. WMO: Six concerns about AGW impact on polar sea ice extent: (1) From 1979 to 2019, Arctic summer minimum sea ice extent (September) had declined at a rate of 12% per decade; (2) In each of the years 2015, 2016, 2017, 2018, and 2019, the Arctic summer minimum (September) and winter maximum (March) sea ice extents were lower than the 1981-2010 average; (3) The four lowest values for Arctic winter maximum sea ice extent (March) since 1979 are found in the five most recent years 2015 to 2019; (4) Summer sea ice extent in Antarctica (February) reached its lowest and second lowest extents in 2017 and 2018 respectively; (5) The second lowest winter maximum sea ice extent in Antarctica (September) since 1979 was recorded in 2017. (6) Most remarkably, Antarctic summer minimum (February) and winter maximum (September) sea ice extent in the period 2015-2019 are well below the 1981-2010 average. This surprising result is in sharp contrast with the rising trends for both winter and summer seen in the periods 1979-2018 and 2011-2015.
    2. RESPONSE: The data show that Antarctic sea ice extent is not declining and if anything it is expanding. Sea ice decline is found in the Arctic particularly so in the summer minimum month of September. However, the correlation needed to attribute the decline to global warming is not found in the data either for the summer minimum in September or for the winter maximum in March. Details provided in a related post [LINK] .















    Carbon budget accounting is based on the TCRE  (Transient Climate Response to Cumulative Emissions). It is derived from the observed correlation between temperature and cumulative emissions. A comprehensive explanation of an application of this relationship in climate science is found in the  IPCC SR 15 2018. This IPCC description is quoted below in paragraphs #1 to #7 where the IPCC describes how climate science uses the TCRE for climate action mitigation of AGW in terms of the so called the carbon budget. Also included are some of difficult issues in carbon budget accounting and the methods used in their resolution.

    1. Mitigation requirements can be quantified using the carbon budget approach that relates cumulative CO2 emissions to global mean temperature in terms of the TCRE. Robust physical understanding underpins this relationship.
    2. But uncertainties become increasingly relevant as a specific temperature limit is approached. These uncertainties relate to the transient climate response to cumulative carbon emissions (TCRE), non-CO2 emissions, radiative forcing and response, potential additional Earth system feedbacks (such as permafrost thawing), and historical emissions and temperature. 
    3. Cumulative CO2 emissions are kept within a budget by reducing global annual CO2 emissions to net zero. This assessment suggests a remaining budget of about 420 GtCOfor a two-thirds chance of limiting warming to 1.5°C, and of about 580 GtCO2 for an even chance (medium confidence). 
    4. The remaining carbon budget is defined here as cumulative CO2 emissions from the start of 2018 until the time of net zero global emissions for global warming defined as a change in global near-surface air temperatures. Remaining budgets applicable to 2100 would be approximately 100 GtCO2 lower than this to account for permafrost thawing and potential methane release from wetlands in the future, and more thereafter. These estimates come with an additional geophysical uncertainty of at least ±400 GtCO2, related to non-CO2 response and TCRE distribution. Uncertainties in the level of historic warming contribute ±250 GtCO2. In addition, these estimates can vary by ±250 GtCO2 depending on non-CO2 mitigation strategies as found in available pathways. {2.2.2, 2.6.1}
    5. Staying within a remaining carbon budget of 580 GtCO2 implies that CO2 emissions reach carbon neutrality in about 30 years, reduced to 20 years for a 420 GtCO2 remaining carbon budget. The ±400 GtCO2 geophysical uncertainty range surrounding a carbon budget translates into a variation of this timing of carbon neutrality of roughly ±15–20 years. If emissions do not start declining in the next decade, the point of carbon neutrality would need to be reached at least two decades earlier to remain within the same carbon budget. {2.2.2, 2.3.5} 
    6. Non-CO2 emissions contribute to peak warming and thus affect the remaining carbon budget. The evolution of methane and sulfur dioxide emissions strongly influences the chances of limiting warming to 1.5°C. In the near-term, a weakening of aerosol cooling would add to future warming, but can be tempered by reductions in methane emissions (high confidence). Uncertainty in radiative forcing estimates (particularly aerosol) affects carbon budgets and the certainty of pathway categorizations. Some non-CO2 forcers are emitted alongside CO2, particularly in the energy and transport sectors, and can be largely addressed through CO2 mitigation. Others require specific measures, for example, to target agricultural nitrous oxide (N2O) and methane (CH4), some sources of black carbon, or hydrofluorocarbons. In many cases, non-CO2 emissions reductions are similar in 2°C pathways, indicating reductions near their assumed maximum potential by integrated assessment models. Emissions of N2O and NH3 increase in some pathways with strongly increased bioenergy demand. {2.2.2, 2.3.1, 2.4.2, 2.5.3} 





