SDG: CLIMATE ACTIVISM DISGUISED AS DEVELOPMENT ASSISTANCE JAMAL MUNSHI ABSTRACT: Implementation of the Sustainable Development Goals (SDG) marks a shift in the priority of the UNDP from tackling poverty to tackling climate change. This shift erodes the ability of the UNDP to perform its primary function of providing development assistance to poor countries. The climate finance proposal is unlikely to mend this fundamental defect in the SDG 1 initiative of the UNDP .
1. INTRODUCTION 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”. About one half the world’s populations live in backward and undeveloped economies mostly in the former colonies – the great majority of them nonresource, 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”. 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 (Butler, 1991) (Cowen, 1984) (Hailey, 1943) (Sachs, 2006) (Doucouliagos, 2009). It is now recognized, as it was by the British Colonial Office in the 1940s, that this 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).
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Date: July 2016, 7/21/2016 minor changes, 7/23/2016 minor changes Author affiliation: Professor Emeritus, Sonoma State University, Rohnert Park, CA, 94928,
[email protected] Key words: climate change, global warming, fossil fuel emissions, Kyoto Protocol, UN, UNEP, UNFCCC, UNDP, MDG, SDG, development economics, foreign aid, colonialism, white man’s burden, Bill Easterly, Colonial Office
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2. 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. 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. The selected countries are listed in Figure 1. The top, middle, and bottom countries are labeled in Figure 1 as TOPGDP, 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 as listed by Wikipedia2. 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). Figure 1: Selected countries at three levels of development and their population in 2011
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https://simple.wikipedia.org/wiki/List_of_countries_by_population
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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. 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. Figure 2: Total development aid received 2009-2014
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).
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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). 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). 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). Conceptually, climate finance embodies the colonial and parental topdown 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.
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3. DATA ANALYSIS AND RESULTS 3.1
Per Capita GDP
Figure 3: Per capita GDP in constant USD: 1970-2014
The left frame of Figure 3 shows a large and growing gap between the developed countries (TOP) and 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). The left panel shows their GDP level 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. 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. All data and computational details used in this study are available for download from an online data archive (Munshi, 2016).
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Human Development Index (HDI)
Figure 4: Human Development Index 1970-2014
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. 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. All data and computational details used in this study are available for download from an online data archive (Munshi, 2016).
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Total Energy Consumption Per Capita
Figure 5: Total energy consumption per capita 1971-2012
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. All data and computational details used in this study are available for download from an online data archive (Munshi, 2016).
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Fossil Fuel Intensity of the Energy Portfolio
Figure 6: Fossil fuel intensity 1971-2013
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 warming3 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. All data and computational details used in this study are available for download from an online data archive (Munshi, 2016).
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The evidence provided for this relationship is a correlation between cumulative emissions and cumulative warming. This correlation has been shown to be spurious (Munshi, The Spuriousness of Correlations between Cumulative Values, 2016).
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Per Capita Carbon Dioxide Emissions
Figure 7: Per capita CO2 emissions 1990-2011
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).
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Figure 8: Per capita electricity consumption 1971-2012
4. 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.
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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. 1. 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. 2. 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. 5. 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. All data and computational details used in this study are available for download from an online data archive (Munshi, 2016).
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