Cndi 2017 – Title I finance Affirmative

Inequality Impact – Economy

Income inequality leads to economic collapse - wealth is concentrated unproductively and asset bubbles lead to fiscal ossification

Lansley 12 (Stewart Lansley, 2-4-2012, Stewart Lansley is a visiting fellow at the Townsend Centre for International Poverty Research, University of Bristol. He is the author of The Cost of Inequality: Why Economic Equality is Essential for Future Growth and coauthor of Breadline Britain: The Rise of Mass Poverty , among other books., "Why economic inequality leads to collapse," Guardian, https://www.theguardian.com/business/2012/feb/05/inequality-leads-to-economic-collapse)mo

During the past 30 years, a growing share of the global economic pie has been taken by the world's wealthiest people. In the UK and the US, the share of national income going to the top 1% has doubled, setting workforces adrift from economic progress. Today, the world's 1,200 billionaires hold economic firepower that is equivalent to a third of the size of the American economy. It is this concentration of income – at levels not seen since the 1920s – that is the real cause of the present crisis. In the UK, the upward transfer of income from wage earners to business and the mega-wealthy amounts to the equivalent of 7% of the economy. UK wage-earners have around £100bn – roughly equivalent to the size of the nation's health budget – less in their pockets today than if the cake were shared as it was in the late 1970s. The Inequality Project: the Guardian's in-depth look at our unequal world Read more In the US, the sum stands at £500bn. There a typical worker would be more than £3,000 better off if the distribution of output between wages and profits had been held at its 1979 level. In the UK, they would earn almost £2,000 more. The effect of this consolidation of economic power is that the two most effective routes out of the crisis have been closed. First, consumer demand – the oxygen that makes economies work – has been choked off. Rich economies have lost billions of pounds of spending power. Secondly, the slump in demand might be less damaging if the winners from the process of upward redistribution – big business and the top 1% – were playing a more productive role in helping recovery. They are not. Britain's richest 1,000 have accumulated fortunes that are collectively worth £250bn more than a decade ago. The biggest global corporations are also sitting on near-record levels of cash. In the UK, such corporate surpluses stand at over £60bn, around 5% of the size of the economy. This money could be used to kickstart growth. Yet it is mostly standing idle. The result is paralysis. The economic orthodoxy of the past 30 years holds that a stiff dose of inequality brings more efficient and faster-growing economies. It was a theory that captured the New Labour leadership – as long as tackling poverty was made a priority, then the rich should be allowed to flourish. So have the architects of market capitalism been proved right? The evidence says no. The wealth gap has soared, but without wider economic progress. Since 1980, UK growth and productivity rates have been a third lower and unemployment five times higher than in the postwar era of "regulated capitalism". The three post-1980 recessions have been deeper and longer than those of the 1950s and 1960s, culminating in the crisis of the last four years. The main outcome of the post-1980 experiment has been an economy that is much more polarised and much more prone to crisis. History shows a clear link between inequality and instability. The two most damaging crises of the last century – the Great Depression of the 1930s and the Great Crash of 2008 – were both preceded by sharp rises in inequality. The factor linking excessive levels of inequality and economic crisis is to be found in the relationship between wages and productivity. For the two-and-a-half decades from 1945, wages and productivity moved broadly in line across richer nations, with the proceeds of rising prosperity evenly shared. This was also a period of sustained economic stability. Then there have been two periods when wages have seriously lagged behind productivity – in the 1920s and the post-1980s. Both of them culminating in prolonged slumps. Between 1990 and 2007, real wages in the UK rose more slowly than productivity, and at a worsening rate. In the US, the decoupling started earlier and has led to an even larger gap. The significance of a growing "wage-productivity gap" is that it upsets the natural mechanisms necessary to achieve economic balance. Purchasing power shrinks and consumer societies suddenly lack the capacity to consume. In both the 1920s and the post-1980s, to prevent economies seizing up, the demand gap was filled by an explosion of private debt. But pumping in debt didn't prevent recession: it merely delayed it. Concentrating the proceeds of growth in the hands of a small global financial elite not only brings mass deflation – it also leads to asset bubbles. In 1920s America, a rapid process of enrichment at the top merely fed years of speculative activity in property and the stock market. In the build-up to 2008, rising corporate surpluses and burgeoning personal wealth led to a giant mountain of footloose global capital. The cash sums held by the world's rich (those with cash of more than $1m) doubled in the decade to 2008 to a massive $39 trillion. Only a tiny proportion of this sum ended up in productive investment. In the decade to 2007, bank lending for property development and takeover activity surged while the share going to UK manufacturing shrank. While the contribution to the economy made by financial services more than doubled over this period, manufacturing fell by a quarter. Far from creating new wealth, a tsunami of "hot money" raced around the world in search of faster and faster returns, creating bubbles – in property, commodities and business – lowering economic resilience and amplifying the risk of financial breakdown. New Labour's leaders were right in arguing that the left needed to have a more coherent policy for wealth creation. That is the route to wider prosperity for all. But the central lesson of the last 30 years is that a widening income gap and a more productive economy do not go hand in hand. An economic model that allows the richest members of society to accumulate a larger and larger share of the cake will eventually self-destruct. It is a lesson that is yet to be learned.

