Forest Preferences & Urbanization Perspective from four Sacred Groves in India’s National Capital Region
By David S. Grace
Dr. Dean Urban, Advisor Dr. Marc Jeuland, Advisor
Masters project submitted in partial fulfillment of the requirements for the Masters of Environmental Management degree in the Nicholas School of the Environment Duke University May 2017
Executive Summary Within its institutional setting, the sacred grove is understood as the forested abode of a deity or multiple deities. However, the relationship between the forest and its divine host/s is expressed in many ways, often in ways which seem incompatible to ‘rational-legal’ discourse and policy. Yet, sacred groves are not confined to the most remote geographies or the most mythic theologies. In fact, the sacred grove is entangled in very ‘modern’ ways of thinking, and bureaucratic governance regimes, in places which are experiencing rapid urban growth and cultural change. Thus, the sacred grove is not easily defined, and its relationship to state protection is complicated. The megacity extending from Delhi, India is formally administered as the National Capital Territory, but is even more broadly conceived as the National Capital Region. The sacred groves in this context have become increasingly characterized by urbanization and related culture change. These conditions present rich theoretical opportunities to analyze changes in preferences among residents in the institutional setting of the sacred forest, empirically, and to interpret implications of any such changes in terms of demand for forest conservation as well as continued collective action potential. To accomplish this task, this present study offers a survey of residents (n=198) within four sacred grove sites in the NCR. This study analyzes the sacred grove as a socio-ecological system situated within a wider landscape consisting of non-sacred forest – with the exception of one site – as well as alternate worship sites outside of sacred forests – here called temple sites. These three site types are important markers in the landscape and are associated with differing types of values and are ascribed different magnitudes of importance. The survey approach seeks to characterize willingness to pay (WTP) for visits to these sites to better understand marginal preferences by site type and characteristic. Details are collected on respondent’s actual visits, and Revealed Preference models infer the characteristics of the visitors to each site type. In specifying these characteristics, this study seeks to determine the impact of important cultural evolution variables and urbanization on preference. Cultural evolution proxy variables utilized include education level, percent calories purchased, and primary worship of native deity. A measure of urbanization is provided by classification of ‘urban’ households based on land-use, land-cover classification as built-up environment. Cultural evolution is analyzed in this Indian context, through the theoretical perspective of Sanskritization, which has been proposed as the cause of institutional decline in the sacred grove. My study contributes to this literature by testing for a Sanskritization effect on forest preferences in a landscape perspective, while controlling for important socio-economic variables in addition to distance to sacred grove and travel cost to nearest sacred grove and temple sites. The Revealed Preference models provide evidence of use-values for the sites, describing actual visits, as well as a baseline for non-use values which may be present. However, Stated Preference survey methods are also utilized to better characterize the range of values present for these sites, by offering hypothetical visits which 1) isolate the sacred vs. non-sacred forest effect on preference, controlling for forest characteristics, and 2) derive relative preference for important forest characteristics – size, temple presence, natural or planted quality, extraction level, and distance from home – as well as marginal preferences among these characteristics by observing trade-offs between varying levels of these attributes. The first item is accomplished with a Contingent Valuation (CV) exercise, which first asks WTP questions for non-sacred forest and then offers the hypothetical choice of a sacred forest visit rather than a non-sacred forest visit, in terms of explicitly additional WTP. The second item is accomplished with a Discrete Choice Experiment (DCE) which offers a series of choice tasks to each respondent, who choose one of two forests described by the attributes above specified at varying levels arranged in a blocked fractional factorial design. This study finds greater WTP for sacred forest than non-sacred forest among residents of the setting of the institutional setting of the sacred grove, in both revealed and stated preference measures. However, a trade-off between sacred forest and temple site visits was observed among those of ‘higher’ urbanization and Sanskritization characteristics. Primary worship of a global deity rather than native deity is one predictor of temple preference, which accords with Sanskritization expectations. However, proximity to sacred grove, holding a professional or vocational degree, or living in an urban environment seem to be better predictors. Overall, relative to the other forest characteristics described, the temple appears more important for visit choice by a factor of two. In this marginal preference context, large forest size and zero extraction level appear to be traded off, suggesting some concern regarding the relation of temple preference and forest conservation demand. Yet, the implications of this temple preference may not be antagonistic to conservation motives. Perhaps unexpected to Sanskritization literature, temple preference seems to correspond with conservation-oriented forest preferences for non-sacred forests. These conservation preferences are suggested by a perception of non-sacred forests as more useful for ecosystem services among temple visitors. An increase in non-use values for non-sacred forest may also be evident in the observation of a greater willingness to pay an entrance fee for non-sacred forest by those who do not worship a native deity primarily. Taken together, these suggest a potential increase in perception of non-sacred forest value and a potential willingness to pay for its non-use ecosystem services. The development of these conservationist preferences for non-sacred forests may yield positive collective action results for community forests facing urbanization threats in which land-use is increasingly contested and alternate uses become feasible tradeoffs with the status quo landscape. Sanskritization, as a transfer from local to global deity worship within the tradition of Hinduism, accords with cultural evolution studies on the relation of complex societies and ‘big Gods.’ The enabling and constraining impacts of these changes for collective action within the institutional setting of the sacred grove are considered to lead to two options: 1). a wider cosmological and social world which creates the enabling conditions for multi-scale governance linkages or 2) a shift in the locus of significance from the forest to the temple, suggestive of a transition from commons to open-access land subject to degradation in the absence of enforcement external to the institutional setting of the sacred grove. This study finds evidence in both directions, and recommends further study of collective action in community forest settings sensitive to cultural evolution.
Table of Contents:
EXECUTIVE SUMMARY…………………………………………………………………………………………………………………I LIST OF ACRONYMS & TRANSLATIONS……………………………..…………………………………………………………II INTRODUCTION…………………………………………………………………………………………………………….……….1
BACKGROUND………………………………………………………………………………………………………………..3 STUDY AREA………………………………………………………………………………………………………………...14 METHODS……………………………………………………………………………………………………….…..……………...21
SURVEY DESIGN ….…………………………………………………………………………………………………….…21 SAMPLING DESIGN ………………………………………………………………………………………………………21 DATA COLLECTION………………………………………………...…………………………………………………….21 DATA EDITING……………………………………………………………………………………………………………...22 MODELLING TECHNIQUE………………………………………………………………………………………………26 Revealed Preferences……………………………………………………………………………………………..27 Stated Preferences…………………………………………….……………………………………………………29 o Contingent Valuation (CV)…………………………………………………………………………..29 o Discrete Choice Experiment (DCE)………….……………………………………………………31 RESULTS…………………………………………………………………………………………………………….….…………….34
Revealed Preferences……………………………………………………………………………………………..34 o Visits to Sacred Forest, Non-Sacred Forest, & Alternate Worship Sites Stated Preferences…………………………………………….……………………………………………………41 o Contingent Valuation (CV)…………………………………………………………………………..41 o Discrete Choice Experiment (DCE)…………….…………………………………………………47 DISCUSSION…………………………………………………………………………………………….……..……………………48 ACKNOWLEDGEMENTS…………………………………………………………………………………………………………….61 REFERENCES…………………………………………………………………………………………………………………………….62 APPENDICIES……………………………………………………………………………………………………………………………65
LIST OF ACRONYMS CV – Contingent Valuation DCE – Discrete Choice Experiment CAP – Collective Action Problem SP – Stated Preference RP – Revealed Preference WTP – Willingness to Pay SF – Sacred Forest NSF – Non-Sacred Forest WS – Temple Site, (Worship Site outside of Sacred Forest)
HINDI/REGIONAL TRANSLATIONS NCR- National Capital Region, includes Delhi, Faridabad, and Gurgaon in my study area and other areas. Perhaps is also inclusive of Hodal based railroad and commuter connection between Delhi and Hodal. Names of Sacred Forest under study Bani – common designator for sacred forest in the region. Chameli Van – Forest Flower Gummat Mandir – ‘Gummat’ Temple Jharna Mandir – Waterfall Temple
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INTRODUCTION URBANIZATION
Urbanization is correlated with cultural change, and possibly a driving factor in social change processes. The twin developments of a global urban majority population and the concentration of population growth in cities calls attention to these processes. While the global population was approximately 70-30 percent rural-to-urban in 1950, projections suggest a near reversal of this distribution by 2050, with a 34-66 percent rural-to-urban population (UN 2014, p.7). Global urbanization comes after the rapid development in the west since the industrial revolution followed by the offset, though not equivalent, growth in the ‘developing’ countries. Shifts in life expectancy, yielding an older aged population with lower fertility, the ‘demographic transition’ is also underway (Lee 2003). It is in the ‘developing’ country context where urban population growth will be concentrated in the future. India features prominently in this trend and is projected to become the most populous country within half a decade.
The urban agglomeration of Delhi, India, now administered as the National Capital Region (NCR), is the second most populous city in the world with recent population growth exceeding that of Sao Paulo, Tokyo, Mexico City, and New York City combined (UN 2014). Recent remote land-use, land-cover research in Delhi and Southwest Delhi in the period of 1977- 2015 shows clear conversions of cultivated area to built-up area and from dense forest to scrub and degraded forest (Jain et. al 2016). Within this geographic context of rapid change, forested areas that hold long-standing spiritual significance among forest-dependent communities – termed sacred groves – are confronted with novel cultural and ecological threats to their persistence. The plight of sacred groves amidst urbanization is a collective action problem and has not been adequately studied in terms of the cultural and ecological variables which have allowed the religious and ecological relationships of forest-dependent communities and sacred groves to persist. It is precisely this knowledge gap that I examine in my work.
The implicit conception of sacred groves in conservation discourse, the canonical model, has been critiqued as a falsely homogenous and idealistically ahistorical entity (Gajula 2007). Certainly heterogeneity is evident in beliefs related to the sacred grove, property ownership type, and management style. Nonetheless, I define the sacred grove as a socio-ecological system where religious valuation is coextensive with a geographically-bounded forest area. As coarsely 2 defined, the canonical sacred grove exists as a geographically-bounded forest located centrally within a mythic social geography. The sacred grove is located in a physical geography and social geography. Stated differently the sacred grove exists in a domain of being, with physical boundaries, in addition to domains of knowing and thinking with social boundaries.
While the social is informed by and mapped onto the physical, it is not a direct, linear relationship. The mind is not a tabula rasa, or blank slate, solely conditioned by sense perception of the physical environment. Rather, the social is conditioned by the historical, and the historical is conditioned by the narratives in which it is understood. These narratives develop as a consequence of cultural evolution with increasing social complexity (Mayer 2014 p.9). These operating conditions comprise the majority of cognition processes and occur before rationalization, which argues against a narrow view of the human species as primarily rational, utility maximizers and instead suggest a view of the human species as “efficient complexity manager” (Levine et. al. 2015). This is not to displace the rational actor theory, but to highlight the bulk of cognitive mechanisms obscured in its appropriation as the sole behavioral logic. This study argues that what is obscured in this narrow behavioral logic is actually of central importance in understanding how preferences are shaped in the long-run and therefore in prediction of how preferences will change with future cultural and ecological change. Within this perspective, rational actor theory becomes more useful for understanding collective action settings sensitive to cultural evolution.
Until very recently, collective action problems have been conceived primarily in the narrow terms of rational actor theory and, in an extended formulation, in an institutionalism which bounds these actors’ rationality within constraints of norms and values. The noteworthy work of Garrett Hardin’s Tragedy of the Commons, pointing toward collective action failure, can be seen as prompting investigation of ‘success stories’ of the commons, most notably in Elinor Ostrom’s Nobel Prize winning research. However, though the work of these authors is different in orientation, both belong to the domain of rational actor theory and institutionalism. This domain of study, however, does not provide an adequate understanding of the cognitive mechanisms driving behavior, as it is constrained by the behavioral logic ascribed in the underlying rational actor model. Chiefly, it does not address cognitive processes identified in cultural evolution theory. For this reason, it is particularly unable to address systems which are 3 defined primarily in their relation to cultural valuation, such as the sacred grove. It is arguable that this is a more general fault, as culture is basic to behavior and thus to collective action problems generally. Empirical data from highly controlled settings in economic games makes this suggestion (Heinrich 2000).
Therefore, a lurking problem in sacred grove conservation initiatives, which depend on cultural protection mechanisms, is the hallmark quality of culture: change, or, more precisely, cultural evolution. Does urbanization in the physical environment of sacred groves lead to ‘urbanization’ of cultural and psychological states, seen as an increase in relative temple-to- forest preference, decreased forest conservation preferences, or decreased forest worship mode choices? I address these questions in my study.
Main Questions: 1) Does forest conservation preference and worship mode choice vary with urbanization level? 2) Does forest conservation preference and worship mode choice depend on distance to sacred groves? 3) Does forest conservation preference depend on worship mode choice, forest type (i.e. sacred or non-sacred), or other factors indirectly related to urbanization?
BACKGROUND: This study attempts to isolate the effect of urbanization on land use from cognitive processes relating to land use preferences. It is important to analytically disaggregate physical and social geography in my study, as culture matters for both cognition and behavior. Culture has been demonstrated to impact spatial cognition (Haun et al. 2006). Additionally, Joseph Heinrich has led a series of cross cultural studies, demonstrating cultural variation of behaviors in economic games (Heinrich 2000; Heinrich et al 2005; Heinrich et al 2010). Using group-level averages of percent calories purchased, rather than grown, hunted, or gathered, Heinrich et al. (2005) found market integration and payoffs to cooperation to significantly explain much of the variation in ultimatum game offers. In a follow-up study, Heinrich et al. (2010) found market integration and participation in a world religion, as opposed to a tribal religion or no religion, to predict increased offer size in both dictator and ultimatum games, which is suggested as increased cooperation. 4
These economic games allow a highly controlled setting in which to analyze motivations for economic behavior, largely based on game theory. Dictator and Ultimatum games are two of the most common. A dictator game is typically played between two individuals and involves one individual starting with funds given to them – an endowment – and they are simply instructed that they can keep the money or they can choose how much they give to the other individual. That is the end of the game. Any offer by the dictator is a violation of the self-interested, utility- maximizing assumptions of rational actor theory, which predicts the dictator to give nothing. This violation has been found consistently across many populations. Additionally, offer size distributions have been found to vary with culture (Heinrich 2000). An ultimatum game is an extension of the dictator game, where the individual who receives the offer can choose whether to accept or reject the offer. In the choice to reject the offer, the individual forfeits their offer but also ‘punishes’ the offeror by cancelling their funds as well. Interpretation of behavior in these games is suggestive for behavioral logics in behavioral economics. The ultimatum game offers insight into ‘conditional cooperation’ where offers may be made on the basis of reciprocation or other concerns, since the giver is aware that their ultimatum can be rejected. The ultimatum game also provides insight into willingness to enforce ‘fairness’ norms, as the recipient can reject the ultimatum, punishing the offeror, but at the personal cost of losing the offered funds. Cooperation and motives other than simple self-interest, such as fairness norms, are suggested in these games, because in the first instance the ‘dictator’ can simply keep the money, without personal cost, and in the second, the ‘ultimatum’ can be rejected, punishment inflicted at personal expense of the opportunity cost of keeping the offered funds.
