The Impact of Rural Electrification on Deforestation and Soil Fertility
By
Andrew M. Tanner
MPP Essay
Submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Master of Public Policy
Presented November 16, 2015
Master of Public Policy essay of Andrew M. Tanner presented November 16, 2015
1 APPROVED
______
Dr. Alison Johnston, representing political science
______
Dr. Elizabeth Schroeder, representing economics
______Dr. Brent S. Steel, representing political science
2
Abstract
Deforestation and loss of soil fertility are two forms of environmental degradation with global importance. Theories of environmental degradation commonly cited in public and academic discourse have historically emphasized the role of human populations and national economic development as being the primary drivers of environmental damage. This thesis utilizes quantitative techniques and a dataset with global scope to assess evidence supporting a different hypothesis: that lack of access to basic electric services in rural areas is a key explanatory factor in assessing deforestation and soil fertility loss. This hypothesis is drawn from the intellectual tradition of political ecology, which emphasizes the material conditions faced by people constrained by exploitative political economic systems, siting the penultimate driver of environmental destruction under the purview of global systems of power, economic and cultural in nature. This thesis seeks to meld the insights of political ecology with contemporary research standards in comparative politics to identify an alternative to environmental policies, which focus on market expansion or population control as means to mitigate environmental degradation.
3 Table of Contents
Introduction...... 5
Conceptualizing Environmental Degradation: Deforestation and Soil Fertility Loss...... 6
Deforestation and Soil Fertility Loss: Drivers and Consequences...... 7
Competing Theoretical Explanations for Environmental Degradation...... 10
A Missing Link?: Political Ecology and Environmental Degradation...... 14
Rural Electrification as a Proxy for Human-Needs Focused Rural Development...... 18
Methods...... 20
Analytical Results...... 24
Discussion...... 33
Policy Implications and Conclusion...... 35
Works Cited...... 48
Appendix...... 42
4 Introduction
Contemporary explanations for environmental degradation tend to emphasize one of two explanations - either the problem is rooted in overpopulation, which causes increased resource demand in order to sustain populations, or it is due to the natural progress of economic development, which requires increasing environmental use until populations become sufficiently wealthy that they can afford to pay the high costs of environmental protection. Even explanations that accept multi-causal processes such as the popular "IPAT" formulation (Impact =
Population + Affluence + Technology) accept overpopulation and economic development as being at the core of environmental degradation.
However, since the 1980s an increasing number of scholars have contributed to the burgeoning field of political ecology, an interdisciplinary area which has sought to counter over-simplified causal explanations of environmental degradation that fail to address what political ecologists view as a crucial explanatory factor: the social and economic structural conditions that mediate local people's use of the environment in order to meet basic human needs. This thesis seeks to apply a broadly political ecological understanding to a quantitative dataset, controlling for oft-cited degradation factors of population and economic wealth to assess on a comparative basis these factors against one that broadly impacts local people's need for direct consumption of environmental resources
- rural electrification.
It is the intent of this work to examine the effect of rural electrification in determining levels of deforestation and soil fertility loss observed at a country level, utilizing a World Bank dataset with global coverage.
It will make the case that rural electrification can serve as a proxy for a mode of rural governance emphasizing people's access to basic public services, a particular political-economic relationship which reduces material demands on landscapes and decreases resistance to state policies promoting environmental protection. Governance is a key factor affecting environmental degradation as the implementation of policy measures often have material impacts on the natural world, directly or indirectly limiting - or enabling - exploitation of natural resources. By applying quantitative methods more commonly associated with evaluations of environmental degradation that emphasize economic and demographic effects, it is argued that local political-economic conditions mediate land user reliance on local landscapes and so are an important and under-examined causal factor in environmental degradation.
5 Conceptualizing Environmental Degradation: Deforestation and Soil Fertility Loss
The rapid uptake and spread of industrial manufacturing processes, beginning in 19th Century Europe and moving to encompass most of the rest of the world within the next hundred years, dramatically accelerated forest loss - to the point that upwards of 70% of original temperate zone forest cover was gone by the time the 21st
Century began (Goudie, 2006). Tropical forests now face the fastest rates of degradation and the bleakest future, with more than half anticipated to be gone by the mid 21st Century (Goudie, 2006; United Nations, 2005). The social welfare implications of this ongoing degradation are clear:
Deforestation is not only a serious threat to achieving
sustainability, but also to progress towards hunger and
poverty reduction and sustainable livelihoods, as forests
provide food, water, wood, fuel and other services used by
millions of the world’s poorest people." (United Nations, 2000)
Perhaps surprisingly, 'Deforestation' is not always a clearly defined concept, although the term is widely used (Brown & Zarin, 2013). When speaking at a regional national level, as is often done in the literature, there arises a problem that Brown and Zarin (Brown & Zarin, 2013) identify as being tied to notions of 'gross' deforestation or 'net' deforestation. Basically, this problem comes down to one of clustering observations into a statistic: if one counts all 'untouched' forest observations as forests, then any modification to these is 'deforestation' in a 'gross' sense. But if one also counts reforested or afforested areas as being forest as well, this then refers to deforestation in a 'net' sense. This distinction is important because most statistics available at a national level report net deforestation.(Brown & Zarin, 2013). This work by necessity adopts a 'net' deforestation definition - that is, if its total forest area decreases between years it experienced deforestation, and vice-versa - as using country-level data obfuscates dynamics within a country, leaving only the reported total change in forest area observed.
Soil fertility loss is a potentially devastating form of degradation, but harder to characterize than deforestation, given the effectively invisible nature of shifts in chemical constituents within soil. Unlike forest loss, which can be observed in a fairly straightforward manner via satellite based remote sensing, measuring soil fertility requires physical samples to be obtained and analyzed in multiple locations and, if one seeks to understand change in soil fertility, at multiple points in time as well (Benjaminson et al, 2010; Manlay et al, 2007; Poudel et al, 2002;
6 Zingore et al, 2011). An indirect measure of soil fertility loss can be made by examining the rate of application of inorganic fertilizers to cropland, as these are typically applied to either replace nutrients which have been lost over time or to enhance existing soils beyond their natural capacity for agricultural production (Taddese, 2001; Zingore et al, 2011).
Deforestation and Soil Fertility Loss: Drivers and Consequences
Deforestation causes have been widely examined in the literature, from a variety of perspectives. Economic drivers play a role in deforestation, and illegal logging has been identified as one of the biggest drivers of deforestation in the tropics (Burgess et al, 2012; Lynch et al, 2013). Legal logging is certainly driven by similar, if not the same, price and cost pressures (Celentano et al, 2011). The boom-bust pattern observed in frontier regions of the Amazon appears to follow economic logic fairly closely, with deforestation at the frontier improving the welfare of local people proximate to it, at least up to a point at which point further logging exhibits a negative effect on welfare (Celentano et al, 2011). International investment is also tied to deforestation, both through timber prices and the increasing demand for agricultural commodities, which promote deforestation in order to increase the stock of agricultural land available for productive use (Celentano et al, 2011; Angelsen, 2009). Agriculture is widely seen as a major contributor to deforestation, with land clearance in order to meet the global demand for commodities dominating the narrative in recent years (Ryan et al, 2012).
A strategy of creating forest preserves free from human use has been widely employed over time, but may itself promote deforestation. The 'preserves' strategy emphasizes the importance of forests in providing vital ecosystem services and identifies the threat to them as fundamentally anthropogenic. This strategy's intellectual roots emphasize the role of people in general, of the idea of inevitable pressures that human populations exert on resources, as the underlying culprit behind deforestation (Goudie, 2006; Ryan et al, 2012; Schaeffer and Rodrigues,
2005; Harris et al, 2012). An exclusionary policy is popular in much of the world, but less so in areas that are officially protected. The reason is fairly clear: more than half of these 'parks' are, in fact, home to people (Almudi &
Berkes, 2010; Rayn & Sutherland, 2011). And, as the UN makes clear in its Millenium Goals and Ecosystem
Assessment, people who live on or near forest lands very often rely on them to preserve their livelihoods.
Preservation efforts have historically stimulated serious resistance on the part of affected locals (Hecht, 2011).
Despite the danger of prosecution, locals in sufficiently desperate circumstances will defy such places' protected
7 status, and deforest them anyway (Pfaff et al, 2014). In examining the effectiveness of establishing forest reserves in
Brazil and Mexico, empirical works have found limited benefits to protection mandates (Almudi & Berkes, 2010;
Pfaff et al, 2014; Rayn & Sutherland, 2011). While certainly having some effect, the magnitude was not found to be as great as predicted.
The impacts of deforestation are increasingly felt at a global level. In terms of impact on climate, deforestation is one of the biggest contributors to emission of carbon dioxide and other greenhouse gases into the atmosphere (Goudie, 2006; United Nations, 2005). It is estimated that illegal logging alone may contribute up to
20% of the total anthropogenic greenhouse gas emission budget (Burgess et al, 2012), and between 1-2 petagrams of anthropogenic carbon totaling 7%-14% of total annual anthropogenic carbon emissions (Harris et al, 2012; Lynch et al, 2013). This contribution to the global problem of climate change has resulted in the widespread push for the reduction of emissions from deforestation in developing countries (REDD), which serves as a mechanism for channeling money from the developed world to the developing in hopes of promoting a development path that does not require the destruction of native forests, as it did in Europe.
