Country ‘choices’ or deforestation paths: A method for global change analysis of human-forest interactions

Journal of Environmental Management (2001) 63, 133–148 doi:10.1006/jema.2001.0466, available online at http://www.idealibrary.com on

Country ‘choices’ or deforestation paths: A method for global change analysis of human-forest interactions Gary Koop† and Lise Tole‡* †

Department of Economics and ‡ Centre for Development Studies, Adam Smith Building, University of Glasgow, Glasgow G12 8RT Received 31 October 1999; accepted 23 April 2001

Data used in quantitative studies of global tropical deforestation are typically of poor quality. These studies use either cross-sectional or panel data to measure the contribution of social and land use factors to forest decline world wide. However, there are pitfalls in the use of either type of data. Panel data studies treat each year’s observation as a distinct, reliable, data point, when a careful examination of the data reveals this assumption to be implausible. In contrast, cross-sectional studies discard most of the time series information in the data, calculating a single average deforestation rate for each country. In this paper, we argue for a middle road between these two approaches: one that does not treat the time series information as completely reliable but does not disregard it altogether. Using a well-known global forest data set (FAO’s Production Series Yearbooks), we argue that the most the data can reliably tell us is whether a country’s deforestation rate falls into one of four categories or country ‘path choices’. We then use the data categorised in this way in a small empirical investigation of the socio-economic causes of deforestation. This multinomial logit framework allows for the determination of the influence of independent variables on the probability that a country will follow one deforestation path vs. another. Results from the logit analysis of key social and land use indicators chosen for their importance in the literature in driving deforestation suggest that the effect of these variables will differ for countries depending on the particular set of deforestation trajectories in question.  2001 Academic Press

Keywords: tropical deforestation; human-forest interactions; multinomial logit analysis.

Introduction Deforestation is a serious and growing problem in many countries of the developing world (WRI, 1994/5, 1996/7). Widespread deforestation leads to a loss of essential supplies of timber, fuelwood, plants, animals, and medicines on which largely poor populations depend. It also leads to a loss of the ecosystem services forests provide. These services range from the maintenance of soil nutrients, to the regulation of climate and water flows, to the stabilization of landscapes and the protection of watersheds. The changes caused by deforestation Ł Corresponding author Email: [email protected]. The second author wishes to acknowledge the financial support of the CVCP (UK).

0301–4797/01/100133C16 $35.00/0

also have socio-economic costs, and threaten future economic growth and welfare. Several studies have quantified the national welfare losses of tropical deforestation for several developing countries and have found them to be substantial (e.g. Repetto et al., 1989; Adger et al., 1995). At the global level, large-scale deforestation increases atmospheric warming due to the destruction of forest carbon sinks and increased burning of forest vegetation and its decomposition (Myers, 1997). The destruction of forest habitats and other ecological changes also contributes to the loss of global biodiversity (Maini and Ullsten, 1993; Pearce and Moran, 1994). International concern about the ecological and socio-economic impacts of deforestation at all levels—local, national, regional and global—has generated a growing body of research into its causal factors and their measurement (Amelung and  2001 Academic Press

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Diehl, 1992; Capistrano and Kiker, 1995; Pearce and Moran, 1994; Kaimowitz and Angelsen, 1998).

Context To date, studies on the causes of tropical deforestation have taken the form of either qualitative case studies or statistical analyses. The latter studies use cross-country or cross-regional data, taking as their dependent variable some measure of average forest loss (e.g. Inman, 1993; Bilsborrow and Geores, 1994; Rudel, 1994; Tole, 1998). The statistical tool used in these studies is always a variant of multiple regression. These statistical studies can be divided into those which are purely cross-sectional and those which use panel data. Cross-sectional analyses do not incorporate underlying trends or patterns over time because the data used is simply an average deforestation rate. This is the case whether deforestation is measured as an average percentage change or average number of hectares lost. Panel data studies (e.g. Allen and Barnes, 1985; Cropper and Griffiths, 1994) avoid the problem of lost information by introducing a time series component into the analysis. Panel data are observed over many years (time series) and across many countries or regions (cross-sectional). Yet panel data deforestation studies suffer from the drawback that good quality time series for a broad range of social and land use indicators (e.g. income, welfare, fuelwood use) at the country and regional level often do not exist. Hence the number of such variables that can be included in any Table 1.