Inequality Impact – Civil War

Income inequality leads to increased risk of civil war

Karls 14 (Eberhard Karls, 6-4-2014, Eberhard Karls University, Tübingen (German: Eberhard Karls Universität Tübingen, sometimes called the "Eberhardina Carolina") is a public university located in the city of Tübingen, Baden-Württemberg, Germany. It is one of Germany's oldest universities, internationally noted in medicine, natural sciences and the humanities. In the area of German Studies (German: Germanistik) it has been ranked first among all German universities for many years., "Rich-poor gap and the risk of civil war," https://phys.org/news/2014-06-rich-poor-gap-civil-war.html)mo

Researchers from the University of Tübingen have found that the greater the disparity in wealth, the greater the risk of civil war. "If unequal division passes a certain level, the effects within a region are enormous," say Economic History specialists Professor Jörg Baten and Christina Mumme. Extremes of wealth inequality could explain the frequency of civil wars in Latin America and Africa in the past 200 years – and could be contributing to the current situation in eastern Europe. "The gap between rich and poor there has widened significantly in recent years. National and ethnic identities are often dug up to distract attention from discontent over income difference," Baten says. The economic historians observe a steadily increasing rich-poor gap in many regions around the globe. In collaboration with Utrecht University in the Netherlands, they compiled a far-reaching study using the first data on the global development of wealth inequality over the past two centuries. The study showed that incomes in Europe and North America had come closer into line in the 1970s ‒ but that since then, the gap between rich and poor has grown, particularly in eastern Europe and the United States. "The current debate over rising inequality demonstrates just how central this phenomenon is for the perception of economic development and the survival of the forms of the market economy," says Baten. The researchers examined inequality both within regions and between regions, using the Gini coefficient. Zero represents the theoretically possible situation in which everyone has exactly the same income, and 1 represents complete inequality. Many countries have an inequality factor of between 0.30 and 0.60 auf.

Inequality Impact – Health

Income inequality causes increased health problems and mortality rates.

Ross et al 00 Ross, Nancy A., Michael C. Wolfson, James R. Dunn, Jean-Marie Berthelot, George A. Kaplan, and John W. Lynch. "Relation between Income Inequality and Mortality in Canada and in the United States: Cross Sectional Assessment Using Census Data and Vital Statistics." BMJ. British Medical Journal Publishing Group, 01 Apr. 2000. Web. 02 July 2017. .