Like deforestation, degradation of soils is widely cited as a major anthropogenic impact on the environment, but there is no single overarching cause that explains it. Unlike deforestation, however, observing and characterizing soil degradation presents serious difficulties (Zingore et al, 2011; Blaikie & Brookfield,1987), which are compounded by there being multiple dimensions of soil degradation which make it important to specify what, exactly, one is talking about when referring to it.
Two major themes emerge from the literature when seeking to define soil degradation's key components.
First, there is the physical presence of the soil itself - soil being the thin, topmost layer of the ground, under which, at some depth, the ground transitions to bedrock (Goudie, 2006). Ultimately, the soil represents the substrate of the biosphere, a source of physical space for plants to take root, anchoring life on Earth. Second, there is the constitution of the soil itself: what chemical elements are present that sustain the growth of plant life, and in what relative proportions?
Both the physical presence of soil and its fertility are threatened by degradation in much of the world, but the developing world is particularly vulnerable. Soil erosion involves the physical displacementof soil between locations, which often results in its deposition in places where it is of no good to living things. This can occur via
8 natural or human processes, or, as is typical, a combination of both. Given that soil takes millenia to generate
(Goudie, 2006) its loss is effectively permanent. In the worst case, erosion losses can become total, and in Ethiopia, for example, formerly productive landscapes have eroded all the way to bare rock(Taddese, 2001), likely as a combined result of human disturbance of natural soil structures via agriculture, and as a natural effect of rain and wind acting on the soil surface. Erosion is implicated in the process of desertification(Geist & Lambin, 2004).
The proximate cause of soil fertility loss is usually over-use of soils by humans trying to grow crops for their consumption or for the market. Economics clearly plays a role, as prices set for agricultural commodities and the costs associated with agricultural production interact to create pressures to intensify and specialize productive use of a given plot of land.
Agriculturalists have long understood that crop rotation can mitigate fertility loss even without added inorganic fertilizers because different plants absorb different nutrients at different rates, and their residues can leave behind enriched sources of nutrients which can be ploughed back into a field to preserve its fertility (Benjaminson et al, 2010; Sheldrick et al, 2003; Haileslassi et al, 2005; Tittonell et al, 2005; Zingore et al, 2011). However, competitive pressures can make healthy crop rotations too expensive to maintain (Poudel et al, 2002), forcing farmers to grow as many cash crops as they can. Such pressures seem to rise as population wealth increases
(Emadodin et al, 2012; Sheldrick et al, 2003), implicating economic growth in affecting farmer ability to practice healthy soil management.
The interplay between soil degradation and forest degradation must be mentioned. The need for access to more agricultural land is a notable driver of deforestation, and one reason more land is needed is the exhaustion of fertility in existing soils (Taddese, 2001). In turn, deforestation can unsettle local hydrological systems and biological habitat patterns, creating rebounding impacts on soils. The DPSIR model - integrating variables classed as
Drivers, Pressures, State, Impacts, Response - emphasizes the complex interactions between different ecological sub-systems (Emadodin et al, 2012). It is becoming increasingly clear that damaging soil fertility can cause feedbacks, spreading damage across the environment.
9 Competing Theoretical Explanations for Environmental Degradation
One of the oldest and most commonly cited causes of environmental degradation is overpopulation. The core logic behind this proposed causal relationship is simple: humans have needs, which can only be satisfied by placing demands on the environment. And, in a gross sense, this logic is irrefutable: there is a fixed limit to the amount of energy the human species can remove from the earth’s natural system (Pimentel, 2009). If, as Malthus argued, if populations grow faster than the systems of goods production that support them, inevitable conflict between supply and demand of resources results. Neo-malthusians such as Erlich (1970) and Garret Hardin (1968) tap this logic to argue that overpopulation is the greatest threat to the planet (Hardin, 1968; Koshland 1993; Alper,
1991; Pimentel, 2009).
Hardin's work has been highly influential, likely because Tragedy of the Commons unites both neo- malthusian thinking and ideas of economic rationality in an explanation for broad-scale environmental degradation.
His argument is that where private property institutions are lacking, unowned resources are exploited by individuals who seek to maximize their own resource extraction activities (Hardin, 1968). Without the bounds of property rights to decide who can take what without paying a price, all users of the commons over-utilize, ultimately degrading, exhausting, and destroying it, to the detriment of all (Hardin, 1968).
Although the logic behind Tragedy of the Commons was initially applied to explain why public goods, in the economic sense, are under-produced and over-exploited, underlying the concept is the argument that population increase is the fundamental driver of over-exploitation (Hardin, 1968). Indeed in a later piece for the journal
Science, Hardin further argued that overpopulation is effectively a disease upon a living planet, which must be treated to prevent the death of the organism (Hardin, 1971). He is far from alone in making similar arguments. In the
1990s neo-Malthusian arguments again were made by scholars, this time claiming to speak for environmentalists as a whole in arguing that overpopulation is the greatest threat to humanity (Alper, 1991; Koshland 1993), and that efforts to introduce affordable family planning are essential in mitigating the issue (Alper, 1991). Counter- arguments that public demand in markets is the real issue, and the efforts by corporations to stimulate consumption, were countered (Koshland 1993). And in recent years, predictions of global populations topping out at more than 9 billion by the mid 21st Century were coupled with fear that climate change would damage an already insufficient food production system (Pimentel, 2009).
10 Numerous issue-specific works that address environmental degradation specifically single out large and/or growing populations as a causal driver of both deforestation (Celentano et al, 2011; Tacconi, 2011, Goudie, 2006) and soil degradation/fertility loss (Emadodin et al, 2012; Sheldrick et al, 2003; Taddese, 2001). The impact of population is explicitly included in the IPAT model, which argues that impacts on the environment (I) are a function of Population (P), Affluence (A), and Technology (T) (Goudie, 2006). This formulation and its wide use in sustainability studies is indicative of the impact 'overpopulation' is widely thought to have on the environment.
If overpopulation arguments are accepted at face value, there are few options available to policy makers.
Family planning is one policy option explicitly considered (Alper, 1991), and options such as China's one-child policy have been tried, even if their effects are not immediately apparent (Pimentel, 2009). In truth, however, overpopulation arguments are generally presented as if they are undeniably factually correct, without offering explicit solutions. Hardin himself seems to offer no palliatives, save for humans being willing to give up the right to have children as part of a wider moral shift in society (Hardin, 1968). Given, however, that it is in developing countries that the majority of population growth is now observed, it is difficult to conceive how western academics can bring such a moral shift about. And can be easily observed that it predominantly has been white, affluent, western scholars who decry population growth that takes place in typically, non-white, poor, non-western contexts.
Another highly influential theory positing causal mechanisms for environmental degradation emerges from economics, in the form of the Environmental Kuznets Curve. The original Kuznets curve was conceptualized and demonstrated by Simon Kuznets in the 1950s as an explanation for inequality in developed societies (Chowdhury
& Moran, 2012). In later years there was an explosion of scholarly research positing that a similar Kuznets curve could be applied to explain environmental degradation over time (Boucekkine et al, 2012; Chowdhury & Moran,
2012). This followed from the observation that, in the experience of the most developed countries - located primarily in Europe, but with Canada, the United States, Australia, and New Zealand also included - environmental degradation increased with economic development, then reached a point where extensive effort was applied to reverse the damage caused, even as economies continued to grow. The Environmental Kuznets Curve literature posits a specific pattern for this phenomenon: as wealth increases in a country, so does demand - which can only be met by increased utilization of natural resources. However the externalities caused by this damage accumulate and begin to have serious impacts on productivity. At a certain point, it becomes economically rational to invest in
11 mitigation efforts, policies, and technologies that keep degradation under control and reverse it, leading to decreasing rates of degradation over time (Boucekkine et al, 2012; Choumert et al, 2013; Chowdhury & Moran,
2012; Culas, 2012; Angelsen, 2009; Tacconi, 2011).
The causal mechanism behind the Environmental Kuznets Curve is increased wealth, which first boosts demand and later accords populations the income flexibility needed to pay for mitigation (Chowdhury & Moran,
2012; Choumert et al, 2013). Significant attention is paid in empirical literature to the 'turning point' in terms of per capita income associated with the moment degradation begins to decrease (Chowdhury & Moran, 2012; Boucekkine et al, 2012; Culas, 2012). This is connected with potential thresholds where policy intervention may be necessary in order to mitigate damages in cases where a threshold of irreversible ecological damage may be crossed (Boucekkine et al, 2012), intervention that was termed by Culas as being akin to 'tunneling through' the otherwise probable curve in order to begin the shift to lower degradation rates earlier than might be otherwise observed in a purely lassez-faire regulatory system (Culas, 2012).
However, empirical results testing the Environmental Kuznets Curve concept have been mixed, with studies conducted in more recent years tending not to find evidence supporting its relevance (Chowdhury & Moran,
2012; Choumert et al, 2013; Boucekkine et al, 2012). Part of this is due to the many types of degradation that have been examined, which range from particulate emissions to physical degradation of forests and soils. Typically, the expected curve has been observed when dealing with certain kinds of pollutant emissions into the air or water, but not so much for broader, more complex forms of degradation such as deforestation (Choumert et al, 2013).