study is limited. Time series data are available in the FAO’s Production Series Yearbooks. This data set is the only comprehensive source for time series estimates of land use and forest cover change. However, results derived from panel data studies based on this data set are questionable. These problems can be highlighted by a detailed examination of the characteristics of this widely used data set. Table 1 lists deforestation rates for 58 tropical developing countries for which data are available on both forest cover and a variety of potential independent variables. An examination of the average annual percentage rate of forest loss reveals a wide heterogeneity across countries. Some countries exhibit extremely high rates of deforestation (Thailand, El Salvador). Some exhibit comparatively moderate rates (Colombia, Dominican Republic). Others exhibit stable or expanding forest cover (Botswana, Pakistan). Closer examination, however, reveals that these average figures hide some unusual time series patterns. The panels in Figure 1 plot the proportion of forest cover for representative countries over time. For the most part, a straight line is observed, with many countries showing extreme deforestation tendencies: Nicaragua, for example, appears to be losing forest cover at the exact same rate per year. This rather implausible pattern is repeated for many countries not reported here. Even when deforestation rates are not constant over the time period, they remain implausibly unchanged for long periods. Viewed from a statistical standpoint, where deforestation rates are the same for all or a substantial number of periods, the slope coefficients in a regression of deforestation rates on

List of countries. Annual average % change in forest levels and years of data availability

Bangladesh Benin Bolivia Botswana Brazil Burkina Faso Burundi Cameroon C. Afr. Rep. Colombia Congo Cote d’Ivoire Dom. Rep. Ecuador El Salvador Ethiopia Ghana Guatemala Guinea

0Ð51 1Ð40 0Ð51 0Ð01 0Ð43 0Ð80 0Ð01 0Ð42 0Ð03 0Ð61 0Ð10 2Ð31 0Ð32 1Ð81 2Ð19 0Ð35 0Ð78 1Ð29 0Ð39

61–92 61–91 61–92 61–89 61–92 61–92 61–92 61–92 61–92 61–92 61–92 61–92 61–92 61–92 61–92 61–92 61–92 61–92 61–92

Haiti Honduras India Indonesia Jamaica Kenya Liberia Malaysia Malawi Mali Mauritania Mexico Myanmar Namibia Nepal Nicaragua Niger Nigeria Pakistan

2Ð30 1Ð89 0Ð63 0Ð44 0Ð50 0Ð77 1Ð12 0Ð93 0Ð40 1Ð23 0Ð22 1Ð14 0Ð00 0Ð17 1Ð05 2Ð31 2Ð11 1Ð92 3Ð46

61–89 61–92 61–92 61–92 61–91 61–92 61–86 61–92 61–92 61–91 61–92 61–92 61–89 61–92 61–86 61–90 61–89 61–92 61–92

Panama Pap.-N.G. Paraguay Peru Philippines Rwanda Senegal Sierra Leone Somalia Sri Lanka Sudan Tanzania Thailand Togo Trin.& Tob Venezuela Zambia Zimbabwe

1Ð26 0Ð05 1Ð63 0Ð34 1Ð70 0Ð49 0Ð33 0Ð15 0Ð11 0Ð39 0Ð61 0Ð28 2Ð43 0Ð84 0Ð43 0Ð85 0Ð23 0Ð39

61–92 61–92 61–92 61–92 61–92 61–92 61–91 61–92 61–92 61–92 70–91 61–88 61–92 61–92 61–91 61–91 61–91 61–92

Country ‘Choices’ or deforestation paths 0.140 0.138 0.136 0.134 0.132 0.130 0.128 0.126

(a)

0.42 0.40 0.38 0.36

(c)

0.36 0.34

(e)

1960 1964 1968 1972 1976 1980 1984 1988 1992

0.560

(f)

0.540 0.530 0.520

(g)

0.42 0.38

1960 1964 1968 1972 1976 1980 1984 1988

(i)

0.510

1960 1964 1968 1972 1976 1980 1984 1988 1992

0.60 0.56 0.52 0.48 0.44 0.40 0.38 0.32 0.28 0.24

(h)

1960 1964 1968 1972 1976 1980 1984 1988 1992

0.044

(j)

0.040 0.036 0.032

1960 1964 1968 1972 1976 1980 1984 1988 1992

(k)

0.68 0.66 0.64 0.62 0.60 0.58

0.32

0.550

0.46

0.70

(d)

0.38

0.50

0.58 0.54 0.50 0.46 0.42 0.38 0.34 0.30

1960 1964 1968 1972 1976 1980 1984 1988 1992

0.44

0.40

1960 1964 1968 1972 1976 1980 1984 1988 1992

0.54

0.34

0.42

1960 1964 1968 1972 1976 1980 1984 1988 1992

0.66 0.62 0.58 0.54 0.50 0.46 0.42 0.38 0.34 0.30

(b)

