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1. The World Meteorological Organization (WMO) has released its State of the Climate report for 2018. The report begins with a statement by the United Nations Secretary-General António Guterres which states that: “The data released in this report give cause for great concern. The past four years were the warmest on record, with the global average surface temperature in 2018 approximately 1°C above the pre-industrial baseline. These data confirm the urgency of climate action. This was also emphasized by the recent Intergovernmental Panel on Climate Change (IPCC) special report on the impacts of global warming of 1.5 °C. The IPCC found that limiting global warming to 1.5 °C will require rapid and far-reaching transitions in land, energy, industry, buildings, transport, and cities, and that global net human-caused emissions of carbon dioxide need to fall by about 45% from 2010 levels by 2030, reaching “net zero” around 2050. To promote greater global ambition on addressing climate change, I am convening a Climate Action Summit on 23 September. The Summit aims to mobilize the necessary political will for raising ambition as we work to achieve the goals of the Paris Agreement. Specifically, I am calling on all leaders to come to New York in September with concrete, realistic plans to enhance their nationally determined contributions by 2020 and reach net zero emissions around mid-century. The Summit will also demonstrate transformative action in all the areas where it is needed. There is no longer any time for delay. I commend this report as an indispensable contribution to global efforts to avert irreversible climate disruption. (A. Guterres United Nations Secretary-General)
2. Yet another introductory comment from the UN is presented by María Fernanda Espinosa Garcés, President of the United Nations General Assembly, as follows: This wide ranging and significant report by the World Meteorological Organization clearly underlines the need for urgent action on climate change and shows the value of authoritative scientific data to inform governments in
   their decision-making process. It is one of my priorities as President of the General Assembly to highlight the impacts of climate change on achieving the sustainable development goals and the need for a holistic understanding of the socioeconomic consequences of increasingly intense extreme weather on countries around the world. This current WMO report will make an important contribution to our combined international action to focus attention on this problem. (María Fernanda Espinosa Garcés).
3. These statements from the UN leadership underscore the predominant role of the United Nations as the conductor of the climate science orchestra and ensures that the science of climate science and meteorology is constructed in ways that serve the activism needs of the UN described in a related post [LINK] . Although the IPCC is treated and thought of as a climate science organization and the WMO is treated as a Meteorology organization, both are in fact UN agencies that serve the needs of the UN first and foremost as articulated by their comments on the WMO State of the Climate report . This report is presented as science and meteorology but it is in fact fear based climate activism that serves the needs of the UN such that climate change and climate action are inserted into what appears to be meteorology. For example, weather forecasting, the primary function of meteorology, is redefined and described in terms of climate change and climate action. This aspect of the WMO will become clearer in the excerpts from the State of the Climate 2018 presented below.
4. Excerpt #1: “The year 2018 was the fourth warmest on record and the past four years – 2015 to 2018 – were the top four warmest years in the global temperature record. The year 2018 was the coolest of the four. In contrast to the two warmest years (2016 and 2017), 2018 began with weak La Niña conditions, typically associated with a lower global temperature”. Comment: Climate science holds that only long term trends and not temperature events serve as evidence of AGW (Anthropogenic Global Warming) and yet when the opportunity presents itself to use temperature events as tools of fear based activism the WMO stoops to activism and abandons science. Specifically we should note that included in the hottest years on record is 2016, a monster El Nino year but this source of natural variability is omitted. The year 2018 was not as hot as 2016 because it was an El Nina year and this natural variability is highlighted in the report. The non-science and biased reporting of the data by the WMO serves as evidence that the organization is not guided by science or meteorology but by the climate catastrophe activism needs of its boss the United Nations.
5. Excerpt #2: “The IPCC special report on the impacts of global warming of 1.5°C reported that the average global temperature for the period 2006–2015 was 0.86 °C above the pre-industrial baseline. For comparison, the average anomaly above the same baseline for the most recent decade 2009–2018 was 0.93 ± 0.07°C, and the average for the past five years, 2014–2018, was 1.04 ± 0.09°C above this baseline. Both of these periods include the warming effect of the strong El Niño of 2015–2016”. Comment: A disclaimer is made that the the data presented are confounded by El Nino effects but they are presented anyway. This kind of data presentation does not show an unbiased pursuit of science but rather quite the opposite. It should be obvious that the WMO is intent on using these temperature data to raise an alarm and thereby to serve the activism needs of its masters at the UN headquarters.
6. Excerpt #3: Above-average temperatures were widespread in 2018. According to continental numbers from NOAA, 2018 was ranked in the top 10 warmest years for Africa, Asia, Europe, Oceania and South America. Only for North America did 2018 not rank among the top 10 warmest years, coming 18th in the 109-year record. Comment: This is a continuation of the use of temperature events that serve fear based activism in violation of the climate science principle that only long term trends and not temperature events are relevant in terms of empirical evidence; but here they take a step further away from the science which emphasizes global means as the only relevant measures of global warming and seeks out regions where high temperatures can be found. As well, in the attempt to create fear with temperature events, the authors commit the so called “Texas Sharpshooter” fallacy in terms of seeking out regions where the temperature event is particularly hot and then submitting that as evidence of AGW and reasons to fear global warming.
7. Excerpt#4: Over the Arctic, warming in annual average temperature anomalies since pre-industrial times exceeded 2°C widely and 3°C in some places. Although Arctic temperatures were generally lower than in the record year of 2016, they were still exceptionally high relative to the long-term average. There were a number of areas of notable warmth. An area extending across Europe, parts of North Africa, the Middle East and southern Asia was also exceptionally warm, with a number of countries experiencing their warmest year on record (Czechia, France, Germany, Hungary, Serbia, Switzerland) or one in the top five (Belgium, Estonia, Israel, Latvia, Pakistan, the Republic of Moldova, Slovenia, Ukraine). For Europe as a whole, 2018 was one of the three warmest years on record. Other areas of notable warmth included the south-western United States, eastern parts of Australia (for the country overall it was the third warmest year) and New Zealand, where it was the joint second warmest year on record. In contrast, areas of below-average temperatures over land were more limited. Parts of North America and Greenland, central Asia, western parts of North Africa, parts of East Africa, coastal areas of western Australia and western parts of tropical South America were cooler than average, but not unusually so. Comment: This text is a continuation of the Texas Sharpshooter fallacy fishing expedition for regions and countries where high rates of warming (the Arctic) and high temperatures (list of countries) can be found. It is not possible for these data to serve as evidence of AGW but the high temperatures and rates of warming create a sense of alarm and serve the activism that the UN needs to push through its agenda of “ambition” described in the Secretary General’s comments.
8. Excerpt #5: Increasing levels of GHGs in the atmosphere are key drivers of climate change. Atmospheric concentrations reflect a balance between sources (including emissions due to human activities) and sinks (uptake by the biosphere and oceans). In 2017, GHG concentrations reached new highs, with globally averaged mole fractions of CO2 at 405.5 ppm, CH4 at 1859 ppb and N2O at 329.9 ppb. These values constitute, respectively, 146%, 257% and 122% of pre-industrial levels (before 1750). Accurately assessing CO2 emissions and their redistribution within the atmosphere, oceans, and land – the “global carbon budget” – helps us capture how humans are changing the Earth’s climate, supports the development of climate policies, and improves projections of future climate change. Comment: A research question stated as “how humans are changing the earth’s climate” contains a bias that allows researchers to hunt for human cause perhaps at the expense of circular reasoning as explained in two related posts [LINK] [LINK] .
9. Excerpt #6: Fossil CO2 emissions have grown almost continuously for the past two centuries a trend only interrupted briefly by globally significant economic downturns. Emissions to date continued to grow at 1.6% in 2017 and at a preliminary 2.0% in 2018. It is anticipated that a new record high of 36.9 ± 1.8 billion tons of CO2 was reached in 2018. Net CO2 emissions from land use and land cover changes were on average 5.0 ± 2.6 billion tons per year over the past decade, with highly uncertain annually resolved estimates. Together, land-use change and fossil CO2 emissions reached an estimated 41.5 ± 3.0 billion tons of CO2 in 2018. The continued high emissions have led to high levels of CO2 accumulation in the atmosphere that amounted to 2.82 ± 0.09 ppm in 2018. This level of atmospheric CO2 is the result of the accumulation of only a part of the total CO2 emitted because about 55% of all emissions are removed by CO2 sinks in the oceans and terrestrial vegetation. Sinks for CO2 are distributed across the hemispheres, on land and oceans, but CO2 fluxes in the tropics (30°S–30°N) are close to carbon neutral due to the CO2 sink being largely offset by emissions from deforestation. Sinks for CO2 in the southern hemisphere are dominated by the removal of CO2 by the oceans, while the stronger sinks in the northern hemisphere have similar contributions from both land and oceans. Comment: The mathematics of the carbon cycle flow accounting includes large and unmeasurable natural flows. These natural flows in the carbon cycle that are an order of magnitude larger than fossil fuel emissions and that cannot be directly measured are inferred with the implicit assumption that the increase in atmospheric CO2 comes from fossil fuel emissions. The flow balance can then be carried out and it does of course show that the increase in atmospheric CO2 derives from fossil fuel emissions [LINK] .
10. Excerpt #7: In November 2017, a marine heatwave developed in the Tasman Sea that persisted until February 2018. Sea-surface temperatures in the Tasman Sea exceeded 2°C above normal widely and daily sea-surface temperatures exceeded 4°C above normal at certain times. The record high sea-surface temperatures were linked to unusually warm conditions over New Zealand, which had its warmest summer and warmest month (January) on record. It was also the warmest November to January period on record for Tasmania. The warm waters were associated with high humidity and February, though past the peak of the marine heatwave, saw a number of extreme rainfall events in New Zealand.  Comment: Once again short term temperature events are being cited as evidence of long term warming claimed to be caused by fossil fuel emissions. This list of events is particularly noteworthy as an example of where climate science has gone on a fishing expedition looking for events that can be used as alarming evidence of the effect of fossil fuel emissions on climate. In that sense it is circular reasoning and the Texas sharpshooter fallacy with a clear motive of fear based activism as described in this related post: [LINK] .
11. Excerpt #8: More than 90% of the energy trapped by GHGs goes into the oceans and ocean heat content provides a direct measure of this energy accumulation in the upper layers of the ocean. Unlike surface temperatures, where the incremental long-term increase from one year to the next is typically smaller than the year-to-year variability caused by El Niño and La Niña, ocean heat content is rising more steadily with less pronounced year-to-year fluctuations. Indeed, 2018 set new records for ocean heat content in the upper 700 m (data since 1955) and upper 2000 m (data since 2005), exceeding previous records
    set in 2017. Comment: Once again the authors embark on yet another fishing expedition looking for events that can be used in raising an alarm about climate change and report these events as effects of and therefore the evidence for AGW. The exercise involves circular reasoning and the Texas sharpshooter fallacy. The claim that the atmosphere is the source of all changes in ocean heat content is circular reasoning used in a heat balance that ignores deep ocean heat sources as explained in a related post [LINK] .
12. Excerpt #9: Sea level is one of the seven key indicators of global climate change highlighted by GCOS4 and adopted by WMO for use in characterizing the state of the global climate in its annual statements. Sea level continues to rise at an accelerated rate. Global mean sea level for 2018 was around 3.7 mm higher than in 2017 and the highest on record. Over the period January 1993 to December 2018, the average rate of rise was 3.15 ± 0.3 mm yr-1, while the estimated acceleration was 0.1 mm yr-2. Accelerated ice mass loss from the ice sheets is the main cause of the global mean sea-level acceleration as revealed by satellite altimetry. Assessing the sea-level budget helps to quantify and understand the causes of sea-level change. Closure of the total sea-level budget means that the observed changes of global mean sea level as determined from satellite altimetry equal the sum of observed contributions from changes in ocean mass and thermal expansion (based on in situ temperature and salinity data, down to 2000 m since 2005 with the international Argo project). Comment: Sea level rise and acceleration of sea level rise are normal in interglacials and these changes are seen in more dramatic form in recent interglacial events such as the Younger Dryas [LINK] and the Eemian [LINK] . These changes do not in themselves imply human cause or that the proposed climate action in the form of reducing fossil fuel emissions will stop sea level rise or change the rate of sea level rise as described in a related post [LINK] .
13. Excerpt #10: In the past decade, the oceans have absorbed around 30% of anthropogenic CO2 emissions. Absorbed CO2 reacts with seawater and
    changes ocean pH. This process is known as ocean acidification. Changes in pH are linked to shifts in ocean carbonate chemistry that can affect the ability of marine organisms, such as molluscs and reef-building corals, to build and maintain shells and skeletal material. This makes it particularly important to fully characterize changes in ocean. Comment: The claim that changes in oceanic CO2 are due to fossil fuel emissions is an assumption and the use of this assumption in this mass balance is a case of circular reasoning as described in a related post [LINK] . No paleo data exists that shows the atmosphere can cause ocean acidification. The primary example of ocean acidification in paleo climatology is the PETM described in a related post  [LINK] . In that major ocean acidification event, the source of the carbon that caused acidification was the ocean itself or perhaps the mantle.
14. Excerpt #11: In 2018, weather and climate events accounted for most of nearly 62 million people affected by natural hazards, according to an analysis of 281 events recorded by the Centre for Research on the Epidemiology of Disasters. Floods continued to affect the largest number, amounting to more than 35 million people in 2018. The CRED statistics also highlight that over 9 million people were affected by drought worldwide, including in Kenya, Afghanistan, and Central America, as well as migration hotspots El Salvador, Guatemala, Honduras and Nicaragua. There are still some challenges to better quantify these impacts and their association with particular categories of hydrometeorological events.  The highest losses affected the United States resulting from two significant hurricane landfalls – Florence and Michael – with a total loss estimated at nearly US$ 50 billion; much less than the US$ 300 billion loss estimated for 2017, which was among the highest losses in recent years, owing to three major hurricanes affecting the United States and the Caribbean. Comment: That “there are still some challenges” in associating these events with AGW means that we don’t know that they do but let’s list them as if they do so the UN can use this report to serve their fear based activism needs. The data do not show for example that tropical cyclones are being driven by climate change as described in related post posts [LINK]  [LINK]
15. CONCLUSION: Although the WMO is a meteorological association with a large number of highly qualified meteorologists, their activities and more importantly, their opinions on and evaluation of the anthropogenic climate change issue are guided by the fact that they are a UN organization reporting to the Secretary General Antonio Guterres. It is well known and it is documented in two related posts [LINK] [LINK] that the UN is a climate activist organization and may well be the real driver of fear based climate alarmism to impose its climate action policies upon all nations of the world. Therefore, it is necessary that the WMO State of the Climate reports must meet the UN’s activism requirements needed to rationalize and impose their climate action policies. The unscientific nature of works like the WMO State of the Climate and their alarmist and activist nature derive not from unbiased scientific inquiry but from the nature of their relationship with the United Nations. The WMO State of the Climate 2018 report is available online [LINK] .