Choumert et al recently conducted a meta-analysis of many if not most pieces of empirical Environmental Kuznets
Curve literature dealing with deforestation published in the past 20 years (Choumert et al, 2013), and discovered that more recent studies do tend to lack support for the theory. Other works have found that, with respect to deforestation in particular, results are similarly mixed, with a tendency not to confirm the theory (Boucekkine et al, 2012;
Choumert et al, 2013; Chowdhury & Moran, 2012; Culas, 2012).
There appear to be significant caveats even in those studies which fail to find an Environmental Kuznets
Curve. First and foremost is the presence of regional effects - it appears that a curve may only be present if an empirical study is restricted to certain regions, particularly Asia (Culas, 2012). Model specification is also extremely important, with most models utilizing a quadratic relationship, but some looking at a cubic relationship, between
12 wealth and degradation (Choumert et al, 2013). The regression analysis employed can also affect results, with cross- section OLS models being less likely to find the expected inverted U shape, but panel models being more likely to find it - time-invariant factors appear to strongly influence findings with respect to deforestation (Choumert et al,
2013).
Despite the mixed empirical evidence, the Environmental Kuznets Curve is still a widely regarded as an explanation for environmental degradation with policy implications that have implicitly or explicitly driven applied efforts to combat destruction of the environment. The 1987 Brundtland report 'Our Common Future', a publication stemming from a United Nations Commission assembled to identify sustainable development pathways that take into account environmental protection objectives, popularized the perception of sustainability as ensuring that humans living presently do not undermine the ability of humans in the future to meet their needs, implicitly accepts the poverty = degradation hypothesis emerging from Kuznets Curve literature (Chowdhury & Moran, 2012;
Choumert et al, 2013). The REDD framework that has been under construction at the United Nations since 2005,
REDD being an acronym for 'Reducing Emissions from Deforestation and Forest Degradation, also emphasizes using redirected wealth to fight environmental degradation, effectively promoting paying for development that does not degrade the environment in developing countries in order to avoid the predicted degradation as a society moves towards the inflection point in the inverted U curve (Culas, 2012). Assertions that developed countries have simply exported pollution to developing countries have not managed to derail these efforts (Kearsley & Riddel, 2009).
Unlike Tragedy of the Commons, at least with respect to the supposed overpopulation crisis, the Environmental
Kuznets Curve presents policy makers with viable options.
Even though the neo-Classical approach embodied by the Environmental Kuznets Curve can inform policy, the sort of policies promoted remain constrained by the near-teleological argument of the Environmental Kuznets
Curve: given that degradation will increase until a high level of development is achieved, and development is inevitable or that current levels require environmental degradation, degradation is inevitable until all populations are fully developed. But given the findings by Rockstrom et al (2009), human civilization may not be able to sustain the degradation needed to develop the entire world. This presents a paradox, which may rectified by identifying an alternative theoretical approach offering alternative policy prescriptions.
13 A Missing Link?: Political Ecology and Environmental Degradation
Political ecology emerged in large part as a counter to the neo-Malthusian ideas expressed by Hardin and others in the 1970s, but eventually included critiques of prevailing economic arguments then and after, including those of the Environmental Kuznets Curve. Political ecology is widely considered to have as its origins in the work of Piers Blaikie (1985) (Robbins, 2004; Peet & Watts, 2004; Simon, 2008; Walker, 2006). This work was intended as a counter to then-dominant narratives explaining degradation of soils, particularly in sub-Saharan Africa and the
Himalayas, which emphasized the role of population, ineffective states, and insufficient access to markets in driving the problem (Blaikie, 1985). However, detailed field work across the spectrum of developing country contexts presented a complicated set of factors underpinning degradation. Both this work and his next key contribution, a collaboration with Harold Brookfield (Blaikie and Brookfield,1987) presented an alternative perspective on degradation - rooted in the idea that "the social relations of production under which land is used is a key and pervasive element in the explanation of soil erosion" (Blaikie, 1985).
Blaikie and Brookfield essentially argue that land managers - any individual using the land, primarily for the purposes of sustaining a livelihood - degrade not through ignorance, overpopulation, or lack of technology, but because of the political economic system in which they are embedded restricts their production options, controls their access to land, and systematically privileges certain groups in society in such a way that the majority of basic land users have little to no voice in matters of policy that determine their ability to sustain their livelihood (Blaikie,
1985; Blaikie & Brookfield, 1987). Degradation of what lands they can access ensues, because they are artificially forced to intensify use in the interest of basic economic survival, even when they are aware of the damage they are causing (Blaikie & Brookfield, 1987).
This initial push to ascertain the causes of land degradation from the perspective of local-level individuals was rapidly joined by other scholars, whose work over the next decade created the corpus of works now seen in
Political Ecology circles as foundational (Robbins, 2004; Peet & Watts, 2004). Hecht and Cockburn's "Fate of the
Forest" took a similar local-scale, almost anthropological approach to understand the roots of the destruction of the
Amazon, and determined that the social relations of production forced upon local people by the demands of Brazil's colonial then military governments were heavily implicated in the Amazon's destruction (Hecht & Cockburn, 1990).
Similar findings emerged from Nancy Peluso's examination of deforestation and forest access in Indonesia,
14 "Rich Forests, Poor People", which saw similar patterns of colonization, military government, and the struggle for access to livelihood resources as defining the land change patterns seen in Indonesia (Peluso, 1994). A similar approach, with broadly similar findings, emerged from Roderick Neumann's work "Imposing Wilderness", which examined the effects of establishing nature preserves in Africa, which also featured the effects of colonialism, but paid special attention to the role of people's imagining of nature in places perceived as 'wild' by westerners, which has often led to establishment of preserves intended to either exclude people, or force them into social relations of production that match externally held images of how they are supposed to interact with landscapes (Neumann,
1998).
This use of local-level, highly contextualized methodological approaches has also generated results demonstrating alternative relationships between local livelihoods and centers of power, with positive results. In post civil-war El Salvador, forests have been observed to be resurgent, in large part because of increased rural governance autonomy, land redistribution, and the emergence of collective institutions at the local level which have pushed for more sustainable modes of management (Hecht, 2004). In Himalayan India, the successes of the Chipko movement and granting of statehood - in federal India a key means of retaining local autonomy - of Uttaranchal
State, reconstructed state-local relationships to more closely connect governance with local needs, resulting in improved local land management (Rangan, 2004).
The emphasis on local cases and the effects of the 'constructivist turn' in the social sciences have, however, largely taken contemporary political ecology far from its roots as a policy-focused critical assessment of how land users respond to the pressures of power in sustaining their lives. Many constructivist scholars have critiqued classic works in Political Ecology as insufficiently addressing its 'political' component (Peet & Watts, 2004; Blaikie, 2011;
Robbins & Bishop, 2008, Walker, 2006). And the local-level focus inherent in studies inspired by anthropological methods have made it difficult to more broadly generalize findings in specific studies, which have trended towards being so interested in the production of local identities that broader contexts are ignored (Robbins & Bishop, 2008;
Simon, 2008). This path of intellectual development has led others to describe Political Ecology as insufficiently attentive to its roots in critiquing policy (Walker, 2007; Blaikie, 2008; Blaikie, 2011). Additionally, Political ecology is increasingly assuming a global view, and rather than focus on the developing world exclusively is exporting lessons learned and focus on material impacts of the social aspects of production to the developed world
15 (Schroeder et al, 2006). 'Classic' patterns of environmental degradation and constrained livelihood options have been observed in US Appalachia (Nesbitt & Weiner, 2001) and in the Exurban US West (Walker, 2003; Walker &
Fortmann, 2003). Recent debates have called for Political Ecology that directly engages policy makers and local governance by explaining environmental degradation's roots in local resource users being unable to secure their livelihoods in an increasingly globalized world.
One of the most significant challenges Political Ecology faces, however, in ensuring its relevance lies in reconciling its epistemological viewpoint with that of more broadly understood bodies of theory, such as those that have given rise to the Environmental Kuznets Curve and overpopulation. Although rooted in post-positivist thinking
- albeit with a heavy dose of critical theory derived from Marxist thought - Political Ecology tends to emphasize individual cases and constructivism. This makes the creation of broadly applicable general theory quite difficult, as noted by Robbins and Bishop (2008), and Political Ecology scholars appear common in their reluctance to make global-level theory backed by universalist arguments.
This work attempts to address the aforementioned global arguments for environmental degradation on their own terms: identifying omitted variables in statistical analysis of the determinants of environmental degradation.
Given the limited attention paid to local-level measures of livelihood in global datasets, it is necessary to look for an indicator of livelihoods despite its probable imperfection. The World Bank reported proportion of rural residents with access to electricity is a promising indicator, as it serves a measure of the level of basic services offered to what are most likely among the poorest, least well-represented people. In terms of the functional connection between rural electrification and environmental degradation, lack of access to electricity means needing alternative means of securing fuel for heat and lighting. The deforestation literature indicates that a significant driver is need for fuel, manure and charcoal, all of which can be obtained by forest clearance (Hecht, 2011; Pfaff et al, 2014; Tacconi,
2011).