    1. The computation of cumulative values requires the use of the same source data item in the computation of more than one cumulative value. This replication implies that there is an information overlap among the cumulative values. The information lost in this manner can be represented in terms of the multiplicity in the use of the time series data when constructing the series of cumulative values. The repeated use of the same source data for computing more than one cumulative value reduces the effective value of the sample size N. Here we present a procedure for estimating the average value of the multiplicity for any sample size and thereby to estimate the effective sample size EFFN in the cumulative series.
    2. The last value in the source series is used only once but all of the other values are used more than once. Under these conditions the statistical significance of the correlation between two time series cannot be evaluated regardless of the magnitude of the correlation coefficient.
    3. If the summation starts at K=2, the series of cumulative values of a time series X of length N is computed as Σ(X1 to X2), Σ(X1 to X3), Σ(X1 to X4), Σ(X1 to X5) … Σ(X1 to XN-3), Σ(X1 to XN-2), Σ(X1 to XN-1), Σ(X1 to XN).
    4. In these N-K+1=N-1 cumulative values, X(N) is used once, X(N-1) is used twice, X(N-2) is used three times, X(N-3) is used four times, X(4) is used N-3 times, X(3) is used N-2 times, X(2) is used N-1 times , and X(1) is also used N-1 times.
    5. In general, each of the first K data items will be used N-K+1 times. Thus, the sum of the multiples for the first K data items may be expressed as K*(N-K+1). The multiplicities of the remaining N-K data items form a sequence of integers from one to N-K and their sum is (N-K)*(N-K+1)/2. Therefore, the average multiplicity of the N data items in the computation of cumulative values may be expressed as:
    6. AVERAGE-MULTIPLE = [(K*(N-K+1) + (N-K)*(N-K+1)/2]/N. Since multiplicity of use reduces the effective value of the sample size we can express the effective sample size as: EFFN = N/(AVERAGEMULTIPLE) = (N^2)/(K*(N-K+1) + (N-K)*(N-K+1)/2).
    7. The usual procedure in the TCRE computation is to use K=2 in which case, EFFN = 1.988. This means that the effective  value of the sample size is less than two  EFFN<2, and that therefore the time series of cumulative values has no degrees of freedom computed as DF=EFFN-2. This is the essence of the statistics issue with the TCRE and the carbon budget procedure that uses the TCRE.
    8. The anomalies and oddities with the carbon budget that climate science struggles to rationalize in terms of “radiative forcings of non-CO2 emissions and Earth system feedbacks such as permafrost thawing are really the creation of a statistics error consisting of the failure to correct the sample size for multiplicity. This failure creates a faux statistical significance of the correlation coefficient and the TCRE regression coefficient that does not actually exist. 
    9. To be able to determine the statistical significance of the correlation coefficient it is necessary that the degrees of freedom (DF) computed as EFFN -2 should be a positive integer. This condition is not possible for a sequence of cumulative values that begins with Σ(X1 to X2) where EFFN=1.988 and therefore DF=1.988-2≈0. Thus a time series of the cumulative values of another time series has no degrees of freedom. Also, since all time spans from unity to the full span are used in the computation, the time series of cumulative values has no time scale. The essential statistics issue in the TCRE and its carbon budget implications is that a time series of the cumulative values of another time series has neither time scale nor degrees of freedom.
    10. EFFN can be increased to values higher than two only by beginning the cumulative series at a later point K>2 in the time series so that the first summation is Σ(X1 to XK) where K>2. In that case, the total multiplicity is reduced and this reduction increases the value of EFFN somewhat but the sample size is reduced by (K-2) and nothing is gained.
    11. These relationships provide the theoretical underpinning for for the spuriousness of correlations between cumulative values. The results show that the time series consisting of the cumulative values of another time series contains neither degrees of freedom not time scale. Therefore both the TCRE correlation and carbon budgets derived from that correlation are spurious and illusory. Numerical results derived from such analysis have no interpretation in terms of the phenomena of nature being studied.