The study of Purzycki et al. 2016, utilizing economic game settings, advances this research in specifying the positive effect of belief in supernatural punishment from the God/s of the world religions, as opposed to local traditions, on cooperation between co-religionists, suggested to support a cultural evolution theory of expanding prosociality alongside increasing social complexity. This study finds that deities of the world religions are thought of as more knowledgeable and moralistic, with greater supernatural punishment, corresponding with greater offer sizes. This, in turn, is interpreted functionally as providing a panoptic means of promoting cooperation in the absence of face-to-face monitoring and enforcement possible in small group sizes. 5
These global, cross-cultural studies are helpful in charting a direction in cultural evolution theory. Chiefly, they empirically support aspects of the classical linear development theories of late 19th and early 20th C. anthropology and sociology after the implications of Social Darwinism made these theories taboo in their own fields. The tendency of understanding cultural evolution as a linear development process, often yielded an interpretation of Europeans as the pinnacle of development, but this arguably reflects the mobilization of theory in service of colonial interests. Further, an absence of long-range studies linking culture and environment, in terms of directional cultural evolution framing, has not stayed the persistence of such theories – explicit or implicit – in popular perception, academia, or in public policy application. Addressing this knowledge gap, empirical research in cultural evolution has suggested directionality in key factors of cultural evolution and proxies for their measurement, such as market integration, measured as percent calories purchased, and world religion, measured as local or global deity type worshipped.
From linear models to non-linear interpretations
Recent scholars have rejected a simple linear ascent of ‘development,’ but have also suggested the possibility of retaining empirical truths in cultural evolution theory while discarding its normative biases (Bellah 2011). Additionally, the non-western world, particularly Asia, is experiencing rapid industrial development alongside novel cultural changes, leading some commentators to suggest the possibility of ‘multiple modernities.’ The concept of modernity is proposed as a social result of structural physical changes, marked by industrialization, and the degree of western influence is a subject of some debate. Prasenjiit Duara’s The Crisis of Global Modernity outlines the migration of western historical imagination, particularly in a loss of authoritative sources of transcendence, to the rest of the world in the late 19th C. This western historical imagination, often recently theorized as one of secularization, has been linked to modernity, industrialization, and consumerism, and resultant ecological problems by contemporary scholars (Northcott 1996, p.105, Wirzba 2015, p.5).i However, the theory of secularization has also been nuanced. Duara provides the term ‘traffic’ as descriptor of the migration of religious ideas into ‘non-religious’ spheres with effects that are hard to predict (p.16). In this way, Duara highlights the possibility of sacred groves in a modern sphere as offering a re-enchantment of the commons, or a subversion of the tragedy of the commons, in his 6 identification of the “embryonic idea of treating commons as a new area of inviolability, sacredness and transcendence” (p.17). This introduction of non-linear schemes of cultural evolution provide a more nuanced understanding of the ways that a change from local to global deity worship, or Sanskritization, may introduce constraining and enabling behavior for collective action around forests – sacred and non-sacred – amidst urbanization.
Sanskritization
The organizing center of my study is the theory of Sanskritization. Malhotra et al. (2001) have provided a concise definition of the theory in relation to the sacred grove: “In many places, local folk deities have been, and continue to be, replaced with Hindu gods and goddesses. This has resulted in the erection of a temple in the sacred grove.”ii It is a shortcoming of the research on Sanskritization that it has mostly been considered historically and anecdotally and without reference to the cultural evolution research which analyzes the effect of local vs. global deity worship in cross cultural contexts spanning populations of hunter-gatherers, agriculturalists, and industrialists. In turn, a short-coming of this cross-cultural, cultural evolution research is the potential conflation of important cultural and historical differences between the cultures compared – especially under non-linear cultural evolution schemes – as well as a reliance on study of behavior as expressed in hypothetical scenarios, such as economic games. In the first case, a source of bias is in the selection of cultures under study. In the second, hypothetical bias seems concerning. Both criticisms can be partially deflated by the number of studies which have been conducted and seem to validate the findings. Outside of selection bias and hypothetical bias, a problematic exists in not attending to the historical aspect of culture which presents non- linear dynamics often uncaptured by such cross-cultural modeling. As any reasonable theory on human behavior must accord with human behavior as observed in non-experimental settings and have positive implications for understanding such behavior, mixed method studies which can combine experimental methods – helpful in isolating causative mechanisms – with analyses of actual behavior seem most promising. Further, attention to historical context, provides a concern for non-linear dynamics which may be critical to interpretation.
This suggests the benefits of deploying a ‘space-for-time substitution’ approach within a specific region, which has a shared history to a closer degree, though one which certainly cannot be assumed to be homogenous. A broad historical survey of sacred groves is provided below, 7 before specific detail is offered regarding the sacred groves of the study area under consideration and its historical context.
Historical Records of Sacred Groves in Global and Indian context
The historical presence of sacred groves seems endemic to locations where oral traditions are mixed with forest ecosystems, rather than being specific to a particular culture. Ancient myths originating outside South Asia provide early reference points in the written record, including the Epic of Gilgamesh and the Garden of Eden in chapter two of Genesis. Frazer’s (1891) Golden Bough provides extended cross-cultural discussion of sacred groves in his landmark comparative study. Certainly, commentators have also pointed to examples of sacred forests within the central texts of Hinduism in the Indian context. The theory of Sanskritization points to how these textual traditions are at some distance from the lived, oral traditions of sacred forests in the subcontinent.
While not a culturally unique category, sacred groves originate in particular cultural and ecological circumstances. Bhagwat et al. (2014) utilized paleoecological dating to provide convincing support for the anthropogenic, cultural origin of two Indian sacred groves, questioning a canonical model of the sacred grove as remnant, virgin forest. However, substantive details regarding origins in historical record are often severely limited. Correspondingly, historical studies or reconstructions can only infer original states from sparse data and are often speculative and, therefore, of limited generalizability.
The sacred grove is thought to persist widely throughout India, but current estimates are often unreliable. However, systematic regional inventories have been conducted of sacred groves. The West Bengal Biodiversity Board inventoried sacred groves of select rural districts of West Bengal (WBBB). Relatedly, community conserved areas – including but not exclusive to sacred groves – were inventoried in the state of Odisha and Madhya Pradesh (UNDP 2012). These inventories often seem focused on the most remote areas, often of the highest tribal populations, but there seems to be less inventory work on sacred groves amidst urbanization or in more mundane contexts. This seems to be due to a belief that sacred groves do not exist in urban or peri-urban areas, and more strongly, that they cannot exist in these areas. Gujar and Gold’s (2007) article Divine Sanction, Community Action argues against this view with the case of one sacred grove in central Rajasthan in which the surrounding population actively incorporated 8
‘rational’ monitoring strategies to protect against increased extraction pressure in the advent of urban growth. The last sentences of this article are worth reproducing here:
Urban proximity can certainly threaten and damage a deity’s protected greenery. However, Malaji’s story reveals that the workings of rural peoples’ religious minds are far from helpless in such circumstances, nor do they necessarily surrender to economic priorities. What is striking about the Malaji model of sacred grove management is that it unites ongoing claims for a deity’s miraculous power with a thoroughly systematic and rationalized mode of landscape protection. What motivates Malaji’s devotees to donate their scarce time and resources to the collective project of sustaining their hillside’s greenery? We believe this has something to do with the ongoing potency of their deity’s life narrative celebrating his miraculous and valorous deeds; with the ways these deeds are fully embedded in Rajasthan’s regional geography; and with the strength of the Mautis Mina community’s adherence to Malaji’s divine order as it permeates both every day and festival experiences (p.13). Here Gujar and Gold are keen to point out that motivations for sacred grove conservation which are embedded in the sacrality of the forest are not opposed to collective action, such as intensification of monitoring and imposition of fines on violators (p.13), but this sacred quality can spur ‘rational’ management and help solve collective action problems. Additionally, what is evident from the wider context of this passage is that the temple and collective action for forest conservation are not antithetical. Contrarily, this article locates the temple as the central locus of forest conservation organizing. It recounts the construction of the temple corresponding with the development of the “Mangat Temple Development Committee,” which oversaw key rituals, fundraising, and forest monitoring (p.12). It is important to note here, that Malaji is a local deity, which reminds that the temple-sacred forest question is related to – as suggested by Malhotra et. al. (2011) – but not the same as the Sanskritization question. However, Gujar and Gold’s article only deals with one particular sacred grove so the results cannot be seen as general. More studies of this kind are necessary to further specify how sacred grove’s respond institutionally to novel urban dynamics and land-use contestation. The role of the temple as locus of collective action for forest conservation is particularly of interest.
The spatiality of the sacred can become mediated by the temple. Rao (2003) provides this view in an extended case study of a Kali and Durga temple in Bhopal, Madhya Pradesh (P.12). Her study is helpful to review in-depth due to her aim “to understand the practices of temple religiosity in the larger context of urban negotiations about an appropriate place for the divine in the human world” (p.9). This study can be conceptualized as occurring not within an ‘urban 9 proximity’ or encroachment model, but totally within the full dynamics of urban space. This is helpful as the encroachment model may be naïve about its understanding of urbanity as encounter with the wholly other. Rao provides context of the ritualistic practice of the Khatik caste, as embedded in the ‘sanskiritized’ norms of Hinduism:
Khatiks have accepted many of these forms of worship that today constitute the standard repertoire of Hindu religion (e.g. the centrality of temple worship, the popularity of a canon of sanskritized gods and goddesses, the general appeal of certain national pilgrimage sites). Both these tendencies, brahmanization as well as the unification of Hinduism, are supported by the temple” (p.41). However, what is supported by the temple and what is of the temple, is an important question for the current study which seeks to not only analyze the effect of temple presence on preference but also to locate the effect of local vs. global deity worship on such preferences, which is the Sanskritization question. Rao specifies that the temple is conceived as a house for the “statues of the different deities” and that it is this deity-centric location which defines the spatiality of the sacred:
“The divine power that is said to be contained in the statues of the different deities transforms the place, sets it apart from its surrounding environment and makes it a sacred territory. This finds expression in people taking off their shoes before entering, sometimes covering their heads in respect, and abstaining from drinking and eating meat” (p.59) It is important to note that the temple is conceived as abode of the deity or deities, since the sacred forest is also conceived in these terms. According to the Rao’s description, the temple becomes the means by which the land surrounding the temple is marked as sacred. Such temple- mediated sacrality is evidenced among some actors in the present study. Importantly, Rao (2003) notes that the spatiality of temple-mediated sacrality is shifting and fluid rather than being purportedly stable, even suggesting that supernatural punishment is invoked in recourse to purposive movement of the sacred in space:
“Yet in spite of the boundless presence of the divine, the temple constitutes a special terrain in popular perception as it serves as a (permanent) home for a deity, thus binding the divine’s presence to this world. In the temple, a divinity’s hall of audience (darbar), the deity is permanently and directly accessible. Once installed, the statue of the goddess is considered absolutely immovable. Popular belief has it that any effort to remove the image would provoke divine revenge. In turn, the boundaries of the temple are rather flexible; they can be shifted through the construction or destruction of any part of the 10
building. Especially during festivals they are temporarily extended onto the streets, incorporating territory not normally considered part of the sacred site” (p.60-61). Rao’s study provides insight into the paradoxical coexistence of a perception of permanent sacred space and the everyday shifts in the actual definition of sacred space. This flexibility present in temple-mediated sacrality amidst urban dynamics is of central importance for understanding the institutional changes in the sacred grove amidst urbanization and potential responses to Sanskritization which may organize the sacrality of the sacred grove in terms of an in-dwelling temple.
Theoretically understood as a linear process, Sanskritization provides an increasingly flexible definition of sacred space, marked by two stages. Firstly, the spatiality of the sacred is centered on the forest. Second, the temple mediates the spatiality of the sacred through its service as the abode of the deities, rather than the forest. In the first instance, the spatiality of the sacred is relatively concrete in its social mapping onto a geographic forest area. Yet, deity replacement from local to global deities (Sanskritization) has been observed to correspond with permissive religious use of sacred trees particularly for temple construction. Once a temple is constructed, or expanded to prominence, a reorientation of sacred space in terms of the temple is possible. In this second instance, the flexibility of sacred spatiality is heightened in proportion to changes in the surrounding landscape matrix which becomes ever-more subject to construction and deconstruction in urbanization processes. Yet, Duara’s analysis reminds, and Gujar and Gold’s attest, there is another potentially positive effect in play – that of the re-enchantment of the commons – which indeed has begun to influence sacred grove conservation initiatives in India.
The temple, in concentrating the locus of sacred spatiality to a specific representation of the deity/s, may also concentrate organizational focus for collective action. When one considers how a transference from local to global deity worship, Sanskritization, both generalizes the scope of divine power and provides a communion of distant co-religionists, opportunities for collective action centered on sacred groves amidst urbanization realize a scaffolding upon which the linkages necessary for multi-scale resource governance can be built. Yet, it can be theoretically conceived that the transference from local to global deity worship in Sanskritization, in generalizing the locus of divine significance, introduces a non-geographical definition of sacred spatiality. 11
The South Asian context is rich for better understanding these relationships, as local deities are commonly worshipped alongside global deities, so a transference from local to global can be analyzed in terms of the effects of relative emphasis on local vs. global deities. Particularly informative is the context of Delhi, India and the surrounding megacity identified as the National Capital Region. Mirialini Rajagopalan’s (2011) essay Postsecular Urbanisms: Situating Delhi within the Rhetorical Landscape of Hindutva argues that Hindutva – a Hindu nationalist movement – is “seeking to recover sacred origins of modernity” and outlines ways Delhi’s re- institution of ancient sacred sites, or their historical imagination, through state-sponsored archeological efforts, mobilizes trafficked ideas of the sacred in the realm of the ‘postsecular’ city. What has resulted are large infrastructural projects, supporting sites which are perceived to bolster this cultural-religious heritage. While this process is viewed critically, due to the relation of Hinduism and Nationalism, this nationalistic orientation may not be the only option and provides a view of how similar logic can be mapped in sacred grove conservation initiatives in the area, perhaps in an ecological orientation.
The Indian Sacred Grove in Rational-Legal Context:
Dietrich Brandis, the first Inspector General of Forests in India’s colonial Forest Department, notes his encounter of sacred groves “in nearly all provinces” in the late 19th C and counts them as one of only three examples of ‘indigenous’ forest conservation in India, along with the setting aside of woodlands and game preserves. (1897, p.12). Notably, Brandis also recounts the strict prohibition on sacred grove extraction as excepted in religious cases: “These sacred forests, as a rule, are never touched by the axe, except when wood is wanted for the repair of religious buildings, or in special cases for other purposes” (p.12). The forth colonial Inspector General of Forests in India, E.P. Stebbing, also provides an account of the use of individually sacred trees, the cedar, for temple construction (Stebbing 1922, p.502).