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FIGURE 1: GLOBAL ACE AT DIFFERENT DATA QUALITY: 1945-2013

FIGURE 2: HADCRUT SEA SURFACE TEMPERATURE DATA 1945-2013

FIGURE 3: GLOBAL ACE VS SST AT DECADAL TIME SCALE

FIGURE 4: CORRELATION & DETRENDED CORRELATION

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1. SUMMARY: The proposed relationship between sea surface temperature (SST) and tropical cyclone activity is tested with data for global mean Accumulated Cyclone Energy (ACE) in all six basins and global mean SST in the study period 1945-2013.  Three different time scales from annual to decadal are studied. Although some strong correlations are seen in the source time series, no correlation is found in the detrended data. A test with only Northern Hemisphere tropical cyclone basins and Northern Hemisphere SST also failed to find the needed correlation. We conclude that no evidence is found in these data to relate the ACE measure of tropical cyclone activity to mean SST. 
2. BACKGROUND: Sea surface temperature (SST) is the link that connects climate change research with tropical cyclone research. Rising SST is observed (Hadley Centre, 2017) and thought to be an effect of Anthropogenic global warming or AGW (Hansen, 2005) . At the same time, the theory of tropical cyclones holds that cyclone formation, and particularly cyclone intensification are related to SST (Vecchi, 2007) (Knutson, 2010). Testable implications of the theory for empirical research are derived from climate model simulations (Knutson, 2010) and also from sedimentary evidence of land-falling hurricanes over a 1500-year period (Mann, 2009). These studies suggest some guidelines and testable implications for empirical tests of the theory that AGW affects tropical cyclone activity (Knutson, 2010). These guidelines as described by Knutson etal are described in a related post at this site [LINK] . A challenge for empirical tests in this line of research in light of Knutson’s work is extremely high variance in tropical cyclone data at an annual time scale or for any single cyclone basin. The variance problem suggests that trend and correlation analysis of tropical cyclone data should be carried out on a global basis for all six tropical cyclone basins and time scales of longer than annual should be used.
3. DATA: The “best track” cyclone data were used as received from the NCDC without corrections, adjustments, additions, or deletions with the exception that the years 1848-1944 were not used because they did not contain data for all six basins. It is generally assumed that these data may contain a measurement bias over time and across basins because of differences in data collection methods and procedures (Kossin, 2013). Although aircraft reconnaissance of tropical cyclones in selected basins began as early as the 1940s, these data did not reach a level of coverage and sophistication until the C-130 was deployed in the 1960s. Satellite data gathering for tropical cyclones began in the 1970s.  Figure 1 is a display of the ACE data for the two different data quality periods. This study uses the longer time span 1945-2013. Global mean and Northern Hemisphere mean SST data for the corresponding period are displayed in Figure 2. 
4. THEORY: The effect of rising atmospheric carbon dioxide and sea surface temperature (SST) in the climate change era on the formation and intensification of tropical cyclones is not well understood (Walsh, 2014). The conventional theory is that rising SST under the right atmospheric conditions will increase both the formation and intensification of tropical cyclones (Gray W. , 1967) (McBride, 1995) (Emanuel K. , The dependence of hurricane intensity on climate, 1987) (Gray W. , 1979). However, historical tropical cyclone data in a warming world as well as future tropical cyclone conditions generated by general circulation climate models imply that the relationship between the warming trend in the climate change era and tropical cyclone formation and intensification may be more complicated (Hodges, 2007) (Kozar, 2013) (Lin, 2015) (Walsh, 2014). For example, localized SST relatively higher than surrounding waters may produce a greater extent of the rainfall area (Lin, 2015). It is also possible that a complex relationship exists between SST and the frequency and intensity of tropical cyclones with rising temperatures implying fewer but more intense storms (Hodges, 2007). On the other hand, a simulation on a millennial time scale by Kozar, Mann, Emanuel, and others suggests that warming will increase the decadal frequency of North Atlantic hurricanes and proportionately, the decadal frequency of hurricanes that make landfall (Kozar, 2013). In this study we use detrended correlation analysis to test whether a relationship between SST and ACE can be found that would imply that ACE is responsive to SST at the time scale of interest. The test is carried out at time scales of annual, 5 years, and 10 years.
5. DATA ANALYSIS AND RESULTS: The results of the detrended correlation analysis of global mean ACE against SST for two regional references (global and northern hemisphere) and three time scales (1, 5, & 10 years) are displayed in Figure 3 and Figure 4. Since the SST series differs among calendar months, the relationship between SST and ACE is studied separately for each calendar month particularly since tropical cyclone activity is strongly seasonal. In Figure 3, the relationship between SST and ACE is depicted graphically for each calendar month in a GIF animation that cycles through the twelve calendar months.
6. Figure 4 is a comparison of the correlations and detrended correlations between ACE and SST (in the ordinate) for the twelve calendar months (labeled 1 to 12  along the coordinate) and the two regional extents studied namely global (GL) and northern hemisphere (NH). Figure 4 consists of three panels for the three time scales with two frames in each panel (global extent in the left frame and northern hemisphere extent on the right frame). These graphics show that source data correlations (in blue) are high and statistically significant for all twelve calendar months with the correlation being highest in the decadal time scale and lowest in the annual time scale. These correlations are stronger for the global extent than they are for the hemispheric extent. Not much difference among calendar months is seen in the global extent but the hemispheric extent appears to show stronger correlations in summer.
7. Source data correlations derive from two independent sources. They are (1) share long term trends and (2) the responsiveness of ACE to SST. It is only the latter that is of interest in this study. The two sources are separated in the detrended correlations shown in red that contain only the responsiveness of ACE to SST at each of the three time scales. Here we find that none of the source data correlation survives into the detrended series for any calendar month and that therefore the correlations seen in the source data are driven only by shared trends and that therefore they do not contain any information about the responsiveness of ACE to SST in any of the twelve calendar months.
8. CONCLUSION:  The data do not show that the total tropical cyclone energy for all six basins worldwide or for the four basins in the northern hemisphere is responsive to the corresponding regional sea surface temperature at any of the three time scales studied from annual to decadal. In a related post it is shown that the apparent rising trend in tropical cyclone activity is mostly the creation of changing data quality with no statistically significant change in tropical cyclone activity measured as total global ACE [LINK] . In this post the the further study of the ACE data we find no evidence that total global ACE is responsive to changes in SST.

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SUMMARY: The overall structure of changes in total column ozone in time and across latitudes shows that the data from the two stations in Antarctica prior to 1985 are unique and specific to that time and place. They cannot be generalized into a global pattern of ozone depletion.

Here we show that declining levels of total column ozone in Antarctica during the months of October and November prior to 1985 do not serve as empirical evidence that can be taken as validation of the Rowland-Molina theory of chemical ozone depletion. The chemical theory implies that ozone depletion must be assessed across the full range of latitudes and over a much longer time span than what is found in Farman etal 1985 which serves as the sole basis for the ozone depletion hypothesis that led to the Montreal Protocol and the ascendance of the UN as a global environmental authority.

Details below. 

FIGURE 1: ROLAND MOLINA THEORY OF OZONE DEPLETION BY CHLORINE

FIGURE 2: THE FARMAN ETAL 1985 FINDINGS

TABLE 1: MEASURING STATIONS USED IN THE STUDY

DATA#1: AMS SOUTH POLE, ANTARCTICA

DATA#2: HLB, HALLEY BAY, ANTARCTICA

DATA#3: LDR, LAUDER, NEW ZEALAND

DATA#4: PTH, PERTH, AUSTRALIA

DATA#5: SMO, AMERICAN SAMOA

DATA#6: MLO, MAUNA LOA

DATA#7: WAI, WALLOPS ISLAND, VA

DATA#8: BDR, BOULDER, CO

DATA#9: CAR, CARIBOU, ME

DATA#10: BIS, BISMARK, ND

DATA#11: FBK, FAIRBANKS, AK

DATA#12: BRW, BARROW, AK

SUMMARY#1: LATITUDINAL PATTERNS AND HEMISPHERIC ASYMMETRY

SUMMARY#1 shows that the range of observed ozone levels is a strong function of latitude. It reaches a minimum of about 20DU in the tropics and increases asymmetrically toward the two poles. The hemispheric asymmetry has two dimensions. The northward increase in range is more gradual than the steep southward increase in range. Also, the northward increase in range is achieved mostly with a greater frequency of high values while southward increase in range is achieved mostly with a declining frequency of low values. The midpoint between the HIGH and LOW values is symmetrical within ±45 latitude but diverges sharply beyond 45 latitude with the northern leg continuing to rise while the southern leg changes to a steep decline. Hemispheric asymmetry in atmospheric circulation patterns is well known (Butchart, 2014) (Smith, 2014) and the corresponding asymmetry in ozone levels is also recognized (Crook, 2008) (Tegtmeier, 2008) (Pan, 1997). These asymmetries are also evident when comparing seasonal cycles among the ground stations. The observed asymmetries are attributed to differences in land-water ratios in the two hemispheres with specific reference to the existence of a large ice covered land mass in the South Pole (Oppenheimer, 1998) (Kang, 2010) (Turner, 2009). The climactic uniqueness of Antarctica is widely recognized (Munshi, Mass Loss in the Greenland and Antarctica Ice Sheets, 2015) (NASA, 2016) (NASA, 2015).