    1. An example TCRE computation appears below in Figure 1The data are annual emissions and mean annual HadCRUT4 temperature reconstructions 1851 to 2015. In the first chart, a value of TCRE=0.0027C/gigaton of emissions is indicated in the source data at an annual time scale but no correlation is found to support that relationship.
    2. The relationship between cumulative values is shown in the next chart of Figure 1 where TCRE=0.0024C/gigaton of emissions is indicated and supported by a near perfect correlation of ρ=0.9017. The absence of correlation in the source data appears to have been overcome by the use of cumulative values.
    3. However, the tabulation under the charts shows that the apparent gain in statistical power is illusory. The effective sample size of a time series of the cumulative values of another time series is EFFN=1.988 yielding negative degrees of freedom of DF=1.988-2 = -0.012. Therefore no conclusion can be made from the spurious and illusory correlation between cumulative values because the time series of cumulative values has neither time scale not degrees of freedom.  It is noted that temperature is cumulative annual warming.
    4. Figure 2 shows that a the effective sample size can be increased to EFFN=2.05, thereby yielding a positive degrees of freedom if the summation starts at n=30 but the t-statistic is too low to reject the null hypothesis that the hypothesized correlation does not exist.
    5. Figure 3  is a split half test of the correlation and TCRE of cumulative values in Figure 1. In the split half test, the first half, the second half. and the mid-half are compared with the full span in terms of correlation and the value of the TCRE coefficient. The comparison shows that there is no agreement between the four TCRE and correlation values for full span, first half, last half, and mid-half. The TCRE is highest for the mid-half (TCRE=0.0035) and lowest for the first half (TCRE=0.0018). The other two values lie somewhere in the middle with full span (TCRE=0.0024) somewhat higher than the second half (TCRE=0.0020). Very little correlation is found in the first half (ρ=0.1784) but the other three spans show statistically significant correlations of ρ=0.9017 for the full span, ρ=0.8860 for the second half and a lower but statistically significant  value of ρ=0.6526 for the mid-half.
    6. These differences in the value of the TCRE coefficient at different locations along the time series of cumulative values provide further evidence of its spuriousness and also offers a simple and more realistic explanation for the so called Remaining Carbon Budget (RCB) issue in climate science. The RCB issue is that the carbon budget accounting over the period of the budget is not linear so that for example, if the budget for the next 20 years is 200 gigatons of carbon dioxide, and after 10 years 80 gigatons of cumulative emissions have been produced, the RCB can’t be assumed to be 200-80=120 gigatons because a fresh computation of a carbon budget for the next 10 years typically yields an entirely different non-proportional figure.
    7. [HOME PAGE]





    8. The correlation and the TCRE value between cumulative warming and cumulative emissions derives from the fact that during a time of long term warming trend, the annual warming values are mostly positive and further that if the rate of warming is rising over the long term the second half of the carbon budget period will contain a greater fraction of positive annual warming values. These values are driven by the fraction of the annual warming values that are positive. In a random series this fraction cannot be assumed to be the same in the two halves of the time series. This is the mundane statistical explanation of the remaining carbon budget issue.
    9. In climate science, the Remaining Carbon Budget  issue is interpreted and resolved in esoteric ways and in terms of climate science variables of convenience. These include non CO2 forcings and the so called “earth system factors” such as release of permafrost methane. The analysis presented here suggests a more banal explanation that goes to the heart of the spuriousness of the TCRE correlation. In terms of the statistics presented here, the RCB anomaly is understood simply in terms of the spuriousness of the TCRE coefficient itself and therefore of its non-proportionality across time time span increments.
    10. A summary of the correlations observed in the data is shown in Figure 3A. The data do not show a correlation at an annual time scale. The somewhat higher correlation seen at a decadal time scale is found to be spurious and driven by shared trends and not at responsiveness at a decadal time scale. The apparent correlation does not survive into the detrended series. The strong correlation between cumulative values does survive into the detrended series but, as shown above, correlations between cumulative values have no interpretation because of the absence of degrees of freedom and time scale in the data.