The founding Inspecting Generals of India’s Forest Department during the colonial period, perhaps most importantly Dietrich Brandis, remain influential within the leadership of today’s Indian Forest Department. P.J. Dilip Kumar held the contemporarily equivalent position to that of Brandis and Stebbing, from 1974 to 2012, and maintains the tradition of Indian Forest Department leaders writing on Indian Forestry in their retirement. Relevant to this study, Kumar maintains a blog titled Forest Matters: Conservation, Communities, and Competing Uses. From 12 a forestry administrators experience in India. Recognizing land-use contestation, Kumar affirms the role of the state in imposing restrictions on forest use, which has been the defining trait of India’s Forest Department since Brandis. Yet, with recognizable tension, these restrictions are intended for the benefit of forest-dependent communities. In his 2016 article Modernizing the Indian Forest Service: from command to collaboration, Kumar reviews the historical case of Germany’s Wood Theft Law, of which an early Marx is observed to advocate against (Kumar 2016, p.13). Kumar then observes Brandis’s commentary on the negative effects for the forest resulting from the lack of prohibition after the dissolution of this law in the revolution of 1848 (p.14). Recognizing a tension between idealistic decentralization advocacy and pragmatic forest management, Kumar sides with Dietrich Brandis and he writes against contemporary decentralization advocates who he titles social environmentalists – such as Madhav Gadgil and Ramachandra Guha – who he cites as arguing in the tradition of Marx and more recently James Scott (p.16).
This historical debate is localized to the situation of sacred groves the present study area through a recent campaign to achieve formal protection of Mangar Bani, in light of mounting threats, where key actors petitioned Kumar in a letter dated 25/03/2012 with the subject “Petition of Faridabad Aravallis deemed forests including Mangar Bani Sacred Grove from real-estate pressures, including agricultural zoning in the Mangar DDP 2031,” which refers dually to real estate speculation and a Mangar Development plan as threats to the forest (Oberoi et al. 2012). The success of this conservation campaign is illustrative of the ways that linkages between state and non-state actors can be mobilized in multi-scale stakeholder collaborations. However, I argue its success cannot be explained outside of the long historical web of connections in which the problem has been conceived.
STUDY AREA In the summer of 2015, I led ethnographic research on this topic in the study region – including five weeks in the sacred grove site, Mangar Bani, in India’s NCR and five weeks outside of the current study region in Kolkata, West Bengal and adjacent areas. My participant observation began in Mangar, Haryana in June 2015 in a village with a relatively large sacred grove, called Mangar Bani, which has received national media coverage, being referred to as ‘Delhi’s Last Sacred Grove.’iii It is the subject of a documentary film, called “The Lost Forest.”iv It is also 13 featured in a well-regarded natural history book, “Trees of Delhi,” due to the presence of Anogeisses pendola or Dhau (Krishen 2006). Stories of the campaign for Mangar Bani’s conservation have been chronicled in the prominent Indian Journal, the Times of India (TOI)— which has also raised public awareness on sacred groves in general.v After arriving in 2015, the TOI reported Haryana’s Chief Minister declaring the forest a “no-construction belt” (Goswami 2015). During my next visit on June 14, 2016, the TOI reported the Bani as a ‘legal’ Forest, demarcating 653.45 acres as sacred forest and incorporating the surrounding 500m “no- construction belt” as a buffer zone (Dash 2016). Though these outcomes are based on the actions of the state, the actions result from the mobilization of public will at multiple scales, which prompts the question of what occurred to generate this collective action. Another question is the degree to which conservationist forest preferences are shared by residents within the institutional setting of sacred groves in this region generally. Accordingly, the present research offers a systematic survey of populations around four sacred grove sites in India’s National Capital Region, including Mangar Bani.
My prior ethnographic research with the forest-dependent communities of Mangar Bani detailed negative consequences of urbanization, including loss of property rights – via imposed land sales, sometimes conducted without informed consent – as well as increases in economic inequality, pollution and deforestation. These struggles are common to many forest-dependent communities experiencing urbanization pressures globally. However, my prior research also details numerous benefits for forest-dependent communities in this region accruing from urbanization, including increases in income, food diversity, education, home quality, roads and motorized vehicles. Consequentially, forest-dependent communities in this region are performing complex cost-benefit calculations regarding how to adapt to the changes posed by rapid urbanization. This ethnographic research was essential in constructing the survey instrument for this study, as well as in providing oral history details regarding the forest-temple dynamic amidst land-use and land management changes. I first introduce the ecological context of these sacred grove sites within a region framed by the Aravalli mountain range and the Jamuna (Yamuna) river.
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STUDY AREA
Aravalli-Jamuna The Aravalli Mountains provides a physical shelter between Delhi and villages just outside of the urban fringe of the highly urban area composed of three principle cities: Delhi, Gurgaon, and Faridabad. The Aravalli range extends in a south-westerly direction from Delhi while the Jamuna extends in a south, south-east direction. My study area centers on four sacred grove sites framed within this Aravalli-Jamuna context (Table 1): Table 1: Selected Sacred Grove Sites
Sacred Grove Location Temple Forest Dist2alt Dist. 2 Name (Y/N) size Forest village. (acre) (km) Gummat Asola & Fatehpur-Beri Y park Adjacent 1.5-2.5 Mandir Delhi Mangar Bani Mangar & Bandwhari, Haryana Y 527- Adjacent 1.5-1.7 653 Nav Durga Gothda Mohbtabad & Pakhal, Y 10-15 Adjacent .5 Jharna Mandir Haryana Chameli Van Hodal, Hatana, & Karman, Y ~175 N/A .35 Haryana/UP
The first three listed sites are also the most northerly, clustered on the border of Delhi, which roughly corresponds with the positioning of the Aravalli mountain range. Gummat Mandir is situated within the plains of Asola and Fatehpur-Beri, Delhi, bordering the Asola Wildlife Sanctuary extending into the Aravalli range. To the south of the Delhi border is Mangar Bani and Jharna Mandir. Each of these are situated in the Aravalli’s in smaller village contexts that will be described more thoroughly below. The fourth site, located approximately 67KM south, southeast, is Chameli Van. This sacred forest is the main forest area among a large surrounding, flat agricultural matrix which includes flooded fields for rice production, which is not found in the Figure 1- Sacred Forests, & administrative borders from other areas, which are dry. Though these sites are at some distance, open street map data Hodal, Haryana is linked to Delhi by railroad, which parallels the path of the Jamuna river, and was opened in 1884 during a rapid phase of urbanization with the colonial railroad construction 15 efforts (IRFC). In Hodal, I encountered various residents who commuted daily to Delhi by rail. Additionally, Hodal, itself, is a city which draws workers from surrounding areas. Railway and highway corridors are significant sites for urbanization developments.
SACRED FOREST CHARACTERIZATION
The sacred forest site at Gummat Mandir includes a small forest that has been bounded by a fence, maintained Delhi Development Authority. This is noted as a recent development with expansion of Delhi’s administrative area. This bounding is a controversial issue in which some residents opposed and others supported conditionally.
Mangar Bani is a relatively large sacred forest, called Bani, located in the Aravalli range. It is surrounded by villages which are characterized by the hill lands and the upland plain. Mangar village is nestled between ridges of the Aravalli’s which enter into the sacred forest. Land area in the valley bottom is limited and has historically been subject to seasonal flooding as well as year- round submersion in select areas. Accordingly, hillside pastoralism has been the historically dominant livelihood strategy. Today, rainfall, the water-level, and flooding have decreased noticeably according to resident memory. Common grazing lands have reduced and livestock numbers have also reduced. The valley area is also the site of recent village development. Bhandwari is located opposite Mangar village, with the sacred forest between, and is located on upland plain, nearer to Gurgaon. Both villages are predominately inhabited by the Gujar caste. This observation is consistent with British observation of Gujar controlling the hills south of Delhi, practicing pastoral livelihoods. Contemporarily, it is bordered to the North by the Haryana, Delhi border.
Jharna Mandir is located nearby Mangar Bani and is also located along the Aravalli range, however it does not include a village characterized by a valley. Rather, villages in this site are located alongside the range in a plains area more suited for agricultural use.
Chameli Van is located outside of the Aravalli range and is clearly more defined by its proximity to the Jamuna River. Its alluvial land is well-suited to agriculture, nearly all of which is cultivated today with the exception of the sacred forest called Chameli Van. The Delhi-Agra railway runs through Hodal, which is a relatively large city for this region outside of the Delhi area. Two smaller villages also surround this sacred forest: Karman, Haryana and Hatana, Uttar 16
Pradesh. Both of which nearly intersect the Haryana, Uttar Pradesh border. Rather, than predominately Gujar, the population tends to be primarily of the Jat caste.
CASTE AND INCOME The demographic, caste context of the total sample is presented in the Figure 1 below:
These categories are maintained by the state of Haryana. In this survey, the majority of caste groups represented were categorized as Backward Castes B. This categorization includes the Gujjar and Jat castes. Historically, Gujjar’s are noted as pastoralists living along hillsides, while the Jat’s are seen as preeminent agriculturalists, living in the plains. The Backward Caste B, as opposed to A, designation is indicative that persons from these castes are thought to often be landowners and more wealth, which accords with the reported average income by caste presented in Figure 2 below: 17
~$6.00 usd / day ~$5.50 usd / day
~$3.25 usd / day
~$2.00 usd / day
FOREST & INSTITUTIONAL CHANGE
In the colonial era, rivers were a central means for timber transportation, and were thus subject to heightened deforestation pressure, and railway construction demanded timber that was often supplied by surrounding lands. Railway and highway corridors continue to extend and concentrate urbanization activity in this region. While population seems to be steadily increasing over the long run, with significant exceptions during notable famines, significant changes have occurred in the recent decades in terms of livelihood changes, new infrastructural projects, and accompanying developments in quality of life. For instance, interviews in Mangar village suggest a main road connection in 1976, the introduction of electricity in 1995, and a transition from mud home construction techniques to concrete in 2009, though the first ‘farmhouses’ – gated upscale houses – were constructed in 1996 and more developed after speculation from outside real-estate interests increased in 2003. A timeline of these developments in the case of Mangar village is presented in the timeline below from details collected in oral histories (Figure 3). 18
A general observation of cow patty cooking fuel was in use in the study area with gas fuel in use in more urban and affluent contexts. Mention of hardship due to forest department restriction on wood collection in non-sacred forest was expressed in multiple sites. However, such extraction was evident since a process of fine collection for forest extraction exists in the Forest Department and is said to not be an irregular occurrence. In multiple contexts, temples also collect fines for extraction from sacred forest.
LAND-USE, LAND=COVER
Land classification data comes from Roy et al. (2015) who provided a decadal LULC classification of India at 100m2 resolution. The data spans from January 1, 1985 to December 31, 2005. Data was assessed with kappa accuracy of 94.46%. The land cover classes observed in the study area during the study period is located in the Appendices (see Appendix A)
Household Locations The LULC classification was extracted from the locations of the 198 surveyed households. Two observations were deleted from the original sample size of 200 due to incompletes surveys that were not resurveyed. The household LULC is interpreted as the LULC of each household surveyed, with the understanding that 100m2 resolution will include a larger area. 19
The main changes in household LULC were in the 1995-2005 period, where 32% of cropland and 8% of grassland was converted to built-up land (Figure 4, Appendix B).
These changes, led to a state in 2005 where 21% of households lived in areas characterized by built-up land whereas none did in 1985 or 1995 (Table 2). Whereas 63% of households lived in cropland areas in 1985, only 43% lived in cropland areas in 2005. (Table 2). The urbanization trend is measured at households in sites 3,4, and 1 (Mohabtabad, Hodal, Mangar) —in order of greatest to least (Table 2). Each of these sites contributed to transition from cropland to built-up land (Table 2). However, Mohabatabad uniquely experienced transition of grassland and cropland to built-up land (Table2). In the Mohabatabad households, 14% lived in grassland areas in 1985 and 1995 whereas 10% lived in grassland areas and 44% lived in built up areas in 2005.
Households in Asola, Delhi (site 2) uniquely did not experience an increase in built-up land (Table 2). This is probably due to the fact that most of this area is classified as shrub land, due to its fragmented, residential character (Table 2). Asola has remained around 84% shrub land in the 20 observed period -- about 82% of total shrub land – and this area contributes 21% of the total household area as shrub land (Table 2).
Table 2 Village Wise Summary of Proportional Land Cover by Year Village 1 2 3 5 14 Cropland Built Up Shrub Grass 1985 0.192 0.000 0.030 0.025 1995 0.182 0.000 0.040 0.025 2005 0.157 0.030 0.035 0.025 Village 2 2 3 5 14 1985 0.035 0.212 1995 0.040 0.207 2005 0.040 0.207 Village 3 2 3 5 14 1985 0.217 0.000 0.035 1995 0.217 0.000 0.035 2005 0.116 0.111 0.025 Village 4 2 3 5 14 1985 0.182 0.000 0.010 0.061 1995 0.182 0.000 0.010 0.061 2005 0.116 0.066 0.010 0.061
These changes in land-cover, impacted by increases in built-up land cover or urbanization, also correspond with institutional impacts for the sacred groves in the study region. This has led to a redefinition of these sacred groves. Firstly, all have changed administrative regimes. Secondly, they have recently been increasingly subject to forest department monitoring and regulation. Mangar Bani’s publicized ‘legal’ forest designation in June 2016 with associated legal restrictions on development highlights these changes. Additionally, Gummat Mandir is located on the far southeastern border of contemporary Delhi. It was however previously within Haryana. Since being administered by Delhi, residents note increasing monitoring and enforcement.
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METHODS
SURVEY DESIGN The survey was crafted after participant observation in the study region. The main sections of the survey are as follows (Table 3):
Table 3: Survey design Actual Visits Days since last visit converted to a monthly visitor classification (<33 days) distance and travel time details Hypothetical Visits Contingent Valuation & Discrete Choice Experiment Levels of the key variables in the hypothetical visit section were randomized on the stratified, random sample points. For the discrete choice experiment, this was a randomization of the choice-set block (1-6) presented to the respondent. For the Contingent Valuation questions, the entry fee and distance offered for non-sacred and sacred forests were randomized among six levels. Perceptions: Re: Site Attributes, Religious beliefs, Household setting Demographics Caste, Age, percent calories purchased, income, education level
SAMPLING DESIGN In the absence of an accurate sampling frame and in order to control for distance relationships, this study utilizes a geographically-based sampling approach. A stratified, random sampling method was conducted based on the observation that household locations are not randomly dispersed around sacred forests but highly concentrated. Accordingly, the sampling design identified residential locations visually in satellite imagery and cropped these candidate zones to a 2.5 KM radius range from sacred forests, centered on temple locations within the forest, as strata. Residential areas were considered as built up locations from the viewing of 2016 satellite images. Within these candidate residential areas as strata, 50 points were randomly plotted into each site as candidate sampling locations.