SUMMARY#2: THE SEASONAL CYCLE AT DIFFERENT LATITUDES

The ground stations in SUMMARY#2 are sorted by latitude from south to north. The right panel shows the relative amplitudes of the seasonal cycles at the twelve ground stations. The left panel indicates the time structure of the seasonal cycle. It shows that at all northern hemisphere stations north of 30 latitude, the seasonal cycle runs from a low in the northern autumn months of September-November to a high in the northern spring months of March-May. In the very shallow seasonal cycles in the northern tropics (MLO) the seasonal low changes from the northern autumn to the northern winter months of December-February but the seasonal high remains at the northern spring months of March-May. Once we cross the equator to the southern tropics the seasonal cycle change abruptly. The low remains at December-February but the seasonal high changes to the southern spring months of September to November. This reversal persists into the southern mid-latitudes down to 60 degrees south where we find that the seasonal low occurs at the southern autumn months of March-May with a high in the southern spring months of September-November. In terms of calendar months the southern ozone cycle is a mirror image of the northern ozone cycle but of course, in terms of seasons, they are exactly the same. Thus, no asymmetry in ozone cycles exists between the hemispheres within ±60o latitude. However, a dramatic asymmetry is found in the continent of Antarctica. Here we find a perfect 180-degree reversal of the seasonal cycle with a high in the southern summer (December-February) and a low in the southern spring (September to November). The timing of the seasonal cycle shown in the left panel of Figure 29 demonstrates the uniqueness of Antarctica. The size and location of the amplitude of the seasonal cycle further underscores Antarctica’s peculiarity on a global scale because it is fairly symmetrical within ±60 degrees but this symmetry is lost when we enter the higher latitudes. In the north, the HIGH value continues to rise from 350DU to well above 400DU while the LOW is stable at above 250 DU. The exact reverse of this pattern is seen in the south where the HIGH is stable at 350DU while the low collapses from 250DU to below 150DU. These data do not indicate that ozone depletion observed in Antarctica from 1975 to 1985 (Farman, 1985) can be generalized as a global chemical phenomenon (Molina, 1974) (UNEP, 2000). It is more
likely that the unique and peculiar changes in atmospheric ozone observed at AMS and HLB reflect changes in the ability of atmospheric circulations to transport ozone from the tropics to the South Pole (Kozubek, 2012) (Tegtmeier, 2008) (Weber, 2011).

SUMMARY#3: LATITUDINAL PATTERN OF OZONE DEPLETION

The left panel of SUMMARY#3 represents the southern hemisphere from AMS (-90o) to SMO (-14 latitude). The right panel represents the northern hemisphere from MLO (+19.5 latitude) to BRW (+71 latitude). The x-axis in each panel indicates the calendar months of the year from September = 1 to August = 12. The ordinate measures the average rate of change in total column ozone for each month among adjacent Lustra for all Lustra estimated using OLS regression of mean total column ozone against Lustrum number for each month. For example, in the left panel we see that in the month of September (x=1) ozone levels at HLB (shown in red) fell at an average rate of 15DU per Lustrum for the entire study period; and in the right panel we see that in the month of July (x=11) ozone levels at FBK (shown in orange) rose at an average rate of more than 2DU per Lustrum over the entire study period. The full study period is 50 years divided into 10 Lustra but it is abbreviated for some stations according to data availability. The concern about ozone depletion is derived from the finding by Farman et al in 1985 that ozone levels at HLB fell more than 100DU from the average value for October in 1957-1973 to the average value for October in 1980-1984. In comparison, changes of ±5DU from Lustrum to Lustrum seem inconsequential. In that light, and somewhat arbitrarily if we describe ±5DU per Lustrum as insignificant and perhaps representative of random natural variability, what we see in Figure 30 is that, except for the two Antarctica stations (AMS and HLB), no average change in monthly mean ozone from Lustrum to Lustrum falls outside this range. It is therefore not likely that the HLB data reported by Farman et al can be generalized globally. All data and computational details used in this study are available in the online data archive for this paper (Munshi, Ozone paper data archive, 2016).

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1. ABSTRACT: Total column ozone data for each calendar month from twelve ground stations in a large range of latitudes are studied in a fifty-year sample period from 1966-2015. The study period is divided into ten Lustra (5-year periods). The average seasonal cycle within each Lustrum and the trends for each calendar month from Lustrum to Lustrum are compared across the range of latitudes from -90 latitude to +71 latitude in the sample period. The overall structure of changes in total column ozone in time and across latitudes shows that the data from the two stations in Antarctica prior to 1995 are unique and specific to that time and place. They cannot be generalized into a global pattern of ozone depletion. The findings imply that declining levels of total column ozone in Antarctica during the months of October and November prior to 1995 do not serve as empirical evidence that can be taken as validation of the Rowland-Molina theory of chemical ozone depletion. The chemical theory implies that ozone depletion must be assessed across a greater range of latitudes and over a much longer period of time than what is found in Farman etal 1985 which serves as the only empirical basis for the ozone depletion hypothesis that led to the Montreal Protocol and the ascendance of the UN as a global environmental authority. It is far more likely that the historical decline of total column ozone in the South Pole during the months of October and November reported by Farman etal 1985 are related to natural cycles in atmospheric circulation patterns that transport ozone from the tropics to the South Pole. Data analysis over a wider range of latitudes does not show evidence of ozone decline. The data presented above imply that the Montreal Protocol is credited with solving a non-existent problem. 
2. BACKGROUND: In 1971, renown environmentalist James Lovelock studied the unrestricted release of halogenated hydrocarbons (HHC) into the atmosphere from their use as aerosol dispensers, fumigants, pesticides, and refrigerants. He was concerned that (1) these chemicals were man-made and they did not otherwise occur in nature and that (2) they were chemically inert and that therefore their atmospheric release could cause irreversible accumulation. In a landmark 1973 paper by Lovelock, Maggs, and Wade he presented the discovery that air samples above the Atlantic ocean far from human habitation contained measurable quantities of HHC (Lovelock, Halogenated hydrocarbons in and over the Atlantic, 1973). It established for the first time that environmental issues could be framed on a global scale and it served as the first of three key events that eventually led to the Montreal Protocol and its worldwide ban on the production, sale, and atmospheric release of HHC (UNEP, 2000). Since HHCs were non-toxic and, as of 1973, environmental science knew of no harmful effects of HHC, the fear of their accumulation in the atmosphere remained an academic curiosity. This situation changed in the following year with the publication of a paper by Mario Molina and Frank Rowland in which is contained the foundational theory of ozone depletion and the rationale for the Montreal Protocol’s plan to save the ozone layer (Molina, 1974). According to the Rowland-Molina theory of ozone depletion (RMTOD), the extreme volatility and chemical inertness of the HHCs ensure that there is no natural sink for these chemicals in the troposphere and that therefore once emitted they may remain in the atmosphere for 40 to 150 years and be transported by diffusion and atmospheric motion to the stratospheric ozone layer where they are subjected to solar radiation at frequencies that will cause them to dissociate into chlorine atoms and free radicals. Chlorine atoms can then act as a catalytic agent of ozone destruction in a chemical reaction cycle described in the paper and reproduced in Figure 1 (Molina, 1974). Ozone depletion poses a danger because the ozone layer protects life on the surface of the earth from the harmful effects of UVB radiation. The Rowland-Molina paper, the second key event that led to the Montreal Protocol, established that the atmospheric accumulation of HHC is not harmless and provided a theoretical framework that links HHC to ozone depletion.
3. FARMAN ETAL 1985: The third key event in the genesis of the Montreal Protocol was the paper by Farman, Gardiner, and Shanklin that is taken as empirical evidence for the kind of ozone depletion described by the RMTOD
   (Farman, 1985). The essential finding of the Farman paper is contained in the top frame of the paper’s Figure 1 which is reproduced here as Figure 2. Ignoring the very light lines in the top frame of Figure 2, we see two dark curves one darker than the other. The darker curve contains average daily values of total column ozone in Dobson units for the 5-year test period 1980-1984. The lighter curve shows daily averages for the 16-year reference period 1957-1973. The conclusions the authors draw from the graph are that (1) atmospheric ozone levels are lower in the test period than in the reference period and (2) that the difference is more dramatic in the two spring months of October and November than it is in the summer and fall. The difference and the seasonality of the difference between the two curves are interpreted by the authors in terms of the ozone depletion chemistry and their kinetics described by Molina and Rowland (Molina, 1974). The Farman paper was thus hailed as empirical evidence of RMTOD and the science of ozone depletion due to the atmospheric release of HHC appeared to be well established by these three key papers. First, atmospheric release of HHC caused them to accumulate in the atmosphere on a global scale because they are insoluble and chemically inert (Lovelock). Second, their long life and volatility ensure that they will end up in the stratosphere where HHC will be dissociated by radiation to release chlorine atoms which will act as catalytic agents of ozone depletion (Molina-Rowland). And third, empirical evidence validates the depletion of ozone and the role of HHC in the depletion mechanism (Farman et al). The Montreal Protocol was put in place on this basis. This study is an extension of a prior work that involved a survey of total column ozone data from ground stations (Munshi, Trends in atmospheric ozone, 2015). Its purpose is to evaluate the Farman findings across a range of latitudes and over a longer time period. The objective is to determine whether the findings by Farman can be generalized globally in terms of the Rowland-Molina theory of chemical ozone depletion.
4. DATA AND METHOD:  Total column ozone (TCO) measurements made with Dobson spectrophotometers at ground stations are used in this study. Twelve stations are selected to represent a large range of latitudes. The selected stations, each identified with a three-character code, are listed in Figure 1. The location of these stations are described and identified with global coordinates. The twelve stations may be classified into five groups according to latitude as (1) high southern latitudes (90o south to 60o south with stations AMS and HLB), (2) mid- southern latitudes (60o south to 30o south with stations LDR and PTH), (3) tropical latitudes (30o south to 30o north with stations SMO and MLO), (4) mid- northern latitudes (30o north to 60o north with stations WAI, BDR, CAR, and BIS), and (5) high northern latitudes (60o north to 90o north with stations FBK and BRW). The data are provided online by the NOAA (NOAA, 2015) (NOAA/ESRL, 2016) (NOAA/ESRL, 2016) and the British Antarctic Survey (BAS, 2016). Most stations provide daily mean values of total column ozone in Dobson units. The time span of the data ranges from 1957 to 2015. The first year of data available varies from station to station in the range of 1957 to 1987, and the last month from August 2013 to December 2015. Some months and some years in the span of measurements do not contain data for many of the stations. The core study period is somewhat arbitrarily defined as consisting of ten Lustra (5-year periods) from 1966 to 2015 (Table 2). The Farman paper may be cited as a precedence for the use of changes in 5-year means in the evaluation of long term trends. The period definitions are not precise for the first and last Lustra. The first Lustrum is longer than five years for some stations and shorter than five years for others. The last Lustrum is imprecise because of the variability in the last month of data availability. The calendar month sequence is arranged from September to August in the tables and charts presented to maintain seasonal integrity. The seasons are roughly defined as follows: September-November (northern autumn and southern spring), December-February (northern winter and southern summer), March-May (northern spring and southern autumn), and June-August (northern summer and southern winter). Daily and intraday ozone data are averaged into monthly means for each period. These monthly means are then used to study trends across the ten Lustra for each calendar month and also to examine the average seasonal cycle for each Lustrum. Trends in mean monthly ozone and seasonal cycles are compared to examine the differences among latitudes. These patterns are then used to compare and evaluate the chemical and transport theories for changes in atmospheric ozone. The chemical explanation of these changes rests on the destruction of ozone by chlorine atoms derived from HHC (Molina, 1974) while the transport theory describes them in terms of the Brewer-Dobson circulation (BDC) and polar vortices that transport ozone from the tropics where they are formed to the greater latitudes where they are more stable (Kozubek, 2012) (Butchart, 2014) (Tegtmeier, 2008) (Weber, 2011)

