AffSustainability genericGrowths inevitable --- adapting strategies to be sustainable is key Spence 12(MICHAEL SPENCE - Nobel laureate in economics, is Professor of Economics at NYUs Stern School of Business, Distinguished Visiting Fellow at the Council on Foreign, The Sustainability Mindset 2 17, 2012, http://www.project-syndicate.org/commentary/the-sustainability-mindset)//hahaMILAN Markets and capitalist incentives have great strengths in promoting economic efficiency, growth, and innovation. And, as Ben Friedman of Harvard University argued persuasively in his 2006 book The Moral Consequences of Growth, economic growth is good for open and democratic societies. But markets and capitalist incentives have clear weaknesses in ensuring stability, equity, and sustainability, which can adversely affect political and social cohesion. Obviously, abandoning market-capitalist systems, and implicitly growth, is not really an option. Collectively, we have little choice but to try to adapt the system to changing technological and global conditions in order to achieve stability, equity (in terms of opportunity and outcomes alike), and sustainability. Of these three imperatives, sustainability may be the most complex and challenging. For many people, sustainability is associated with finite natural resources and the environment. The global economy will probably triple in size in the next quarter-century, largely owing to growth in developing countries as they catch up to developed-country incomes and adopt similar consumption patterns. Thus, there is a well-founded fear that the planets natural resources (broadly defined) and recuperative capacities will not withstand the pressure. To some, this logic leads to the conclusion that growth is the problem, and that less growth is the solution. But, in developing countries, where only sustained growth can lift people out of poverty, limiting it cannot be the answer. The alternative is to change the growth model in order to lighten the impact of higher levels of economic activity on natural resources and the environment. But there is no existing alternative to which we can all switch. Changing the growth model means inventing a new one over time, step-by-step, from complementary parts. The two key ingredients seem to be education and values. Everyone, not just policymakers, needs to understand the consequences of our individual and collective choices. We need to be aware for example, that population growth and rising consumption levels have intergenerational consequences, and that how we conduct ourselves will affect the lifestyles and opportunities of our children and grandchildren. Thus far, the quality of our choices has been unimpressive, reflecting little sensitivity to sustainability and the impact of our choices on future generations. As a result, many developed countries have built up dangerously large public debts and even larger non-debt liabilities, owing to unsustainable growth patterns. Most of us, I believe, do not knowingly make choices that adversely affect future generations. So perhaps incomplete knowledge of the consequences of our choices is responsible. Moreover, an unfunded liability path, once taken, is hard to leave, because at the point of departure, some generation is paying for past commitments and at least beginning to fund future ones. That seems unfair, because it is. Most people might agree that living beyond our means in the aggregate, via unfunded social services and insurance, or disproportionate use of resources, imposes a burden on our offspring. But we might still fail to reach agreement on who should pay for funding these programs, or for reducing our consumption of resources. Too often, it is easier to deal with the distributional problem by shifting the burden to those who are not present, and who are insufficiently represented by those who are. Education and values are the foundation of sound individual and, ultimately, collective choices. Without them, the incentives and policies that economists rightly argue are needed to increase energy efficiency, limit carbon emissions, economize on water usage, and much more will lack support and fail in the democratic decision-making process. If sustainability is to triumph, it must be predominantly a bottom-up process. Environmentalists are right to focus on education and individual choices, even when their policy proposals are not always on target. Education and values will drive local innovation, alter lifestyles, and shift social norms. They will also affect business behavior via choices by customers and employees, including business leaders. Thus, they are essential components of the formulas needed to pursue sustainable patterns of growth. But, while education and values are necessary, they clearly are not sufficient. Complementary national policies and international agreements will require careful scientific and economic analysis and thoughtful choices. The need for burden-sharing, particularly between advanced and developing countries, will not magically disappear. Climate-change risks, though serious, should not be mistaken for the entire sustainability agenda. There are clear steps that can be taken. Appropriate regulation and sufficiently long time horizons can make structures of all kinds much more energy-efficient, without imposing burdensome costs. In a similar way, transportation can become less energy-intensive without restricting mobility. Some of these shifts might be subject to international coordination, in order to avoid adverse competitive consequences, whether real or perceived. But too much coordination can be a bad thing. That is why climate-change negotiations are shifting from the misguided objective of seeking risky 50-year commitments to binding carbon-emissions targets to focusing on parallel, step-by-step processes, including higher energy efficiency, better urban planning, improved transportation systems, and on learning as we go. Likewise, businesses and industries that are heavy water users will simply develop new technologies and thrive in the face of scarcity. Progress has been helped by growing awareness in populous Asia and in developing countries generally that sustainability is the key to achieving their longer-term growth objectives. This perspective perhaps comes more naturally in an environment of rapid growth, because their growth models require continual review and adaptation to be sustainable. Over time, values shift as knowledge is acquired and disseminated. Policies aimed at sustainability are likely to follow. What is unknown is whether we will reach that point fast enough to avoid major disruptions, or even potential conflict.EKCgenericEKC model is accurate --- proves environmental degradation isnt an opportunity cost to growth Levinson 8(Arik Levinson professor economics Georgetown university, Environmental Kuznets Curve