    1. The anomalous behavior of correlation and the TCRE demonstrated with Figure 1, Figure 2, and Figure 3 above can be understood in terms of how the correlation and TCRE values are generated by the time series. The tabulations in Figure 1 and Figure 2 demonstrate that temperature is cumulative annual warming. It can be shown that the high correlation seen between cumulative emissions and cumulative warming is not an indication of correlation but of a fortuitous sign pattern of annual emissions and annual warming. The sign pattern is that annual emissions are always positive; and, at a time of an overall long term warming trend, annual warming is mostly positive. It is this sign pattern and not real responsiveness of warming to emissions that creates the faux correlation of the TCRE and the faux statistical significance of the TCRE itself. For this reason these values have no interpretation in terms of the phenomena of nature being studied in terms of a responsiveness of warming to emissions.
    2. The role of the sign pattern in the TCRE correlation is demonstrated in Figure 4 and Figure 5. The left frame of these figures shows two time series of random numbers X and Y generated by a random number generator such that the X-values in both Figure 4 and Figure 5 are all positive (as in emissions). The Y-values in Figure 4 are completely random with no sign bias. However, the Y-values in Figure 5 are contain a small bias for positive values in the random number generator. The differences between Figure 4 and Figure 5 are therefore understood only in terms of this only difference between them.
    3. In both Figure 4 and Figure 5, the left frame shows no sign of correlation between X and Y at an annual time scale verifying that these are random numbers independently generated. However, a significant difference is seen between Figure 4 and Figure 5 in their right frames where the cumulative values of the random numbers are tested graphically for correlation. Here we find a strong positive correlation in Figure 5, where a common positive sign pattern exists with X-values all positive and Y-values with a bias for positive values. No such evidence of a positive correlation exists in Figure 4 where though the X-values are all positive, no positive bias exists in Y-values. Therefore the difference in visual correlation between the two figures is understood on that basis.
    4. This demonstration supports the statement above that the TCRE correlation is a creation of a sign pattern. The sign pattern is that emissions are always positive and annual warming is mostly positive during a time of overall warming.
    5. that do not show any correlation at all. Their right frames show the relationship between the cumulative values of these completely uncorrelated random numbers.
    6. The only difference between Figure 4 and Figure 5 is sign pattern. In Figure 4, the numbers are completely random with no bias for positive values but in Figure 5, a small bias of 5% for positive values is inserted into the random number generator.
    7. The strong TCRE linear correlation between cumulative values is seen in Figure 5 but not in Figure 4.  This difference is thus interpreted in terms of the bias for positive values in Figure 5. We conclude from the demonstration in Figure 4 and Figure 5 that the TCRE correlation seen in emissions and warming data is a spurious and illusory creation of a sign pattern. The other, and perhaps more important interpretation of the strong visual correlations seen in Figure 5 is that, under the same conditions and using the same procedures that yield the strong proportionality and statistically significant TCRE value in climate data, the same strong proportionality and statistical significance is seen in random numbers.
    8. The only information content of the TCRE is the sign pattern. If the the two time series have a common sign bias, either both positive or both negative, the correlation will be positive. If the the two time series have different sign biases, one positive and the other negative, the correlation will be negative. If the the two time series have no sign bias, no correlation will be found. Therefore, the only information content of the TCRE is the sign pattern and no rational interpretation of such a proportionality exists in terms of a causal relationship that can be used in the construction of carbon budgets. The TCRE carbon budgets of climate science is a a meaningless exercise with an illusory statistic.


















    by Roger Pielke Jr published as a Twitter Thread

    1. I started studying extreme events in 1993 when I began a post-doc at NCAR on a project focused on lessons learned in Hurricane Andrew & the Midwest floods of 1993. I worked for MickeyGlantz, who was one of my most significant mentors. On March 15, 2006 I received an award from the NAS & gave a lecture to a large audience at the Smithsonian Natural History Museum in DC. My work was viewed to be important, novel and, legitimate. Two months later, An Inconvenient Truth came out, focused on politicizing extreme weather in the climate debate. Extreme weather had always been part of the debate, but it was becoming more central as advocates tried to make climate more relevant to the public.