Abstract Objective: To compare the relation between mortality and income inequality in Canada with that in the United States. Design: The degree of income inequality, defined as the percentage of total household income received by the less well off 50% of households, was calculated and these measures were examined in relation to all cause mortality, grouped by and adjusted for age. Setting: The 10 Canadian provinces, the 50 US states, and 53 Canadian and 282 US metropolitan areas. Results: Canadian provinces and metropolitan areas generally had both lower income inequality and lower mortality than US states and metropolitan areas. In age grouped regression models that combined Canadian and US metropolitan areas, income inequality was a significant explanatory variable for all age groupings except for elderly people. The effect was largest for working age populations, in which a hypothetical 1% increase in the share of income to the poorer half of households would reduce mortality by 21 deaths per 100 000. Within Canada, however, income inequality was not significantly associated with mortality. Conclusions: Canada seems to counter the increasingly noted association at the societal level between income inequality and mortality. The lack of a significant association between income inequality and mortality in Canada may indicate that the effects of income inequality on health are not automatic and may be blunted by the different ways in which social and economic resources are distributed in Canada and in the United States. Introduction A large body of research reports an association between income distribution and health1–14 and a range of hypotheses articulates possible mechanisms operating between income inequality and poor health outcomes.15 16 Among American states, mortality is more weakly correlated with mean or median state income than it is with various measures of how that income is shared within a state.5 6 US metropolitan areas with greater income inequality also have significantly higher mortality than metropolitan areas with more equal income distributions, independent of the median income of the metropolitan area.8 Collectively these studies point to the conclusion that populations in areas where there is an unequal income distribution have higher mortality than populations in more homogeneous areas. While some have claimed that the relation between income inequality and mortality is an artefact of the non-linear relation between income and mortality at the individual level,17 Wolfson and colleagues18 and others reporting findings from multilevel analyses19–22 provide substantial evidence for a non-artefactual explanation. We compared income inequality and age grouped mortality in Canada and the United States. We considered two levels of geographic aggregation: state/provincial and metropolitan area. The comparison of states/provinces and US metropolitan areas is compelling in that it has the potential to highlight characteristics and policies specific to particular social contexts that could affect health. While the product of similar economic, social, and cultural forces,23 Canada and the United States also have some major differences, especially with regard to social policy and racial divisions. US metropolitan areas differ greatly from Canadian metropolitan areas in terms of the degree of economic and social inequality they generate and the ways in which unequal material circumstances and social relations are institutionalised through policy and urban political structure.24 25 While economic segregation and social polarisation are less pronounced in Canadian cities, some studies have suggested that they increased in the last decade of the 20th century.26 27 Incomes at the bottom of the distribution are higher in Canada than in the United States, and while inequality in net income rose between 1985 and 1995 in the United States it actually fell slightly in Canada because of the redistributive effects of Canadian taxation and transfer policies.28 Furthermore, since the 1980s, pay inequality in Canada has widened much less than in the United States.28 29 In the United States, labour market prospects for low skilled workers have been poor over the past two decades. Hypotheses such as the growing skill requirements of a global economy, deindustrialisation, relocations of employers to suburban areas, and racial discrimination have been offered to explain these trends.30 Methods Associations between income inequality and mortality were studied in the 50 US states and the 10 Canadian provinces, as well as in 282 US and 53 Canadian metropolitan areas with populations greater than 50 000 (as of 1990 in the United States and 1991 in Canada). All mortalities were age standardised to the Canadian population in 1991. The associations were examined separately by the following age and sex groupings for the states and provinces: infants (less than 1 year), children and youth (1 to 24 years), working age men (25 to 64 years), working age women (25 to 64 years), elderly men (65 years and older), and elderly women (65 years and older). Age groupings were the same for metropolitan areas but breakdowns by sex were unavailable. Inequality was operationalised as the proportion of total household income accruing to the less well off 50% of households within an area (that is, the “median share” of income). In a setting of perfect equality, the bottom half of the income distribution receives 50% of the total income and the area then has a median share value of 0.50. The indicator has recently been used in similar studies on inequality and mortality, 5 8 and thus allowed for comparability of results. Moreover, tests with a range of other measures of inequality and polarisation suggested that this choice did not substantially affect the results. US data Mortality data for the 50 US states came from the Centers for Disease Control (CDC) Wonder website. Mortalities by state, sex, and age were averaged over three years (1989-91) to improve the stability of the estimates. State median share proportions and the median income values were generated from the 1990 US census and have appeared in a previous paper by Kaplan and colleagues.5 Metropolitan area mortalities and median share proportions were from the work of Lynch and colleagues.8 Canadian data The income inequality data for Canada came from a micro data file of the 1991 census of Canada. The income definition used in the Canadian calculations, like that for the United States, included income from wages and salaries, net income from self employment, government transfers, and investment income. Canadian mortality data were based on three year averages (1990-2) by province, sex and age group, and by metropolitan area and age group. Model building and general linear testing Multiple regression analyses were conducted only on the metropolitan area data because of the small number of Canadian provinces. Given that the reliability of the estimated mortality is related to the populations of metropolitan areas we used weighted regression with population size as the weight. Use of these weights ensures that the regression line goes through the mean mortality of the entire population under study. Furthermore, the use of such a weighted regression allows for the unobserved differences in mortality between Canada and the United States, potentially because of differences in social structure, to be taken into account through the use of a dummy variable.31 The regression analyses proceeded in four steps. Firstly, models specific for age group were fitted for the 282 US metropolitan areas with median share of total metropolitan area household income as an explanatory variable. Secondly, median income for the US metropolitan areas was added as an explanatory variable. Thirdly, the 53 Canadian metropolitan areas were added. In the combined models, metropolitan median household income for the Canadian cities was adjusted downwards by a factor of 0.8 (this is Statistics Canada's purchasing power parity rate, applied to personal final expenditure, for 199523) to achieve purchasing power parity between the two countries. We also included a dummy variable to indicate whether the metropolitan area was Canadian or American to adjust for the mortality differentials between the two countries.32 Finally, we tested whether the relation between income inequality and mortality in Canada differed significantly from the US relation and whether the coefficients for median share for Canada differed significantly from zero. The approach involved specifying full models, including all two way interactions, and then specifying reduced models with the effect of interest removed (the multicollinearity present in the fully fitted models made it difficult to assess the slope differences; the approach comparing the error sum of squares of the full and reduced models circumvents the problem). The test statistic entailed a comparison of the error sum of squares of each model and followed an F distribution.33 Results States and provinces