DATA COLLECTION In the survey implementation stage, households were identified as the nearest available household location to each sample point. Latitude-longitude coordinates were re-recorded at the 22 actual locations of surveyed households. All data was collected in the NCR, during 7 weeks in this region in 2016.
The survey began with an introduction and an oral consent statement – approved by IRB review – focusing on identifying myself and the nature of the research as well as stating the respondent’s ability to stop the survey at any time and the guarantee of the confidentiality of any potentially identifying information. Since the survey includes latitude-longitude locations, locations are not referenced specifically but only in aggregate.
I recruited the survey team, using the web-based service Internshala, from an applicant pool consisting on university students from urban areas proximate to the study location. Five translators assisted during the course of the project, all fluent in Hindi and English. However, a core team of two interviewers completed the majority of the surveys, in the presence of the author. Surveys were conducted in Hindi, with the exception of two or three surveys enumerated in English among respondent’s proficient in the spoken English language. All surveys were administered orally by an enumerator.
DATA EDITING CONSTRUCTING TRAVEL COST VARIABLES
To deal with outliers in reported distance and time values, cut-off values for distance and time to site type were selected. Cut-off distance selected was 11 KM. Cut-off travel time selected was 200 minutes for walkers and 40 minutes for drivers. Values exceeding the cut-off were imputed with OLS regression of travel time ~ distance for each site type (Table 4). The intercept was forced to equal zero in these models, given a known intercept of zero, since travel time equals zero when distance to site equals zero. Eisenhauer (2003) recounts other examples where a constant is theoretically impossible, and suggests “Regression through the origin” is indeed appropriate in these cases.
Table 4: Model Fit for Regressions for Imputation of Walk and Drive Time by Site Site Type Walk Time Drive Time Sacred Forest R2=.6922, F=383.2, p<.001 R2= .7159, F=409.3, p<.001 Worship Site R2=.8109, F=738.4, p<.001 R2=.7595, R=490.5, p<.001
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This imposes minimal travel speeds for long trips, for walkers, a minimum walking speed of 3.3km/hour is imposed, and a minimal driving speed of 16.5 km/hour is imposed for drivers. Short trips can be expected to have an inflated travel rate due to the disproportionately large length of time occupied with starting and stopping, when total trip length is nominal. Thus, the imposed values seem reasonable in comparison to the median walking travel rate of 5.3km/hour and a median driving rate of 16km/hour for sacred forest visits before this imputation.
Classification of Drivers & Walkers
The primary travel modes for all site types was walking or a vehicular transportation mode, e.g. motorcycle or car drivers. All respondents were classified as walkers or drivers. NAs are classified based on a threshold distance of 2km, where those who reported a distance less than 2 km to the site were deemed walkers and those over 2km reported distance to site were deemed drivers.
Calculation of Travel Rate for Sacred Forest & Temple Visits
Travel Rate by transportation mode was calculated for Sacred Forest and Temple visits by dividing total travel time to site – walking time for walkers and driving time for drivers – by reported distance to site. This yielded integrated travel time variables by site type. NA values were imputed by an OLS regression of travel rate ~ transportation mode (SF, R2=.4439, F=139.1, p<.0001, WS: R2=.1486, F=30.84, p<.001). This imputation includes those who reported both travel time and distance values greater than the imposed cut off. Thus, those who reported very distant trips are assigned average times for their travel mode.
Revealed Preference Travel Cost Variables Travel cost variables were constructed by site type according to the following basic formula:
Travel Cost = distance cost + time cost eq(1) where distance cost represents the pecuniary cost of travel – valued as Rs. 2.1 per km for drivers – and time cost represents the value of individual time, based on a fraction of the wage rate in cases where income is reported and fraction of median wage rate where it is not.
Distance Cost: Rs. 2.1/KM is based on fuel price per kilometer. Drivers and walkers were identified from a 24 survey question asking what transportation mode to respondents use to travel to sacred forests. Drivers were charged the distance cost, while walkers were not. Where a distance was not reported for the nearest sacred forest, a measured distance was provided for all sites excepting Asola, which is excluded from analyses using travel cost and distance measures for actual visits
Time Cost:
Time cost = Travel time *1/3 wage rate eq(2)
Travel time is calculated from the multiplication of reported distance to site by the constructed travel rate to site variable.
Wage rate was calculated in terms of Rupees per minute by converting reported wages per month into the time unit of minutes. Since it is impossible to work during all hours of the day, this value can be seen as a lower bound valuation of time. For individuals earning an income, a third of this income was calculated. For individuals who did not report a personal income but who reported a household income, a third of the proportional household income – household size/household income – was calculated. A reported income of zero or an unreported income were imputed as a third of the median wage rate.
Stated Preference Travel Cost Variables The calculations of travel cost for the stated preference models, based on hypothetical trips utilized the same formula.
Travel Cost = distance cost + time cost eq(1)
However, distance cost was based on offered distances rather than reported distances. Another important distinction is that whereas the revealed preference travel cost variables are calculated according to reported distances and travel times by nearest site type, the stated preference travel cost variables utilize the travel rate and driver vs. walker classification – reported for sacred forest trips – to compute travel cost for hypothetical site type distance offer. This general travel cost specification, using the sacred forest rates, was chosen because of the high number of missing values in non-sacred forest travel rates as well as the assumption that travel time and mode for non-sacred forest visits is relatively similar to sacred forest visits, which are the site types under consideration in the stated preference models. 25
DATA PROBLEMS
The largest distance error between actual household locations surveyed and their respective sampling points was in Asola, Delhi. This was due to both uncertainty in prior knowledge of the main sacred forest of significance – Gummat Mandir – as well as greater discrepancy between sampling points and housing locations. For instance, some sampling points were located in non- residential industrial areas while others were in inaccessible gated household areas. This sampling approach was therefore, least suited to the particular character of Asola. Accordingly, inaccessible sampling points were avoided and replaced by means of ‘convenience sampling’ in areas proximate to Gummat Mandir, among households nearer to the significant sacred grove site. Since sampling is geographically based, within a 2.5km radius of the sacred forest site, for other sites, measured distance could not be used for Asola. Perceived distance to a sacred forest in the Asola sample was 3-5km (1st-3rd Quartile) versus 1.5-3KM for the remainder of the respondents in other sites. For this reason, Asola is excluded in revealed preference models which analyze actual trips with travel cost variables constructed based on the reported distances. However, I argue that Asola can still reasonably be included in stated preference models – without a measured distance variable – as respondents were aware of sacred forests in their area and thus would also conceive of the tradeoffs in visits to sacred forests, non-sacred forests, and temples outside of sacred forests. Potential bias is possible in analysis of revealed preference questions, because of the additional distance required for an actual visit to a sacred forest site. To avoid complications from this bias in revealed preference measures, this site is dropped in the sacred forest and temple visitors (Table 5). The stated preference models, since they do not rely on controlling for measured distance to sacred grove but rather distance offer, this is not considered to be a key source of bias.
Table 5: Sites Dropped from Analysis Revealed Preference Models SF & WS NS Stated Preference Sites Dropped Asola Hodal & Asola NONE
Non-sacred forest models, in addition to excluding Asola due to the above mentioned sampling inconsistency, exclude Hodal because alternate forest to the sacred forest – non-sacred forest – is largely absent.
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Modeling Technique My research employs methods common to marketing studies adapted for research in non-market valuation by the discipline of environmental economics, including revealed and stated preference methods, specifically a discrete choice experiment (DCE) and contingent valuation (CV) exercise. This survey approach is deployed within a landscape ecology framework to query the spatial dependencies of a social-ecological system amidst urbanization, the sacred grove. Distance relationships are considered in terms of measured distance to sacred grove, in addition to constructed travel cost variables for visits to nearest sacred forest and temple site outside of sacred forest.
This study utilizes empirical analyses of variation among the preferences and perceptions of the forest-dependent communities surrounding four extant sacred groves in India’s NCR. By inferring revealed and stated preferences with individual characteristics, perceptions, deity worship type, and built-up land-use classification of household locations, it is possible to determine the impact of Sanskritization and urbanization on forests conservation preferences relative to other factors on which these preferences depend.
In my treatment, Sanskritization is the replacement of local with global deities, specifically the globally recognized deities of Hinduism. I understand ‘replacement’ in terms of primacy or relative emphasis. Since multiple deities are worshipped, commonly of each category, I consider the deity type which is most regularly worshipped. A variable of most-regularly worshipped deity as native is interpreted throughout the rest of the paper as local deity worship, and the non- primacy of native deity worship is considered as global deity worship. This global deity classification, consequently, includes 2% of the sample which indicated worship of no deities as well as 2.5% of the sample which indicated primary worship of Allah as a global deity, noting that 2 out of 7 of this Muslim population indicated Allah as a local deity, which is a deity who is from their area of residence rather than a different geographical area/ I consider urbanization in terms of classification of households as urban and non-urban within each site – measured with built-up area classification of remote sensing data. A variety of socio-economic and demographic variables are also included in the model, along with site as a fixed effect.
Predictor variables considered are presented in the table below (Table 6). Sacred Forest and Temple Site RP models include all variables. The RP model for non-sacred forest (NSF) 27 excludes the travel cost to temple variable. Lastly, the SP models do not include the distance to sacred grove variable nor the travel cost variables based on actual visits – but a constructed travel cost variable based on hypothetical distance offer.
Table 6: Predictor Variables and Median Values or Percentages By Modelling Technique for the noted Site Types
Variables RP: RP: SP: SF & WS NSF CV Distance to Sacred Grove 1.87 km 1.72km - (1.88 km) Travel Cost to Sacred Grove 5.2 Rs 4.7 Rs - (5.9 Rs) Travel Cost to Temple .65 Rs -(1.2 Rs) - (.87 Rs) Built 28% 28% 21% Most Regular Deity Native 39% 51% 34% Age 32 32 32 Income 3750 Rs 4000 Rs 4406 Rs All Food Purchased 49% 51% 57% Degree 59% 57% 60% 10-12 Years Edu 34% 38% 34% NSF Perceived Useful for Services* 3.5 4 4
Caste Categories (upper) 5% 3% 9%
Site 1, 3, 4 1, 3 1, 2, 3, 4 *Ordinal ranking from strongly disagree(0) to strongly agree (4) - values in parentheses indicate variable’s non-inclusion in the respective model. Revealed Preference Models
Dependent Variable: Classification of Visitors in the past month by Site Types
Respondents were asked the number of days since their last visit to each site type. The continuous, numeric responses were converted into a dichotomous variable indicating whether or not the respondent has visited the site type in the past 33 days. Visitor by site type classifications 28 included visitors of the following site types: sacred forest (SF), non-sacred forest (NSF), and temple outside of sacred forest. Classification of visitors to sacred forests and not temple sites further specifies personal characteristics of the exclusive visitors to sacred forests.
Analyses Monthly visitor by site type classification supplied dichotomous dependent variables for revealed preference models assessing the personal characteristics of the visitors to Sacred Forest, Non- Sacred Forest, Temple Sites, and Sacred Forest when Temple Site is not visited. The models consisted of generalized linear models, in which the predictors are related to a binary dependent variable by a logit link function, referred to as logistic regression, formulated as follows:
Site Visitor ~ Dist. to Sacred Grove + Sacred Forest TC + Temple Site TC + Predictors eq(3)
The distance to sacred grove variable captures the Euclidian distance between Sacred Forest, as measured at the temple, to the respondent’s household location, using ArcGIS. The Sacred Forest travel cost variable reflects reported distance to the nearest sacred forest by respondent. Thus in addition to capturing non-Euclidian travel routes to the sacred forest, this value may also reflect a trip distance an alternate sacred forest to the central one upon which sampling is based and distance to sacred grove measures. This helps to control for the existence of alternate sacred forest sites. The Temple Site travel cost variable is also based on distance to the nearest temple, outside of sacred forest, for each respondent. The Temple Site and the Sacred Forest travel cost variables are important to include together in the separate Sacred Forest and Worship Site models because these sites are alternate places of worship. Thus, the Sacred Forest and Temple visitor models, include the full suite of predictor variables in Table 6.
However, since the non-sacred forest model excludes two sites, the travel cost variable for worship sites was dropped due to its introduction of additional NA values. However, this variable is of less relevance because temple sites and non-sacred forests are arguably not conceived as alternatives.
Test of Significance:
The test of the model coefficients with the logit link function was performed with a χ² test in an ANOVA. A P-value for the model was generated with a χ² test based on deviance and degrees of 29 freedom. An approximation of the r2 value was generated by dividing the model’s deviance by the null deviance and subtracting this value from one.
Analyses were conducted using the base GLM function in R v. 3.2.2 (R Development Core Team 2015).
Stated Preference Models
A. Contingent Valuation (CV)
Dependent Variable: Yes/no responses to hypothetical site visit questions
A CV exercise was crafted to assess the relative importance of the sacrality of the sacred forest as opposed to a non-sacred forest of like characteristics. CV exercises have five components: introduction and context to the exercise; description of the good and its context; a payment vehicle; debriefing questions; and, lastly, there are questions about the individual’s characteristics (Jeuland 2015).
This method has been well-developed in non-market valuation studies and has often been applied to similar counterfactual landscape visitation scenarios, such as in valuations of the value of a proposed park land, in environmental economics research. Critics of the method primarily suggest that it is susceptible to hypothetical bias, however stated preference models been shown to significantly explain current travel behavior and to provide results which correspond with revealed preference models (Waldman 1988). Further, stated preferences methods are required to consider non-use values, as they are not accounted for by behavior. This method is adopted to help discern the non-use values evident in the sacrality of the sacred forest rather than its material characteristics, which may be reflected in behavior.
The Scenario
This survey’s CV exercise consisted of a hypothetical scenario, regarding the respondent’s choice to visit a hypothetical forest at an offered entry fee or distance, the latter being converted to a travel cost for analysis. The CV scenario included visit offers to non-sacred forest – including an entry fee and distance offer – as well as an offer to travel additional distance from the distant non-sacred forest to visit a forest of the same characteristics but a forest which is a sacred forest (See Appendix). Levels of the entry fee and distance in each offer were randomized 30 among respondents. Each respondent was offered one discrete value for each scenario in order to elicit more realistic responses than an open-ended response question. However, the respondents were also asked for maximum travel distances and entry fees that they would be willing to travel/pay. In each of the forest visit offers, the characteristics of the forest were held constant, as presented below:
Table 7: Forest Characteristics of CV Offers
In the case of the non-sacred forest, the respondents were given two offers. The first, to visit a non-sacred forest at a nearby distance from their home, but one which charged an entry fee. The second, one which did not charge an entry fee, but which was located at some distance from their home. At this point in the exercise, the interviewer would remind the respondent of the distance that they said they would or would not travel to the non-sacred forest. After which, an additional distance from this non-sacred forest would be offered in which the respondent could choose whether they would like to visit a sacred forest rather than a non-sacred forest, at an additional distance. The randomized levels of the offers are presented below:
Table 8: Randomized Entry Fee and Distance Offers for CV Questions: Non-Sacred Forest, Entry Fee Offers (Rs) 10 20 40 60 150 200 Non-Sacred Forest, Distance Offers (KM) 2 4 6 10 13 20 Sacred Forest, Distance Offers (KM) 1 5 10 15 20 60
Analysis:
The same modelling techniques for the CV exercise were used as for the RP models. However, since this was a hypothetical scenario, travel cost variables were constructed based on the offered distances to the respective site type, rather than reported distance to any site type. The construction of the logistic regression is presented below: 31
Choice ~ Price + Predictor Variables eq(4)
Where price is either entry fee offer or travel cost constructed based on distance offer. With the exception of the measured distance to sacred grove variable, all other predictor variables were used in these models.