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REFERENCES

1. BAS. (2016). Ozone data. Retrieved 2016, from BAS: https://legacy.bas.ac.uk/met/jds/ozone/index.html
   Butchart, N. (2014). The Brewer-Dobson circulation. Reviews of Geophysics , 52:2.
2. Crook, J. (2008). Sensitivity of Southern hemisphere climate to zonal symmetry in ozone. Geophysical Research Letters , 35 L07806.
3. Dunkerton, T. (1978). On the mean meridional mass motions of the stratosphere and mesosphere. Journal of Atmospheric Science , 35: 2325-2333.
4. Farman, J. (1985). Large losses of total ozone in Antarctica reveal seasonal ClOx/NOx interaction. Nature, 315.207-210.
5. Hirota, I. (1980). Observational evidence of the semiannual oscillation in the tropical middle atmosphere. Pure Applied Geophysics , 118: 217-238.
6. Kang, S. (2010). Why is the Northern Hemisphere Warmer than the Southern Hemisphere? New York: Columbia University.
7. Kozubek, M. (2012). Change of Brewer -Dobson circulation and its impact on total ozone in the middle and high latitude stratosphere. Retrieved 2015, from Researchgate: https://www.researchgate.net/publication/258620941_Change_of_Brewer_Dobson_circulation_and_its_impact_on_total_ozone_in_the_middle_and_high_latitude_stratosphere
8. Lovelock, J. (2007). GAIA. Retrieved 2015, from ecolo.org: http://ecolo.org/lovelock/what_is_Gaia.html
9. Lovelock, J. (1973). Halogenated hydrocarbons in and over the Atlantic. Nature , 241. 194-196.
10. Molina, M. (1974). Atmospheric sink for chlorofluoromethane: chlorine atom catalyzed destruction of ozone. Nature , 249(5460) 810-812.
11. Munshi, J. (2015). Mass Loss in the Greenland and Antarctica Ice Sheets. Retrieved 2016, from ssrn.com/author=2220942: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2684427
12. Munshi, J. (2016). Ozone paper data archive. Retrieved 2016, from dropbox.com: https://www.dropbox.com/sh/mmebqnw0v5vk3v6/AACZaIFMLW8bmtmtz2B-GHcja?dl=0
13. Munshi, J. (2015). Trends in atmospheric ozone. Retrieved 2016, from ssrn.com:
    http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2612810
14. NASA. (2016). Antarctic Sea Ice Reaches New Record Maximum. Retrieved 2016, from nasa.gov: https://www.nasa.gov/content/goddard/antarctic-sea-ice-reaches-new-record-maximum
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    https://www.nasa.gov/feature/goddard/nasa-study-mass-gains-of-antarctic-ice-sheet-greater-thanlosses
16. NOAA. (2015). Ozone depletion. Retrieved 2015, from NOAA:
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    ftp://aftp.cmdl.noaa.gov/data/ozwv/Dobson/
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19. Oppenheimer, M. (1998). Global warming and the stability of the West Antarctic Ice Sheet. Nature , 393, 325-332.
20. Pan, L. (1997). Pan, L., S. Solomon, W. Randel, J.-F. Lamarque, P. Hess, J. Hemispheric asymmetries and seasonal variations of the lowermost stratosphere water vapor and ozone derived from AGE II data. Pan,
    L., S. Solomon, W. Randel, J.-F. Lamarque, P. Hess, J. Gille, E.-W. Chiou, and M. P. McCormick, 1997:Hemispheric asymmetries and seasonal variations ofJournal of Geophysical Research , 102, 28 7–28 184.
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ROWLAND-MOLINA OZONE DEPLETION CHEMISTRY

1. The ultraviolet spectrum in incident solar radiation comes in three frequency bands. The high energy band (200-240 nanometers in wavelength) and the medium energy band (240-300 nanometers in wavelength) are harmful to living matter and are absorbed in the ozone layer while the low energy band (300-480 nanometers in wavelength) reaches the earth’s surface and causes tanning. Ozone plays a role in the absorption of harmful UV radiation. It is both created and destroyed in the absorption process.
2. The high-energy band UV is absorbed by oxygen molecules. The energy absorbed causes the oxygen molecule to break apart into extremely reactive oxygen atoms. A subsequent chance collision of these particles with other oxygen molecules causes the formation of ozone. The ozone thus formed then absorbs the medium-energy UV band and disintegrates back into oxygen.The UV absorption process is a cyclical one that begins and ends with oxygen. Ozone is a transient intermediate product of this process.
3. The reason that there is any ozone accumulation at all in the stratosphere is that, of the three reactions, the second is the slowest. Sunset finds the stratosphere with an excess of single oxygen atoms still looking for a date with an oxygen molecule. Overnight, with no radiation to destroy their product, these particles build up an inventory of ozone whose destruction will begin anew at sunrise. There is therefore, a diurnal cycle in the ozone content of the stratosphere whose amplitude, incidentally, is of the same order of magnitude as reported ozone depletion that caused Montreal Protocol to be invoked.
4. A longer but irregular cyclical pattern in stratospheric ozone coincides with the sunspot cycle. The period is approximately eleven years. It has been as long as 17 and as short as 8 years. High-energy band UV increases by 6 to 10% during periods of high sunspot activity but the medium-energy UV emission is largely unaffected. Therefore, high sunspot activity favors ozone accumulation and low sunspot activity is coincident with ozone depletion.
5. A somewhat similar pattern exists in the case of polar ozone holes. The UV induced reactions described above occur only over the tropics where sunlight is direct. Ozone is formed over the equator and not over the poles. Equatorial ozone is distributed to the poles by the Brewer-Dobson Circulation (BDC). The shape and position of the BDC changes seasonally and also shifts over a longer time cycle. Therefore, the efficiency of the BDC in transporting ozone to the greater latitudes changes seasonally and also over longer time cycles. Brewer, A. W. “Evidence for a world circulation provided by the measurements of helium and water vapour distribution in the stratosphere.” Quarterly Journal of the Royal Meteorological Society 75.326 (1949): 351-363.
6. When the distribution of ozone is not efficient, localized “ozone depletion” appears to occur in the extreme latitudes in the form of what has come to be called an ozone hole. These holes come and go in natural cyclical changes and are not the creation of chemical ozone depletion.
7. Concurrent with the ozone hole scare, climate scientists report that the warming trend has weakened the Brewer Dobson circulation . This connection between climate and ozone appears to indicate that warming can create more frequent and larger ozone holes. Butchart, N., et al. “Simulations of anthropogenic change in the strength of the Brewer–Dobson circulation.” Climate Dynamics 27.7-8 (2006): 727-741. However, the effect of global warming or of changing atmospheric composition on the Brewer Dobson Circulation remains controversial. Garcia, Rolando R., and William J. Randel. “Acceleration of the Brewer–Dobson circulation due to increases in greenhouse gases.” Journal of the Atmospheric Sciences 65.8 (2008): 2731-2739.
8. The case against CFCs is that when they get to the stratosphere by diffusion, they absorb high-energy band UV and form unstable and reactive chlorine atoms. The chlorine atom particles then participate as catalytic agents to convert ozone back to oxygen. In other words they mediate the reaction between atomic oxygen particles and ozone. It is alleged that the destruction of ozone by this mechanism exposes the surface of the earth to dangerous levels of medium-band UV because there is not enough ozone in the stratosphere to absorb them. Although these reactions can be carried out in the chemistry lab, there are certain rate constraints that make them irrelevant in the stratosphere.
9. The air up there in the stratosphere is rather thin, containing less than one percent of the molecular density of air at sea level. It is not easy for a molecular particle in random thermal motion to find another particle to react with. Photochemical reactions occur instantaneously while those that require a collision of two particles take much longer. This difference in the reaction rate is the reason that ozone accumulates overnight and why there is an inventory of ozone in the ozone layer.
10. The atomic oxygen particles that react with oxygen molecules to form ozone could in theory react with an ozone molecule instead and cause its destruction or it could react with another atomic oxygen particle and form oxygen instead of ever forming any ozone. Some of the oxygen atoms do behave in this manner but these reactions proceed too slowly to be important to the chemistry of the stratosphere. The reason is that the stratospheric chemicals in question exist in minute quantities.
11. One in a million particles is an ozone molecule or an atomic oxygen particle and one in a billion is CFC or chlorine generated from CFC. The accidental collision between chlorine atoms and ozone molecules or between chlorine atoms and oxygen atoms are rarer than those between two oxygen atoms or that between an oxygen atom and an ozone molecule. Therefore the latter collisions are more important to ozone depletion than those mediated by chlorine.
12. Considering that more than 200,000 out of a million molecular particles in the stratosphere are oxygen molecules it is far more likely that charged oxygen atoms will collide with oxygen molecules rather than with each other or with ozone. Therefore ozone rather than oxygen is formed. Ozone formation is a rate phenomenon.
13. Since chlorine atoms are a thousand times rarer in the stratosphere than atomic oxygen particles, it is not likely that chlorine’s mediation in short circuiting ozone generation will occur sufficiently fast to be important. Nature already contains an ozone destruction mechanism that is more efficient than the CFC mechanism but ozone forms anyway.
14. However, the argument can be made that overnight after sunset, as charged oxygen atoms are used up the charged chlorine atoms take on a greater role in ozone destruction and also when these chemicals are distributed to the greater latitudes where sunlight is less direct and too weak to be ionizers of oxygen, the only ozone destruction chemistry left is that of charged chlorine atoms colliding with ozone. The  relative importance of these overnight and greater latitude reactions in making changes to latitudinally weighted mean global ozone can be checked by examining its overall long term trends as well as its trend profiles. These data are shown in the data analysis documents linked below. They do not show the ozone depletion described in the Montreal Protocol.