The median share values ranged from 0.17 (least equal) in Louisiana to 0.23 (most equal) in New Hampshire for the US states, while the range for the Canadian provinces was 0.22 (least equal) for Saskatchewan to 0.24 (most equal) for Prince Edward Island. The median proportion of income received by the less well off half was 0.21 for US states, while for Canadian provinces it was 0.23. There was little overlap between US states and Canadian provinces in regard to income inequality with only Wisconsin, Vermont, Utah, and New Hampshire sharing similar income distributions to the Canadian provinces. Median share of income was correlated (P<0.01) with infants (r =m-0.69), children/youth (r =−0.62), working age men (r =−0.81), working age women (r =−0.81), elderly men (r =−0.44), elderly women (r =−0.42), and all age (r =−0.68) mortality in combined US states and Canadian provinces calculations. Figure 1 shows a weighted linear fit (the areas of the circles are proportional to the population size) between income inequality and mortality for working age men at the state/provincial levels. The strongest relation with inequality was for working age populations. The Canadian provinces seem almost like a more equitable extension of the US data, by having lower mortality and lower inequality. Within Canada, however, the slope of the weighted regression line was in the expected direction but was not significantly different from zero. Mortality in working age men by proportion of income belonging to the less well off half of households, US states (1990) and Canadian provinces (1991). Mortality standardised to Canadian population in 1991. State abbreviations: LA-Louisiana; MS-Mississippi; AL-Alabama; SC-South Carolina; FL-Florida; TX-Texas; CA-California; AR-Arkansas; NH-New Hampshire; MN-Minnesota. Province abbreviations: QC-Quebec; NS-Nova Scotia; NB-New Brunswick; ND-Newfoundland; PE-Prince Edward Island; ON-Ontario; AB-Alberta; BC-British Columbia; MB-Manitoba; SK-Saskatchewan Metropolitan areas The populations of the 282 metropolitan areas in the United States ranged from 56 735 (Enid, Oklahoma) to 18 087 251 (New York city) with a median size of 242 847. The populations of the 53 metropolitan areas in Canada ranged from 50 193 (Saint-Hyacinthe, Quebec) to 3 893 046 (Toronto, Ontario) with a median size of 116 100. The median share values ranged from 0.15 (least equal) in Bryan, Texas, to 0.25 (most equal) in Jacksonville, North Carolina, for the United States while the range in Canada was 0.22 (least equal) for Montreal, Quebec, to 0.26 (most equal) for Barrie, Ontario. The median proportion of income received by the less well off half of households for US metropolitan areas was 0.21 while for the Canadian metropolitan areas it was 0.23. There were significant correlations (P<0.01) between median share and mortality for infants (r =−0.37), children and youth (r =−0.38), the working age population (r =−0.55), the elderly population (r =−0.25), and all ages combined (r =−0.43) for the pooled 335 metropolitan areas in the United States and Canada. Within Canada, however, there was no statistical relation between inequality and mortality at the metropolitan area level as evidenced by the weighted linear fit (dashed line) to the Canadian data points for working age mortality in figure 2. Mortality in all working age people by proportion of income belonging to the less well off half of households, US (1990) and Canadian metropolitan areas (1991). Mortality standardised to Canadian population in 1991. State abbreviations: LA-Louisiana; GA-Georgia; AR-Arkansas; SC-South Carolina; NY-New York; TX-Texas; CA-California; IA-Iowa; NH-New Hampshire; WI-Wisconsin. Province abbreviations: QC-Quebec; ON-Ontario; BC-British Columbia In the first set of multiple regression models, the median share was a significant explanatory variable for all but the model of mortality in elderly people for the 282 US metropolitan areas (table). The largest effect was in mortality in working age people, where a 1% increase in the share of household income to the poorer half of the income distribution was associated with a decline in mortality of nearly 22 deaths per 100 000. In general, the size of the effect of the median share variable changed little with the addition of the median state income variable, the second set of regressions. The inclusion of the 53 Canadian metropolitan areas, the third set of regressions, improved the explanatory significance of the models with, for example, the adjusted R2 (squared multiple correction) increasing from 0.02 to 0.27 for infants and from 0.33 to 0.51 for the working age population. The country dummy variable was significant in each of the models and may be interpreted as the difference in mortality between the two countries after adjustment for the distribution of household income and median household income. Thus there were 91 fewer deaths per 100 000 in Canadian metropolitan areas than in US metropolitan areas after adjustment for median share and median income. Finally, the general linear testing indicated that the slope of the relation between median share and mortality for Canadian metropolitan areas was significantly different than the US slope for children and youth (F 1,329=5.98, P<0.05), working age populations (F 1,329=8.79, P<0.01), and all age groups combined (F 1,329=6.22, P<0.05). In all cases, however, after the three main effects variables (median share, median income, and the dummy country indicator) and all two way interactions in the Canada and US models were accounted for, the slope of the relation between median share and mortality in Canada was not significantly different from zero. Discussion Our analysis of data from Canada and the United States has shown that variations in the equality of the income distribution are associated with mortality. The relation was strongest for working age populations but was much weaker in elderly populations. Other research has suggested that differential working age mortality across populations may be a more powerful measure of relative disadvantage than the traditionally studied infant mortality differential.20 34 35 As for the attenuation seen in elderly populations, current household income may not be a useful measure for this group given that income levels before retirement or measures of wealth better reflect their social position.36 There were no significant asociations between income inequality and mortality in Canada at either the provincial or metropolitan area levels, whereas such associations were apparent in the United States. The absence of an effect within Canada may indicate that the relation between income inequality and mortality is non-linear (that is, at higher levels of equality there is a diminishing effect on health) or that the relation between income inequality and mortality is not universal but instead depends on social and political characteristics specific to place. The first explanation suggests that reducing income inequality would be beneficial for population health. The latter explanation suggests that specific policies can be implemented to buffer the health effects of income inequality.15 The juxtaposition of Canadian and US policies in these analyses raises questions about differences in the social and material conditions of the two countries that mute (in Canada) and exaggerate (in the United States) the relation of inequality to mortality. One plausible difference is the greater degree of economic segregation in large US cities.20 Such segregation can create a spatial mismatch between workers and jobs and large inequalities in provision of public goods and services (for example, schools, transportation, health care, policing, housing, etc) because of concentrations of people with high social needs in municipalities with low tax bases.37 The population health effects of inequalities in provision of these public goods and others like parks, libraries, and recreation facilities need to be the focus of future research.15 38 Another major difference between the two countries is the way in which resources such as health care and high quality education are distributed. In the United States these resources tend to be distributed by the marketplace so their utilisation tends to be associated with ability to pay; in Canada they are publicly funded and universally available. As a consequence, in the United States an individual's income, in both a relative and absolute sense, is a much stronger determinant of life chances and, in turn, “health chances” than in Canada. These comments underscore the point that observations of contexts in which income inequality has health consequences and those in which it does not provide opportunities to examine the role of variations in economic and social policy which structure the availability of resources and demands placed on individuals. Collectively, these resources and demands modify the day to day experiences of individuals thereby creating different patterns of health and disease in different places.