Test of Significance:
The test of the model coefficients with the logit link function was performed with a χ² test in an ANOVA. A P-value for the model was generated with a χ² test based on deviance and degrees of freedom. An approximation of the r2 value was generated by dividing the model’s deviance by the null deviance and subtracting this value from one.
Analyses were conducted using the base GLM function in R v. 3.2.2 (R Development Core Team 2015).
A. Discrete Choice Experiment
Dependent Variable: Yes/no responses to choice sets, offering forest visits by varying characteristics
The Scenario
In order to provide a measure of relative and marginal preferences, a discrete choice experiment (DCE) was conducted. DCE presents a menu of hypothetical items to a respondent where each item is characterized by the same set number of attributes, but which are comprised by varying levels within that attribute. Assuming rational choice, the menu of choices presented suggest trade-offs being made by the respondent. By observing numerous menu item choices per respondent over a sample of a population, it is possible to infer marginal utilities for attributes and attribute levels. Ideally, each respondent would observe all combinations of attribute levels in a full factorial design. However, this is often practically infeasible, due to the burden of observing the many choice sets required by all combinations of levels. A fractional factorial design allows redundant choice sets to be culled, based on a measure of efficiency. Further, blocking the choice sets, with randomized assignment, allows individuals to observe a reasonable number of choice sets. A blocked, fractional-factorial design was pursued based on the difficulty of administering the DCE in a foreign context, and one in which translation services would be required. 32
Like the contingent valuation exercise, this involved the introduction of a hypothetical scenario. However, the DCE involved the presentation of numerous choice tasks to the same respondent. Each choice task consisted of two hypothetical forests, in which the levels of the forest attributes varied. A critical component of the DCE is that the attributes reflect a realistic scenario for respondents, and reflect the actual attributes in the consideration of the respondent. The attributes were selected based on prior participant observation in the study region, consideration of the literature, and in relevance to the hypotheses under consideration in the study. These attribute levels were set to approximate the variation in forest attribute levels evident within the study region. These attributes and attribute levels are presented in Table 9 below: Table 9: Discrete Choice Experiment (DCE): Attribute Levels by Attribute
These levels are color coded according to the hypothesized forest conservation implication of each level, with dark green representing hypothesized conservationist preference and dark red representing non-conservationist preferences. This color scheme is followed throughout this paper. For clarity, lopping is a practice of removing the braches of a tree without resulting in tree mortality.
To assign attribute levels for each choice task I utilized two free software sources. First, in order to create an orthogonal array, I used a package in R called DoE.base, using the function oa.design (Groemping 2016). This yielded an array with 16 attribute level combinations. Then, 33 from this orthogonal array, I used the online, free software from Burgess (2007) to generate the choice set tasks, specified to two options in each choice set. This yielded 32 choice set tasks.
The construction of the choice sets, yielded combinations of attribute levels into ‘discrete choices’ between two forests of differing characteristics. One choice set is displayed below (Table 10):
Forest 1 Forest 2 Forest Quality natural Forest Quality planted Temple in Forest absent Temple in Forest absent Forest Size 150 Forest Size 45 Forest Activity lopping Forest Activity no extraction Distance to Forest 5 Distance to Forest 3
The 32 choice sets were blocked into sets of six and four choice tasks. I created two ‘dummy’ tasks for the blocks with four choice sets, so that each respondent observed six choice sets. The dummy tasks were not analyzed.
This is a blocked fractional factorial design in which only main effects can be reliably estimated. After each choice task, 2 questions are asked: which attribute was most important (1), and which attribute was least important (2). Before administering the Discrete Choice Experiment, respondents were asked directly for their preferences among these attributes by their respective attribute level. This provides the absolute preferences of respondents which can be compared with the marginal preference information garnered in the DCE. Analysis
A mixed conditional logit model was constructed, and specified as follows:
(Choice, Choice set) ~ DCE Attributes eq(5) This formulation assumes personal characteristics to vary randomly since choice is analyzed within choice-sets rather than within individual respondents. This assumption is justified since the blocks were randomly assigned.
Analyses were conducted using R v. 3.2.2 (R Development Core Team 2015), and contributed package ‘mclogit’ (Elff 2016). 34
RESULTS Revealed Preferences The site type with the highest percent visitation in the past month was the Sacred Forest, followed closely by the Temple, with the site type of lowest percent visitation being the Non- Sacred Forest (Table 11). However, the majority of respondents had visited each site type in the past month, excepting the subset of sacred forest visitors who had not visited temple sites.
Table 11: Percent Visitation in the last month, site-wise Actual Sacred Non-Sacred Temple Sacred Forest Forest Forest not Temple
% Visitors 79% 60 % 71% 22%
Avg. One-way 6.2 3.2 2.4 5.9 Travel Cost (Rs)
While sacred forest and temple site are similar in percent visitation, they differ in terms of average travel cost with travel cost to sacred forest being greater by a factor of three. Travel cost for non-sacred forest is also greater than for temple sites. It is important to note that this average travel cost presents a measure of willingness to pay for a one-way trip by site. However, respondents travel to these sites with differing frequencies, which is not captured here.
RP Models: Visitor Characteristics Sacred Forest Visitors The RP model for sacred forest visitors was not significant (α >.05). Variables with significant effects are presented below (Table 12):
SF model Sig. Variable Estimate Std. Error Pr(>Chi) Upper Caste -2.3182 1.0509 0.0357 * P-value = 0.6848 D2 = 0.0966 35
In the sacred forest model, only one variable yielded a significant effect. Here, upper caste has a significant, negative effect on sacred grove visitation in the past month (α < .05). This suggests a decreased probability of being a sacred forest visitor for persons of upper castes compared to persons of other caste categories, a difference of approximately 20% to 70% predicted probability of being a visitor (Figure 5).
However, this effect may or may not reflect a significant difference between the visitation patterns between upper castes persons and those of other caste categories, reflected in the large, overlapping error bars.
Though the model was not found to be significant, the model explained approximately 10% of the null deviance (D2 = .096). This suggests that approximately 90% of the deviance in the dataset is unexplained by the model. This small explanatory power, tempers the interpretation of the predictor variables as adequate predictors of sacred forest visitors.
Non-Sacred Forest Visitors The RP model for non-sacred forest visitors was significant at α < .10. Variables with significant effects are presented below (Table 13):
NS model Sig. Variables Estimate Std. Error Pr(>Chi) Primary Deity = Local 0.9957 0.5859 0.0288 * Age -0.0253 0.0206 0.0435 * log(Income) -0.3984 0.2540 0.0600 . 36
100% Food Purchased 0.6855 0.6810 0.0606 . P-value = 0.077 . D2 = 0.1834
Two variables were significant: local deity as primary deity and age (α <.05). Additionally, two variables were nearly significant: income and 100% food purchased (α < .10). Respondents who indicated local deity worship as their primary deity were over 20% more likely to have visited a non-sacred forest than those who did not worship a local deity primarily, indicated here as global deity (Figure 6)
However, once again it is uncertain that this significant effect represents a significant difference between these groups in terms of non-sacred forest visitation.
With increasing age and income, respondents were less likely to have visited non-sacred forest in the past month. Figures 7 and 8 present the non-sacred forest visitation by age and income respectively among persons who worship global and local deities primarily, in addition to the effect of 100% food purchase when local deity is primary: 37
Those who worship a global deity primarily are relatively less likely to have visited non-sacred forest than those who worship a local deity primarily. Additionally, those who live in a household which purchases all of its food are more likely to have visited non-sacred forest.
Though the model was found to be significant (α < .10), the model only explained approximately 20% of the null deviance (D2 = .1834). This suggests that approximately 80% of the deviance in the dataset is unexplained by the model. This small explanatory power, tempers the interpretation of the predictor variables as good predictors of non-sacred forest visitors.
Temple Visitors
The RP model for temple site visitors is very significant (P-value < .001). Variables with significant effects are presented below (Table 14):
Temple RP model Sig. Variables Estimate Std. Error Pr(>Chi) log(Distance to Sacred Grove) 1.99 23 0.9906 0.0240 * log(Travel Cost to Temple) -0.0103 0.1642 0.0399 * Built-up Area Residence 1.3894 0.6947 0.0107 * Primary Deity = Local -0.9513 0.5931 0.0266 * Degree Holder (higher ed) 2.0550 0.9141 0.0453 * Perception NSF 4 Services 0.5426 0.2045 0.0045** P-value = 0.000966 *** D2 = 0.267 38
Six predictor variables were significant in the temple visitor model (α <.05). The effect size of holding a professional or vocational degree is approximately the same as the difference in effect between strong disagreement and strong agreement to the statement that non-sacred forests are useful for ecosystem services. Additionally, the positive effect size of residence in an urban environment is approximately the same, though opposite, the negative effect size of primary worship of a local deity.
Figures 9, 10, and 11 present the Sanskritization effect in revealed temple preference. When global deity is primary, Figure 10 includes the effect of residence in a built-up area, and Figure 11 further includes the effect of holding a professional or vocational degree.
Sanskritization – primary worship of a global rather than local deity – and urbanization effects – residence in a built up area and holding a professional or vocational degree – increase probability of being a temple visitor. However, the Sanskritization effect on revealed temple preference is unclear, until added with other urbanization factors, shown here as built-up area residence and holding a vocational or professional degree.
Further, measured distance to the central sacred grove and travel cost to temple sites, based on reported distances, have significant increasing and decreasing effects on revealed temple preference respectively, as shown in Figures 12 and 13 below: 39
Respondents who live farther from the central sacred grove have a higher probability of being temple site visitors. Travel cost to temple site has a slight negative effect on temple site visitors. Disagreement that sacred forests are useful for ecosystem services, however, has a much larger negative effect.
This significant model explained approximately 27% of the null deviance (D2 = .267). This suggests that approximately 73% of the deviance in the dataset is unexplained by the model. This modest explanatory power is greater than the other RP models, though it is still small, which suggests caution in over-interpretation of the predictor variables in classification of temple visitors.
Sacred Forest – Temple Substitution, SF Visits when Temple is not visited When the subset of people who visited both temple sites and sacred forest were removed from the analysis of sacred forest visitors the model was significant (P-value < .05) with interesting, negative, and significant Sanskritization and urbanization effects as well as the negative effect of perception of NSF as useful for ecosystem services and distance to sacred grove (Table 15).
SF model (when not temple visitor) Estimate Std. Error Pr(>Chi) Sig. Variable log(Distance to Sacred Grove) -2.06656 1.05144 0.03243 * Built-up Area Residence -1.43294 0.73268 0.01225 * 40
Primary Deity = Local 0.84190 0.62645 0.06664 . Perception NSF 4 Services -0.44282 0.20869 0.02652 * P-value = 0.0187 * D2 = 0.2346463 It seems possible that this model reflects characteristics of those who do not visit temples more than the characteristics of those who visit sacred forests. Two factors support this claim: 1). The Sacred forest visitor’s model was insignificant. 2). the directionality of effects in the SF not temple model are opposite from the temple visitor model – which includes some who also visit sacred forest. Thus, this model is considered to offer characteristics of those who do not substitute sacred forest for temple visits.
Figures 14, 15, and 16 present a sacred forest-temple substitution by illustrating the intersection point between temple visitors and sacred forest visitors who are not temple visitors. The first two figures present the Sanskritization effect, while the third figure presents the Sanskritization and urbanization effect on the distance from sacred grove in which sacred forest-temple substitution is likely:
This point provides the distance from sacred grove in which one is less likely to visit a sacred forest and not a temple. When a local deity is worshipped primarily, the distance at which sacred forest visitation loses primacy is approximately 1.5 km. The Sanskritization effect reduces this distance by approximately half a kilometer. Lastly, the cumulative Sanskritization and urbanization effect further reduces this distance by more than a half kilometer, to a proximity nearer to sacred grove temple than the closest observed household. 41
This significant model explained approximately 23% of the null deviance (D2 = .267), suggesting approximately 77% of the deviance in the dataset is unexplained by the model. Interpretation is tempered with given this unexplained deviance.
Stated Preferences Contingent Valuation: Non-Sacred Forest The CV model for non-sacred forest visitors based on travel cost is very significant (P-value < .001). Variables with significant effects are presented below (Table 16):
NSF, Dist. model (CV) Sig. Variables Estimate Std. Error Pr(>Chi) Built-up Area of Residence -0.6401 0.4667 0.0410 * Age -0.0175 0.0132 0.0334 * log(Income) 0.3565 0.1454 0.0326 * Perception NSF 4 Services 0.4258 0.1579 0.0039 ** Mangar, Bandwhari 1.2828 0.7641 0.0040 ** P-value = 0.000640 *** D2 = 0.18079
Five variables were significant (α <.05), including built up environment residence, age, income, and perception of non-sacred forest as useful for ecosystem services. A notable variable that is not significant is the travel cost to the hypothetical non-sacred forest, based on the randomized distance offer.
Figure 17 presents the probability of accepting the offer to visit distant non-sacred forest by income, while Figure 18 is presented by age. Each figure also presents the effect of residence in a built environment and the effect of strong disagreement to strong agreement that non-sacred forests are useful for ecosystem services. 42
Higher income persons have a much higher probability of accepting the offer to visit the distant non-sacred forest than low-income persons (figure ?). Older aged persons are less likely to accept this offer than younger persons (Figure ?). Disagreement that non-sacred forests are useful for ecosystem services also has a substantial negative effect on non-sacred forest visitation probability, with residence in a built-up area having a more modest negative effect.
This significant model explained approximately 18% of the null deviance (D2 = .180). This suggests that approximately 82% of the deviance in the dataset is unexplained by the model. This modest explanatory power is in a similar range as the RP models, and suggests caution in over- interpretation of the predictor variables in understanding stated preferences for non-sacred forest.
Contingent Valuation: Sacred Forest rather than Non-Sacred Forest
The CV model for sacred forest visitors is very significant (P-value < .01). Variables with significant effects are presented below (Table 17):
SF model (CV) Sig. Variables Estimate Std. Error Pr(>Chi) log(SF Offered Travel Cost) -0.7720 0.2190 2.975e-05 *** 100% Food Purchased -1.5252 0.6123 0.0234 * Perception NSF 4 Services 0.2730 0.1744 0.0571 . P-value = 0.001122 ** D2 = 0.21833 43
Travel cost did not appear significant in the NSF model, whereas travel cost had a significant negative effect in the additional distance offers to visit sacred forest rather than non-sacred forest. The hypothetical travel cost variable and a variable indicating households which purchase 100% of the food they consume were significant (α <.05). Additionally, perception of non-sacred forests as useful for ecosystem services was close to significance at the α <.05 level.