This indicator is not without its problems. A logical connection exists between level of development and rural access to electricity, and it appears widely accepted that electric access underpins development (Gomez &
Silveira, 2010; Matsika et al, 2013; Narula et al, 2012). Controlling for the effect of increased development on rural electrification is necessary if rural electrification itself is to be an acceptable explanatory variable for environmental degradation. However, even given the probable connection, there are logical reasons to believe that rural
16 electrification is not predominantly an indicator of a country's development. Income disparities within even a more developed country may lead to markets failing to see a benefit in providing electricity to rural residents, and power disparities may lead to political systems failing to ensure services are provided. Relative poverty in rural areas is identified as a reason for continued lack of electric access, even in rapidly developing economies like Brazil, India, and South Africa (Gomez & Silveira, 2010; Matsika et al, 2013; Narula et al, 2012). By contrast, rapidly developing
China has achieved nearly 100% rural electrification, but not according to a typical neo-Classical model where markets expand as incomes rise: China has instead embarked on an explicit rural electrification policy backed by the state, and achieved almost complete success (Bhattacharyya & Ohiare, 2012). Given these authors' arguments, it seems reasonable to believe that while development does affect rural electrification, it does not fully explain it.
There is likely to be a reverse-causal relationship in cases, as improved rural electric access itself stimulates growth.
Political Ecology focuses on the artificial nature of resource scarcity forced on the most vulnerable by poor systems of governance and the unequal distribution of power in a society. It claims that population is not necessarily a negative driver of impacts on the environment - high populations have historically been sustained even on vulnerable landscapes, because of the phenomenon of 'landesque capital'. This is a form of human-constructed environmental degradation prevention systems that can be and often have been sustained for centuries because sufficient labor was available for the job (Blaikie & Brookfield, 1987). Political Ecology has traditionally emphasized that populations whose survival depends on their natural environment are often seen carefully managing it to ensure long-term sustainability, and will often incorporate important knowledge about proper management into long-standing cultural traditions (Zimmerer, 1997). Clearly there are situations where this fails - consider the cases of Easter Island and the Maya as noted by Jared Diamond (2005), but even early Political Ecology noted cases where populations had sustained the natural environment for millenia (Blaikie, 1985; Blaikie & Brookfield, 1987).
Political Ecology ultimately argues that most local land managers degrading vulnerable lands would choose a different option if systems of political and economic power did not constrain them (Blaikie, 1985, Blaikie
& Brookfield, 1987; Robbins, 2004; Peet & Watts, 2004). Even if wealth increases as a consequence of development, its concentration in the hands of the powerful interests who are typically better placed to take advantage of new markets will leave poor residents in largely the same situation as they were before - unable to pay for the accoutrements of development such as electrification, but now increasingly susceptible to global market
17 pressures which often force them to make painful decisions in order to survive - including destroying the natural world they historically have relied upon for their livelihoods (Blaikie, 1985, Blaikie & Brookfield, 1987; Robbins,
2004; Peet & Watts, 2004; Peluso, 1994; Hecht & Cockburn, 1990).
This is where neo-classical theorist derived policy prescriptions that emphasize development as a solution to environmental degradation fail: in a political-economic system riven by structural economic inequality, where land users are bound in "relational webs shot through with power" (Rocheleau & Roth, 2007), development in and of itself is no guarantee of mitigating environmental degradation. Directly meeting the basic needs of people inhabiting landscapes - and here rural areas are emphasized due to their spatial proximity to the natural environment
- is a more reliable means of addressing environmental degradation.
Rural Electrification as a Proxy for Human-Needs Focused Rural Development
Drawing on a political ecology emphasis on power relations provides a connection between theory and this data: given structural inequalities that privilege the interests of wealth accumulators over the livelihoods of typical rural resource users - who are assumed to be lower income and more reliant on physical labor productivity to meet basic needs - under capitalist political-economic conditions, increased national wealth will not automatically result in improved access to electricity. Only if responsive governance mechanisms are sufficiently robust that rural residents are able to successfully demand national investment in infrastructure development that benefits primarily rural areas, will improved electrification rates be observed.
This theoretical prediction is supported by a basic fact of most rural electrification, so implicit in the literature that it is not often directly stated: rural electricity provision is typically a result of mandates by government entities - not private markets (Narula et al, 2012; Bhattacharyya & Ohiare, 2012; Gomez & Silveira, 2010; Oda &
Tsujita, 2011). The root of this fact resides in the cost of expanding electricity infrastructure to universally or even partially bring rural communities onto the grid - costs of electrification rapidly increase with distance
(Bhattacharyya & Ohiare, 2012; Gomez & Silveira, 2010; Narula et al, 2012; Zahnd & Kimber, 2009), to the point that for some more distant communities, grid extension may form the bulk of costs, surpassing even generation costs. Given the well-established connection between rural life and increased poverty rates, it almost goes without saying that rural communities are often incapable of bearing the cost of grid extension without significant
18 government intervention.
The next-best solution for communities is typically installation of some sort of decentralized electricity generation system (Gomez & Silveira, 2010; Narula et al, 2012; Oda & Tsujita, 2011), but even this can be cost prohibitive for a typical rural community. The rapid increase in rural electrification observed in rapidly developing nations like Brazil, India, and China is not considered directly attributable to simple increases in per capita GDP, rather it is a result of direct government intervention to ensure rural electric access (Assuncai et al, 2014;
Bhattacharyya & Ohiare, 2012; Gomez & Silveira, 2010; Oda & Tsujita, 2011) - something that reflects the western experience with rural electrification as well, where grid connections are largely taken for granted even in most remote locations.
This is not to say that there is no wealth effect on rural electrification, given that a wealthy household in a rural area may pay for its own generator system, or that a wealthier country may be more willing to pay for universal rural electric access. However, there is equally no guarantee that increased national wealth will inevitably result in greater rural investment - if rural areas are politically marginalized, the state may have little incentive to pursue rural electrification investments. Additionally, though a connection is observable between increased wealth and increased rural electric access, a good number of scholars and development professionals would argue that rural electrification is itself a prerequisite to widespread economic development, rather than the reverse (Bhattacharyya & Ohiare, 2012;
Gomez & Silveira, 2010; Matsika et al, 2013; Oda & Tsujita,2011 ).
Aside from being connected to broadly conceived human well-being and economic development, rural electrification is a crucial component of environmental degradation. Even when lacking access to electricity, human populations still require access to some form of energy to use in household cooking, heating, and lighting - likely part of the reason it is an official UN Millenium Goal to provide universal electric access (Narula et al, 2012). And so it can be readily observed virtually wherever rural residents have insufficient access to electricity that they are forced to rely on environmental sources of fuel - all too often forest and agricultural sources, leading to deforestation and insufficient natural organic fertilizer supply (Cai & Jiang, 2010; Matsika et al, 2013; Zahnd & Kimber, 2009). It is important to note, however, that rural electrification can potentially itself be associated with environmental degradation, For example, farmers with access to cheap electricity tend to be more likely to irrigate their fields, which can contribute to soil fertility loss due to increased crop yields. In places where irrigation greatly improve
19 agricultural performance, farmers often have an incentive to cut down nearby forests in order to expand their cropping areas. The process of electrification itself may devastate large areas of existing farm and forest land due to the construction of dams. However, this thesis hypothesizes that the net direction of rural electrification effects on environmental degradation is negative - that is, increased rural electric access has a net reducing effect on degradation.
Which leads to the overarching hypothesis here derived from an application of political ecology insights to empirical case study evidence from major developing countries: environmental degradation is significantly connected to rates of rural electrification, which is at least as important as population and economic development in explaining deforestation and soil fertility loss. And this hypothesis is directly linked to a particular argument: given that rural electrification is largely a matter of government policy, a governance strategy for mitigating environmental degradation is the promotion of rural electrification in developing regions where 100% electric access is not guaranteed.
Methods
To assess this hypothesis, OLS multivariate regression was employed for a panel of 162 countries from the period 2002-20121. All data originated from the World Bank (2015). In order to estimate the significance and magnitude of the effect of rural populations having access to electricity on environmental degradation, the modeled effects of this indicator were assessed against indicators connected to the more typically utilized approaches to explaining environmental degradation as outlined above, specifically rural population.and national wealth. Rural population is considered, as opposed to total national population, because individuals in rural areas are the ones who must directly and materially engage in environmentally degrading activities in their physical location, and often by necessity as part of pursuing their livelihoods. Those living in urban areas are separated from the site of degradation by the intervening effects of distance and markets, and often have multiple sourcing options for material goods, so they are not inherently tied to environmental degradation in one particular region, complicating meaningful comparative country level analysis. The fraction of total population living in rural areas was used here as this eliminates differences in gross population size between countries, and serves as an indication of the relative degree
1 These are the countries for which World Bank data is available for the relevant variables. See Appendix.
20 to which environmental degradation tied to direct population use of the environment is important in each country, given that a high rural fraction of the population likely indicates increased rural reliance on local natural resources.