Income inequality causes an increase of health risks

Kondo et al 09 Kondo, Naoki, Grace Sembajwe, Ichiro Kawachi, Rob M. Van Dam, S. V. Subramanian, and Zentaro Yamagata. "Income Inequality, Mortality, and Self Rated Health: Meta-analysis of Multilevel Studies." BMJ. British Medical Journal Publishing Group, 11 Nov. 2009. Web. 02 July 2017. .

Empirical studies have attempted to link income inequality with poor health, but recent systematic reviews have failed to reach a consensus because of mixed findings. The stakes in the debate are high because many developed countries have experienced a surge in income inequality during the era of globalisation, and if economic inequality is truly damaging to health, then even a “modest” association can amount to a considerable population burden. More than three quarters of the countries belonging to the Organisation for Economic Cooperation and Development (OECD) have in fact experienced a growing gap between rich and poor during the past two decades.1 Income inequality could damage health through two pathways. Firstly, a highly unequal society implies that a substantial segment of the population is impoverished, and poverty is bad for health. Secondly, and more contentiously, income inequality is thought to affect the health of not just the poor, but the better off in society as well. The so called spillover (or contextual) effects of inequality have in turn been attributed to the psychosocial stress resulting from invidious social comparisons,2 3 as well as the erosion of social cohesion.4 The public health importance and burden of income inequality are obviously broader under the second scenario.4 5 6 7 8 We sought to provide quantitative evaluations of the income inequality hypothesis by conducting a meta-analysis of prospective cohort studies and cross sectional studies on the association of income inequality with mortality and self rated health. We also quantitatively evaluated the potential factors explaining the differences between studies—for example, the “threshold effect” hypothesis posits the existence of a threshold of income inequality beyond which adverse impacts on health begin to emerge.4

XT – BioD Impact

Biosphere collapse spills over until complete extinction

Anthony D Barnosky,1 James H Brown,2 Gretchen C Daily,3 Rodolfo Dirzo,3 Anne H Ehrlich,3 Paul R

Ehrlich,3 Jussi T Eronen,4 Mikael Fortelius,4 Elizabeth A Hadly,3 Estella B Leopold,5 Harold A Mooney,3 John Peterson Myers,6 Rosamond L Naylor,3 Stephen Palumbi,3 Nils Chr Stenseth7 and Marvalee H Wake1 2014(1University of California, USA 2University of New Mexico, USA 3Stanford University, USA 4University of Helsinki, Finland 5University of Washington, USA 6Environmental Health Sciences, USA 7University of Oslo, Norway "Introducing the Scientific Consensus on Maintaining Humanity’s Life Support Systems in the 21st Century: Information for Policy Makers", http://anr.sagepub.com/content/1/1/78.full.pdf+html//AKP)

Loss of ecosystem services. Extinctions irreversibly decrease biodiversity, which in turn directly costs society through loss of ecosystem services (Cardinale et al., 2012; Daily et al., 2000; Ehrlich et al., 2012). ‘Ecosystem services’ (see the quote below) are attributes of eco- logical systems that serve people. Among the ecosystem services that support human life and endeavors are: moderating weather; regulating the water cycle, stabilizing water sup- plies; filtering drinking water; protecting agricultural soils and replenishing their nutrients; disposing of wastes; pollinating crops and wild plants; providing food from wild species (especially seafood); stabilizing fisheries; providing medicines and pharmaceuticals; con- trolling spread of pathogens; and helping to reduce greenhouse gases in the atmosphere . In contrast to such directly quantifiable benefits promoted by high biodiversity, reducing bio- diversity generally reduces the productivity of ecosystems, reduces their stability, and makes them prone to rapidly changing in ways that are clearly detrimental to humanity (Cardinale et al., 2012). For example, among other costs, the loss of tropical biodiversity from defor- estation often changes local or regional climate, leading to more frequent floods and droughts and declining productivity of local agricultural systems. Tropical deforestation can also cause new diseases to emerge in humans, because people more often encounter and disrupt animal vectors of disease (Patz et al., 2004; Quammen, 2012).

Invisible threshold for species extinction----causes global ecosystem collapse

Barnosky et al 12 Anthony D. Barnosky, Elizabeth A. Hadly, Jordi Bascompte, Eric L. Berlow, James H. Brown, Mikael Fortelius, Wayne M. Getz, John Harte, Alan Hastings, Pablo A. Marquet, Neo D. Martinez, Arne Mooers, Peter Roopnarine, Geerat Vermeij, John W. Williams, Rosemary Gillespie, Justin Kitzes, Charles Marshall, Nicholas Matzke, David P. Mindell, Eloy Revilla & Adam B. Smith, Nature 486, 52–58 (07 June 2012), “Approaching a state shift in Earth’s biosphere,” http://www.nature.com/nature/journal/v486/n7401/full/nature11018.html