Figure 19 presents the probability of accepting the additional travel cost to visit a sacred forest, and the effect of 100% food purchase and strong disagreement that non-sacred forests are useful for ecosystem services.
Travel cost has a slight negative effect, but is more pronounced among those of households which purchase all of the food they consume and those who strongly disagree that non-sacred forests are useful for ecosystem services.
This significant model explained approximately 22% of the null deviance (D2 = .218). This suggests that approximately 78% of the deviance in the dataset is unexplained by the model. This modest explanatory power is in a similar range as the RP models, and suggests caution in over- interpretation of the predictor variables in understanding stated preferences for sacred forest.
Contingent Valuation: Entry Fee for Nearby Non-Sacred Forest The CV model for non-sacred forest visitors based on entry fee is very significant (P-value < .001). Variables with significant effects are presented below (Table 18): 44
NSF Fee model (CV) Sig. Variables Estimate Std. Error Pr(>Chi) log(NSF Entry Fee) -0.58691 0.17757 0.0127 * Built-up Area of Residence -1.49088 0.48443 0.0019** Primary Deity = Local -0.79258 0.44999 0.0836 . Age 0.00364 0.01262 0.0937 . 10-12 Years Edu -1.41033 0.50665 0.0274 * Perception NSF 4 Services 0.38859 0.15551 0.0079 ** P-value = .0000563 *** D2 = 0.1868
The acceptance of hypothetical non-sacred forest visit with entry fee offers has four significant predictor variables (α <.05).: entry fee, built environment residence, 10-12 years of education, and perception of non-sacred forest as useful for ecosystem services. Two variables were nearly significant (α <.10): local deity as primary deity and age.
Figure 20 presents the Sanskritization effect on acceptance of the entry fee offer to nearby forest, while Figure 21 presents the sanksritization and urbanization effect on offer acceptance.
Sanskritization seems to cause an approximately 20% increase in offer acceptance, noting, however the overlapping confidence interval suggesting uncertainty. However, Sanskritization 45 and urbanization’s cumulative effect is an approximately 10% decrease in offer acceptance. Residence in a built up area – urbanization – has a larger negative effect than the positive effect of Sanskritization, here primary worship of a non-local deity.
Figure 22 and 23 presents the entry fee offers acceptance for nearby – hypothetical – non-sacred forest by entry fee and age, respectively. Figure 22 also provides urbanization and Sanskritization effects, while Figure 23 shows the negative effects of 10-12 years of education and disagreement that non-sacred forests are useful for ecosystem services.
Entry Fee’s negative effect, presented on a natural log scale, has a sharper rate of decrease at the lowest cost interval. Rate of decrease is more constant at higher cost intervals. Here, Sanskritization and urbanization variables have opposite effects, with urbanization decreasing probability of accepting the NSF entry fee offer.
Age has a slight increasing effect on NSF entry offer acceptance probability. Additionally, strong disagreement that non-sacred forests are useful for ecosystem services, relative to strong agreement, has an approximately equal negative effect as 10-12 years of education.
This significant model explained approximately 19% of the null deviance (D2 = .187). This suggests that approximately 81% of the deviance in the dataset is unexplained by the model. This modest explanatory power is in a similar range as the RP and SP models, and suggests caution in 46 over-interpretation of the predictor variables in understanding stated preferences for non-sacred forest.
Absolute Preferences: The absolute preferences among attributes levels presented in Figure 24
Absolute Forest Preferences
The attribute level of majority preference is bounded by a red box in the figure above, with the exception of activity, where a majority was not achieved. The second most common level preference was debris removal and is boxed in yellow. The most homogenous and the most heterogeneous preferences were for the attributes Temple and Activity, and in each case approximately 5% of respondents indicated no preference. For the attributes, the no-preference response is approximately 10%. Temple Presence and Natural Quality are nearly a consensus preference. 500 acre size was preferred by approximately 80% of respondents; 1 KM distance was preferred by approximately 60%. Within the activity levels: No extraction, debris removal, lopping and logging were preferred by approximately 45, 25, 12, 18 percent of the sample respectively.
47
Discrete Choice Experiment The magnitude of temple preference relative to important forest characteristics – size, natural quality, activity – as well as distance from home to forest is greater by more than a factor of two (Figure 25)
The insignificance of the attribute: size, suggests that increases from the baseline of 14 acres did not have a significant impact on forest choice relative to the other forest attributes. Additionally, the activity level, lopping did not have a significant impact on forest choice, relative to no- extraction level and the other attributes. Greater distances from home to forest, and logging relative to no extraction have a negative impact on forest choice.
48
DISCUSSION Revealed preferences
Sacred Forest Visitors The characteristics of sacred forest visitors within a 2.5km radius of the sacred grove seem heterogeneous given the lack of significant effects among predictor variables as well as the overall non-significant model. This lack of significance, suggests that the negative effect of upper caste on sacred forest visits should not be pursued in too much detail. However, upper castes have been associated with temple worship, suggesting the possibility of a Sanskritization effect. It may be the case that ritual practices of upper castes overlap less with the ritual practices associated with the sacred grove. However, upper castes were not found to visit temples more than other castes. Further, upper castes are not observed to be more likely to visit other site types compared to other caste groups. While it seems possible that upper castes do not prefer sacred forests as much as other caste groups, this effect may or may not represent actual differences in sacred forest preference.
Temple Site Visitors The temple site RP model is highly significant and offers the most explanatory power of the RP models. Consistent with expectations, but under supported in theoretical consideration, Sanskritization and urbanization increase probability of temple site visitation. However, it is notable to Sanskritization research that higher education has a much larger effect on probability of visiting a temple site than Sanskritization or urbanization. What is also notable is the geographic effect of distance from the central sacred grove, when controlling for reported travel cost to nearest sacred grove. The increased likelihood of visiting a temple among those who live farther away from the central sacred forest suggests a possible substitution of sacred forest visits for temple site visits, discussed in the RP conclusions section. An unexpected finding for Sanskritization studies is that temple site visitors seem more likely to think that non-sacred forests are useful for ecosystem services. Thus, increases in temple site visitation may correspond with higher valuation of non-sacred forest.
Non-Sacred Forest Visitors 49
The significant predictors (α < .05) of NSF visitation are Sanskritization and age. Assuming NSF visits reflect use-values for NSF, Sanskritization’s negative effect on NSF visits suggests decrease in the use of NSF with primary worship of a non-local deity. The significance of Sanskritization for NSF visitors provides evidence that Sanskritization is tied to livelihoods. The negative effect of age is also consistent with this interpretation, suggesting lower use of NSF probably due to lower mobility among older persons.
The less significant predictors which are significant at (α < .10) are income and 100% food purchase. The negative income effect seems to be simply explainable, since higher incomes have non-forest dependent livelihoods and NSF visits are not a necessity. Further, unnecessary NSF visits may be seen as a luxury experience in this case since higher income persons are less likely to visit NSF but were more likely to accept hypothetical distance offers to visit NSF (see CV, NSF distance). Across incomes, the increased likelihood of persons from 100% food purchasing households to visit NSF seems to also reflect the role of livelihoods on forest preferences, discussed below.
Sacred Forest – Non-Sacred Forest: Considering Livelihoods & Non-Extraction Geographies
Sacred Forests are conceived differently than alternate forests, which are not sacred. The latter being often the site of extraction for forest products. Thus, non-sacred forest visits provide insight into the impact of livelihoods on site type preferences. The effect of Sanskritization on decreasing sacred forest preferences was not found, but a Sanskritization effect was found in decreasing non-sacred forest revealed preference. Such visitation of non-sacred forests suggests use-values from non-sacred forests. Thus, local deity worship seems tied to practices of forest dependent communities at the site of sacred groves. However, Sanskritization does not contribute an effect to sacred forest visits, possibly because of the association of sacred forests with both local and global deities. This flexibility in association is the chief reason for comparing temple and forest preference, since the forest can be traded-off for temple sites within or outside of the sacred forest. This is discussed in terms of RP models looking at temple-sacred forest substitution in terms of actual visits and the SP, DCE model considering temple-forest substitution in terms a multiple-attribute hypothetical forest visit choice setting (See DCE).
The impact of livelihood changes which reduce forest dependence on NSF visitation was unclear. Increasing income and food purchase, here analyzed where 100% food is purchased, 50 suggest the possible measures of livelihood change reducing forest dependence. However, these variables were significant in the NSF model at a higher α level (α < .10), and with opposing directionality with income being negatively related to NSF visits.
Percent food purchased may not be clearly interpretable as reducing forest dependence. Percent food purchased – the continuous variable by which 100% food purchased was constructed – was negatively, though very weakly, correlated with income (-.09). Additionally, a significant difference in means was not found between incomes of the 100% food purchased group and those who have grown some quantity of their food (two-sample t (88.5) = -1.28, p = .21). This suggests 100% food purchase is unrelated to income. Households that purchase all food can be seen to be high or low income in urban or rural settings. For instance, wealthy rural, such as land owners, and wealthy urban persons could be expected not be included in the 100% food purchased category. This calls attention to the 100% food purchased effect as potentially reflecting lack of access to the means of agricultural production and increased dependence on forests, perhaps for cooking fuel or other non-edible goods. Alternately, it could simply reflect an increased value of non-sacred forest for luxury services, without reflecting a necessity. This variable’s lack of significance at α < .05 in addition to the low model significance, suggest caution in over-interpretation. However, both interpretations are possible in consideration that the 100% food purchased group was less likely to accept the additional distance offer to visit a sacred forest rather than a non-sacred forest in the CV exercise (See CV, SF). This is discussed as a potential deflation of the value of sacred forest relative to non-sacred forest in that section, which can be a matter of necessity or not. If NSF visits were more necessary for this group, WTP for additional distance to sacred forest could be limited by budget constraints. If NSF visits were valued for luxury services, WTP for additional distance to sacred forest would be unnecessary due to potential substitutability of SF with NSF for satisfying indirect use and non-use values.
Sacred Forest – Temple Substitution: Considering Substitution in Worship Sites The institutional setting of the sacred grove seems sensitive to geography, which appears evident in the identification of a distance from sacred grove at which sacred forest visits without temple visits becomes less likely than temple visits with or without sacred forest visits. The distance at which those who worship a local deity primarily appear more likely to visit a sacred forest and not a temple is approximately 1.5km residence from the sacred grove center. This substitution 51 distance is decreased by Sanskritization, urbanization, and higher education such that degree holders, residing in a built-up area who do not primarily worship a local deity appear less likely to visit a sacred forest and not visit a temple site at the closest household locations to the sacred grove center observed. This suggests that the cumulative effects of Sanskritization, urbanization, and higher education may highly reduce or eliminate the role of geography in sacred forest preferences. This may further suggest a non-geographical conception of sacred space or sacred sites. As a consequence, this seems to suggest a decline in the institution of the sacred grove since sacred groves are recognized geographically by definition. However, rather than decline, I argue that this result suggests a reconceptualization of sacred forest in a change to a definition of the sacred grove given shape by temple-mediated sacrality. Definitions of the sacred grove based on geographic-mediation and temple mediation were offered in interviews of actors living in proximity to these sacred groves. The geographical definition of sacrality appears in that some interviewees indicated preference for a particular temple in a particular sacred forest versus alternate temple sites due to perception of greater benefits conferred by worship at that particular site. This is what I term geographically mediated sacrality. Contrarily, some respondents expressed the mere quality of temple presence to create sacred space, which I term temple- mediated sacrality. It seems possible that this geographically mediated sacrality is reduced, or traded-off at greater distances from sacred forest, given the temple-sacred forest substitution effect. Further, the geographically-mediated definition of the sacred grove may be eliminated and reconfigured in terms of temple-mediated sacrality under Sanskritization, urbanization, and higher education scenarios.
Revealed Preference Conclusions
The Revealed preference models in this study suggest an institutional setting of the sacred grove which appears to depend on geographical proximity to the sacred grove as well as the existence of non-sacred forest in the surrounding landscape matrix. Therefore, change in the sacred grove institution seems likely to be traced to changes in the landscape composition surrounding sacred groves, particularly in non-sacred forests, as well as cultural changes. The landscape perspective on the sacred grove suggests some unanticipated effects in theories of sacred grove decline based on Sanskritization: 1). Revealed preference for non-sacred forest and temple sites are more influenced by Sanskritization than revealed sacred forest preferences. Where Sanskritization’s 52 effect is significant, it is one among other factors, and arguably not the most important, such as the substantial effect of higher education on temple preference. 2). While Sanskritization appears to reduce revealed NSF preference and increase temple preference, temple visitors perceive NSF to be more useful in terms of provisioning ecosystem services. These effects call attention to the unanticipated and multiple factors of landscape and cultural change in the institutional setting of the sacred grove. Attention to the effect of livelihood change may best infer use-values for forest while attention to the effect of Sanskritization and urbanization may best infer use-values in choice of worship site. Additionally, the increased ecosystem service perception from NSF of temple site visitors and NSF visitors highlights the need to attend to indirect and non-use values for these site types – which the SP models are considered to provide insight.
Lastly, Sanskritization, urbanization, and higher education seem to be particularly important factors in changing the sacred grove institution as their cumulative effects seem to differentiate temple visitors from non-temple visitors and prompt a substitution of exclusive visitation of sacred forest as a worship site with alternate temple site visits. Based on the likelihood of persons of these characteristics to appear to substitute temple visits for sacred forest visits, these characteristics are hypothesized to introduce a non-geographic conception of sacred space, that of temple-mediated sacrality. The relationship between temple and forest is further specified through the SP models.
Stated preferences The Contingent Valuation exercise provides nuance to the observation that Sanskritization and urbanization impact preferences in the institutional setting of the sacred grove. Particularly, these models provide insights into how indirect use values, and possibly non-use values, influence preferences for SF, NSF, and temple sites as well as preference for forest characteristics, especially the magnitude of temple-forest preference.
Non-Sacred & Sacred Forest, Distance Offers (CV) Travel Cost
While the respondents evidenced greater WTP for sacred forest rather than non-sacred forest, total WTP for these sites is difficult to derive due to multiple issues. Firstly, each demand curve has very ‘fat tails’ due to the fact that around half of the respondents accepted the highest offers, 53 making an upper bound for demand speculative. However, the total WTP for visits to these sites is less important than understanding variation in the demand for these hypothetical visits according to the urbanization and Sanskritization characteristics under study.
Travel cost did not appear significant in the NSF model, though it had a negative effect, whereas travel cost had a significant negative effect in the model for additional distance offers to visit sacred forest rather than non-sacred forest. This could suggest that the distances offered were either not far enough or were decisions were subject to hypothetical bias. Alternately, since distance is converted to travel cost in terms of income, the significant positive effect of income seems to be explaining offer acceptance better than travel cost in the CV NSF model based on distance. The positive effect of income in the CV, NSF distance offer model contrasts with the negative effect of income in the RP, NSF model. This can suggest that higher income persons were subject to hypothetical bias or that they hold higher non-use values for NSF.