The basic model used for analysis was:
Degradation was operationalized using two dependent variables, deforestation rate and kilograms of fertilizer applied per hectare of farmed land. The latter was a proxy for soil fertility loss, used under the assumption that fertilizer is applied in accordance with underlying lack of fertility in soils. Deforestation was calculated by differencing the percent of total forested land area in each country across two sequential years, the reference year -
2002 and 2012 - and the year immediately prior: 2001 and 2011. Taking t1-t2 - so 2001-2002 and 2011-2012 - a rate of chance was obtained. This resulted in a measure of deforestation between the two years, with positive values
- occurring where t2
The primary independent variable of interest was the proportion of rural residents with access to electricity, as outlined above. However, for several models, a modified form of rural electrification was utilized. This alternative measure of rural electrification was the residual of an OLS regression where rural electrification rates serves as the dependent variable, run on per capita GDP and rural population proportion as the independent variables. The logic behind using a residual is that it removes movements in population growth and development from variation in rural electrification (in other words, the residual encompasses variables other than those controlled for that lead to changes in rural electrification). The standardized residuals for this model were calculated, and were used as a robustness check in further analysis, as a control for the significant correlations between rural electrification and environmental degradation
The competing hypotheses, rural population and economic development, are directly operationalized using
World Bank data for each country. Rural population is expressed by the proportion of the total national population living in rural areas, while economic development is represented by the measurement of per capita GDP. However, to use per capita GDP as a measure of wealth affected by the proposed Environmental Kuznets Curve, it was necessary to employ a non-linear term in the basic regression to capture the predicted non-linear relationship between degradation at high levels of wealth. A quadratic transformation of the basic per capita GDP value was employed.
21 To account for omitted variable bias, several further control variables needed to be incorporated into the model. These included: Land area in square kilometers, to control for the effect of country size and associated governance difficulties; Arable land in hectares per person, which controls for the relative scarcity of farmlands, an often cited factor in promoting deforestation, and; average annual precipitation in millimeters, which is an important control of basic agricultural productivity and forest cover.
In addition, regional effects were assessed by incorporating simple dummy variables signifying whether a particular observation (country) was located in a particular region. Six regions were created, along broadly continental lines: North America, Europe, Asia, Africa, Latin America, and Oceania. North America served as the standard baseline category against which others are assessed, except where the data is restricted to non-OECD nations, where non-OECD Europe serves as the baseline, due to both nations in the North America region being
OECD members. It should be noted that North America consisted only of the United States and Canada, in order to isolate these geographically and economically similar, very large countries, while Central America from Mexico to
Panama was grouped along with South America under 'Latin America'. Additionally, Oceania was coded to include the continent of Australia along New Zealand and the many islands and archipelagos scattered across the Pacific.
To validate the model, both cross section and panel OLS regressions were employed for each dependent variable of interest. The simple cross-section regressions utilized data from 2002 or 2012, ensuring no time-effects complicated analysis within individual cross-sections. Sub-models were assessed for each dependent variable in each year, allowing a comparison of results under different specifications. Sub-models investigated the baseline model as described above and a semi-log form involving natural-log transformation of all independent variables - but not the dependent variable, for which observations with negative and zero values would be dropped in a log transformation. Each of these three sub-models was further evaluated both including all observations in the sample, and with OECD nations excluded from the sample. This is done to mitigate the potential skew effect inherent in the more wealthy, developed economies, which tend to have 100% electrification rates and little to no deforestation, as well as more money to spend mitigating soil fertility loss without necessarily relying on fertilizers. OECD countries represent a sub-class of observations with common characteristics that may obscure trends inherent only in contemporary developing countries.
To ensure the model met additional OLS assumptions, HC3 Robust Standard Errors were utilized to
22 account for heteroskedasticity, VIF scores and pairwise correlation tables were generated and evaluated to account for multicollinearity, and the tactic of employing different functional forms was utilized to account for model specification. All metrics were standardized to ensure ease of evaluating relative magnitude of effects.
Time effects were accounted for in two ways: creating a panel by pooling the cross sections from each year, and in applying first-differences transformation. Panel regressions followed the same pattern as the cross-section regressions: sub-models were assessed and compared to account for OECD status - making non-OECD European countries the new baseline category for the regional dummy variables due to all members of the North American category being OECD members, eliminating this category entirely, natural-log transformations for independent variables, and a transformation of the rural electrification indicator to compensate for collinearity with GDP and rural population. All panels utilized GLS random effects, justified by the non-significance of a Hausman test comparing performance of models using fixed and random effects, with potential fixed effects further controlled for by use of the n-1 dummy variables, and near-significance of a Durbin-Watson test, indicating a degree of first degree serial correlation. A binary dummy variable for time was also included to control for time-dependent effects.
Heteroskedastic-robust standard errors were employed in all models2.
2 See Appendix for further model robustness checks, including sample jackknifing
23 Analytical Results
Table 1: 2002 cross sections for deforestation 2002 Deforest I II III IV X-Section OECD=yes OECD=yes OECD=no OECD=no raw rurelec. log transform raw rurelec. log transform Rural Electric β coeff -.320** -.101 -.313* .095 Access std.error .159 .062 .174 .066 fraction p-stat .046 .108 .075 .153 GDP Per Capita β coeff .106 .063 -.250 .074 ($ 2005) std.error .295 .081 .688 .092 p-stat .718 .440 .717 .421 Kuznets Effects β coeff -.027 N/I .366 N/I (Gdp/cap)^2 std.error .248 .670 p-stat .912 .586 Rural Population β coeff .115 .218 .112 .338 fraction std.error .116 .196 .117 .272 p-stat .326 .270 .339 .217 Arable Land β coeff .112 .106 .094 .092 hectares per person std.error .071 .092 .114 .108 p-stat .116 .250 .411 .394 National Land Area β coeff .009 -.006 .016 -.001 kilometers^2 std.error .109 .045 .134 .053 p-stat .930 .889 .903 .976 Precipitation β coeff .158** .179** .164* .157 millimeters per annum std.error .080 .085 .089 .102 p-stat .051 .038 .067 .127 Europe β coeff -.034 -.289 N/I N/I dummy std.error .422 .214 p-stat .935 .179 Asia β coeff .429 .542 .560 .998*** dummy std.error .461 .297 .370 .376 p-stat .353 .070* .133 .009 Latin America β coeff .611 .605* .763* 1.10** dummy std.error .546 .344 .453 .431 p-stat .265 .081 .095 .012 Africa β coeff .214* .415 .335 .830* dummy std.error .518 .308 .447 .369 p-stat .068 .181 .456 .026 Oceania β coeff -.488 -.239 -.559 -.009 dummy std.error .498 .391 .529 .556 p-stat .329 .541 .293 .987 Constant -.297 -2.17 -.449 -3.10 N 162 161 128 127 R^2 .228 .205 .203 .181 a. *indicates p<.10, 90% significance; **indicates p<.05, 95% significance; ***indicates p<.01, 99% significance b. all values have been standardized or log transformed (models II and IV) c. dummy variables coded as 0/1 binaries, 0 indicating non-applicability d. in models I, II, III, regional dummies are relative to the United States and Canada e. in models IV, V, VI, regional dummies are relative to non-OECD Europe f. observations systematically excluded from all models include countries lacking data or where data is unreliable due to conflict during time period under examination g. observations excluded in non-OECD models include all countries who are members of the OECD h. in log-transformed models (II and IV) the Kuznets effect is omitted due to perfect collinearity
24 Table 2: 2012 cross sections for deforestation 2012 Deforest I II III IV X-Section OECD=yes OECD=yes OECD=no OECD=no raw rurelec. log transform raw rurelec. log transform Rural Electric β coeff -.298*** -.169** -.293** -.164* Access std.error .110 .077 .126 .085 fraction p-stat .007 .030 .022 .056 GDP Per Capita β coeff -.055 .015 -.366 .019 ($ 2005) std.error .222 .074 .575 .096 p-stat .805 .843 .526 .843 Kuznets Effects β coeff .075 N/I .396 N/I (Gdp/cap)^2 std.error .171 .713 p-stat .663 .580 Rural Population β coeff .086 .146 .080 .229 fraction std.error .084 .145 .087 .202 p-stat .309 .316 .359 .259 Arable Land β coeff .113 .070 .068 .035 hectares per person std.error .072 .077 .103 .089 p-stat .116 .364 .506 .690 National Land Area β coeff -.001 .005 .015 .028 kilometers^2 std.error .082 .040 .099 .047 p-stat .986 .892 .883 .557 Precipitation β coeff .137** .138* .134* .123 millimeters per annum std.error .069 .073 .077 .090 p-stat .049 .060 .084 .173 Europe β coeff -.114 -.237 N/I N/I dummy std.error .337 .197 p-stat .736 .231 Asia β coeff .319 .412 .482** .731*** dummy std.error .373 .275 .235 .274 p-stat .394 .135 .042 .009 Latin America β coeff .521 .561** .739** .958*** dummy std.error .437 .283 .330 .307 p-stat .235 .050 .027 .002 Africa β coeff .030 .181 .190 .496** dummy std.error .408 .273 .288 .253 p-stat .942 .507 .511 .052 Oceania β coeff .183 .078 -.210 .294 dummy std.error .419 .347 .438 .436 p-stat .662 .822 .633 .502 Constant -.114 -1.04 -.308 -1.97 N 162 161 128 127 R^2 .224 .207 .195 .180 a. *indicates p<.10, 90% significance; **indicates p<.05, 95% significance; ***indicates p<.01, 99% significance b. all values have been standardized or log transformed (models II and IV) c. dummy variables coded as 0/1 binaries, 0 indicating non-applicability d. in models I, II, III, regional dummies are relative to the United States and Canada e. in models IV, V, VI, regional dummies are relative to non-OECD Europe f. observations systematically excluded from all models include countries lacking data or where data is unreliable due to conflict during time period under examination g. observations excluded in non-OECD models include all countries who are members of the OECD h. in log-transformed models (II and IV) the Kuznets effect is omitted due to perfect collinearity