The New Palgrave Dictionary of Economics, 2008, http://faculty.georgetown.edu/aml6/pdfs&zips/PalgraveEKC.pdf)//hahaEnvironmental Kuznets curve Some forms of pollution appear first to worsen and later to improve as countries incomes grow. The worlds poorest and richest countries have relatively clean environments, while middle-income countries are the most polluted. Because of its resemblance to the pattern of inequality and income described by Simon Kuznets (1955), this pattern of pollution and income has been labelled an environmental Kuznets curve (EKC). Grossman and Krueger (1995) and the World Bank (1992) first popularized this idea, using a simple empirical approach. They regress data on ambient air and water quality in cities worldwide on a polynomial in GDP per capita and other city and country characteristics. They then plot the fitted values of pollution levels as a function of GDP per capita, and demonstrate that many of the plots appear inverse-U-shaped, first rising and then falling. The peaks of these predicted pollution-income paths vary across pollutants, but in most cases they come before a country reaches a per capita income of $8000 in 1985 dollars (Grossman and Kruger, 1995, p. 353). In the years since these original observations were made, researchers have examined a wide variety of pollutants for evidence of the EKC pattern, including automotive lead emissions, deforestation, greenhouse gas emissions, toxic waste, and indoor air pollution. Some investigators have experimented with different econometric approaches, including higher-order polynomials, fixed and random effects, splines, semi- and non-parametric techniques, and different patterns of interactions and exponents. Others have studied different groups of jurisdictions and different time periods, and have added control variables, including measures of corruption, democratic freedoms, international trade openness, and even income inequality (bringing the subject full circle back to Kuznets's original idea). Some generalizations across these approaches emerge. Roughly speaking, pollution involving local externalities begins improving at the lowest income levels. Fecal coliform in water and indoor household air pollution are examples. For some of these local externalities, pollution appears to decrease steadily with economic growth, and we observe no turning point at all. This is not a rejection of the EKC; pollution must have increased at some point in order to decline with income eventually, and there simply are no data from the earlier period. By contrast, pollutants involving very dispersed externalities tend to have their turning points at the highest incomes, or even no turning points at all, as pollution appears to increase steadily with income. Carbon emissions provide one such example. This, too, is not necessarily a rejection of the EKC; the turning points for these pollutants may come at levels of income per capita higher than in todays wealthiest economies. Another general empirical result is that the turning points for individual pollutants differ across countries. This difference shows up as instability in empirical approaches that estimate one fixed turning point for any given pollutant. Countries that are the first to deal with a pollutant do so at higher income levels than following countries, perhaps because the following countries benefit from the science and engineering lessons of the early movers. Most researchers have been careful to avoid interpreting these reduced-form empirical correlations structurally, and to recognize that economic growth does not automatically cause environmental improvements. All of the studies omit country characteristics correlated with both income and pollution levels, the most important being environmental regulatory stringency. The EKC pattern does not provide evidence of market failures or efficient policies in rich or poor countries. Rather, there are multiple underlying mechanisms, some of which have begun to be modelled theoretically. Beyond this aggregate decomposition of the EKC, some attempts have been made to formalize structural models that lead to inverse-U-shaped pollution-income patterns. Many describe economies at some type of corner solution initially, where residents of poor countries are willing to trade environmental quality for income at a faster rate than possible using available technologies or resources. As the model economies become wealthier and their environments dirtier, eventually the marginal utility of income falls and the marginal disutility from pollution rises, to the point where people choose costly abatement mechanisms. After that point, the economies are at interior solutions, marginal abatement costs equal marginal rates of substitution between environmental quality and income, and pollution declines with income (see Stokey, 1998). In frameworks of this type, there is typically zero pollution abatement until some threshold income level is crossed, after which abatement begins and pollution starts declining with income. To date, the practical lessons from this theoretical literature are limited. Most of the models are designed to yield inverse-U-shaped pollution-income paths, and succeed using a variety of assumptions and mechanisms. Hence, any number of forces may be behind the empirical observation that pollution increases and then decreases with income. Moreover, that pattern cannot be interpreted causally, and is consistent with either efficient or inefficient growth paths. Perhaps the most important insight is in Grossman and Kruegers original paper: We find no evidence that economic growth does unavoidable harm to the natural habitat (1995, p. 370). Economists have long argued that environmental degradation is not an inevitable consequence of economic growth. The EKC literature provides empirical support for that claim. Economic growth doesnt hurt the environmentLevinson 2k(Arik Levinson Professor of economics at Georgetown University, The Ups and Downs of the Environmental Kuznets Curve 2000, http://units.georgetown.edu/economics/pdf/0108.pdf)//hahaTo make the point that pollution can decline with income per capita, all one needs do is plot pollution levels against GDP per capita for some sample pollutants and countries. As an example, consider SO2, the pollutant most frequently found to have an inverse-U-shaped pattern, and internationally the best-monitored pollutant. The GDP per capita data come from Summers and Heston's (1991) Penn World Tables. Data on ambient pollution levels used by the World Bank and Grossman and Krueger in their original work were collected by the Global Environmental Monitoring System (GEMS), sponsored by the World Health Organization (WHO) and the United Nations. The EPA maintains these data in its Aerometric Information Retrieval System (AIRS). For SO2, the GEMS data contain 2401 annual observations