    2. That same May, 2006 I was busy organizing a major international workshop in partnership with MunichRe in Hohenkammer, Germany. We wanted to assess the science of disasters and climate change as input to the upcoming IPCC AR4 report. We wanted to assess scientific understandings on the causes of the trend shown in the data. Why were disasters getting more costly (Chart#1)?  We started by thinking that we’d produce a “consensus/dissensus” report, but wound up with 100% consensus. The workshop [LINK] involved 32 participants & we comissioned 24 background papers. We produced a summary that was published in Science [LINK] [LINK] .
    3. The three consensus statements most relevant to this talk are: [Analysis of long term records of disaster losses indicate that societal change and economic development are the principal factors responsible for the increasing losses to date] and [Because of issues related to data quality, the stochastic nature of extreme event impacts, length of time series, and various societal factors present in the disaster loss record, it is still not possible to determine the portion of the increase in damages that can be attributed to climate change due to GHG emissions]. If increasing disaster losses were the result of climate change due to GHG emissions, we could not detect that. It was not a close call, it was unanimous.
    4. So when the IPCC AR4 came out, I excitedly looked to see what role this report played in their report. I went to Section “ Summary of disasters and hazards” only to be blindsidedThe report cited “one study” that was apparently at odds with what we had concluded in our assessment. How could 32 experts have all missed this “one study”? The IPCC report said, [ Summary of Disasters and Hazards: Global losses reveal rapidly rising costs due to extreme weather related events Since the 1970s. One study has found that while the dominant signal remains that of the significant increases in the values of the exposure at risk, once losses are normalized for exposure, there still remains an underlying rising trend].
    5. The IPCC AR4 included a mysterious chart [Chart#2] that relates increasing catastrophic losses to rising global temperature. It had never seen that chart before. The reference was [Muir-Wood et al 2006]. It was one of the papers we had commissioned for the Hohenkammer workshop. But I knew it did not include that mysterious graph nor any analysis of temperatures and disasters [Chart#3]. The mysterious temperature to catastrophic loss chart was mysteriously slipped into the IPCC report by one of its authors. who mis-cited it to our Hohenkammer white paper to circumvent an IPCC deadline. He had expected the chart to appear in a future publication but had to mis-cite it to meet the IPCC deadline [Chart#4].
    6. That future paper was eventually published well after the IPCC AR4 was released. The paper says [We find insufficient evidence to claim a statistical relationship between global temperature increase and normalized catastrophe losses.That seemed like a big deal & it was. The Sunday Times did a very good news story on it [Chart#5]. I was interviewed by Christina Larson and it was this interview in which I described the IPCC AR4 irregularities, that resulted in the hit job below [Chart#6] which turned me into a climate “denier”. The Center for American Progress amplified the hit job & continued a campaign of de-legitimization of me & my work. It was relentless. In 2015 Pulitzer Prize winner Paige St. John quoted me innocuously, only to have others calling for her to be fired for doing so. She wrote: [You should come with a warning label. Quoting Roger Pielke will bring a hail storm down on your work from the Guardian, Mother Jones, and Media Matters]. 
    7. In 2012 the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) was published. It arrived as the same conclusion as we had at Hohenkammer. Key conclusion: [Long term trends in economic disaster losses adjusted for wealth and population increases have not been attributed to climate change, but a role for climate change has not been excluded. Medium evidence. High agreement] [Chart#7].
    8. Despite the IPCC consensus aligned with and drawing upon the work of our  Hohenkammer workshop, the de-legitimization efforts intensified. In 2015 I was the subject of an Congressional “investigation” (w/ 6 other academics), accused of secretly taking Exxon money  [Chart#8]. I cannot describe how penal, professionally and personally, it is to be identified as the subject of a congressional investigation.
      I suppose that was the entire point. I never have taken any money from energy companies. I was cleared in the “investigation [Chart#9]. The reality is Dr. Holdren got caught out while articulating the Al Gore version of disasters & climate change and ignoring the latest IPCC version. I had testified to the IPCC version before the Senate in 2013.