25 Table 3: Deforestation panels Deforest I II III IV Panel OECD=yes OECD=yes OECD=no OECD=no raw rurelec. log transform raw rurelec. log transform Rural Electric β coeff -.280*** -.085** -.277** -.079** Access std.error .104 .037 .116 .039 fraction p-stat .007 .021 .017 .042 GDP Per Capita β coeff .011 -.004 -.348 -.025 ($ 2005) std.error .214 .068 .403 .083 p-stat .959 .951 .387 .756 Kuznets Effects β coeff -.006 N/I .288 N/I (Gdp/cap)^2 std.error .158 .327 p-stat .966 .378 Rural Population β coeff .038 .054 .002 .050 fraction std.error .086 .136 .097 .193 p-stat .658 .692 .982 .797 Arable Land β coeff .093* .111* .070 .098 hectares per person std.error .051 .060 .078 .083 p-stat .069 .065 .367 .155 National Land Area β coeff .003 -.008 .006 .004 kilometers^2 std.error .068 .034 .077 .040 p-stat .960 .817 .934 .914 Precipitation β coeff .165*** .189*** .169** .188** millimeters per std.error .064 .070 .070 .193 annum p-stat .010 .007 .016 .023 Time β coeff .067* .061* .076* .061 dummy std.error .036 .037 .044 .046 p-stat .062 .094 .085 .191 Europe β coeff -.137 -.279* N/I N/I dummy std.error .263 .165 p-stat .602 .091 Asia β coeff .310 .480** .570** .904*** dummy std.error .306 .216 .265 .295 p-stat .311 .026 .032 .00 Latin America β coeff .449 .521* .710** .968*** dummy std.error .385 .288 .361 .358 p-stat .243 .071 .049 .007 Africa β coeff .121 .371 .356 .780*** dummy std.error .338 .236 .306 .272 p-stat .721 .115 .245 .004 Oceania β coeff -.343 -.000 -.245 .374 dummy std.error .328 .276 .350 .357 p-stat .721 1.00 .484 .294 Constant .291 -1.11 -.006 -1.54 N 324 322 256 254 R^2 .221 .198 .191 .166 chi^2 62.48 62.78 45.25 45.11 a. *indicates p<.10, 90% significance; **indicates p<.05, 95% significance; ***indicates p<.01, 99% significance b. all values have been standardized or log transformed (models II and IV) c. dummy variables coded as 0/1 binaries, 0 indicating non-applicability d. in models I, II, III, regional dummies are relative to the United States and Canada e. in models IV, V, VI, regional dummies are relative to non-OECD Europe f. observations systematically excluded from all models include countries lacking data or where data is unreliable due to conflict during time period under examination g. observations excluded in non-OECD models include all countries who are members of the OECD h. in log-transformed models (II and IV) the Kuznets effect is omitted due to perfect collinearity
26 Table 4: 2002 cross sections for fertilizer use 2002 Fertilizer I II III IV X-Section OECD=yes OECD=yes OECD=no OECD=no raw rurelec. log transform raw rurelec. log transform Rural Electric β coeff .037 -.020 -.050 -.011 Access std.error .112 .049 .154 .056 fraction p-stat .743 .690 .748 .839 GDP Per Capita β coeff .417 -.031 1.99 -.103 ($ 2005) std.error .390 .071 1.42 .134 p-stat .287 .664 .166 .445 Kuznets Effects β coeff -.355 N\I -2.26 N/I (Gdp/cap)^2 std.error .430 1.69 p-stat .410 .185 Rural Population β coeff -.163 -.179 -.112 -.245 fraction std.error .139 .161 .124 .253 p-stat .243 .269 .368 .337 Arable Land β coeff -.136* -.340* -.118 -.394 hectares per person std.error .069 .198 .089 .247 p-stat .052 .088 .190 .115 National Land Area β coeff -.028 -.046 -.044 -.073 kilometers^2 std.error .073 .070 .078 .091 p-stat .701 .513 .574 .420 Precipitation β coeff -.066 -.205 -.119 -.212 millimeters per annum std.error .139 .224 .185 .220 p-stat .637 .362 .522 .339 Europe β coeff -.123 -.499 N/I N/I dummy std.error .397 .544 p-stat .757 .360 Asia β coeff .439 -.573 .588 -.147 dummy std.error .421 .649 .369 .253 p-stat .298 .379 .115 .563 Latin America β coeff .104 -.524 .193 -.078 dummy std.error .394 .528 .258 .175 p-stat .792 .323 .457 .659 Africa β coeff .240 -.636 .264 -.194 dummy std.error .388 .697 .202 .221 p-stat .538 .363 .196 .383 Oceania β coeff .539 -.198 .128 -.681 dummy std.error .454 .835 .251 1.16 p-stat .238 .812 .611 .560 Constant -.201 2.80 -.245 3.41 N 133 132 99 98 R^2 .176 .313 .281 .350 a. *indicates p<.10, 90% significance; **indicates p<.05, 95% significance; ***indicates p<.01, 99% significance b. all values have been standardized or log transformed (models II and IV) c. dummy variables coded as 0/1 binaries, 0 indicating non-applicability d. in models I, II, III, regional dummies are relative to the United States and Canada e. in models IV, V, VI, regional dummies are relative to non-OECD Europe f. observations systematically excluded from all models include countries lacking data or where data is unreliable due to conflict during time period under examination g. observations excluded in non-OECD models include all countries who are members of the OECD
27 Table 5 2012 cross sections for fertilizer use 2012 Fertilizer I II III IV X-Section OECD=yes OECD=yes OECD=no OECD=no raw rurelec. log transform raw rurelec. log transform Rural Electric β coeff .045 -.083 .209** -.123 Access std.error .092 .127 .085 .162 fraction p-stat .625 .514 .016 .447 GDP Per Capita β coeff .188 -.093 -1.65 -.058 ($ 2005) std.error .604 .133 1.20 .139 p-stat .757 .488 .175 .678 Kuznets Effects β coeff .227 N/I 4.17* N/I (Gdp/cap)^2 std.error .756 2.40 p-stat .765 .086 Rural Population β coeff -.128 -.647 -.071 -.931 fraction std.error .092 .669 .076 1.00 p-stat .169 .336 .349 .356 Arable Land β coeff -.128 -.210* -.061 -.169 hectares per person std.error .092 .116 .053 .140 p-stat .169 .072 .248 .232 National Land Area β coeff -.013 -.042 .038 -.050 kilometers^2 std.error .114 .075 .040 .093 p-stat .909 .581 .345 .590 Precipitation β coeff -.108 -.175 .064 -.073 millimeters per annum std.error .220 .187 .078 .116 p-stat .624 .352 .414 .531 Europe β coeff .163 -.237 N/I N/I dummy std.error .686 .312 p-stat .812 .449 Asia β coeff 1.15 -.127 .026 .106 dummy std.error 1.06 .323 .182 .301 p-stat .279 .695 .888 .725 Latin America β coeff .881 -.227 .041 -.238 dummy std.error 1.04 .347 .154 .310 p-stat .399 .514 .792 .445 Africa β coeff .873 -.284 .162 -.072 dummy std.error .877 .377 .132 .214 p-stat .322 .453 .221 .736 Oceania β coeff 1.02 .178 .059 -.071 dummy std.error .955 .543 .200 .678 p-stat .287 .744 .769 .916 Constant -.820 4.84 .619 5.15 N 141 140 107 106 R^2 .232 .301 .930 .364 a. *indicates p<.10, 90% significance; **indicates p<.05, 95% significance; ***indicates p<.01, 99% significance b. all values have been standardized or log transformed (models II and IV) c. dummy variables coded as 0/1 binaries, 0 indicating non-applicability d. in models I, II, III, regional dummies are relative to the United States and Canada e. in models IV, V, VI, regional dummies are relative to non-OECD Europe f. observations systematically excluded from all models include countries lacking data or where data is unreliable due to conflict during time period under examination g. observations excluded in non-OECD models include all countries who are members of the OECD
28 Table 6: Panels for fertilizer use Fertilizer I II III IV Panel OECD=yes OECD=yes OECD=no OECD=no raw rurelec. log transform raw rurelec. log transform Rural Electric β coeff .045 -.045 .110 -.061 Access std.error .065 .038 .082 .042 fraction p-stat .483 .233 .182 .141 GDP Per Capita β coeff .218 -.076 -.536 -.098 ($ 2005) std.error .223 .095 .772 .124 p-stat .326 .425 .488 .432 Kuznets Effects β coeff .030 N/I 1.91** N/I (Gdp/cap)^2 std.error .294 .929 p-stat .919 .040 Rural Population β coeff -.155* -.462 -.135 -.685 fraction std.error .083 .329 .083 .471 p-stat .061 .160 .104 .146 Arable Land β coeff -.136*** -.277*** -.104* -.273*** hectares per person std.error .046 .085 .057 .095 p-stat .003 .001 .067 .0004 National Land Area β coeff -.021 -.043 -.004 -.059 kilometers^2 std.error .044 .035 .026 .042 p-stat .629 .228 .884 .154 Precipitation β coeff -.086 -.181* .013 -.120 millimeters per annum std.error .113 .093 .098 .082 p-stat .447 .052 .897 .142 Time β coeff -.024 -.021 .016 -.004 dummy std.error .109 .107 .164 .147 p-stat .824 .846 .922 .976 Europe β coeff -.019 -.365* N/I N/I dummy std.error .267 .210 p-stat .942 .083 Asia β coeff .747** -.364 .253 -.031 dummy std.error .404 .274 .190 .171 p-stat .065 .183 .182 .855 Latin America β coeff .438 -.394 .059 -.191 dummy std.error .388 .269 .134 .217 p-stat .259 .144 .662 .380 Africa β coeff .504 -.467* .206* -.139 dummy std.error .339 .276 .108 .135 p-stat .137 .091 .057 .302 Oceania β coeff .782** .010 .122 -2.92 dummy std.error .370 .380 .106 .276 p-stat .035 .979 .249 .291 Constant -.448 4.03 .230 4.60 N 274 272 206 204 R^2 .180 .286 .399 .323 Wald chi^2 30.67 38.31 100.93 26.61 a. *indicates p<.10, 90% significance; **indicates p<.05, 95% significance; ***indicates p<.01, 99% significance b. all values have been standardized or log transformed (models II and IV) c. dummy variables coded as 0/1 binaries, 0 indicating non-applicability d. in models I, II, III, regional dummies are relative to the United States and Canada e. in models IV, V, VI, regional dummies are relative to non-OECD Europe f. observations systematically excluded from all models include countries lacking data or where data is unreliable due to conflict during time period under examination g. observations excluded in non-OECD models include all countries who are members of the OECD h. in log-transformed models (II and IV) the Kuznets effect is omitted due to perfect collinearity
29 Results indicate robustness to the various model specifications applied, however, the independent variable having the predominant impact on the dependent variable of interest differs depending on the dependent variable's identity, that is, whether it is deforestation or fertilizer application that is being explained.