from 285 monitoring stations in 102 cities in 45 countries, from 1971 to the present. Because the Summers and Heston data only extend to 1992, this analysis stops at that date. Figure 1a depicts a cross-section of mean SO2 readings from each monitoring station in 1980, plotted against GDP per capita. The observations are stacked up because there are multiple readings from each country, each with a single value of GDP per capita in 1980. These are the numbers used to run the regressions plotted by Grossman and Krueger, and by the World Bank. By looking at figure 1a, one can see the difficulty inherent in discerning any particular pattern. The data are roughly consistent with an environmental Kuznets curve, with the highest pollution readings coming from middle income countries. However, middle-income countries also have monitoring stations with low SO2 readings, so one cannot draw immediate conclusions from this figure. Figure 1b plots the average SO2 reading across all monitoring stations within a country, against GDP per capita. So by contrast to Figure 1a, Figure 1b has only one observation per country. One has to squint a little harder at this diagram to make the claim that cross-section evidence supports any particular decline in pollution levels at high incomes. However, if the fundamental point to be made by this literature is that pollution does not inevitably increase with income, then cross-sectional evidence is irrelevant. Five of the studies reviewed in Table 1 contain only cross-sections of pollution and incomes at single points in time. While such evidence may suggest that richer countries are cleaner than middle-income countries, it does not necessarily show that richer countries have become cleaner over time. For that, we need time series evidence. Most of the studies in Table l do use panels of data, but they typically pool time series and cross-section evidence. Grossman and Krueger, for example, estimate panel data models with random effects. The coefficients on GDP per capita are thus identified partly from cross- sectional comparisons of countries within a given year, and partly from time series comparisons within given countries. Again, however, if the fundamental point to be made by this literature is that pollution does not deterministically increase with income, then all we need do is show some countries whose pollution levels have declined with economic growth. Take airborne Sulfur pollution in the U.S., for example. Showing that a decline in pollution levels has occurred contemporaneously with economic growth is slightly more complicated than merely plotting average monitoring station readings against GDP per capita. That is because over time, countries have expanded the number of monitoring stations. If new stations are added in successively cleaner locales (the dirtiest places are targeted first), then the averages will display a spurious downward trend. To avoid the bias inherent in the selection of monitoring station locale, in figure 2 I have plotted average SO2 readings from the 22 monitoring stations in the U.S. that were continuously active from 1979 through 1992. As is clear from the picture, economic growth and environmental cleanup are not mutually exclusive. Though other countries have fewer monitoring stations and fewer years of continuous data, the same trends are notable among industrialized countries.1 In sum, aggregate panel data on pollution levels across countries over time are noisy, and patterns are difficult to discern in the raw data. A large variety of empirical specifications attempting to detect such patterns have, in the literature, yielded an equally large variety of predictions. Nevertheless, for some pollutants it is quite easy to document steady improvements in ambient air quality, concurrent with economic growth. This is consistent with the claim that economic growth does not necessarily degrade the environment. Environmental quality and growth are not a tradeoff --- their evidence conflates common misconceptionsLevinson 2k(Arik Levinson Professor of economics at Georgetown University, The Ups and Downs of the Environmental Kuznets Curve 2000, http://units.georgetown.edu/economics/pdf/0108.pdf)//hahaConclusion Grossman and Krueger (1995), who sparked this literature, wrote in their abstract that most pollution problems appear to begin improving before countries' per capita incomes reach $8000. This description of an inverse-U-shaped pollution-income pattern set off an empirical hunt for other inverse-U-shaped patterns, and a theoretical hunt for general theories of this pattern. Meanwhile, in the text of their paper is the less eye-catching conclusion that there is "no evidence that environmental quality deteriorates steadily with economic growth." Though unsurprising to economists, who can demonstrate the result using simple theory, this finding is useful in policy circles where environmental and economic issues are often seen solely as a tradeoff. Based on this brief perusal of the literature to date, the conventional wisdom on the state of knowledge on economic growth and the environment can be summarized as follows. Empirically, many researchers have used a variety of specifications to tease inverse-U-shaped pollution-income patterns out of noisy aggregate data, though skeptics have argued that these results are not replicable, and are sensitive to functional forms and specifications. Theoretically, inverse-U-shaped pollution-income paths can be the result of numerous causes, modeled in increasingly complex ways. In some cases, the inverse-U shape may be evidence for market failures. In other cases, the shape is consistent with efficient resource allocation. The key insight therefore are that (a) pollution does not inevitably increase with growth, (b) inverse-U-shaped pollution-income paths are neither necessary nor sufficient evidence for market failures or efficiency, and (c) the inverse-U derives from a technological link between a desirable good and an undesirable side-effect, which is broader and more general than the environment. All of these points can be made without most of the empirical and theoretical mechanics in the literature. To demonstrate the first point, all we need do is show that some pollutants have declined, even in countries growing rapidly. For the second point, all that is required is a static, one-good, model, in which both the centralized (efficient) and decentralized (inefficient) pollution-income relationships are inverse-U-shaped. For the third, a few extensions into other applications suffice. As this literature inevitably proliferates, these three points will be important to keep in mind.at inaccurateThe EKC is accurate --- theoretical modeling provesDinda 4(Soumyananda Dinda - Professor (Associate) Sidho Kanho Birsha University Economics, Environmental Kuznets Curve Hypothesis: A Survey Economic Research Unit, Indian Statistical Institute, Ecological Economics, 14 July 2004, http://ac.els-cdn.com.proxy.lib.umich.edu/S0921800904001570/1-s2.0-S0921800904001570-main.pdf?