FIGURE 1: The affected areas of environmental damage attributed to acid rain

FIGURE 2: THE CLAIMED DEVASTATION OF THE ADIRONDACKS BY ACID RAIN

FIGURE 3: LOCATION OF LAKE ACIDITY MEASUREMENTS & EPA REGIONS

FIGURE 4: SO2 EMISSIONS FOR EACH AFFECTED EPA REGION

FIGURE 5: RAIN AND LAKE ACIDITY DATA

FIGURE 6: HYPOTHESIS TESTS FOR CORRELATION 

FIGURE 7: CORRELATION BETWEEN EMISSIONS AND ACIDITY

FIGURE 8: CORRELATION ANALYSIS RESULTS: SUMMARY TABLE

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SUMMARY OF FINDINGS OF THIS STUDY

1. ABSTRACT: Detrended correlation analysis in the sample period 1992-2015 shows that on an annual time scale the acidity of rain in NY is responsive to the combined anthropogenic SO2 emissions in EPA Regions 1, 2, and 3; but that the acidity of lakes in the Adirondacks is not responsive to the acidity of rain in NY. An attempt to explain lake acidity in terms of dry deposition of anthropogenic SO2 emissions also failed. Our results are consistent with previous research which found no relationship between rain acidity and lake acidity and explained lake acidity in terms of soil chemistry in the catchment area of the lakes.
2. Anthropogenic emissions of sulfur dioxide (SO2) from industrial combustion of fossil fuels particularly coal became an environmental issue in the 1960s and 1970s because SO2 combines with water in the atmosphere and falls back to the surface in acidic form. Such acid deposition is referred to as acid rain and deemed undesirable because it is thought to kill fish in lakes and streams by increasing the acidity of the water and also to kill trees and damage crops by virtue of its acidity (Schindler, 1988) (Schofield, 1976) (Beamish, 1972) (Siccama, 1982) (Vogelmann, 1988) (Vogelmann, 1985) (Irving, 1981) (Cape, 1993) (Newbery, 1990). In 1970 the Clean Air Act (CAA) Amendment of 1970 was passed and the Environmental Protection Agency (EPA) was formed to enforce it. In 1971 the EPA put in place regulations to limit SO2 emissions from power plants on a per megawatt basis to achieve national air quality standards for SO2 mandated by the 1970 Amendment (Melnick, 2010) (Taylor, 2005). These were command and control regulations that prescribed to each affected firm, the emission reduction targets to be met and the methods to be used to meet them (Cole, 1999). SO2 emissions fell 30% from 1972 to 1982 but with no measurable change in the acidification of lakes and streams, or of the other cited environmental harm associated with SO2 emissions (NAPAP, 1987). 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. 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). 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. In the cap-and-trade market of the Acid Rain Program2, 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)3 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. 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. 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 discovered (Chan, 2012) (Conniff, 2009) (Dales, 1968) (Ellerman A. , 2002). 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). 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.
4. The purpose of the Acid Rain Program is not to reduce SO2 emissions as an end in itself but as a means to solve the acid rain problem. The acid rain problem is that acid deposition ascribed to anthropogenic SO2 emissions (1) kills trees, (2) increases the acidity of lakes and streams and kills or harms fish, and (3) damages crops. The objective of the Acid Rain Program is to effect a reduction in the environmental harm attributed to anthropogenic SO2 emissions. Therefore, the appropriate measure of the effectiveness or the success of the Acid Rain Program is not just whether SO2 emissions have been reduced but whether the Program has reduced the acidity of rain and the crop damage and the killing of trees and fish attributed to acid rain. The Acid Rain Program was motivated primarily by measurements that showed increased acidity of rainfall in the Northeastern Seaboard of the United States (Figure 1) with devastating environmental effects on high elevation forests in the Northeast particularly on Camel’s Hump Mountain in Vermont and on fish in highland lakes and ponds particularly on the Adirondack Mountains in New York (Figure 2) (Schindler, 1988) (Siccama, 1982). As an evaluation of the effectiveness of the Program we therefore consider whether the environmental concerns with respect the Adirondack Region of New York State (hereafter referred to as the “Adirondacks”) have been adequately addressed by the Acid Rain Program. Specifically, we look at the effect of the Acid Rain Program on the acidity of rain in the state of New York and the acidity of lakes in the Adirondacks. The evaluation of the Acid Rain Program is presented in three parts. Part 1 considers whether the Program achieved its objectives as of this writing. Part 2 looks at the design and efficiency of the emissions trading system for SO2 emissions. Part 3 compares the SO2 emissions trading system with market structures proposed for trading CO2 emissions. This document is Part 1 of this series.
5. DATA AND METHODS: Historical annual anthropogenic emissions of SO2 are provided by the Socio Economic Data and Application Center of Columbia University country by country on an annual basis from 1850 to 2005 (SEDAC, 2009). The patterns in these data are used to interpolate SO2 emissions for years missing in the EPA data. The primary source of anthropogenic SO2 emissions data in this study is the EPA Air Markets Program Data service (AMPD) which provides annual emissions data state by state for the years 1980, 1985, 1990, and annually from 1995 to 2015 (EPA-AMPD, 2016). The SEDAC data provide “total” SO2 emissions as well as “emissions from coal”. The EPA emission figures for the lower 48 states add up to values that closely match the “emissions from coal” figures in the SEDAC dataset for the years that the two datasets have in common. The year to year patterns in the SEDAC coal data are therefore used to interpolate the EPA emissions data for the missing years between 1990 and 1995. The interpolation yields estimated emission figures for 1992, 1993, and 1994 for the EPA dataset and generates a continuous annual emission data series state by state for the sample period 1992 to 2015. The state by state data are aggregated into data for EPA Regions (Figure 5) (EPA, 2016).
   The study period is chosen as 1992-2015. It is constrained by the availability of acidity data for lakes in the Adirondacks. The lake acidity data are provided by the Adirondack Lakes Survey Corporation (ALSC) as part of their Adirondack Long Term Monitoring (ALTM) program (ALSC, 2016). The dataset used in this study are the average SO4 concentrations in water samples taken from near the surface of lakes and ponds at over forty measuring stations located throughout the Adirondack (Figure 3). A continuous time series of annual mean SO4 concentration data in mg/liter are available from 1992 to 2015. These data constitute out “lake acidity” dataset. The importance of the ALTM lake acidity dataset in the evaluation of the Acid Rain Program is underscored by the rationale cited by the ALSC for their ALTM program. The text of this statement taken from the ALSC website is included in Figure 3 in abbreviated form along with the map showing the location of the ALTM measuring stations. The sensitivity of Adirondack lake acidity to acid rain and thereby to anthropogenic SO2 emissions is widely recognized (ALSC, 2016) (Driscoll, 2003) and this sensitivity forms the basis of our empirical test of the hypotheses that relate lake acidity to rain acidity and rain acidity to SO2 emissions. Acid deposition data are provided by the National Atmospheric Deposition Program (NADP, 2016) which maintains a large number of stations for the measurement of pollutants in precipitation (rain, snow, sleet, and hail). Included in the data are measurements of SO4 concentration in mg/liter weighted by the amount of precipitation. Since the focus of this work is the acidity of lakes in the Adirondack Region which is located wholly within New York State (NY), the relevant NADP data are identified as those taken from 21 NADP stations located within NY. The NADP dataset provides 4,127 SO4 acid deposition measurements taken at the 21 NADP measuring stations in NY during the period 1979 to 2015. These values are aggregated into annual means from 1992 to 2015, and this time series serves as the “rain acidity” measure in our study.
   We test the hypotheses that (1) rain acidity is responsive to anthropogenic SO2 emissions at an annual time scale, (2) lake acidity is responsive to rain acidity at an annual time scale, and (3) lake acidity is responsive directly to anthropogenic SO2 emissions at an annual time scale. For hypothesis #2, the relevant data are well defined as mean rain acidity in NY and mean lake acidity in the Adirondacks but for hypotheses #1 and #3, it is necessary to identify the relevant geographical area for SO2 emissions. Four different geographical extents that include NY are considered. They are EPA Regions 1, and 2, EPA Regions 1, 2, and 3, EPA Regions 1, 2, 3, and 4, and EPA Regions 1, 2, 3, and 5. Hypotheses #1 and #3 are tested four times once for each geographical extent. Thus there are 9 hypothesis tests, four for emissions and rain acidity, four for emissions and lake acidity, and one for rain acidity and lake acidity.
   The possibility of a direct relationship between anthropogenic SO2 emissions and lake acidity that does not act through acid rain derives from the “dry deposition” theory (Cosby, 1985) (Delmelle, 2001) (Driscoll, 2003) (Driscoll, 2001) (Graedel, 1989). It holds that SO4 in dry sulfate form in the atmosphere can affect lake acidity independent of precipitation of any form. The hypotheses are tested using detrended correlation analysis. Detrending is necessary to determine if a positive relationship exists that could be interpreted in terms of a theory of causation in a year to year annual time scale net of a shared drift in time in terms of overall trend during the sample period (Chatfield, 1989) (Prodobnik, 2008) (Munshi, 2016). Pearson’s correlation coefficient is computed with Excel’s CORREL () function and Bowley’s procedure is used to estimate the standard deviation of the correlation coefficient. One tailed t-tests are used to test the null hypothesis H0: ρ>0 against HA: ρ≤0. The test corresponds with the assumed positive relationships among the variables studied. For example, SO2 emissions are thought to increase not decrease rain acidity and rain acidity is thought to increase and not decrease lake acidity. All hypothesis tests are made at a maximum false positive error rate of α=0.001 per comparison consistent with “Revised standards for statistical evidence” published by the NAS to address an unacceptable rate of irreproducible results in published research (Johnson V. , 2013) (Siegfried, 2010). Since 9 comparisons are made each at α(comparison)= 0.001, the study-wide maximum false positive error rate is estimated as α(study) = 0.009 (Holm, 1979). This means that there is a 0.9% probability of at least one false positive result in nine tries with samples taken from the H0 distributions. All data and computational detains used in this work are available in an online data archive (Munshi, SO2-Part1-Archive, 2016).
6. DATA ANALYSIS AND RESULTS: Data for anthropogenic SO2 emissions in each of eight EPA regions (Figure 5) from 1980 to 2015 are displayed in Figure 4. It shows a wide range of SO2 emissions at the inception of the Acid Rain Program in 1995 from over 4,000 kilo tons per year (KTY) to less than 400 KTY. With the exception of regions 6&8, where emissions are relatively low to begin with, all regions show a steep decline in SO2 emissions from 1995 to 2015.
   The bottom panel of Figure 4 compares the rate of decline in SO2 emissions from coal in the USA (SEDAC, 2009) before the Acid Rain Program (1970-1994) with the rate of decline in the Acid Rain Program era since 1995. The comparison indicates a four-fold increase in the rate of decline in SO2 emissions since 1995. Figure 5 displays the data for the acidity of precipitation in the state of New York (left panel) and the acidity of lakes and streams in the Adirondacks (right panel) for the sample period 1992-2015. The sample period is constrained by the availability of lake data. The graphic display of the data indicates a dramatic decline in SO4 acidity5 of rain in NY and of lakes in the Adirondacks over a period when anthropogenic SO2 emissions have also declined (Figure 4). To determine whether these declining trends are related at an annual time scale we carry out detrended correlation analysis among anthropogenic SO2 emissions, SO4 acidity of rain in NY, and SO4 acidity of lakes in the Adirondacks. Natural emissions of SO2, though much larger than anthropogenic emissions (HSDB, 2016), are not included in this analysis because they are sporadic and random with great uncertainty and therefore unquantifiable on an annual basis. The analysis for the relationship at an annual time scale between acidity of rain in NY and the acidity of lakes in the Adirondacks is presented graphically in Figure 5. A high correlation of R = 0.9612 is observed between the time series in the lower left panel of Figure 5. This correlation can be described as being derived from two sources. They are (1) the common and perhaps incidental direction of the drift in time of the two time series being compared and (2) the responsiveness of the acidity of lakes to the acidity or rain at an annual time scale. It is the second effect that is relevant to a theory of causation6 that relates lake acidity to the acidity of rain and it is extracted from the overall correlation with detrended correlation analysis (Chatfield, 1989) (Prodobnik, 2008) (Munshi, 2016). The detrended correlation at an annual time scale is depicted graphically in the bottom right panel of Figure 5 and it shows some evidence of a positive correlation. The highlighted column in Figure 6 shows that the value of the detrended correlation coefficient is computed as R = 0.3655 and using Bowley’s procedure we estimate its standard deviation as S = 0.1984 (Bowley, 1928). The null hypothesis H0: ρ≤0 against HA: ρ>0 is tested with the t-distribution and the one-tail pvalue is computed as p=0.03951. At a maximum false positive error rate of α=0.001 per comparison (Johnson V. , 2013), we find that the pvalue > α and we therefore fail to reject H0 in this case and conclude that the data do not provide sufficient evidence that lake acidity is responsive to rain acidity at an annual time scale. This result is consistent with prior research that found no correlation between the acidity of rain and the acidity of lakes and streams and attributed changes in the acidity of lakes not to the acidity of rain but to the acidity of the soil in the catchment area or drainage basin through which rainwater drains into lakes and streams (NAPAP, 1987) (Krug, 1989) (Cosby, 1985). Soil chemistry of the catchment area was found to be the dominant factor that determines the acidity of lakes and streams in which direct rainfall is an insignificant source of water.