Localized ecological systems are known to shift abruptly and irreversibly from one state to another when they are forced across critical thresholds. Here we review evidence that the global ecosystem as a whole can react in the same way and is approaching a planetary-scale critical transition as a result of human influence. The plausibility of a planetary-scale ‘tipping point’ highlights the need to improve biological forecasting by detecting early warning signs of critical transitions on global as well as local scales, and by detecting feedbacks that promote such transitions. It is also necessary to address root causes of how humans are forcing biological changes. Introduction Introduction Basics of state shift theory Hallmarks of global-scale state shifts Present global-scale forcings Expecting the unexpected Towards improved biological forecasting and monitoring Guiding the biotic future References Acknowledgements Author information Comments Humans now dominate Earth, changing it in ways that threaten its ability to sustain us and other species1, 2, 3. This realization has led to a growing interest in forecasting biological responses on all scales from local to global4, 5, 6, 7. However, most biological forecasting now depends on projecting recent trends into the future assuming various environmental pressures5, or on using species distribution models to predict how climatic changes may alter presently observed geographic ranges8, 9. Present work recognizes that relying solely on such approaches will be insufficient to characterize fully the range of likely biological changes in the future, especially because complex interactions, feedbacks and their hard-to-predict effects are not taken into account6, 8, 9, 10, 11. Particularly important are recent demonstrations that ‘critical transitions’ caused by threshold effects are likely12. Critical transitions lead to state shifts, which abruptly override trends and produce unanticipated biotic effects. Although most previous work on threshold-induced state shifts has been theoretical or concerned with critical transitions in localized ecological systems over short time spans12, 13, 14, planetary-scale critical transitions that operate over centuries or millennia have also been postulated3, 12, 15, 16, 17, 18. Here we summarize evidence that such planetary-scale critical transitions have occurred previously in the biosphere, albeit rarely, and that humans are now forcing another such transition, with the potential to transform Earth rapidly and irreversibly into a state unknown in human experience. Two conclusions emerge. First, to minimize biological surprises that would adversely impact humanity, it is essential to improve biological forecasting by anticipating critical transitions that can emerge on a planetary scale and understanding how such global forcings cause local changes. Second, as was also concluded in previous work, to prevent a global-scale state shift, or at least to guide it as best we can, it will be necessary to address the root causes of human-driven global change and to improve our management of biodiversity and ecosystem services3, 15, 16, 17, 19. Basics of state shift theory Introduction Basics of state shift theory Hallmarks of global-scale state shifts Present global-scale forcings Expecting the unexpected Towards improved biological forecasting and monitoring Guiding the biotic future References Acknowledgements Author information Comments It is now well documented that biological systems on many scales can shift rapidly from an existing state to a radically different state12. Biological ‘states’ are neither steady nor in equilibrium; rather, they are characterized by a defined range of deviations from a mean condition over a prescribed period of time. The shift from one state to another can be caused by either a ‘threshold’ or ‘sledgehammer’ effect. State shifts resulting from threshold effects can be difficult to anticipate, because the critical threshold is reached as incremental changes accumulate and the threshold value generally is not known in advance. By contrast, a state shift caused by a sledgehammer effect—for example the clearing of a forest using a bulldozer—comes as no surprise. In both cases, the state shift is relatively abrupt and leads to new mean conditions outside the range of fluctuation evident in the previous state. Threshold-induced state shifts, or critical transitions, can result from ‘fold bifurcations’ and can show hysteresis12. The net effect is that once a critical transition occurs, it is extremely difficult or even impossible for the system to return to its previous state. Critical transitions can also result from more complex bifurcations, which have a different character from fold bifurcations but which also lead to irreversible changes20. Recent theoretical work suggests that state shifts due to fold bifurcations are probably preceded by general phenomena that can be characterized mathematically: a deceleration in recovery from perturbations (‘critical slowing down’), an increase in variance in the pattern of within-state fluctuations, an increase in autocorrelation between fluctuations, an increase in asymmetry of fluctuations and rapid back-and-forth shifts (‘flickering’) between states12, 14, 18. These phenomena can theoretically be assessed within any temporally and spatially bounded system. Although such assessment is not yet straightforward12, 18, 20, critical transitions and in some cases their warning signs have become evident in diverse biological investigations21, for example in assessing the dynamics of disease outbreaks22, 23, populations14 and lake ecosystems12, 13. Impending state shifts can also sometimes be determined by parameterizing relatively simple models20, 21. In the context of forecasting biological change, the realization that critical transitions and state shifts can occur on the global scale3, 12, 15, 16, 17, 18, as well as on smaller scales, is of great importance. One key question is how to recognize a global-scale state shift. Another is whether global-scale state shifts are the cumulative result of many smaller-scale events that originate in local systems or instead require global-level forcings that emerge on the planetary scale and then percolate downwards to cause changes in local systems. Examining past global-scale state shifts provides useful insights into both of these issues. Hallmarks of global-scale state shifts Introduction Basics of state shift theory Hallmarks of global-scale state shifts Present global-scale forcings Expecting the unexpected Towards improved biological forecasting and monitoring Guiding the biotic future References Acknowledgements Author information Comments Earth’s biosphere has undergone state shifts in the past, over various (usually very long) timescales, and therefore can do so in the future (Box 1). One of the fastest planetary state shifts, and the most recent, was the transition from the last glacial into the present interglacial condition12, 18, which occurred over millennia24. Glacial conditions had prevailed for ~100,000 yr. Then, within ~3,300 yr, punctuated by episodes of abrupt, decadal-scale climatic oscillations, full interglacial conditions were attained. Most of the biotic change—which included extinctions, altered diversity patterns