_tid=96d45986-1811-11e4-8241-00000aab0f6c&acdnat=1406742635_6a63fd3ab115fb7787bcad54d4c87fba)//hahaThe conceptual arguments make the EKC conceivable from a theoretical viewpoint. Recently, the EKC has been explained theoretically. Income growth is driven by accumulation of production factors (Lopez, 1994), which increases firms' demand for polluting inputs. At the same time, demand for environmental quality rises with income as the willingness to pay for a clean environment increases. A basic comparative static analysis of the costs and benefits associated with a better environmental quality provides an interesting conceptual insight as to how the EKC might emerge. The Environmental Kuznets Curve is derived from the interaction points of marginal cost (MC) and marginal benefit (MB) curves (Munasinghe, 1999). An EKC can be derived directly from the technological link between consumption of a desired good and abatement of its undesirable byproduct" (Andreoni and Levinson, 2001). It is also consistent with either Pareto efficient policy or a decentralized market economy. If pollution is not priced, firm will use it until its marginal product is zero, when pollution is considered as a factor of production, but not the stock of environmental capital. Extending this model, stock of environmental quality is included as a factor of production (Lopez, 1994), then the predictions of this model depend crucially on the existence of property rights. The EKC emerges from a dynamic process, as one part of capital goes for development of the environmental sectors. Total capital is divided into two parts-one is used in production process that creates pollution and damage the existing environment and the other is used to clean up environment or improve it (Dinda, 2002). The role of abatement expenditure is crucial to reduce the pollution in production side (Dessus and Bussolo, 1998; Jaeger, 1998; Selden and Song, 1994). But the abatement expenditure may not be a determining factor behind the EKC for long-lived pollutants like hazardous waste sites that are neither easily abated nor shifted elsewhere. A stylized theoretical model of the EKC based on perfect mobility of household and labour is developed, and a general equilibrium model that emphasizes spatial separation on the consumer side as the reason behind the EKC for hazardous waste sites" (Gawande et al., 2001). Under various conditions, the EKC relationship between pollution and income can be obtained theoretically (Beltratti, 1997; Bulte and van Soest, 2001; Dinda, 2002; John and Pecchenino,24 1994; Jones and Rodolfo, 1995; Kadekodi and Agarwal, 1999; Selden and Song, 1995; Stokey, 1998). It should be noted that the EKC relation may also take shape from the interaction between ecological and economic factors (Ezzati et al., 2001).at exportation of pollution/variance Even if theres some variance, that doesnt disprove the broader trend of the curveDinda 4(Soumyananda Dinda - Professor (Associate) Sidho Kanho Birsha University Economics, Environmental Kuznets Curve Hypothesis: A Survey Economic Research Unit, Indian Statistical Institute, Ecological Economics, 14 July 2004, http://ac.els-cdn.com.proxy.lib.umich.edu/S0921800904001570/1-s2.0-S0921800904001570-main.pdf?_tid=96d45986-1811-11e4-8241-00000aab0f6c&acdnat=1406742635_6a63fd3ab115fb7787bcad54d4c87fba)//hahaThe empirical analyses are based on data for various sources. Most of the data used in empirical tests are drawn from cross-sections of countries, cross-sectional panel data and pooled data. Most of the studies have used water and air pollution data from GEMS, ORNL (CO2), World Resources, UN Statistical Yearbook, compendium of the OECD, FAO Production Yearbook, WHO's Health database, IEA, EPA for US data, different sources for micro-data, etc. Economic data (GDP per capita, trade, etc.) are taken mostly from the Penn World Tables (Summers and Heston) or the World Bank. Using these data, several authors study the EKC hypothesis and their empirical evidences provide controversy about it. In the absence of a single environmental indicator, it is possible to distinguish three main categories that have been used in the literature: air quality, water quality and other environmental quality indicators. 4.2. 1. Air quality indicators The urban or/and local air quality indicators (SO2: sulphur dioxide, SPM: suspended particulate matters, CO: carbon monoxide and NOX: nitrous oxides, etc., directly affect human health) generally reveal the inverted-U relationship with income. Several studies26 confirm this outcome. Generally, the literature does not find the evidence of EKC for air pollutants that have direct little impact on health. Both early and recent studies find that the global pollutants (such as carbon dioxide emissions) either monotonically increase or decrease as income grows. 4.2.2. Water quality indicators For water quality indicators, empirical evidence of EKC is even more mixed. Three main categories of indicators are used as measures of water quality: (a) concentration of pathogens in water (fecal and total coliforms), (b) amount of heavy metals (lead, cadmium, mercury, arsenic and nickel) and toxic chemicals' dis- charge in water by human activities and c) measure of deterioration of the water oxygen regime (dissolved oxygen, biological and chemical oxygen demand, i.e., BOD and COD). There is evidence of EKC for some indicators, but many studies reach conflicting results about the shape and peak of the curve (Beede and Wheeler, 1992; Hettige et al., 2000b). Several authors find evidence of N-shaped curve for some indicators (for example, fecal coliforms in river water, see Shaiik, 1994). 4.2.3. Other environmental indicators Some other environmental indicators (municipal solid wastes, urban sanitation, access to safe drinking water, energy use and traffic volumes, etc.) have been used to test the EKC hypothesis. Most of these indicators do not support EKC. All studies find that environmental problems having direct impact on human health (such as access to urban sanitation and clean water) tend to improve steadily with economic growth. On contrary, when environmental problems can be externalized (as in the case of municipal solid wastes) curve does not even fall at high-income levels. The empirical evidence of EKC is controversial in case of deforestation (Bhattarai and Hammig, 2001; Bulte and van Soest, 2001; Koop and Tole, 1999). 