0.44

1960 1964 1968 1972 1976 1980 1984 1988 1992

0.56 0.52 0.48 0.44 0.40 0.36 0.32 0.28

0.46

135

1960 1964 1968 1972 1976 1980 1984 1988 1992

0.028

1960 1964 1968 1972 1976 1980 1984 1988 1992

0.50 0.48 0.46 0.44 0.42 0.40 0.38 0.36 0.34 0.32

(l)

1960 1964 1968 1972 1976 1980 1984 1988 1992

Figure 1. Proportion of forest cover in (a) Dominican Republic, (b) Ghana, (c) Nicaragua, (d) Venezuela, (e) Costa Rica, (f) Bolivia, (g) Nepal, (h) Thailand, (i) Paraguay, (j) Mali, (K) Indonesia, (l) Guatemala.

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any independent variable other than an intercept will be zero or nearly so. The presence of such ‘flats’ causes problems in statistical analysis. They make it impossible to statistically measure the relationship between the dependent variable and independent variables. Possible explanations for such deforestation patterns may be: (1) the data are a true reflection of reality (i.e. Nicaragua really did lose its forest cover at exactly the same rate every year); or (2) the data contain a degree of measurement error. There is no way of knowing for sure which is the case (short of a careful analysis of the original data sources and their mode of construction). However, it is probably a reasonable assumption that the data are of questionable quality. This conclusion is not too surprising in view of the fact that the FAO data is based primarily on on-the-ground surveys carried out by government agencies, often intermittently. The use of differing definitions, interpretations and measurement techniques in these national forest inventories has also been noted as a source of heterogeneity in the data (WRI, 1994/5, 1996/7). Obtaining accurate forest data is a laborious process even for the most resourceful of industrialised nations, let alone for the low-income country with a limited budget and a lack of adequately trained personnel. Further, governments may under-report deforested areas to deflect attention away from poor forest management and conservation practices. The FAO is politically obliged to accept member country reports and data regardless of their unreliability. These limitations may explain some of the patterns noted above. Data for Nicaragua, for example, may be based on ground surveys of forest cover taken some years apart, yielding two time series data points, with all other points derived by simple straight-line interpolation. If this is the case then the series contains only two real data points. It is questionable to assume that there are more, as does the panel data study. In view of these problems, we argue that all the data can reliably reveal are very broad deforestation patterns (e.g. that ‘deforestation in Nicaragua is extreme’ or that ‘deforestation, once extreme in Costa Rica, now appears to be improving’). However, it probably cannot be reliably said, as a panel data study implies, that ‘Nicaragua lost its forest cover at a rate of 2Ð31% in each year from 1961 through 1991’. In fact, given the probable magnitude of measurement error, it cannot even be reliably said, as in the cross-sectional study that the average deforestation rate in Nicaragua was 2Ð31% over the period 1961 to 1991. All the

available data can reliably reveal is very broad deforestation patterns in the data; that is, whether forest loss has been rapid or slow, accelerating or decelerating. The data cannot determine the % rate of change in forest cover in every year within a reasonable latitude of accuracy, only a broad but crude outline of change. Given the scarcity of land use data containing a substantive time series component, and the fact that the FAO data set is widely used in agricultural, economic and environmental studies, researchers need to use the information in this data set in a way that quantitatively considers its limitations.

Objectives Probable measurement error in the FAO Production Series data means that conclusions about the empirical relationships between deforestation and various independent variables based on panel data are questionable. Cross-sectional studies use an average deforestation rate, but do not incorporate potentially interesting patterns across time in the data. Both limitations should not lead researchers to reject the data altogether, however. This paper develops a more reliable way of categorising the information in the Production Series data. It constructs a dependent deforestation variable from the data, defined as a set of alternative deforestation trajectories. It then uses this variable in an investigation of key socio-economic influences (e.g. welfare level, population growth) on deforestation in 58 developing countries. The aim of the latter is not so much to identify the specific underlying or driving factors in deforestation globally as it is to compare findings with other cross-sectional and panel data analyses of these factors. In particular, it aims to determine whether the proposed approach reveals any new findings of significance for researchers in the field of global forest change analysis.