and new community compositions—occurred within a period of 1,600 yr beginning ~12,900 yr ago. The ensuing interglacial state that we live in now has prevailed for the past ~11,000 yr. Box 1: Past planetary-scale critical transitions and state shifts Full box Occurring on longer timescales are events such as at least four of the ‘Big Five’ mass extinction s25, each of which represents a critical transition that spanned several tens of thousands to 2,000,000 yr and changed the course of life’s evolution with respect to what had been normal for the previous tens of millions of years. Planetary state shifts can also substantially increase biodiversity, as occurred for example at the ‘Cambrian explosion’26, but such transitions require tens of millions of years, timescales that are not meaningful for forecasting biological changes that may occur over the next few human generations (Box 1). Despite their different timescales, past critical transitions occur very quickly relative to their bracketing states: for the examples discussed here, the transitions took less than ~5% of the time the previous state had lasted (Box 1). The biotic hallmark for each state change was, during the critical transition, pronounced change in global, regional and local assemblages of species. Previously dominant species diminished or went extinct, new consumers became important both locally and globally, formerly rare organisms proliferated, food webs were modified, geographic ranges reconfigured and resulted in new biological communities, and evolution was initiated in new directions. For example, at the Cambrian explosion large, mobile predators became part of the food chain for the first time. Following the K/T extinction, mammalian herbivores replaced large archosaur herbivores. And at the last glacial–interglacial transition, megafaunal biomass switched from being dominated by many species to being dominated by Homo sapiens and our domesticated species27. All of the global-scale state shifts noted above coincided with global-scale forcings that modified the atmosphere, oceans and climate (Box 1). These examples suggest that past global-scale state shifts required global-scale forcings, which in turn initiated lower-level state changes that local controls do not override. Thus, critical aspects of biological forecasting are to understand whether present global forcings are of a magnitude sufficient to trigger a global-scale critical transition, and to ascertain the extent of lower-level state changes that these global forcings have already caused or are likely to cause. Present global-scale forcings Introduction Basics of state shift theory Hallmarks of global-scale state shifts Present global-scale forcings Expecting the unexpected Towards improved biological forecasting and monitoring Guiding the biotic future References Acknowledgements Author information Comments Global-scale forcing mechanisms today are human population growth with attendant resource consumption 3, habitat transformation and fragmentation3, energy production and consumption28, 29, and climate change3, 18. All of these far exceed, in both rate and magnitude, the forcings evident at the most recent global-scale state shift, the last glacial–interglacial transition (Box 1), which is a particularly relevant benchmark for comparison given that the two global-scale forcings at that time—climate change and human population growth27, 30—are also primary forcings today. During the last glacial–interglacial transition, however, these were probably separate, yet coincidental, forcings. Today conditions are very different because global-scale forcings including (but not limited to) climate change have emerged as a direct result of human activities. Human population growth and per-capita consumption rate underlie all of the other present drivers of global change. The growth in the human population now (~77,000,000 people per year) is three orders of magnitude higher than the average yearly growth from ~10,000–400 yr ago (~67,000 people per year), and the human population has nearly quadrupled just in the past century31, 32, 33. The most conservative estimates suggest that the population will grow from its present value, 7,000,000,000, to 9,000,000,000 by 204531 and to 9,500,000,000 by 205031, 33. As a result of human activities, direct local-scale forcings have accumulated to the extent that indirect, global-scale forcings of biological change have now emerged. Direct forcing includes the conversion of ~43% of Earth’s land to agricultural or urban landscapes, with much of the remaining natural landscapes networked with roads1, 2, 34, 35. This exceeds the physical transformation that occurred at the last global-scale critical transition, when ~30% of Earth’s surface went from being covered by glacial ice to being ice free. The indirect global-scale forcings that have emerged from human activities include drastic modification of how energy flows through the global ecosystem. An inordinate amount of energy now is routed through one species, Homo sapiens. Humans commandeer ~20–40% of global net primary productivity1, 2, 35 (NPP) and decrease overall NPP through habitat degradation. Increasing NPP regionally through atmospheric and agricultural deposition of nutrients (for example nitrogen and phosphorus) does not make up the shortfall2. Second, through the release of energy formerly stored in fossil fuels, humans have substantially increased the energy ultimately available to power the global ecosystem. That addition does not offset entirely the human appropriation of NPP, because the vast majority of that ‘extra’ energy is used to support humans and their domesticates, the sum of which comprises large-animal biomass that is far beyond that typical of pre-industrial times27. A decrease in this extra energy budget, which is inevitable if alternatives do not compensate for depleted fossil fuels, is likely to impact human health and economies severely28, and also to diminish biodiversity27, the latter because even more NPP would have to be appropriated by humans, leaving less for other species36. By-products of altering the global energy budget are major modifications to the atmosphere and oceans. Burning fossil fuels has increased atmospheric CO2 concentrations by more than a third (~35%) with respect to pre-industrial levels, with consequent climatic disruptions that include a higher rate of global warming than occurred at the last global-scale state shift37. Higher CO2 concentrations have also caused the ocean rapidly to become more acidic, evident as a decrease in pH by ~0.05 in the past two decades38. In addition, pollutants from agricultural run-off and urban areas have radically changed how nutrients cycle through large swaths of marine areas16. Already observable biotic responses include vast ‘dead zones’ in the near-shore marine realm39, as well as the replacement of >40% of Earth’s formerly biodiverse land areas with landscapes that contain only a few species of crop plants, domestic animals and humans3, 40. Worldwide shifts in species ranges, phenology and abundances are concordant with ongoing climate change and habitat transformation41. Novel communities are becoming widespread as introduced, invasive and agricultural species integrate into many ecosystems42. Not all