    CHART#1 & CHART#2


    CHART#3 & CHART#4


    CHART#5 & CHART#6

    CHART#7 & CHART#8




    1. The chart above is seen in social media and denier blogs with the start year fixed at 2005 and the end year varying from 2017 to 2019 depending on when it was posted. It is meant to show that there is no global warming evident in the data. The chart shows mean temperatures from 114 USCRN station data across the USA. It is claimed that the unique property of this chart is that the start date of 2005 ensures that the UHI (urban heat island) effect has been removed from the data. It is thus argued based on this chart that AGW global warming is a faux creation of UHI because when UHI is removed the evidence for warming disappears from the data. It is further argued that therefore, all datasets that show warming, including the UAH satellite data, are flawed because they show warming when it is now clear that global warming is a creation of UHI. 
    2. This post is a critical evaluation of this line of reasoning. First, mean USA temperature cannot be expected to mimic global mean temperature because of the geographical limitation and second, the time span of 15 years is too short for this kind of test as it has been shown in related posts that even strong warming trends do not show consistent monotonic warming year after year but rather that the long term trend is simply the net result of violent warming, cooling, and no trend cycles at decadal and multi-decadal time scales [LINK] [LINK] .
    3. FIGURE 1: The chart and regression table in Figure 1 below show UAH global mean temperatures for the eight calendar months January to August over the time span, 2005-2019. The chart appears to show a warming trend and in the corresponding regression table we find p-values<0.05 in four of the eight months with an average p-value for all eight calendar months computed as p-value=0.0488 < 0.05. We conclude that there appears to be some evidence of a warming trend in the UAH global mean temperatures 2005-2019 though it does not appear to be a very strong warming trend.
    4. Figure 2: A corresponding analysis of UAH temperatures for the USA lower 48 states is presented in Figure 2. Here the chart appears flat with no indication of a warming trend. The regression table shows very high p-values with an average p-value of 0.2637. The UAH data appear to agree with the USCRN data above that no trend in USA temperatures is evident for the time span 2005-2019.  A comparison of Figure 1 and Figure 2 indicates that the absence of trend in the USCRN temperatures cannot be ascribed to the removal of UHI but with peculiarities of the geographical limitation of the analysis. This comparison does not support the interpretation of the USCRN data in  terms of UHI. In other words, the data do not show that a warming trend was removed because of the exclusion of the UHI effect.
    5. Figure 3: That the 15-year time span chosen for the UHI study is too short for drawing conclusions about AGW is shown in the four charts of Figure 3. Here we see large fluctuations in trends for a moving 15-year window as its end year moves through the full span from 1993 to 2019. The overall trend for the full span is therefore understood as a long term warming that accumulates from these violent swings. These charts also provide convincing evidence that 15-year temperature trends do not provide useful information nor information relevant to an empirical test of AGW theory.
    6. Figure 4: Shows results for a full span trend analysis of the UAH USA48 data. Very little evidence for statistically significant trends is found in stark contrast with the UAH global temperatures presented in Figure 6 where robust warming trends are found for all eight calendar months January to August with an aveage p-value=0.0002. The comparison of Figure 4 and Figure 6 provide additional evidence that the difference in trends between the USA and the world is not due to UHI but to geographical limitations of the USA-only analysis. It is noted in this context that AGW theory does not claim nor require that all geographical locations should warm at the same rate but only that a long term warming trend should exist in the global mean temperature.
    7. SUMMARY AND CONCLUSION: The data presented in the chart at the top of this post are too limited by geography and time span in its trend analysis to have significant implications for AGW where only long term trends in deseasonalized mean global temperature are relevant. Also, the claim that the absence of trend can be attributed to the removal of the UHI effect in 2005 is not supported by the trend profile showing large changes in trends in a moving 15-year window where trends as low or lower are seen prior to 2005. The comparison of USA data from USCRN and UAH does not indicate that the absence of trend since 2005 is due to the removal of the UHI effect because no such intervention exists in the UAH data. 





    FIGURE 3: TRENDS IN A MOVING 15-YEAR WINDOW: UAH-USA48 1979-20190102030405060708




    FIGURE 5: UAH GLOBAL TRENDS IN MOVING 15-YEAR WINDOW 1979-20190102030405060708