4.2.4. Turning point It is clear that the EKC-type relations exist for some environmental pressure factors and a transition is expected at a crucial point, i.e., tuming point. The turning points of these inverted-U-shaped relation- ships vary for different pollutants or environmental indicators.27 For most of the pollution indicators, the estimated turning point lies within the income range of US$3000-10,000 (at a constant price, 1985 US dollar). Moreover, there are also large variations across studies for same indicators. Economic growth may be associated with worsening environmental conditions in less developed or poor countries but air and water quality appears to be benefited from economic growth if the critical level of income has been reached. Several pollution indicators, such as SO2, NOX, CO, CO2, SPM and air toxic emission matters; oxygen regime in river basins (BOD, COD), fecal contamination of river water, heavy metals in water (mercury, arsenic, cadmium, nickel, lead); hazardous" and municipal waste, deforestation (Bhattarai and Hammig, 2001; Koop and Tole, 1999), etc.; have been used to study the EKC relation. These studies assume that each country should follow EKC with same shape but level of the curve may vary across countries as per their economic position. The social and political factors are also crucial for shaping the EKC.Tech innovationgenericTech innovations solve growth --- policy proposals to sustainable growth keyDinda 4(Soumyananda Dinda - Professor (Associate) Sidho Kanho Birsha University Economics, Environmental Kuznets Curve Hypothesis: A Survey Economic Research Unit, Indian Statistical Institute, Ecological Economics, 14 July 2004, http://ac.els-cdn.com.proxy.lib.umich.edu/S0921800904001570/1-s2.0-S0921800904001570-main.pdf?_tid=96d45986-1811-11e4-8241-00000aab0f6c&acdnat=1406742635_6a63fd3ab115fb7787bcad54d4c87fba)//haha4.3.5. Technological progress Generally, technological progress leads to greater efficiency in the use of energy and materials. Thus, a given amount of goods can be produced with successively reduced burdens on natural resources and environment. One aspect of this progress may be better and more efficient reuse and recycling of materials, which (coupled with greater efficiency in use) can yield large resource savings. 4.3.5.1. R&D. As income grows, people can adopt better and efficient technology that provide cleaner environment. This preferential behavior of people should be reflected through their income elasticity. The income elasticity of public research and development finding for environmental protection is positive (Komen et al., 1997). It is true for public expenditure on R&D for environmental protection in the case of 19 OECD countries over the period 1980-1994 (Magnani, 2000). This indicates the key role of such public investments for environmental improvements in reducing environmental degradation as income levels rise and even decreasing relationships found for some pollution indicators in developed countries. The effect of economic growth on pollution/emissions differs substantially among high-income countries. Relative income and political framework in which policy decisions are taken determine the emergence of downward sloping segment of EKC. This also depends on the adoption of new technology. 4.3.5.2. Innovation and adoption. New technologies, unambiguously, improve productivity but create potential dangers to the society such as new hazardous wastes, risk and other human problems. These externalities are unknown in the early phase of diffusion of technology, in later stages regulation becomes war- ranted to address it. Once the technology is regulated, this may stimulate the gradual phase out of existing technology. So, a cyclical pattern arises in technologies, which first diffuse, then become regulated and finally are phased out by next generation of technologies (Smulder and Bretschger, 2000). Thus, an inverted-U shape can be observed with reference to each technology. Since the pattern of innovation, income growth and pollution over cycles, a sequence of Environmental Kuznets Curves emerge related to each technology. This may produce an envelope of EKCS, which may be again an inverted-U- or N- shaped or inverted-L curve (Dinda, 2003b). The Environmental Kuznets Curve hypothesis is con- firmed with empirical evidence for several pollutants. Earlier EKCs studies provide that some pollutants follow N-shaped relationship with income, and pollutants have different turning points. This implies that over a certain period during which income grows, one pollutant may decline but another may rise due to adoption of new technology." 4.3.5.3. Technological and organizational change. Improved technology not only significantly increases productivity in the manufacture of old products but also the development of new products. There is a growing trend among industries to reconsider their production processes and thereby take environmental consequences of production into account. This concerns not or1ly traditional technological aspects but also the organization of production as well as the design of products. Technological changes associated with the production process that may also result in changes in the input mix of materials and fuels (Lind- mark, 2002). Material substitution may be an important element of advance economics (Labys and Wadell, 1989) that may result in lower environmental impacts. The economy-wide reforms often contribute simultaneously to the economic, social and environmental gains (Anderson and Cavandish, 2001; Pasche, 2002). The EKC approach seeks to relate the stages of economic development of a country to that of environmental degradation. Developing countries could learn from the experiences of industrialized nations, and restructure growth and development to tunnel through (Munasinghe, 1999) any potential EKC- thereby avoiding going through the same stages of growth that involve relatively high (and even irreversible) levels of environmental harm. However, it is not clear which effective environ- mental policies should encompass to reduce pollution. Yet, virtually all of the studies that investigated EKCs have hinted at the important policy implications of their work.Technological development solves biodiversity collapseCzech 8(BRIAN CZECH - Ph.D. in renewable natural resources studies from the University of Arizona with a minor in political science, Prospects for Reconciling the Conflict between Economic Growth and Biodiversity Conservation with Technological Progress Conservation Biology Volume 22, No. 6, 2008, http://steadystate.org/wp-content/uploads/Czech_Technological_Progress.pdf)//hahaIn economic terms technological progress results in in- creasing technical efficiency (productivity) (i.e., greater production of output per unit input [Perelman 1995]). Engineers may view such an increase primarily in physical terms (e.g., an increase in steel production from the same amount of iron and energy). Economists level the field by using monetary units, such as dollars, to mea- sure inputs and outputs (Fried et al. 1993). They also distinguish between product innovation and process