Non-Sacred Forest (CV)
Acceptance of hypothetical NSF distance offers seems to also reflect livelihoods, like the RP NSF model. Here the negative effect of residence in a built up environment seems to reflect a lack of necessity for visiting NSF, and age seems to reflect lack of mobility. Additionally, the positive effect of greater perception of NSF as useful for ecosystem services suggests that indirect use values from these sites can increase preference despite declining use values.
The significant, positive effect of the Mangar Bani site location on accepting hypothetical distance offers to non-sacred forest may reflect endogenous preferences for NSF since this site arguably has the most available non-sacred forest. Site was not significant in any of the other RP or SP models.
Sacred Forest Rather than Non-Sacred Forest (CV)
The negative effect of the belonging to a household which purchases all household calories consumed on acceptance of Sacred Forest visit offers represents a decrease in WTP for sacred forest visits – relative to non-sacred forest visits – for households that do not grow any of their own food. This needs to be interpreted alongside the RP, NSF model showing that households which purchase 100% of the food they consume seem to prefer NSF over non-purchasing households (RP, NSF). As a proxy for market integration, or alternately lack of access to the 54 means of agricultural production, the significance of ‘100% food purchased’ in the hypothetical scenario for sacred forest, as well as the RP, NSF model, may reconfigure how sacred forest is differentiated from non-sacred forest. This effect can be stated as follows: with increasing market integration, the differential in sacred forest to non-sacred forest value is deflated. This could be due to 1). Increased luxury visits for NSF, with decreased forest dependence, corresponding with migration of non-use values from SF to also be shared with NSF; or 2). Increased necessity visits, with increased forest dependence, corresponding with greater budget constraints for additional travel distance for SF visits.
In the first case, with livelihood changes away from subsistence agricultural practices, the functional distinction between sacred and non-sacred forest would seem to decrease since forests in general are less utilized for material goods, loosening a functional definition of the sacred grove as a non-extraction zone. It seems likely that the value associated with the distinction of sacred forest relative to non-sacred forest would decrease. In other terms, all forests can functionally hold non-use values. This presents the particular conservation-oriented values associated with the sacred forest to migrate to forests in the surrounding landscape, or the non- sacred forest.
Non-Sacred Forest, Entry Fees (CV) The offer of Entry Fees to a hypothetical, nearby non-sacred forest provides significant urbanization and Sanskritization effects which I suggest further highlight the distinction in use and non-use values for non-sacred forest that occurs during urbanization. With the rise of urban space, non-sacred forests lose use-value due to decreasing direct-use dependence on forests. However, non-sacred forests also increase in non-use value as they present an increasingly rare land-use as urban space increases. In the context of more urban sites in my sample, entry fees may be associated with status quo visit options which require entrance fees to park lands, such as the Asola Wildlife Sanctuary in the study area. In the sites more distant from Delhi, this association may not be present.
Households in built environments may show less WTP in acceptance of entry fee offers than do those in non-built environments due to difference in use-values between these settings, where non-built settings have higher demand for direct-use of forest resources. On the other hand, the increase in WTP with Sanskritization, may be explained by difference in non-use values. 55
Sanskritization may increase WTP for the non-sacred forest entry fee because the forest is not needed for direct use of its materials, but for the indirect use of its recreational or other value. This may be a case where Sanskritization has generalized the particular non-use or indirect use values previously reserved for sacred forest to alternate forest.
DISCRETE CHOICE EXPERIMENT The interpretation of the DCE results provide insight into marginal preferences for forest characteristics. The figure displaying absolute preferences is overlaid with the marginal preferences derived from the DCE, where blue stars indicate marginal preferences, the no symbol indicates attribute levels with marginal disutility, and the blue slash indicates places of disagreement between absolute and marginal preference (Figure 26)
Marginal preference information agrees largely with the respondent’s stated absolute preferences. The key exception is 500 acre forest size, which is insignificant in a marginal preference context. This suggests respondents would be willing to trade-off large forest sizes given a choice setting involving land-use decisions. While individuals in the study would not be able to individually decide land-use at this scale, this preference is insightful for collective action settings. Here, the trade-off of large forest size – in preference for nearby, natural forests, with temples and debris removal – may represent a Sanskritization impact of increasing preference for small ‘temple forests’ rather than large sacred forests independent of the temple. Due to the heterogeneity of absolute activity level preference, debris removal is less clearly interpreted as a 56 trade-off rather than the expression of an absolute preference. However, change in extraction preferences are also important to consider.
Stated Preference Conclusions Taken together, the findings from the CV and DCE data generally agree expectations produced by consideration of the RP models. Further, these SP models enrich understanding of values for these sites and their characteristics. These SP models provide information on how respondent’s value visits of sacred versus non-sacred forests in addition to providing a view of the most important characteristics in forests. As with the RP models, urbanization appears to decrease NSF preference, and livelihoods appear important in preference. In terms of forest type: while sacred forest is the most preferred site, market integration, measured as 100% food purchased, travel cost, and negative ecosystem service perception of NSF reduce this differential preference. In terms of forest characteristics: while natural, large, nearby forest that has a temple and no extraction are the highest absolute preferences, size appears to be traded off as an attribute of less importance than the others. In particular, large forest size appears to lose to temple presence in the forest, which is the most important ‘forest’ attribute by a factor of two. Since such great magnitude difference in temple preference relative to forest attributes, attention to the conservation of sacred and non-sacred forests amidst urbanization and culture change is demanded.
Theoretical Implications for Sanskritization Research as offered by the Sacred Grove The study adds evidence to the observation that Sanskritization and urbanization present threats to the conservation of sacred forests. Quantitative analysis of this observation in a landscape framework provides the unexpected finding that Sanskritization and urbanization effects on the sacred grove institution may be best observed in the marginal preferences for alternative site types: temple sites and non-sacred forest in the landscape matrix surrounding sacred forest. This finding demands increased attention to the institutional setting of the sacred grove, and particularly the relationship between changes in the landscape matrix surrounding sacred groves and the land-use preferences of residents in this setting.
While Sanskritization suggests a potential threat, evidence is also presented that Sanskritization trends involve a reconceptualization of non-sacred forest. While primary worship of global deity, Sanskritization, had a negative effect on probability of actual visit to non-sacred forest, 57 hypothetical visit questions suggest greater WTP for non-sacred forest entry fees with Sanskritization. The finding of an opposite effect for urban households further highlights the Sanskritization effect by helping to differentiate the effect of deity type worshipped from household area land-use, land-cover. This differentiation between deity type worshipped and land-use, with the same directionality, is evidenced in revealed preference for temple sites and sacred forest when temple is not visited. The RP, NSF model also provides an interesting differentiation between Sanskritization and 100% food purchased.
The development of these conservationist preferences in non-sacred forest may yield positive collective action results for community forests facing urbanization threats in which land-use is increasingly contested and alternate uses become feasible tradeoffs with the status quo landscape. Sanskritization, as a transfer from local to global deity worship within the tradition of Hinduism, accords with cultural evolution studies on the relation of complex societies and ‘big Gods.’ The enabling and constraining impacts of these changes for collective action within the institutional setting of the sacred grove are considered to lead to two options: 1). a wider cosmological and social world which creates the enabling conditions for multi-scale governance linkages or 2) a shift in the locus of significance from the forest to the temple, suggestive of a transition from commons to open-access land subject to degradation in the absence of enforcement external to the institutional setting of the sacred grove. This study finds evidence in both directions, and recommends further study of collective action in community forest settings sensitive to cultural evolution.
Implications of Urbanization & Culture Change for Conservation of Culturally Significant Forests If Sanskritization as decline in the sacred forest institution accurately describes real changes in this complex socio-ecological system, conservation initiatives need to attend to the forests outside of sacred forests, those that are ‘non-sacred.’ The sacred forest in the form of the definition as a place ‘set apart’ – and where extraction is accordingly prohibited – is at stake if surrounding non-sacred forest is depleted and livelihood changes have not reduced the demand for timber and non-timber forest products. This is because non-extractive SF visits can only be supported when forest products are required if NSF is available.
Overall, relative to the other forest characteristics described, the temple appears more important for visit choice by a factor of two. In this marginal preference context, large forest size and zero 58 extraction level appear to be traded off, suggesting some concern regarding the relation of temple preference and forest conservation demand. Some respondents clearly articulated a view of the sacred forest as only sacred by virtue of temple presence.
Yet, the implications of this temple preference may not be antagonistic to conservation motives. Perhaps unexpected to Sanskritization literature, temple preference seems to correspond with conservation-oriented forest preferences for non-sacred forests. These conservation preferences are suggested by a perception of non-sacred forests as more useful for ecosystem services among temple site and non-sacred forest visitors. An increase in non-use values for non-sacred forest may also be evident in the observation of greater willingness to pay for an entrance fee for non- sacred forest with Sanskritization. Taken together, these suggest a potential increase in perception of non-sacred forest value and a potential willingness to pay for its indirect use value from their provision of ecosystem services.
Revealed preference for temple sites indicates a greater perception of benefits of ecosystem services from non-sacred forest. Additionally, each stated preference model – for sacred and non-sacred forest – also evidences a positive effect between offer acceptance and NSF ecosystem service perception. This seems to suggest that actual temple site visitors hold a greater value for non-sacred forest than others. Contrarily, sacred forest visitors, when temple is not visited, are less likely to view NSF as useful for ecosystem services.
Additionally, temple preference appears to correspond with preferences against use-values for forest, in terms of the marginal disutility of logging in the discrete choice context. These findings may demonstrate a reorientation of forest preferences, in a migration of non-use values from exclusively expression toward sacred forest to also be applied to non-sacred forest.
Further, these changing preferences, in addition to supplying new motivations for forest conservation, may correspond with new capacities for addressing collective action problems. Recent cultural evolution research (Heinrich, Purczyski) illustrates that cooperation may be enhanced among strangers, particularly co-religionist strangers – in societies with more market integration and global deity worship. Cooperation is now understood as foundational for collective action. Additionally, the role of shared narrative as solution to collective action problems has recently received renewed attention (Mayer). These enabling collective action conditions are densely present in the case of the sacred grove amidst urbanization. Here, global 59 deities are worshipped alongside local deities, and the traditions of these local deities become intertwined with the Sanskrit traditional forms of the global Hinduism. Thus, the sacred grove provides a case where the narrative subject has a clear and defined geography in the landscape. Further, the sacred grove amidst urbanization expands the institutional setting of the sacred grove from the forest-dependent community and the specialist to a general set of actors, spanning urban and rural contexts. This expansive new geography of the sacred grove is brought about in the very mechanisms of Sanskritization, which links local and global deity worship, and urbanization, which increases market integration. In this expansive context, new capacities for collective action come to being which can serve the conservation of sacred groves. Duara’s interest in sacred groves in terms of re-enchantment, and his suggestion of the commons as a new sphere for the sacred, points to the ability of the localized sacred grove to take on much broader significance in wider conceptions of commons among wider sets of actors (p.17).
I argue that this generalization of the sacred grove institution is taking place in terms of an increased perception of the non-use values of non-sacred forests. However, it seems that in the urbanization and Sanskritization dynamic, this generalization can be reduced to abstraction, leading to a situation of institutional decline and transition from commons to open-access land. The prominence of the temple relative to important forest characteristics, particularly forest size, calls attention to this potential abstraction. Yet, concrete organizing campaigns which have centered on temples situated in sacred forests suggest that the temple can be a particularly influential site of forest conservation organization. The temple, as home to local and global deities, provides an institutional center to such organizing which can draw connections between those devoted to particular local deities in forest-dependent communities and distant co- religionists devoted to global, Hindu deities. These connections can be given concrete form through actual visits to temple sites, visits often repeated at regular intervals. Through Sanskritization, the place-based devotion to the deity of the sacred forest may be made increasingly conversant with wider, non-geographical or global, forms of devotion to Hindu deities. Alongside Sanskritization, narrative forms linking the ‘local’ and the ‘global,’ can provide coherence between groups of disparate deity devotees and offer the possibility of creating stakeholders in the conservation of forests extending from the sacred grove. 60
Research on collective action settings sensitive to cultural evolution should take into account the variety of institutional changes occurring within an appropriate landscape scale. This landscape scale needs to attend closely to particular socio-ecological shifts at the site of conservation consideration. This site level must be linked to the policy scale and the interconnections between stakeholders mediating stakeholders must be appreciated. Lastly, often disregarded but of critical importance to conservation initiatives, operative narratives at each stakeholder scale should be analyzed to identify points of agreement and disagreement. Though sometimes difficult to apprehend, these narrative linkage provide the opportunities for dynamic conservation movements which can ‘scale-up’ the particular site-level conservation initiative for a culturally significant forest into a scale appreciable for more significant conservation outcomes.