   We now test the responsiveness of rain acidity in NY to anthropogenic SO2 emissions from a relevant geographical area. Four different geographical areas are tested for relevance in terms of SO2 emissions that can be related to the SO4 acidity of rain in NY. Statistically significant results are found in two of these emission regions (Figure 6). The results for the geographical area defined as EPA Regions 1, 2, and 3 are shown in the highlighted column of Figure 6 and depicted graphically in Figure 7. The bottom right panel of Figure 10 appears to show evidence that the acidity of rain in NY is responsive to SO2 emissions In EPA Regions 1, 2, and 3 at an annual time scale and this visual intuition is confirmed in the highlighted column of Figure 6. These results provide strong evidence that the SO4 acidity of precipitation in NY is related to anthropogenic SO2 emissions in the relevant geographical extent. The results appear to be paradoxical. If anthropogenic SO2 emissions explain the acidity of rain in New York but the acidity of rain in NY does not explain the acidity of lakes in the Adirondacks, then what explains the observed changes in the acidity of lakes in the Adirondacks? One possibility is the role played by “dry deposition” of sulfates. The dry deposition hypothesis implies that anthropogenic SO2 emissions can impose a direct effect on the acidification of lakes independent of acid rain. We test this hypothesis with detrended correlation analysis between SO4 acidity of lakes in the Adirondacks and SO2 emissions in the four EPA Regions described in Figure 6. The test for EPA regions 1, 2, and 3 is depicted graphically in Figure 11 and summarized in the highlighted column of Figure 12. Since the pvalue of p=0.00353 is greater than our comparison α=0.001, we fail to reject H0 and conclude that the data do not provide sufficient evidence that changes in the SO4 acidity of lakes in the Adirondacks can be explained in terms of the sum of SO2 emissions from EPA regions 1, 2, and 3 at an annual time scale. Three other emission regions are tested (Figure 12). No statistically significant result is found.
7. SUMMARY AND CONCLUSIONS: The Acid Rain Program described in Title IV of the 1990 Amendments to the Clean Air Act of the USA was a response to two high profile environmental events of the 1970s and 1980s in the Northeast region of the USA (Figure 1). First, a high elevation boreal forest of red spruce on Camel’s Hump Mountain in Vermont was found to contain a large number of dead or dying trees (Siccama, 1982) (Johnson A. , 1983) (Vogelmann, 1988) (NAPAP, 1987) (Vogelmann, 1985) (Cape, 1993) and secondly, lakes and ponds in the Adirondacks in New York8 showed an increase in acidity and simultaneously a decrease in the populations of fish and other aquatic creatures9 (Cowling, 1982) (Cowling, 1990) (Russell, 1993) (Garland, 1988) (NAPAP, 1987) (Beamish, 1972) (Krug, 1989) (Wildavsky, 1997). These discoveries came on the heels of the discovery of “acid rain”, a reference to a high level of acidity of precipitation (rain, sleet, hail, and snow) (NADP, 2016).
   Taking note of more than 4,000 KTY of SO2 emissions from coal burning power plants mostly in the states to the west and south of the affected areas (EPA, 2016), environmental science developed a theoretical framework that connected these events in a causation chain. According to this theory anthropogenic SO2 emissions cause acid rain. Acid rain kills red spruce in Camel’s Hump Mountain and acidifies lakes in the Adirondacks. Acid lakes in turn kill aquatic life in the lakes and ponds of the Adirondacks. The Acid Rain Program is based on this theoretical assessment (EPA, 2016). Our evaluation is based on the principle that environmental programs must be judged based on achievement of the environmental objectives at the end of the chain of causation and not on intermediate results (Keith, 1983) (Coen, 2000). It is assumed that these effects occur within one year and that they can therefore be measured at an annual time scale (Langner, 1991) (Cosby B. , 1985) (Larssen, 2000) (NAPAP, 1987) (NAPAP, 2003) (NAPAP, 2011). Although a broader interpretation could be made, we identify the environmental goals of the Acid Rain Program as two-fold: first, to restore the health of the high elevation boreal forest particularly red spruce on Camel’s Hump Mountain that is alleged to have been damaged by the acidity of precipitation; and second, to restore the health of aquatic life in the lakes and ponds of the Adirondacks that is alleged to have been adversely affected by lake acidity. Data for the relationship between red spruce decline on Camel’s Hump Mountain and anthropogenic SO2 emissions or rain acidity are not presented in this study because of the historical nature of this issue and in view of the overriding effect of the drought of the 1960s in Vermont on forest health (NAPAP, 1987) (NAPAP, 1991) (Siccama, 1982). From 1960 to 1969 the state of Vermont suffered its most severe drought on record as of 1989 (USGS, 1989) and the data show that the observed decline in red spruce on Camel’s Hump in the 1970s and 1980s is a singular event that can be traced to this drought. Since then, the health of the high elevation forest on Camel’s Hump Mountain has been generally improving and not declining (Johnson A. , 1983) (Siccama, 1982) (NAPAP, 1987). Adverse effects on crops and on human health were originally suspected but these were never confirmed and are not a serious concern in the current state of the assessment of adverse effects of acid precipitation (NAPAP, 1987) (NAPAP, 1991). Accordingly, in this study we address the more unsettled question regarding the relationships among the acidity of precipitation in NY, the acidity of aquatic ecosystems in the Adirondacks, and anthropogenic SO2 emissions in a relevant geographical extent that can be described in terms of EPA Regions (EPA, 2016). We use publicly available secondary data from the sources listed in Section 2 of the paper to present an empirical test of the effect of anthropogenic SO2 emissions on the SO4 acidity of precipitation in NY and on the SO4 acidity of lakes in the Adirondacks and the effect of SO4 acidity of precipitation in NY on the SO4 acidity of lakes and ponds in the Adirondacks. Detrended correlation analysis is used to extract correlations at an annual time scale among these variables. The findings are summarized in Figure 13. The first row in Figure 13 is marked “CORR”. It shows that the correlations among the source data are very high and well over 90% in all cases. For detrended correlation analysis at an annual time scale, these correlations may be described as being derived from two sources and they are (1) a year to year responsiveness of the theoretical effect variable to the theoretical cause variable and (2) a shared and perhaps incidental long term drift in time that cannot be interpreted in terms of causation. The second row marked “DET CORR” contains the correlations at an annual time scale that survives after the long term trends in the data are removed. These detrended correlations are tested for statistical significance and where possible they are interpreted in terms of causation hypotheses at an annual time scale.
   The columns marked “EMIS-RAIN” contain hypothesis tests for the effect of anthropogenic SO2 emissions (EMIS) in four different geographical extents on the SO4 acidity of precipitation in NY (RAIN). Very high detrended correlations are observed for SO2 emissions in EPA Regions 1, 2, and 3 and EPA Regions 1, 2, 3, and 4. Both of these correlations are found to be statistically significant at α=0.001 per comparison. We conclude that the data provide sufficient evidence that the SO4 acidity of precipitation in NY is responsive to anthropogenic SO2 emissions in a large geographical area identified by the indicated EPA regions (Figure 3). In the column marked EMIS-RAIN we test the hypothesis that theSO4 acidity of lakes in the Adirondacks is responsive to the SO4 acidity of precipitation in NY on an annual time scale. The test failed. The observed detrended correlation in the sample is too low to be generalized beyond the sample data. We conclude that the data do not contain evidence of a relationship between the acidity of lakes in the Adirondacks and the acidity of precipitation in NY. This result is consistent with previous research which ascribes changes in lake acidity to the acidity of the soil in the catchment area of the lakes and not to acidity of precipitation because lakes derive an insignificant portion of their water from direct rainfall. The acidity of the soil derives from other sources including vegetation and therefore lake acidity is unlikely to be responsive to rain acidity (Krug, 1989) (NAPAP, 1987) (Wildavsky, 1997) (Munton, 2011) (Brookes, 1989) (Reville, 2008).
8. 
   However, it is known that SO2 emissions fall back to the surface not only in the form of acid precipitation but also in dry sulfate form independent of precipitation. “Dry deposition” of this form may affect both soil and lake acidity directly without the intermediation of acid precipitation (Delmelle, 2001) (Driscoll, 2001) (Driscoll, 2003) (Cosby, 1985). This hypothesis is tested for four geographical extents in the columns marked “EMIS-LAKE” (Figure 13). No statistically significant correlation is found but it is noted that the pvalue of p=0.00353 for the geographical extent of emissions defined as EPA Regions 1, 2, and 3 is very close to α=0.001 and that therefore a failure to reject H0 may imply insufficient statistical power. We conclude that the data do not provide sufficient evidence that dry deposition of sulfates due to anthropogenic SO2 emissions in EPA Regions 1, 2, and 3 can explain SO4 acidity of lakes in the Adirondacks with the caveat that greater statistical power may yield different results. It is also possible that the absence of the expected effects of anthropogenic SO2 emissions at an annual time scale may be due to the effects of natural emissions of SO2 from volcanoes and geothermal vents that have not been taken into account (Kazahaya, 2004) (Malinconico, 1987) (McGee, 2010) (Sutton, 2003) (Symonds, 1990) (Wallace, 1994) (Young, 1998). These emissions don’t occur on a continuous year-to-year basis but sporadically and randomly. Yet, they account for more than two thirds of SO2 emissions globally (HSDB, 2016). It may be difficult to detect the impact of the much lower anthropogenic emissions on an annual time scale in a context in which the larger natural flows are excluded in the analysis possibly due to insurmountable measurement issues.
9. EVALUATION OF THE ACID RAIN PROGRAM: In view of the data and analysis presented in this study, our overall evaluation of the Acid Rain Program is that:
10. (1) The Program coincides with a corresponding increase in the rate of decline of SO2 emissions from coal fired power plants in the USA. We therefore conclude that the program has been effective in reducing anthropogenic SO2 emissions in the targeted EPA Regions.
11. (2) The abatement of anthropogenic SO2 emissions noted in item 1 above has reduced the SO4 acidity of precipitation in NY at an annual time scale.
12. (3) There is no evidence that the reduction of the SO4 acidity of precipitation in NY has had a measurable effect on the SO4 acidity of lakes in the Adirondacks.
13. (4) The reduction of anthropogenic SO2 emissions noted in item 1 above may have reduced the SO4 acidity of lakes in the Adirondacks that can be attributed to dry deposition of sulfates independent of acid precipitation; but no clear indication of that effect could be found at an annual time scale for the sample period 1992-2015 used in this study.
14. (5) It is not possible to evaluate the Acid Rain Program in terms of health of the high altitude boreal forest on Camel’s Hump Mountain in Vermont because of the much stronger effect of the drought of 1960-1969.
15. (6) The evaluation of the success of emissions trading systems must be based not only on the basis of abatement of emissions but also on the basis of whether the environmental objectives that served as the rationale for the reduction of emissions have been achieved.
16. (7) The study of the environmental impact of SO2 emissions suffers from a data availability bias (Das, 1998) because it relies entirely on anthropogenic emissions that are easy to measure and ignores much larger natural flows that are difficult to measure and impossible to quantify on an annual basis.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         All data and computational details used in this study are available in an online data archive (Munshi, SO2-Part1-Archive, 2016). Two additional installments of this research are planned. They involve the evaluation of the SO2 emissions trading system and its application in the area of climate change. 
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Reference: The decline of the caribou, Bangkok Post, November 2, 2009