in- novation (Li et al. 2007). Product innovation is synonymous with invention, and process innovation pertains to reconfiguring the production process. Paying particular attention to the prospects for alleviating environmental impact, Wils (2001) suggests a classification system with tl1ree categories of innovation: explorative, extractive, and end-use innovation. Explorative innovation allows the user to locate stocks of natural capital that were not previously detectable, and extractive innovation allows the user to extract known resources that were previously inaccessible. Explorative and extractive innovations contribute to economic growth by increasing the amount of natural capital reallocated from the economy of nature to the human economy (Wils 2001). This leaves end-use innovation as the lone source of technological progress that could conceivably reconcile economic growth with biodiversity conservation. End- use innovation is essentially synonymous with increasing technical efficiency (Wils 2001). A good example for our purposes is increasing fuel efficiency of fishing vessels. Product innovation such as vessel design, or process innovation such as optimizing fishing schedules, may in- crease the amount of fish caught per unit of fuel consumed. Two basic scenarios may follow: (1) the same amount of fish are caught and sold, but less fuel is purchased, and ceteris paribus, economic growth does not result, and (2) the same amount of fuel is purchased, more fish are caught and sold, and, ceteris paribus, economic growth results. In the first scenario, economic growth is not reconciled with biodiversity conservation because economic growth does not occur. In the second scenario, economic growth is not reconciled with bio- diversity conservation because more fish are reallocated from the economy of nature to the human economy. In an intermediate scenario, somewhat less fuel is purchased by the fishing fleet and somewhat more fish are caught and sold. In other words, theoretically the economy may grow somewhat, with somewhat less natural capital reallocated from the economy of nature to the human economy, at least relative to the amount of natural capital that would have been allocated pursuant to the same amount of economic growth in the absence of the end-use innovation. Nevertheless, this theoretical out- come is based on the assumption that different forms of natural capital (fuel and fish in this case) are substitutable; an assumption not conducive to fish conservation (Daly & Farley 2005). It is also based on the assumption that the fish harvest has not reached the stage of liquidation. These theoretical considerations say nothing quantitatively about the technical efficiency gains required to reconcile the conflict between economic growth and bio- diversity conservation. Nevertheless, to the extent that biodiversity is a function of intact landscapes, or lands from which natural capital has not been liquidated for human economic production, studies pertaining to growth of the ecological footprint are highly relevant. Dietz et al. (2007) estimated "an annual growth rate in the global footprint of 2.12% per year" and surmised that "the requisite technological improvement needs to exceed 2% per year" for environmental protection. Annual productivity gains exceeding 2% typified the "advanced capitalist economies" during the third quarter of the 20th century (Maddison 1987), but gains falling well below 2% have befuddled growth theorists and national income accountants ever since. In countries with less-advanced economies, much of the recent economic growth has resulted from increases in factor inputs (land, labor, and capital), not from the efficiency with which those factors were used (Oguchi 2005).
NegEKC generic
EKC models are wrong Stern 4(DAVID I. STERN - energy and environmental economist with an interdisciplinary background in geography and economics, The Rise and Fall of the Environmental Kuznets Curve 2004, World Development Vol. 32, No. 8, pp. 14191439, http://steadystate.org/wp-content/uploads/Stern_KuznetsCurve.pdf)//hahaThe evidence presented in this paper shows that the statistical analysis on which the environmental Kuznets curve is based is not robust. There is little evidence for a common inverted U-shaped pathway that countries follow as their income rises. There may be an inverted U- shaped relation between urban ambient concentrations of some pollutants and income though this should be tested with more rigorous time-series or panel data methods. It seems unlikely that the EKC is an adequate model of emissions or concentrations. I concur with Copeland and Taylor (2004), who state that: "Our review of both the theoretical and empirical work on the EKC leads us to be skeptical about the existence of a simple and predictable relationship between pollution and per capita income." The true form of the emissions-income relationship is likely a mix of two of the scenarios proposed by Dasgupta et ul. (2002) illustrated in Figure 3. The overall shape is that of their "new toxics" EKC-a monotonic increase of emissions in income. But over time this curve shifts down, which is analogous to their "revised EKC" scenario. Some evidence shows that a particular innovation is likely to be adopted preferentially in high-income countries first with a short lag before it is adopted in the majority of poorer countries. However, emissions may be declining simultaneously in low- and high-income countries over time, ceteris paribus, though the particular innovations typically adopted at any one time could be different in different countries. It seems that structural factors on both the input and output side do play a role in modifying the gross scale effect though they are mostly less influential than time-related effects. The income elasticity of emissions is likely to be less than one-but not negative in wealthy countries as proposed by the EKC hypothesis. In slower growing economies, emissions- reducing technological change can overcome the scale effect of rising income per capita on emissions. As a result, substantial reductions in sulfur emissions per capita have been observed in many OECD countries in the last few decades. In faster growing middle income economies, the effects of rising income over- whelmed the contribution of technological change in reducing emissions.EKC models are wrong --- their ev misreads data points and doesnt assume feedback from environmental damage to economic production Stern 4(DAVID I. STERN - energy and environmental economist with an interdisciplinary background in geography and economics, The Rise and Fall of the Environmental Kuznets Curve 2004, World Development Vol. 32, No. 8, pp. 14191439, http://steadystate.org/wp-content/uploads/Stern_KuznetsCurve.pdf)//hahaA number of critical surveys of the EKC literature have been published (e.g., Ansuategi, Barbier, & Perrings, 1998; Arrow et al., 1995; Copeland & Taylor (2004); Dasgupta et al., 2002; Ekins, 1997; Pearson, 1994; Stern, 1998; Stern et al., 1996). This section discusses the criticisms raised against the EKC in the earlier surveys on theoretical (rather than methodological) grounds. The more recent surveys raise similar points but have more evidence to marshal. The key criticism of Arrow et al. (1995) and others was that the EKC model, as presented in the 1992 World Development Report and else- where, assumes that there is no feedback from environmental damage to economic production as income is assumed to be an exogenous variable. The assumption is that environmental damage does not reduce economic activity sufficiently to stop the growth process and that any irreversibility is not so severe that it reduces the level of income in the future. In other words, there is an assumption that the economy is sustainable. But, if higher levels of economic activity are not sustainable, attempting to grow fast in the early stages of development when environmental degradation is rising may prove counterproductive. 8 It is clear that emissions of many pollutants per unit of output have declined over time in developed countries with increasingly stringent environmental regulations and technical innovations. But the mix of residuals has shifted from sulfur and nitrogen oxides to carbon dioxide and solid waste so that aggregate waste is still high and per capita waste may not have declined. 9 Economic activity is inevitably environmentally disruptive in some way. Satisfying the material needs of people requires the use and disturbance of energy flows and materials stocks. Therefore, an effort to reduce some environmental impacts may just aggravate other problems. 10 Both Arrow et al. (1995) and Stern et al. (1996) argued that, if there was an EKC type relationship, it might be partly or largely a result of the effects of trade on the distribution of polluting industries. The Hecksher-Ohlin trade theory suggests that, under free trade, developing countries would specialize in the production of goods that are intensive in the factors that they are endowed with in relative abundance: labor and natural resources. The developed countries would specialize in human capital and manufactured capital intensive activities. Part of the reduction in environ- mental degradation levels in the developed countries and increases in environmental degradation in middle income countries may reflect this specialization (Hettige, Lucas, & Wheeler, 1992; Lucas, Wheeler, & Hettige, 1992; Suri & Chapman, 1998). Environmental regulation in developed countries might further encourage polluting activities to gravitate toward the developing countries (Lucas et al., 1992). These effects would exaggerate any apparent decline in pollution intensity with rising income along the EKC. In our finite world the poor countries of today would be unable to find further countries from which to import resource- intensive products as they, themselves, become wealthy. When the poorer countries apply similar levels of environmental regulation they would face the more difficult task of abating these activities rather than outsourcing them to other countries (Arrow et al., 1995; Stern et al., 1996). Copeland and Taylor (2004) conclude that, in contrast to earlier work (e.g., Jaffe, Peterson, Portney, & Stavins, 1995), recent research shows that increased regulation does tend to result in more decisions to locate in less regulated locations. On the other hand, there is no clear evidence that trade liberalization results in a shift in polluting activities to less- regulated countries. Furthermore, Antweiler, Copeland, and Taylor (2001) and Cole and Elliott (2003) argue that the capital-intensive activities that are concentrated in the developed countries are more polluting and hence developed countries have a natural comparative advantage in polluting goods in the absence of regulatory differences. There are no clear answers on the impact of trade on pollution from the empirical EKC literature. Stern et al. (1996) argued that early EKC studies showed that a number of indicators: S02 emissions, NOX, and deforestation, peak at income levels around the current world mean per capita income. A cursory glance at the available econometric estimates might have lead one to believe that, given likely future levels of mean income per capita, environmental degradation should decline from the present onward. This interpretation is evident in the 1992 World Development Report (IBRD, 1992). Income is not however, normally distributed but very skewed, with much larger numbers of people below mean income per capita than above it. Therefore, it is median rather than mean income that is the relevant variable. Selden and Song (1994) and Stern et al. (1996) performed simulations that, assuming that the EKC relationship is valid, showed that global environmental degradation was set to rise for a long time to come. Figure 2 presents projected sulfur emissions using the EKC in Figure l and UN and World Bank forecasts of economic and population growth. Despite this and despite recent estimates that indicate higher or nonexistent turning points, the impression produced by the early studies in the policy, academic, and business communities seems slow to fade (e.g., Lomborg, 2001).Best studies proves growth causes pollution and environmental degradation Stern 4(DAVID I. STERN - energy and environmental economist with an interdisciplinary background in geography and economics, The Rise and Fall of the Environmental Kuznets Curve 2004, World Development Vol. 32, No. 8, pp. 14191439, http://steadystate.org/wp-content/uploads/Stern_KuznetsCurve.pdf)//hahaEconometric criticisms of the EKC fall into four main categories: heteroskedasticity, simultaneity, omitted variables bias, and cointegration issues. Stern et al. (1996) raised the issue of heteroskedasticity that may be important in the context of regressions of grouped data (see Maddala, 1977). Schmalensee et al. (1998) found that regression residuals from OLS were heteroskedastic with smaller residuals associated with countries with higher total GDP and population as predicted by Stern et al. (1996). Stern (2002) estimated a decomposition model using feasible GLS. Adjusting for heteroskedasticity in the estimation significantly improved the goodness of fit of globally aggregated fitted emissions to actual emissions. Cole et al. (1997) and Holtz-Eakin and Selden (1995) used Hausman tests for regressor exogeneity to directly address the simultaneity issue. They found no evidence of simultaneity. In any case, simultaneity bias is less serious in models involving integrated variables than in the traditional stationary econometric model (Perman & Stern, 2003). Coondoo and Dinda (2002) test for Granger Causality between