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ACKNOWLEDGEMENTS
Dean Urban Marc Jeuland Prasenjiit Duara Steven Anderson Ram Oren Randy Kramer Baba, Deepak, Jaiveer, Pratibha Nikhil, Akash Mangar Bani
Funding Sources: Forest History Society Environmental Humanities in Asia Nicholas School of the Environment Related Funds below: Nicholas School International Internship Fund KLN Fund Whitney Chamberlin Fund
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APPENDICIES Appendix A: Land-Use, Land-Cover Analysis
Observed Land Cover Pixel Classes Values Description Deciduous Woody vegetation with a percent cover >60% and height exceeding 2 m. Consists of broadleaf tree communities with an annual cycle of leaf-on and leaf-off 1 periods. Cropland Temporarily cropped area followed by harvest and a bare soil period (e.g. single and multiple cropping systems). Note that perennial woody crops will be classified as either forest or shrubland, whichever is appropriate. Includes orchards. Different types of cropland based on seasons (e.g. kharif, rabi, zaid) were 2 not subclassified. Built up Land covered by buildings and other man-made 3 structures Mixed Trees with a percent cover >60% and height exceeding 2 Forest m. Consists of tree communities with interspersed mixtures or mosaics of the other four forest types. None 4 of the forest types exceeds 60% of landscape Shrub Land with woody vegetation less than 2 m in height and with greater than 10% shrub canopy cover. The shrub 5 foliage can be either evergreen or deciduous Water Areas with surface water, either impounded in the form of ponds, lakes, reservoirs or flowing as streams, rivers, 9 etc. Can be either fresh or salt-water bodies Grass Herbaceous types of cover. Tree and shrub cover is less 14 than 10%
Appendix B
Appendix B: Land Cover Change 1985 - 1995 Land 2 3 5 14 Cover Cropland Built Shrub Grass Up 2 0.984 0.000 0.016 0.000 5 0.020 0.000 0.980 0.000 14 0.000 0.000 0.000 1.000 1995 - 2005 66
Land 2 3 5 14 Cover 2 0.667 0.317 0.016 0.000 5 0.059 0.000 0.941 0.000 14 0.000 0.083 0.000 0.917 Summary of Proportional Land Cover by Year Land 2 3 5 14 Cover 1985 0.626 0.000 0.253 0.121 1995 0.621 0.000 0.258 0.121 2005 0.429 0.207 0.253 0.111
Revealed Preferences: Appendix C: Sacred Forest Model (RP) SF model Estimate Standard Pr(>Chi) Variable Error (Intercept) 2.06166 2.16845 log(dis2sg) -0.38834 0.87826 0.64330 log(discstWS) -0.02198 0.15215 0.77506 log(discstR) 0.26257 0.26649 0.49087 built 0.37026 0.57427 0.80495 mst.reg.nativ == 1TRUE -0.34149 0.58021 0.86973 age -0.01139 0.01767 0.67803 log(IncRemix) -0.09983 0.22208 0.57709 as.factor(allfoodpurch)1 -0.76444 0.67203 0.20917 Degreed -0.37313 0.73105 0.75521 l8high 0.52084 0.66700 0.47975 perep.ns.useserv 0.06339 0.18992 0.76084 as.factor(SC)1 -1.04005 0.79783 0.18359 as.factor(BCa)1 0.21213 0.79230 0.83536 as.factor(UCaste)1 -2.31825 1.05090 0.03573 * site == 1TRUE 1.52203 0.94338 0.28942 site == 4TRUE 1.03549 0.75140 0.17011 P-value = 0.6848 67
D2 = 0.0966
Appendix D: Non-sacred Forest Model (RP) NS model Estimate Standard Pr(>Chi) Variable Error (Intercept) 1.90368 2.50526 log(dis2sg) -0.44434 0.75336 0.62286 log(discstR) 0.36102 0.28900 0.38592 built 1.01061 0.62784 0.33136 mst.reg.nativ == 1TRUE 0.99574 0.58592 0.02881 * age -0.02538 0.02062 0.04353 * log(IncRemix) -0.39840 0.25409 0.06004 . as.factor(allfoodpurch)1 0.68558 0.68107 0.06064 . Degreed 0.34625 0.87202 0.21370 l8high 0.67719 0.79908 0.41855 perep.ns.useserv 0.19942 0.18238 0.35263 as.factor(SC)1 0.41972 1.06291 0.73728 as.factor(BCa)1 0.19245 0.78276 0.93679 as.factor(UCaste)1 0.77620 1.76282 0.64783 site == 1TRUE 0.68828 0.75799 0.36220 P-value = 0.077 D2 = 0.1834
Appendix E: Temple Site Model (RP) WS model Estimate Standard Pr(>Chi) Variable Error (Intercept) -4.552678 2.309452 log(dis2sg) 1.992353 0.990629 0.024085 * log(discstR) 0.325451 0.281006 0.576636 log(discstWS) -0.010313 0.164217 0.039954 * built 1.389429 0.694702 0.010733 * mst.reg.nativ == 1TRUE -0.951305 0.593175 0.026694 * age 0.063688 0.025002 0.263338 log(IncRemix) 0.001485 0.202928 0.797338 68 as.factor(allfoodpurch)1 1.007284 0.689938 0.689060 Degreed 2.055043 0.914109 0.045363 * l8high -1.157655 0.829289 0.246837 perep.ns.useserv 0.542628 0.204580 0.004519 ** as.factor(SC)1 -1.302749 0.845479 0.136476 as.factor(BCa)1 -0.329597 0.774969 0.774202 as.factor(UCaste)1 -1.156501 1.358603 0.413076 site == 1TRUE -0.625209 0.983762 0.706506 site == 4TRUE -0.547151 0.981920 0.572322 P-value = 0.000966 D2 = 0.267
Appendix F: Sacred Forest and not Temple Visitor Model (RP) SF model (when not temple Estimate Standard Pr(>Chi) Visitor) Error Sig. Variable (Intercept) 4.26878 2.35122 log(dis2sg) -2.06656 1.05144 0.03243 * log(discstR) 0.03379 0.31969 0.41350 log(discstWS) 0.04017 0.17134 0.24839 built -1.43294 0.73268 0.01225 * mst.reg.nativ == 1TRUE 0.84190 0.62645 0.06664 . age -0.06609 0.02793 0.13839 log(IncRemix) -0.08354 0.20160 0.83785 as.factor(allfoodpurch)1 -0.90239 0.73828 0.31645 Degreed -1.81322 0.93950 0.13594 l8high 1.16936 0.87157 0.24917 perep.ns.useserv -0.44282 0.20869 0.02652 * as.factor(SC)1 -0.28785 0.96575 0.69664 as.factor(BCa)1 0.34013 0.81125 0.68055 as.factor(UCaste)1 -15.34014 1643.05146 0.32663 site == 1TRUE 0.39637 1.05400 0.89796 site == 4TRUE 0.80921 1.03525 0.42442 P-value = 0.0187 D2 = 0.2346463 69
STATED PREFERENCES Appendix G: Contingent Valuation (CV) Questions I will now ask you questions about your willingness to travel to forest sites based on their sacred quality. I will ask for your preference for visiting two hypothetical forest areas, which differ based on their sacred quality.
Assume that the two hypothetical forests have the following characteristics: Each is 150 acres with no extraction, and they both include a temple and are natural or not planted. However, one forest is a sacred forest and the other is not. Even though the two forests have the same characteristics above, they differ based on their sacred quality: One is a sacred forest and is very spiritually powerful, rewarding those who worship there by fulfilling their wishes. The other is a non-sacred forest, being not spirituality powerful and one’s wishes are rarely fulfilled when visiting. We will ask you a few questions about your willingness to regularly travel to visit these forest sites on an approximately monthly basis.
In thinking about your forest visit choice, please keep in mind the following: Yours and your family’s income and economic status, Your current spending, Any alternative worship sites less than 10 km from your home, and Your transportation mode options. Q1: Suppose the non-sacred grove was very convenient and close to your home (a very short walk away). Also suppose there were an entry fee to go into the non-sacred forest. What is the highest entry fee you would pay to visit the non-sacred forest on a regular basis? In other words, you would not visit the non-sacred forest if the entry fee were above this fee. [RANDOMIZED ENTRY FEE PRESENTED, Rs 10,20,40,60,150, or 200]
Q2: Now suppose there were no entry fee, but the non-sacred forest was not close by. How far would you be willing to go to visit the non-sacred forest with this same frequency, if it did not cost anything? [RANDOMIZED DISTANCE OFFERED: 2,4,6,10,13,or 20 KM]
Q3: You just stated: You would travel _____ to go to this non-sacred forest. Now assume the sacred forest is located farther away than the non-sacred forest. I want to know, would you be willing to travel an additional distance to visit the sacred forest instead of this non-sacred one, or would you choose the non-sacred one instead since the sacred one is located farther away. Please answer if you are willing or unwilling to travel ______additional KM to visit the sacred forest. [RANDOMIZED DISTANCE OFFERED, 1,5,10,15,20, or 60 KM]
Appendix H: Non-Sacred Forest Entry Fee model (CV) NS Entry Fee model (CV) Estimate Standard Pr(>Chi) Variable Error (Intercept) 0.512717 1.807277 log(NS Entry Fee) -0.586913 0.177571 0.012729 * built -1.490881 0.484431 0.001964 ** mst.reg.nativ == 1TRUE -0.792585 0.449997 0.083686 . age 0.003646 0.012621 0.093776 . log(IncRemix) 0.058452 0.138804 0.590737 as.factor(allfoodpurch)1 -0.433261 0.452822 0.302199 Degreed 1.929901 0.555330 0.114724 l8high -1.410333 0.506656 0.027480 * 70 perep.ns.useserv 0.388598 0.155511 0.007993 ** as.factor(SC)1 0.692555 0.670485 0.354042 as.factor(BCa)1 0.277930 0.509001 0.408337 as.factor(UCaste)1 -0.678980 0.690332 0.301573 site == 1TRUE 0.647780 0.608536 0.117188 site == 3TRUE 0.236444 0.605624 0.147581 site == 4TRUE -0.657280 0.570880 0.244587 P-value = 5.633938e-05 D2 = 0.1868283
Appendix I: Non-Sacred Forest – Distance Offer Model (CV) NS, Distance model (CV) Estimate Standard Pr(>Chi) Variable Error (Intercept) -0.44660 1.67121 log(NS Offered Travel Cost) - 0.51648 0.17941 0.135418 built -0.64012 0.46679 0.041099 * mst.reg.nativ == 1TRUE -0.66797 0.48170 0.699244 age -0.01757 0.01320 0.033498 * log(IncRemix) 0.35657 0.14541 0.032617 * as.factor(allfoodpurch)1 -0.33051 0.51174 0.721881 Degreed -0.12834 0.54643 0.615263 l8high 0.27650 0.51505 0.224874 perep.ns.useserv 0.42584 0.15798 0.003978 ** as.factor(SC)1 -1.00181 0.67430 0.233763 as.factor(BCa)1 -0.26260 0.58985 0.732628 as.factor(UCaste)1 -1.01412 0.75168 0.178492 site == 1TRUE 1.28283 0.76410 0.004020 ** site == 3TRUE -0.38117 0.66017 0.758309 site == 4TRUE -0.72171 0.59482 0.219758 P-value = 0.0006402666 D2 = 0.1807928
Appendix J: Sacred Forest rather than non-sacred forest Model (CV) SF model (CV) Estimate Std. Error Pr(>Chi) Variable 71
(Intercept) 5.19489 2.10937 log(SF Offered Travel Cost) - 0.77203 0.21906 2.975e-05 *** built 0.32679 0.62180 0.98482 mst.reg.nativ == 1TRUE 0.85577 0.60304 0.16435 age -0.02386 0.01586 0.20889 log(IncRemix) 0.07527 0.16580 0.71019 as.factor(allfoodpurch)1 -1.52527 0.61237 0.02346 * Degreed -0.54233 0.62192 0.54321 l8high 0.63083 0.59624 0.35124 perep.ns.useserv 0.27305 0.17446 0.05712 . as.factor(SC)1 -0.37576 0.76436 0.38944 as.factor(BCa)1 0.69377 0.73409 0.43666 as.factor(UCaste)1 -0.49592 0.78937 0.67584 site == 1TRUE -1.17997 0.75364 0.45797 site == 3TRUE -1.62941 0.83910 0.14656 site == 4TRUE -1.02719 0.72339 0.14725 P-value = 0.001122238 D2 = 0.2183295
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Discrete Choice Experiment (DCE) Appendix K: Choice Sets of the DCE
FOREST OPTION 1 FOREST OPTION 2 Size Distance Size Distance Set Quality Temple Activity Quality Temple Activity (Acres) (KM) (Acres) (KM) 1 natural absent 150 lopping 5 planted absent 45 no extraction 3 2 natural absent 150 lopping 5 planted present 500 debris rem. 1 3 natural absent 500 no extraction 3 planted absent 150 lopping 1 4 natural absent 500 no extraction 3 planted present 14 logging 7 5 natural present 150 debris rem. 1 planted present 45 logging 7 6 natural present 150 debris rem. 1 planted absent 500 no extraction 5 7 planted absent 14 no extraction 1 natural absent 500 lopping 7 8 planted absent 14 no extraction 1 natural present 45 logging 5 9 planted absent 45 lopping 7 natural absent 14 no extraction 5 10 planted absent 45 lopping 7 natural present 150 debris rem. 3 11 planted present 500 lopping 1 natural present 150 no extraction 7 12 planted present 500 lopping 1 natural absent 14 debris rem. 5 13 planted present 45 debris rem. 3 natural present 14 logging 1 14 planted present 45 debris rem. 3 natural absent 150 no extraction 7 15 natural present 45 no extraction 5 planted present 14 lopping 3 16 natural present 45 no extraction 5 planted absent 150 logging 1 17 natural present 500 logging 7 planted present 150 debris rem. 5 18 natural present 500 logging 7 planted absent 14 lopping 3 19 planted present 14 logging 5 natural present 500 debris rem. 3 20 planted present 14 logging 5 natural absent 45 lopping 1 21 planted absent 500 debris rem. 5 natural absent 150 logging 3 22 planted absent 500 debris rem. 5 natural present 14 no extraction 1 23 natural absent 14 debris rem. 7 planted absent 500 logging 5 24 natural absent 14 debris rem. 7 planted present 45 no extraction 3 25 planted absent 150 logging 3 natural absent 45 debris rem. 1 26 planted absent 150 logging 3 natural present 500 lopping 7 27 natural present 14 lopping 3 planted present 500 no extraction 1 28 natural present 14 lopping 3 planted absent 45 debris rem. 7 29 natural absent 45 logging 1 planted absent 14 debris rem. 7 30 natural absent 45 logging 1 planted present 150 lopping 5 31 planted present 150 no extraction 7 natural present 45 lopping 5 32 planted present 150 no extraction 7 natural absent 500 logging 3
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Appendix L: DCE Results
Mixed Conditional Logit Results
Dependent
variable: Choice Base = Planted, No Temple, No Activity *** Quality: Natural 0.420 (0.075) *** Temple: Present 1.098 (0.111) *** Activity: Debris Removal 0.431 (0.141) Activity: Lopping 0.129 (0.113) *** Activity: Logging -0.470 (0.135) *** Distance: 1,3,5,7 KM -0.172 (0.040) Size: 14,45,150,500 Acres 0.020 (0.042) Observations 927 * p<0.1; ** Note: p<0.05; *** p<0.01
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RP Travel Cost Variables
SF Travel Cost = Reported dist.near.sf*sf.travrate)* TimeValueConvert)+( sfdriver* dist.near.sf*2.1)
WS Travel Cost = ((Reported dist.near.ws*ws.travrate)* TimeValueConvert)+(wsdriver*dist.near.ws*2.1) SP Travel Cost Variables
SF travel cost (CV) <- SF Distance Offered*sf.travrate*(TimeValueConvert)+( sfdriver* SF Distance Offered*2.1)
NSF travel cost (CV)<- NSF Distance Offered*sf.travrate*(TimeValueConvert)+(sfdriver* NSF Distance Offered*2.1)
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i Despite disagreement on underlying causes, each author’s project shares properties as an attempt to confront the contemporary social fact that God is dead in the order of knowing and the ecological fact that the world is dying, as concurrent and correlated events ii Also see
Ormsby, Alison A. and Bhagwat, Shonil A. (2010). Sacred forests of India: a strong tradition of community based Natural Resource management.
Das (2006) Cultural Diversity, Religious Syncretism and People of India: An Anthropological Interpretation iii https://mangarbani.wordpress.com/2013/04/19/save-mangar-bani-delhis-last-sacred-forest-grove/ iv http://bani-dham.blogspot.com/2012/06/sacred-forest-struggles-for-survival-in.html v See a 2015 article noting the documentation of sacred groves in Maharashtra, described as “First Mission to Save ‘Sacred Groves’ in 30 years” – since the work of noted forest historian Gadgil. : http://timesofindia.indiatimes.com/city/kolhapur/First-mission-to-save-sacred-groves-in-30- years/articleshow/48783868.cms