The hypothesis that rural electrification has a significant and negative impact on deforestation rates - negative here in the mathematical sense of its increase being associated with reducing deforestation - is borne out by the results of the cross section and panel models. In the individual year cross-sections for 2002 and 2012, rural electrification remains significant across all model specifications, save for log-transformed models in 2002. In these cross-section models, the other consistently significant variables are precipitation levels, which are positively associated with deforestation, and rural population fraction of general population, which is also positively associated with deforestation in some models. In the sample restricted (non-OECD) models, a country being in Asia, Latin
America, or Africa is significantly and positively associated with observed deforestation compared to non-OECD
European countries. However, in the panel models, rural population ceases to remain a significant predictor of deforestation (the indicate lacks significance in all but one model), with precipitation and location in Asia or Latin
America remaining significantly and positively associated with deforestation.
Given the significance of virtually every model variation addressing deforestation, it becomes necessary to argue for which should be considered the 'true' model. The most likely 'true' model bearing the most significant impact for future policy options should be considered to be the panel model that excludes OECD countries from the sample. This is due to the unique circumstances facing less-developed countries, which are structurally encouraged by the global market system to emphasize exploitation of local natural resources in pursuit of national comparative advantage. In addition, practically speaking, less-developed countries are also unique relative to wealthier OECD countries in that they typically have not achieved full electrification within their jurisdictions. In 2002, for example, the average rural electrification rate in OECD countries was over 99%, compared to just under 53% in non-OECD countries. Non-OECD countries had only, by 2012, reached an average rural electrification rate of just over 61%.
This indicates it is in the less wealthy parts of the world where a rural electrification strategy to mitigate deforestation can have a potentially significant impact.
Restricting the sample to the 128 non-OECD countries for which there is reliable data, a .277 standard deviation reduction in deforestation rates is observed for a one standard deviation increase in rural electrification
30 rates. Given that the average non-OECD deforestation rate is .087% of total national land area per year as of 2012, with a standard deviation of .275, and given that one standard deviation in terms of rural electric access in non-
OECD countries is roughly the difference between average rural electrification rates around the world and full electrification - the norm in OECD countries - these results indicate that successfully achieving full electrification in rural areas in the non-OECD world can be predicted to reduce average deforestation rates by .079% of average total national land area per year, a 90% reduction in the deforestation rate observed in 2012 in the non-OECD world.
However, it must be noted that there was a .011% reduction in average total national land area per year lost to deforestation from 2002 to 2012 in those countries experiencing deforestation rates greater than 0, concurrent with a 7% increase in rural electrification rates in the non-OECD world countries with deforestation rates greater than 0 during the same decade. The results above would predict that this approximately .184 standard deviation improvement in rural electrification rates would result in a .051 standard deviation reduction in deforestation, which translates to a predicted .013% of land area per year reduction in the affected countries. This discrepancy indicates that caution should be exercised when applying the model results to the real world.
In terms of its effect on fertilizer application, assuming this indicator is a sufficient proxy for degradation in terms of soil fertility, rural electrification rates appear to have no reliable statistical impact, regardless of model specification. Only in 2012 cross sectional models IV and VI, sample restricted to non-OECD countries, does rural electrification show as significantly associated with fertilizer application, and there as a positive predictor. This fleeting effect disappears in the panel data, and given the suspiciously high R-squared of .93 in these models, there appears to be something external driving the association between 2012 rural electrification and fertilizer application.
In fact, the most reliable predictor of fertilizer application is the control of arable land per person, with a decidedly negative impact on fertilizer application rates. Second to it in terms of reliable significance across the cross-section and panel models is the rural population fraction and Kuznets curve effects, which given the collinearity between GDP per capita and its square term should be interpreted as meaning there is a significant connection between fertilizer application rates and GDP, with a quadratic relationship occurring at higher levels of
GDP per capita.
Of particular interest here is that both rural population fraction and GDP are negatively associated with fertilizer application - with the GDP effect reversing as wealth increases, representing an inverse Kuznets curve.
31 This indicates that countries with greater fractions of the population living in rural areas see less incidence of inorganic fertilizer application, and that increases in wealth contribute to decreased fertilizer use, up to a point, at which increases in GDP begin to exert a positive influence on fertilizer application. It does appear that the Kuznets effect only exists in non-OECD countries, perhaps indicating that once a country becomes sufficiently wealthy that fertilizer application is no longer a function of wealth.
It is important to note that, in accordance with the methods laid out above, additional models utilizing first- difference regressions were employed exactly as in the cross sections and panels above. However, tables have been omitted as in no case was any variable in any model statistically significant at even a 90% level. This may mean that, despite the results reported in full here, time-invariant factors are strongly significant and exhibit collinearity with the indicators used in this work.
There are other factors which may be influencing these results. With particular respect to fertilizer application, this indicator may simply be an insufficient proxy for degradation in soil fertility. Unlike deforestation, which can be fairly readily observed using aerial and satellite remote sensing technologies, soil fertility requires intensive sampling and analysis. And also unlike deforestation, which involves a fairly clear physical transformation
- trees to no trees - fertilizer application does not necessarily involve a change from no fertility to fertility, and may occur out of a desire by a land user to enhance existing fertility rather than replace fertility which has been lost. In addition, unlike in the deforestation case, there is no direct substitution effect possible - electricity itself cannot physically impact soil fertility, but it can replace heating, cooking, and lighting which can be provided by either electric appliances or direct use of biomass from trees.
Finally, the use of a country-level unit of analysis is not ideal for land change variables given the spatial heterogeneity within many, if not most, countries. In many countries, reforestation or managed plantations which increase forest area in some jurisdictions offsets deforestation of native forests in other areas, so that while overall deforestation rates are suppressed in a national metric, serious deforestation is actually occurring in ecologically significant areas.
32 Discussion
In terms of the specific hypothesis put forth above, that rural electrification rates have a significant impact on deforestation rates, the above analysis indicates that a strong argument can be made that rural electrification does indeed offer a means of mitigating deforestation, particularly in developing countries who have not adequately electrified their rural districts.
This accords with the experience of several countries whose recent emphasis on rural electrification has been observed to exhibit positive impacts on rural use of forest lands as sources of fuel. China has publicly pursued a policy of 100% rural electrification, achieving its goal by the 2010s, and has observed a marked decrease in rural people's need for forest fuels (Bhattacharyya & Ohiare, 2012; Cai & Jiang, 2012). India has likewise pursued rural electrification, albeit less fully, (Narula et al, 2012; Oda & Tsujita, 2011), and has itself maintained negative deforestation rates throughout the 2000s to the present. In Nepal, a small Himalayan state which has seen a dramatic improvement in rural electrification, the material benefit of reduced reliance on forests for fuel have been accompanied by improvements in health and education (Zahnd & Kimber, 2009). Studies of Brazil's rural electrification efforts and simultaneous reductions in deforestation have additionally posited that rural electrification is strongly responsible for improved economic conditions, sparking a virtuous cycle of increased wealth, which further enabled electrification. (Assuncai et al, 2014; Gomez & Silveira, 2010)
This analysis undermines the Malthusian arguments that tie environmental degradation to population pressures as well as Development arguments which claim a connection between development and environmental degradation, at least with respect to deforestation. There does indeed appear to be a signal, albeit faint, associating rural populations with deforestation. However, in the panel models above this signal is only statistically significant when OECD countries, which tend to be fairly well-developed and urbanized, are included in the sample. This may indicate that a few highly-developed countries are driving the observed effect. In no case does wealth appear to have a significant impact on deforestation rates, a surprising result but one that remains robust even when the potential collinearity between rural electrification and GDP per capita is removed.