community modification is leading to species reductions; on local and regional scales, plant diversity has been increasing, owing to anthropogenic introductions42, counter to the overall trend of global species loss5, 43. However, it is unknown whether increased diversity in such locales will persist or will eventually decrease as a result of species interactions that play out over time. Recent and projected5, 44 extinction rates of vertebrates far exceed empirically derived background rates25. In addition, many plants, vertebrates and invertebrates have markedly reduced their geographic ranges and abundances to the extent that they are at risk of extinction43. Removal of keystone species worldwide, especially large predators at upper trophic levels, has exacerbated changes caused by less direct impacts, leading to increasingly simplified and less stable ecological networks39, 45, 46. Looking towards the year 2100, models forecast that pressures on biota will continue to increase. The co-opting of resources and energy use by humans will continue to increase as the global population reaches 9,500,000,000 people (by 2050), and effects will be greatly exacerbated if per capita resource use also increases. Projections for 2100 range from a population low of 6,200,000,000 (requiring a substantial decline in fertility rates) to 10,100,000,000 (requiring continued decline of fertility in countries that still have fertility above replacement level) to 27,000,000,000 (if fertility remains at 2005–2010 levels; this population size is not thought to be supportable; ref. 31). Rapid climate change shows no signs of slowing. Modelling suggests that for ~30% of Earth, the speed at which plant species will have to migrate to keep pace with projected climate change is greater than their dispersal rate when Earth last shifted from a glacial to an interglacial climate47, and that dispersal will be thwarted by highly fragmented landscapes. Climates found at present on 10–48% of the planet are projected to disappear within a century, and climates that contemporary organisms have never experienced are likely to cover 12–39% of Earth48. The mean global temperature by 2070 (or possibly a few decades earlier) will be higher than it has been since the human species evolved. Expecting the unexpected Introduction Basics of state shift theory Hallmarks of global-scale state shifts Present global-scale forcings Expecting the unexpected Towards improved biological forecasting and monitoring Guiding the biotic future References Acknowledgements Author information Comments The magnitudes of both local-scale direct forcing and emergent global-scale forcing are much greater than those that characterized the last global-scale state shift, and are not expected to decline any time soon. Therefore, the plausibility of a future planetary state shift seems high, even though considerable uncertainty remains about whether it is inevitable and, if so, how far in the future it may be. The clear potential for a planetary-scale state shift greatly complicates biotic forecasting efforts, because by their nature state shifts contain surprises. Nevertheless, some general expectations can be gleaned from the natural experiments provided by past global-scale state shifts. On the timescale most relevant to biological forecasting today, biotic effects observed in the shift from the last glacial to the present interglacial (Box 1) included many extinctions 30, 49, 50, 51; drastic changes in species distributions, abundances and diversity; and the emergence of novel communities49, 50, 52, 53, 54. New patterns of gene flow triggered new evolutionary trajectories55, 56, 57, 58, but the time since then has not been long enough for evolution to compensate for extinctions. At a minimum, these kinds of effects would be expected from a global-scale state shift forced by present drivers, not only in human-dominated regions but also in remote regions not now heavily occupied by humans (Fig. 1); indeed, such changes are already under way (see above5, 25, 39, 41, 42, 43, 44). Given that it takes hundreds of thousands to millions of years for evolution to build diversity back up to pre-crash levels after major extinction episodes25, increased rates of extinction are of particular concern, especially because global and regional diversity today is generally lower than it was 20,000 yr ago as a result of the last planetary state shift37, 50, 51, 54, 59. This large-scale loss of diversity is not overridden by historical increases in plant species richness in many locales, owing to human-transported species homogenizing the world’s biota42. Possible too are substantial losses of ecosystem services required to sustain the human population60. Still unknown is the extent to which human-caused increases in certain ecosystem services—such as growing food—balances the loss of ‘natural’ ecosystem services, many of which already are trending in dangerous directions as a result of overuse, pollutants and climate change3, 16. Examples include the collapse of cod and other fisheries45, 61, 62; loss of millions of square kilometres of conifer forests due to climate-induced bark-beetle outbreaks;63 loss of carbon sequestration by forest clearing60; and regional losses of agricultural productivity from desertification or detrimental land-use practices1, 35. Although the ultimate effects of changing biodiversity and species compositions are still unknown, if critical thresholds of diminishing returns in ecosystem services were reached over large areas and at the same time global demands increased (as will happen if the population increases by 2,000,000,000 within about three decades), widespread social unrest, economic instability and loss of human life could result 64.

XT – Democracy Impact

Prefer our statistics—democracy is the best at stopping war

Valerie Epps, Professor of Law, Suffolk University Law School, Boston, Spring, ’98
4 ILSA J Int'l & Comp L 347

One scholar who has perhaps tried the hardest to separate out other possible influences on conflict is Professor Bruce Russett. Through a series of calibrated tables he has looked at the influence of a variety of factors as well as the fact of democracy itself on conflict. He tests such factors as wealth, economic growth, alliances, contiguity, and military capability ratio. What he finds is that "the effect [of democracy] is continuous, in that the more democratic each member of [any two possible warring states] is, the less likely is conflict between them." n32 He also looks at such variables as political stability, structural/institutional constraints, normative cultural restraints, and even the levels of deaths resulting from political conflict within countries. From his studies he  [*354]  concludes that: The more democratic are both members of a pair of states, the less likely is it that a militarized dispute will break out between them, and the less likely it is that any disputes that do break out will escalate. This effect will operate independently of other attributes such as the wealth, economic growth, contiguity, alliance or capability ratio of the countries. n33 Russett concludes that the "results do suggest that the spread of democracy in international politics . . . can reduce the frequency of violent conflicts among nations." n34