1. It is reported that in 1989 there were 178000 Porcupine caribou in the Yukon and that “their number now is estimated to be 100,000” and from these data we may conclude that global warming is killing off the caribou because warming causes freezing rain in the calving season and that makes it hard for calving caribou to feed (The decline of the caribou, Bangkok Post, November 2, 2009).
2. There are two things wrong with this analysis. First the data reported for 2009 was estimated by the authors to suit their purpose and it does not represent actual data. Second, if you don’t cherry pick the start of the study period as 1989 but look at the entire available time series you get a very different picture because it shows that the population rose steadily from 100000 in 1972 to 178000 in 1989 and then decreased steadily down to 120000 in 2005. These data suggest, and caribou biologists agree, that caribou populations go through a 30 to 40 year cycle of growth and decay.
3. This population dynamic cannot be related to global warming or carbon dioxide. Presenting only half of the caribou population cycle as a global warming phenomenon is just the kind of flimflam chicanery with which the war against carbon dioxide is now associated. Climate science has become corrupted by activism [LINK] .

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ABSTRACT: Implementation of the Sustainable Development Goals (SDG) marks a shift in the priority of the UNDP from its primary purpose and function of tackling poverty to a UNEP and UNFCCC task of tackling climate change. This shift erodes the ability of the UNDP to perform its primary function of providing development assistance to poor countries and creates a vacuum in the United Nation’s poverty eradication program. We have learned from the poor performance of Cold-War-era development plans that combining the strategic interests of the donor (fighting communism) with the needs of the poor (economic and human development) do not mix. Yet, the SDG is just such a confounded and conflicted racist “White Man’s Burden” development program because it combines the strategic needs of the donor with respect to climate change with the most modest of needs of the very poor.

From an SDG perspective, an important consideration is the environmental constraint on economic growth which holds that human development is not necessarily a good thing because it comes at the expense of environmental degradation, finite resource depletion, greenhouse gas (GHG) emissions, and climate change. In this equation, optimality exists only when human development is constrained by environmental considerations. Imposition of these values of the rich North on the poor SOUTH is an egregious form of racism.

[RELATED POST ON RACISM]

Perhaps the UNDP recognizes the contradiction of making poor countries fight climate change as part of a development assistance program. Accordingly, climate finance is proposed to address this issue. It refers to additional funds from donor countries that will be made available to development aid recipients to compensate them for the cost of climate change mitigation. The flaw in this logic is that, if additional funding is available, its use is best determined according to the urgent needs and priorities of the poor countries receiving the funds and not according to the social and environmental concerns of the rich countries providing these funds. 