Simply examining the raw data, one can see examples of the opposite effect as that argued by the
Malthusian Tragedy of the Commons narrative: the Phillippines, Macedonia, Armenia, and Sri Lanka all experienced decreases in deforestation rates despite only slight improvements in wealth and significant increases in
33 their rural populations, but while simultaneously improving rural electric access between 2002 and 2012. As mentioned above, China and India have also sought to improve rural electric access, and are observing increasing domestic forest coverage despite being home to approximately 1/3 of the global population. Brazil, home to much of the Amazon, and a focal point in global efforts to reduce deforestation, has seen its rate of forest loss decline dramatically from the 1990s, when predictions of the Amazon's complete destruction were repeatedly made (Hecht,
20). Path dependency may play a role here, as there are diminishing returns associated with exploiting a given forest, so that once a forest is reduced in size, there is less physical material to cut in a given future year.However, significant tracts of Brazilian forest remain and its continued positive rates of deforestation indicate that potential supply has not been exhausted, so the path dependency may not play a significant role as yet. In all these cases, improving the access to what is now broadly considered a basic human need has correlated with a reduction in forest loss, as predicted by the model and significant portions of the literature. This demonstrates the capability of political ecology to inform policy analysis, and also the utility of quantitative measures to improve results of political ecology-oriented works.
It therefore seems reasonable to argue that policy efforts to mitigate deforestation should consider investing time and effort in to further promoting rural electrification efforts. Given that approximately 30% of rural populations, the people who tend to be proximate to and reliant upon forest resources, lack access to electricity - and in all likelihood, many more lack reliable electric access - there is a great deal of potential for mitigating deforestation via electrification of rural areas.
The results do not support the hypothesis that rural electrification is associated with fertilizer application.
What evidence of association that does exist is suspect given the relevant models' apparent over-fitting problem, and even if legitimate indicate the reverse effect from that predicted - that is, rural electrification is in 2012 associated with increased fertilizer use. This could well be connected to observations that rural electrification can enable increased irrigated agriculture, which may require fertilizer investments to bring to full productive fruition. In addition, soil and electricity are not directly substitutable for one another, while forests and electricity are, to a degree.
The connection between arable land availability and fertilizer use appears reasonable on its face Increased availability of arable land likely reduces the need for inorganic fertilizers, as supply of food is sufficient to meet the
34 needs of a country's people. This accords with Malthusian arguments to a degree, but not in any compelling way: neither competing theoretical approach motivating this study would suggest that if there is no shortage of food - at least in some context - farmers would still apply fertilizer or, frankly, degrade their soils in the first place. However, the association between increased rural population and decreased fertilizer use is quite antithetical to a strictly
Malthusian view. One possibility here is that larger rural populations ensure a steady supply of labor for agricultural works, which can substitute for increased fertilizer inputs and mitigate soil degradation due to the creation of
"landesque capital" - an observation stemming from classic political ecology (Blaikie, 1985; Blaikie & Brookfield,
1987, Peet & Watts, 2004).
However, the inverse Kuznets curve, which appears to be significant for non-OECD countries, does offer an interesting result, one not easily predicted using typical Kuznets arguments. There appears to be a tendency for increased wealth to militate against fertilizer application, at least until a certain degree of national wealth is reached.
Thus, economic growth does not seem to necessitate environmental degradation, at least of soils. The change in sign of the GDP per capita and fertilizer use relationship may in fact be better predicted by referring to some of the classic literature in political ecology. Zimmerer's work in the Andes indicates that class strongly impacts demand for types of food, with wealthy people often purchasing imported food, which would mitigate the need for local agricultural productivity (Zimmerer, 1997). However, as a country becomes more integrated in the global economic system, ensuring continued wealth generation will tend to subject local agricultural lands to global market discipline, forcing exploitation of soils in order to produce surplus for export.
Policy Implications and Conclusion
This work largely confirms a hypothesis regarding systems with human and natural components: complexity is guaranteed. Single explanations for any form of environmental degradation are unlikely to be satisfactory, regardless of the theoretical perspective employed.
However, this work has demonstrated that insights derived from political ecology are at least as relevant for environmental degradation as those emerging from neo-classical economics and neo-malthusian population concerns. While environmental degradation may be complex, a key driver is the political-economic status and local- governance involvement of those living proximate to the natural world: residents of rural areas. Their relationship
35 with their environment is not one of pure exploitation, rather it is contingent on satisfying basic needs of life. Simply controlling rural population in terms of its size, or pursuing national economic development without ensuring that benefits accrue to all members of the population - these are insufficient policy prescriptions for mitigating environmental destruction. It is necessary to understand what specific factors cause local-level over-utilization of the proximate natural world, and then seek to address them. The causes and remedies will not be the same from place to place, or across time. This work offers further support to a view that governance which directly addresses local material needs has a role to play in mitigating environmental degradation. Meeting one of these needs, energy, is associated with mitigating deforestation, and recent evidence of successful efforts by China, India, and Brazil in particular to accomplish this is very likely connected to reduction in deforestation rates in each country. Although direct evidence for policy efforts which can mitigate soil degradation are lacking in this work, the same logic ought to hold: if local populations are observed to be increasing agricultural output by exhausting soils, understanding the material connection between this behavior and the results pursued by land users will shed light on policy prescriptions which may mitigate the consequences. And although this cannot be easily assessed quantitatively, it seems reasonable to extend this logic to argue that successful policy interventions which mitigate degradation while helping people meet their needs are likely to have a strong legitimizing effect on further policy efforts in the area.
This can potentially offer a path forward in situations where governance has been previously characterized by local resistance to policy, such as in cases where environmental preserves have been created, yet are unsuccessful in their objective.
Deforestation and soil fertility loss have material effects that manifest across spatial levels. It is now broadly accepted that human impacts on the biosphere constitute a threat to its long-term survival. Innovative policy is necessary to arrest and reverse these impacts before they further undermine the biosphere's capacity to support human civilization. However, the roots of these impacts are highly complex and vary in nature from place to place.
A net effect of this complexity and variability is to render them less than amenable to one-size-fits-all policy prescriptions. Nuanced understanding of human-environmental impacts are needed, allowing for identification of general principles of mitigation, aiding development of contextualized policy.
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41 Appendix
1) Although the World Bank nominally provides data for all countries and territories in the world, in reality many do not report values for all indicators, are too small to be included as independent observations, report extreme values, or undergo changes in national borders and sovereignty which undermine the reliability of results. Therefore, the following countries for which the World Bank nominally reports data were eliminated from consideration in this work:
Aruba, Andorra, American Samoa, Bahrain, Bermuda, Channel Islands, Curacao, Cayman Islands, Faroe Islands, Greenland, Guam, Hong Kong, Iraq, Isle of Man, Jamaica, Kosovo, Lichtenstein, St. Martin, Macao, Monaco, Montenegro, Myanmar, Mariana Islands, Marshall Islands, Palau, French Polynesia, North Korea, Qatar, San Marino, Somalia, Turks and Caicos Islands, Singapore, Serbia, Sudan/South Sudan, Syria, Tuvalu, United Arab Emirates, and Virgin Islands
2) Results for the deforestation data remain robust when the ten to twelve countries exhibiting the largest changes in forest area in 2012, whether positive (deforestation) or negative (reforestation), are removed. This indicates model robustness even without the presence of the deforestation observed in Brazil and Indonesia, which together account for around 1/3 of total global deforestation.
However, jackknifing the data by region reveals that the countries of Asia are driving much of the relationship between rural electrification and deforestation. When all of Asia is excluded from the sample, rural electric access becomes non-significant. This may mean that something about Asia, which is classified as extending from the Mediterranean in the west to the Pacific in the East, and excluding Russia, is special when it comes to the effects of rural electrification on deforestation.
However, it is difficult to be certain if this is not due to the sample becoming unduly biased as a result of removing fully 1/3 of the non-OECD nations. Further investigation is warranted.
An additional model was estimated in an attempt to mitigate interrelationships between rural electrification, rural population, and GDP per capita, which regressed the latter onto rural electrification, then used the resultant residuals to replace the raw value for rural electrification rates. The significance of rural electric access in these models, for deforestation, remained robust to this altered expression of rural electric access.
Finally, an attempt to account for potential path dependency by including a t-1 lagged indicator, the deforestation rate as of 1993, resulted in a significant and positive correlation between deforestation rates in the past and in the present, but little to no change in the significance or magnitude of the effect of rural electric access.
42