CO2 emissions and income in various individual countries and regions. As the data are differenced to ensure stationarity, this test can only address short-run effects. The overall pattern that emerges is that causality runs from income to emissions or there is no significant relationship in developing countries, while in developed countries causality runs from emissions to income. This suggests that simultaneity is not important. Stern and Common (2001) use three lines of evidence to suggest that the EKC is an inadequate model and that estimates of the EKC in levels can suffer from significant omitted variables bias: (a) Differences between the parameters of the random-effects and fixed-effects models, tested using the Hausman test; (b) differences between the estimated coefficients in different subsamples, and (c) tests for serial correlation. Table 2 presents the key results from an EKC model estimated with data from 74 countries (in the World sample) over 1960- 90. For the non-OECD and World samples, the Hausman test shows a significant difference in the parameter estimates for the random-effects and fixed-effects model. This indicates that the regressors - the level and square of the logarithm of income per capita-are correlated with the country effects and time effects. As these effects model the mean effects of omitted variables that vary across countries or across time, this indicates that the regressors are likely correlated with omitted variables and the regression coefficients are biased.11 The OECD results pass this Hausman test but this result turned out to be very sensitive to the exact sample of countries included in the subsample. As expected, given the Hausman test results, the parameter estimates are dependent on the sample used, with the non-OECD estimates showing a turning point at extremely high- income levels and the OECD estimates a within sample turning point (Table 2). As mentioned above, these results exactly parallel those for developed and developing country samples of carbon emissions. The Chow F-test tests whether the two subsamples can be pooled, and therefore that there is a common regression parameter vector, a hypothesis that is rejected. The parameter p is the first order autoregressive coefficient of the regression residuals. This level of serial correlation indicates misspecification either in terms of omitted variables or missing dynamics. Harbaugh et al. (2002) carry out a sensitivity analysis of the original Grossman and Krueger (1995) results. They use an updated and larger version of the ambient pollution data set and test a number of alternative specifications. Using the new extended dataset with Grossman and Krueger's original cubic specification results in the coefficients changing sign and peak and trough levels altering wildly. Altering the specification in various ways-adding explanatory variables, using time dummies instead of a time trend, using logs, removing outliers, and averaging the observations across monitors in each country-changes the shape of the curve. The final experiment they carry out is to include only countries with GDP per capita above $8,000. In contrast to Stern and Common (2001), this results in a monotonic curve. The authors comment: This may seem counterintuitive. S03 concentrations in Canada and the United States have declined over time at ever decreasing rates the regressions include a linear time trend after detrending the data with the time function, pollution appears to increase as a function of GDP (p. 548). There are several differences between the Harbaugh et al. (2002) model and the Stern and Common (2001) model that may explain the different results obtained for high income countries. Harbaugh et al. (2002) use concentrations data, a linear time trend and a dynamic specification, while Stern and Common (2001) use emissions data, individual time dummies, and a static specification. Stern and Common's (2001) first difference results (Table 2) are very similar to the Harbaugh et al.'s (2002) results, which suggests that the dynamic specification could be important.
empirics
Their author concludes neg --- lack of structural correlation and empirical support Levinson 8(Arik Levinson professor of economics Georgetown University, Environmental Kuznets Curve The New Palgrave Dictionary of Economics, 2008, http://faculty.georgetown.edu/aml6/pdfs&zips/PalgraveEKC.pdf)//haha
Most researchers have been careful to avoid interpreting these reduced-form empirical correlations structurally, and to recognize that economic growth does not automatically cause environmental improvements. All of the studies omit country characteristics correlated with both income and pollution levels, the most important being environmental regulatory stringency. The EKC pattern does not provide evidence of market failures or efficient policies in rich or poor countries. Rather, there are multiple underlying mechanisms, some of which have begun to be modelled theoretically. Beyond this aggregate decomposition of the EKC, some attempts have been made to formalize structural models that lead to inverse-U-shaped pollution-income patterns. Many describe economies at some type of corner solution initially, where residents of poor countries are willing to trade environmental quality for income at a faster rate than possible using available technologies or resources. As the model economies become wealthier and their environments dirtier, eventually the marginal utility of income falls and the marginal disutility from pollution rises, to the point where people choose costly abatement mechanisms. After that point, the economies are at interior solutions, marginal abatement costs equal marginal rates of substitution between environmental quality and income, and pollution declines with income (see Stokey, 1998). In frameworks of this type, there is typically zero pollution abatement until some threshold income level is crossed, after which abatement begins and pollution starts declining with income. To date, the practical lessons from this theoretical literature are limited. Most of the models are designed to yield inverse-U-shaped pollution-income paths, and succeed using a variety of assumptions and mechanisms. Hence, any number of forces may be behind the empirical observation that pollution increases and then decreases with income. Moreover, that pattern cannot be interpreted causally, and is consistent with either efficient or inefficient growth paths. Perhaps the most important insight is in Grossman and Kruegers original paper: We find no evidence that economic growth does unavoidable harm to the natural habitat (1995, p. 370). Economists have long argued that environmental degradation is not an inevitable consequence of economic growth. The EKC literature provides empirical support for that claim.
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