FIGURE 1: COMPARISON OF COUNTRIES AT THREE LEVELS OF DEVELOPMENT

FIGURE 2: TOTAL DEVELOPMENT AID RECEIVED BY LDCs

FIGURE 3: PER CAPITA GDP AND RELATIVE GROWTH

FIGURE 4: HUMAN DEVELOPMENT INDEX HDI

FIGURE 5: TOTAL ENERGY CONSUMPTION PER CAPITA

FIGURE 6: FOSSIL FUEL INTENSITY

FIGURE 7: PER CAPITAL CO2 EMISSIONS AND RELATIVE GROWTH

FIGURE 8: PER CAPITA ELECTRICITY CONSUMPTION

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1. Overview of Development Assistance: Significant social, political, financial, scientific, and technological advances in Europe since the 18th century left most of Asia and Africa behind and dependent on Europeans, initially as colonies and later in terms of development assistance. In the post-war and post-colonialism world about one fifth of the world’s population live in industrialized and technologically advanced rich countries, their economies driven by capitalism, innovation, trade, and well developed legal, political, and financial institutions that favor economic growth (Young, 2016) (Weightman, 2010) (Rosen, 2012). These countries, mostly former colonizers, are collectively referred to as the “North”.
2. About one half the world’s populations live in backward and undeveloped economies mostly in the former colonies – the great majority of them non-resource, non-industrialized, non-mechanized, agrarian subsistence economies mired in poverty, hunger, disease, illiteracy, and inadequate sanitation and infrastructure. These countries are collectively referred to as the “South”.
3. The great disparity in wealth and human development between the North and the South in the colonial and post-colonialism worlds (Therien, 1999) gave rise to the idea that the North could and should provide development assistance to the South in terms of capital, technology, and ideology to build them up in the North’s image – a model of development economics derived from colonial development plans and described as “The White Man’s Burdn” model of development assistance (Butler, 1991) (Cowen, 1984) (Hailey, 1943) (Sachs, 2006) (Doucouliagos, 2009).
4. It is now recognized, as it was by the British Colonial Office in the 1940s, that the White Man’s Burden model of North to South top-down and paternal development assistance is flawed because it overlooks the political dimension of economic growth and differences in social values and development priorities between North and South (Butler, 1991) (Easterly, 2002) (Easterly, 2007) (Easterly, 2015). Yet, it is still the norm in development assistance programs including the Sustainable Development Goals (SDG) development program of the UNDP (UNDP-SDG, 2015). In this brief note we examine the SDG with respect to the issues raised by the Colonial Office (Butler, 1991) and by Bill Easterly (Easterly, Senseless Dreamy Garbled, 2015) (Easterly, 2015) and deliberate whether this program is a paternal development model that imposes contentious climate change priorities of the North on a poverty-ridden South without due consideration of whether they serve the urgent needs of the South (Diekmann, 1999) (Vegter, 2012).
5. Data and Methods: Per capita GDP data for 104 countries are available for the sample period 1970 to 2014 in nominal USD terms from the UNDATA data archive (UNdata, 2016). The data are inflation adjusted using GDP deflators published by the World Bank (World Bank- Data, 2016) and converted to constant 2014 dollars. The countries are then sorted according to the average per capita GDP in the last decade of the study period 2005-2014 from highest to lowest.
6. At the top of the sorted list are the richest countries in our sample and in the bottom are the poorest. In the middle, marked by the median per capita GDP are countries that are between these two extremes, perhaps previously poor countries or colonies that are in a process of industrialization and development. Somewhat arbitrarily we select 18 countries from each group for a comparative study. This sample size provides a margin of safety so that that even in cases where data are sparse a relevant and usable sample average can be derived.
7. The selected countries are listed in Figure 1. The top, middle, and bottom countries are labeled in Figure 1 as TOP-GDP, MID-GDP, and BOT-GDP. Within each group, the 18 selected countries are listed alphabetically. The population for each country in millions of people is listed in Figure 1 in the column labeled POP. Most of these population figures are actual census values for a year somewhere between 2010 and 2015. They are used to compute population weighted means of different object variables for each group. Since the year of the census is not the same for all countries and since the populations vary across the study period we consider the weighting to be approximate. An additional issue in this regard is that the use of linear population weighting across a large range of populations in a small sample can cause information about small countries to be overwhelmed by one large country. This is mitigated with the Penrose square root weighting system (Zyczkowski, 2004).
8. Six different object variables are studied in the comparison of the three country groups. They are per capita GDP, the human development index (HDI), per capita energy consumption (ENERGY), per capita electricity consumption (ELEC), fossil fuel intensity of the energy portfolio (FFI), and per capita carbon dioxide emission (CO2). Data for all six object variables are taken from the UN data archive (UNdata, 2016). The time spans of the datasets vary. Also, data for all countries in Figure 1 are not available in all datasets. Data for the BOT-GDP countries are sparse. The comparisons made should therefore be considered approximate.
9. The amount of aid received by these countries over the period 2009 to 2014 is provided by the OECD database in a year by year table (OECD, 2016). A simple sum of the annual aid amounts is used to compute the per capita aid received by the BOT and MID countries (Figure 2). The data show that MID countries receive almost twice the aid of BOT countries on a per capita basis. The ratio of per capita aid received is MID/BOT ≈1.7. The apparent paradox of greater aid flow to richer countries can be explained in terms of absorptive capacity (Rosenstein-Rodan, 1961) (Feeny, 2009). The BOT countries likely do not contain the extant infrastructure to absorb more aid.
10. Effectiveness of Development Assistance: A simple estimation of the effectiveness of development aid can be computed as the observed increase in the objective variable on a per capita basis per dollar of development aid received over the same time period. The BOT and MID country groups can then be compared in terms of this measure of aid effectiveness (Burnside, 2004) (Bearce, 2010). From an SDG perspective, an important consideration is the environmental constraint on economic growth which holds that human development is not necessarily a good thing because it comes at the expense of environmental degradation, finite resource depletion, greenhouse gas (GHG) emissions, and climate change. In this equation, optimality exists only when human development is constrained by environmental considerations (Pearce, 1993) (Jorgenson, 2010) (Redclift, 2005) (Rich, 2013) (Stern, 1996).
11. This equation was developed in the highly industrialized advanced economies where concerns for resource depletion, environmental degradation, and climate change due to human development may be justified and where the high level of wealth and standard of living already achieved have redefined the priorities of the citizens (Diekmann, 1999) (Gelissen, 2007). This value system cannot be expected to coincide with the needs of poor countries (Vegter, 2012). The set of Sustainable Development Goals (SDG) of the United Nations Development Program (UNDP) is derived from the environmentalist equation that balances human development against resource depletion and environmental degradation with a specific emphasis on GHG emissions and climate change (Salleh, 2016) (UNDP-SDG, 2015).
12. The SDG initiative has effectively changed UNDP’s priority from eradicating poverty to tackling climate change (Antrobus, 2009) (Obeng-Odoom, 2013). It is thought that climate finance offers a workable solution to this development dilemma of the SDG. It proposes that the cost of emission mitigation by SDG development aid recipients will be borne by the donor countries in the form of additional funding of up to $100 billion per year by 2020 earmarked for both climate change adaptation and mitigation (Buchner, 2011) (Stewart, 2009) (Glemarec, 2011) (Brown J. , 2010) (Bowen, 2011). However, the climate finance solution does not address the issue of the difference in social priorities between rich and poor countries. Specifically, if additional funding is available, its use is best determined according to the urgent needs and priorities of the poor countries receiving the funds and not according to the social and environmental priorities of the rich countries providing these funds (Diekmann, 1999) (Gelissen, 2007) (Vegter, 2012).
13. Conceptually, climate finance embodies the colonial and parental top-down development aid model that is widely known to have failed (Butler, 1991) (Doucouliagos, 2009) (Easterly, 2015) (Easterly, 2007) (Easterly, 2002). Additional issues with climate finance involve the details of its implementation with respect to the amount of funding involved, how it will be collected and disbursed, and which international body will carry out the disbursement and on what basis (Ballesteros, 2010). History contains at least two examples of development aid policies that failed when unrelated needs of the donor were combined with the development needs of the recipient. British colonial development aid policy 1940-1948 failed because the aid policy contained confused and conflicted goals of colonial development and British economic needs (Butler, 1991); and Free World development programs during the Cold War contained confused and conflicted goals of providing development assistance and at the same time fighting communism by keeping aid recipients on the side of the Free World and away from Soviet influence (Bearce, 2010). The SDG of the UNDP suggests that we have not learned from history and are about to commit the same error yet again.
14. Data analysis: The left frame of Figure 3 shows a large and growing gap among the developed countries (TOP), the developing (MID), and least developed (BOT) countries over the 45-year sample period from 1970 to 2014. In percentage growth terms, we find in the right panel a strong growth performance by the developing countries (MID) perhaps attributable in part to China. The TOP developed countries also show strong percentage growth. These growth patterns are in sharp contrast with the extremely poor performance of the least developed countries (BOT).
15. The left panel shows the GDP level of the BOT countries to be almost zero relative to the GDP of the MID and TOP countries; and we find in the right panel that their percentage growth performance is very poor. Strong growth from the 1970s to 1980 is followed by GDP collapse from 1980 to the year 2000 when their per capita GDP reached 50% of its value in the 1970s. Modest growth is seen from 2000 to 2014 but not enough to overcome the losses from 1980 to 2000.
16. The improvement in GDP growth in the post-Cold-War era (1995-2014) shown by all three groups is consistent with the observation by Bearce and Tirone that the fall of communism in 1991 has ended the strategic goals model of foreign aid and improved foreign aid effectiveness (Bearce, 2010). In terms of aid effectiveness we find that from 2009 to 2014 per capita GDP in the MID countries grew by 37% on foreign aid of $1368 per capita or 27% per thousand dollars of aid. Over the same period per capita GDP in the BOT countries grew by 33% on foreign aid of $808 per capita or 40.5% per thousand dollars of aid. In terms of this measure of aid effectiveness, development assistance appears to have been more effective in the BOT than in the MID in the post-Cold-War period of 2009-2014.
17. Human Development Index: The human development index (HDI) is a composite of per capita GDP, life expectancy at birth, and literacy computed as a geometric mean and normalized to values 0≤HDI≤1 (UNDP-HDI, 2016). It is intended to measure the effect of development assistance in a more comprehensive way than GDP alone by including child mortality, health, education, as well as economic growth. In the right panel of Figure 4 we see that, in terms of percentage growth, the BOT countries show the best performance in HDI growth over the sample period 1980-2014, particularly so in the post-Cold-War era (Bearce, 2010). The better performance of the MID over the TOP countries is likely explained by the TOP countries being already up against the upper limit of HDI=1.0. The left panel of Figure 4 shows that the absolute difference in HDI between TOP and MID in 1980 of ΔHDI≈0.3 has narrowed to ΔHDI≈0.2 in 2014 and at the same time the difference between MID and BOT has not narrowed but widened.
18. Thus, in absolute terms, BOT is not only in the bottom of the HDI ranking but does not appear to be closing the gap with MID. Yet it is a positive sign for the development aid programs that the HDI of their clients BOT and MID is rising and that this rise appears to have accelerated in the post-Cold-War era for the BOT consistent with Bearce and Tirone (Bearce, 2010). In terms of aid effectiveness, in the period 2009-2014, HDI for MID increased from 0.7274 to 0.7469 which we attribute to per capita aid of $1368 and estimate aid effectiveness as 2% growth in HDI per $1000 of aid per capita. The BOT countries fared better in this measure of performance. Their HDI increased from 0.4576 to 0.4779 in the same period on aid of $808 per capita for an effectiveness of 5.5% per $1000 of aid per capita. Combining these results with those for GDP we conclude that development aid is more effective in the BOT than in the MID on a per dollar basis.
19. Per Capita Energy Consumption: The sample period begins in 1971 with per capita energy consumption of the TOP countries about five times that of the MID countries; and ended in 2012 with the MID countries having reduced TOP’s advantage significantly to three times. Rapid and accelerated growth in per capita energy consumption by the MID countries is evident in the chart (Figure 5). A high percentage growth rate in per capita energy consumption by the MID countries is seen in the right panel of Figure 5. Per capita energy consumption in the TOP and BOT countries is relatively flat over this period. Growth in per capita energy consumption (Figure 5) closely parallels the growth in per capita GDP (Figure 3). These energy consumption patterns are consistent with the generally accepted principle of a two-way causality between energy consumption and economic growth in developing countries and an absence of this relationship in developed countries (Zhang, 2009) (Lee, 2008) (Mahadevan, 2007) (Kander, 2002) (Odhiambo, 2009) (Soytas, 2009). The comparison of the TOP and MID countries in Figure 5 and Figure 3 are consistent with this principle. These relationships suggest that economic development (Figure 3) and human development (Figure 4) in the South require a corresponding increase in energy consumption and that this imperative of the South is not well served by imposing climate change initiatives upon them in the way that they are imposed on the North. This distinction between North and South is recognized by the “common but differentiated” principle of the UNFCCC which recognizes differences in the responsibilities of developed and developing countries (UNFCCC, 2014). Imposition of Sustainable Development Goals #7 Renewable Energy, #11 Sustainable Cities, #12 Responsible Consumption, and #13 Climate Action (UNDP-SDG, 2015) may interfere with basic development needs of the South and they violate the “common but differentiated” principle of the UNFCCC. It should be mentioned that energy data are sparse in the UNDATA datasets (UNdata, 2016) particularly for the BOT countries where energy data were found for only one third of the countries in Figure 1.
20. Fossil Fuel Intensity:  Fossil fuel intensity is measured as the percent of the energy consumed that is derived from fossil fuels. Use of fossil fuels generates emissions that add new extraneous carbon dioxide from below ground into the surface atmosphere carbon cycle and climate system. These emissions are thought to cause warming which in turn is expected to cause catastrophic climate change (IPCC, 2014). The relevant Sustainable Development Goals are #7 Renewable Energy, and #13 Climate Action (UNDP-SDG, 2015). We note in the left panel of Figure 6 that the fossil fuel intensity of the TOP countries has gradually declined from 92% to about 72% over the entire sample period while at the same time that of the MID countries has increased from 63% to 80% overtaking the intensity of the TOP countries. In view of the large difference in per capita GDP between the TOP and MID countries (Figure 3), we can derive from this graphic that the MID and BOT countries are far below the per capita GDP wealth level at which fossil fuel intensity will arise as a concern in their system of social values. Relative changes in fossil fuel intensity appear in the right panel of Figure 6 where we find a steep rise in the fossil fuel intensity of the BOT, a more gradual increase in the MID, and a decline in the TOP countries. This graphic provides further support that climate change mitigation initiatives arise in societies when they have reached far greater wealth levels than that of the clients of development aid. It is noted that energy data are sparse in the UNDATA datasets particularly for the BOT countries where energy data were found for only one third of the countries in Figure 1.
21. Per Capita CO2 Emissions:  Carbon dioxide emissions from fossil fuels add new extraneous CO2 from below ground into the surface atmosphere carbon cycle and climate system. These emissions are thought to cause warming which in turn is expected to cause catastrophic climate change (IPCC, 2014). The relevant Sustainable Development Goals are #7 Renewable Energy, and #13 Climate Action (UNDP-SDG, 2015). CO2 emissions per capita in tonnes per person per year are depicted graphically in the left panel of Figure 7 for the 22-year sample period 1990-2011. The chart shows a large difference in per capita emissions among the three country groups with emissions by the BOT countries indistinguishable from zero in the context of the TOP countries. The period begins with the TOP country per capita emissions at four times those of the MID countries in 1990 but by the end of the study period in 2011, the difference is halved by declining per capita emissions in the TOP countries and rising per capita emissions in the MID countries. The stark contrast between rich and poor is clearly displayed in the right panel of Figure 7 which shows percentage growth in per capita emissions. Declining emissions in the rich and high-emission TOP countries is contrasted by rapid growth in per capita emissions in the poor and low-emission MID and BOT countries. Figures 5, 6, and 7, together with the electricity consumption data in Figure 8 show a pattern that suggests that increasing energy consumption and increasing CO2 emissions are essential for economic growth of poor countries in their aspiration to become rich countries. They also show, that once countries become rich and their per capita consumption and emissions reach the high levels shown in these graphs, their social values and priorities change so that they value investments in efficiency, conservation, environmental quality, and emission reduction (Cotgrove, 1981) (McMichael, 2016) (O’Brien, 2009) (Inglehart, 2005) (Billett, 2010) (Leiserowitz, 2007) (Wolf, 2011). Therefore an arbitrary imposition of rich country priorities upon poor countries does not serve the development needs of the poor countries (Hickel, 2015).
22. SUMMARY AND CONCLUSIONS: In the year 2000, the United Nations Development Agency (UNDP) announced its Millennial Development Goals (MDG) for the year 2015 (MDG Task Force, 2015). The MDGs include poverty eradication, universal primary education, gender equality, reducing child mortality, improving maternal health, and combating AIDS and malaria as well as a “global partnership for development”. Each of these goals contains multiple targets that are actionable and measurable. The MDG program is considered to be a success (MDG Task Force, 2015) although its apparent success may be an artifact of the way poverty was defined by the UNDP (Easterly, 2015) and by the fall of communism and the end of the Cold War. In the Cold War era development aid programs were confounded by the dual and conflicting objectives of the North to fight poverty and at the same time to contain communism. This dual track strategy is now known to have been a failure (Bearce, 2010) (Dunning, 2004) (Meernik, 1998). The development goals for the next fifteen years 2016-2030 have been redefined as Sustainable Development Goals or SDG (UNDP-SDG, 2015) with the eight economic and human development goals of the MDG supplanted by five political and human rights ideals and four climate change mitigation initiatives. The development goals related to climate change are SDG#7 Renewable Energy, SDG#11 Sustainable Cities, SDG#12 Responsible Consumption, and SDG#13 Climate Action. The complex mix of seventeen goals combines economic and human development with political ideals and climate change initiatives. This study is a critical evaluation of the role of climate change initiatives in a development plan to fight and to end poverty. Our findings are five-fold and they have to do with (1) energy poverty of the poor countries, (2) the dependence of development needs on the level of wealth and human development already achieved, (3) the failure of top-down externally conceived development plans, (4) the failure of combining development goals with the unrelated strategic goals of the donor, and (5) a pragmatic interpretation of climate finance. 
23. The energy poverty of poor countries is evident in Figures 5, 6, 7, and 8. We conclude from these data that, to eradicate poverty, it is necessary for poor countries to increase their energy consumption and carbon dioxide emissions. Imposition of climate change mitigation on poor countries will make it harder to bring them out of energy poverty.
24. The climate initiatives of the SDG represent the social values and priorities of rich countries and it cannot be assumed that they fit the needs of the poor countries when viewed from their perspective. It is necessary for the poor to get out of poverty and become rich before they view development as a balance between human wellbeing and environmental issues such as climate change. 3. A large volume of literature in development economics exists on the subject of the failure of top down paternal development plans against the success of home grown development models that are drawn from the perspective and the priorities of the aid recipient rather than those of the donors. The various works of Bill Easterly are pertinent in this regard. 4. We have learned from the poor performance of Cold-War-era development plans that combining the strategic interests of the donor (fighting communism) with the needs of the poor (economic and human development) do not mix. Yet, the SDG is just such a confounded and conflicted development program because it combines the strategic needs of the donor with respect to climate change with the most modest of needs of the very poor. Perhaps the UNDP recognizes the contradiction of making poor countries fight climate change as part of a development assistance program. Accordingly, climate finance is proposed to address this issue. It refers to additional funds from donor countries that will be made available to development aid recipients to compensate them for the cost of climate change mitigation. The flaw in this logic is that, if additional funding is available, its use is best determined according to the urgent needs and priorities of the poor countries receiving the funds and not according to the social and environmental concerns of the rich countries providing these funds. 

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