Materials and methods This section details a method for researchers in the field of global human forest interactions that allows for meaningful quantitative conclusions to be drawn from the Production Series data. Given measurement error in the data, all we can meaningfully do is classify the dependent variable as falling into one of several categories. Given a categorical dependent variable, multiple regression

Country ‘Choices’ or deforestation paths

techniques are no longer appropriate and thus we use multinomial logit methods instead. Table 1 lists countries in this study along with their average annual deforestation rates. We categorise the countries as follows: (1) Stable or expanding forest cover (12 countries): Botswana, Burundi, Central African Republic, Congo, India, Myanmar, Namibia, Pakistan, Papua New Guinea, Sierra Leone, Somalia, Sri Lanka. In these countries average deforestation rates are equal to or less than 0Ð2% per year. (2) Moderate forest loss (23 countries): Brazil, Burkina Faso, Cameroon, Colombia, Cote d’Ivoire, Dominican Republic, Ethiopia, Ghana, Guinea, Jamaica, Kenya, Madagascar, Malawi, Mauritania, Peru, Rwanda, Sudan, Tanzania, Trinidad and Tobago, Venezuela, Zambia, Zimbabwe. In these countries average deforestation rates were greater than 1% per year at a roughly constant rate. (3) Worsening or extreme rates of forest loss (18 countries): Bangladesh, Benin, Ecuador, Guatemala, Haiti, Honduras, Indonesia, Liberia, Mali, Mexico, Nicaragua, Niger, Nigeria, Panama, Paraguay, Philippines, Senegal, Togo. In these countries average deforestation rates significantly accelerated during the 1980s (i.e. worsened) or were greater than 1% per year at a roughly constant rate (i.e. extreme). (4) Improving forest loss (6 countries): Bolivia, Costa Rica, El Salvador, Malaysia, Nepal, Thailand. In this category, average deforestation rates improved significantly during the 1980s. A few words of explanation about our categories are in order. The categorization of stable or expanding forest cover is based on a deforestation rate of 0Ð2% or less per year. Thus, a country losing forests at an annual average rate of 0Ð2% will lose approximately 10% of its forest cover in 50 years; alternatively, 90% of its forest will be standing in 50 years. This is a reasonable and conservative metric for defining the upper limit of a moderate deforestation rate or less. Given the dependency of developing countries on forests for a wide range of essential produce (e.g. fuelwood, food, medicine), some deforestation is to be expected; however, the limit of a moderate or better deforestation scenario (i.e. improving or expanding forest cover) should not be defined too high. The uncertainties surrounding tropical forest regeneration and the slow pace of government forest protection and reforestation efforts necessitate that this limit be

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set conservatively. Likewise, countries losing forest cover at an annual average rate of 1% per year, will have approximately 50% of their forests standing in 50 years. This is a reasonable dividing line between the ‘moderate’ and ‘extreme’ deforestation category. Larger thresholds, for instance, 0Ð5% or 1%, are clearly unreasonable. For instance, the designation of a 0Ð5% per annum forest loss threshold for separating the ‘expanding/stable’ forest category from the ‘moderate’ category rather than the current designation of 0Ð2% implies that a country will have only 77% of its forest cover left after 50 years. A 1% threshold implies that roughly 50% of its forests will be standing in 50 years. Neither figure (or any higher) implies a common sense notion of an expanding/stable forest scenario. Slight changes in the threshold definitions (e.g. the use of 0Ð15% or 0Ð25%) for the dividing line between ‘stable/expanding’ forest cover and ‘moderate’ forest loss had no impact on the categorization of countries. Countries classified as having ‘worsening’ and ‘improving’ forest loss were selected by graphic examination of their deforestation trajectory over time. Figures 2 and 3 provide representative examples of ‘worsening’ and ‘improving’ patterns, respectively. Although this method can be criticised for being subjective, for virtually every country the choice of category turned out to be unambiguous and uncontroversial. Consequently, all empirical results presented in the paper are robust to small changes in definitions. (Note that countries were originally classified into six groups (i.e. ‘worsening’ and ‘extreme’ were two separate categories). Statistical tests (i.e. likelihood ratio) indicated that it was acceptable to collapse these categories into the four). It is possible that some countries have lower rates of deforestation than others in that their forest cover is now virtually gone. Although this interpretation is possible for a few countries, it is not plausible for most. If we assume a cut-off point of 10% land area in forest as an indicator of when forests are depleted (noting that the cut-off point is a subjective one and that some countries such as Haiti have forest cover of around 5% of land area and still continue to experience rapid deforestation) then only two countries out of a total of 18 in the expanding, stable or improving forest cover groups have depleted forests: Burundi and El Salvador. It is also possible that countries on improving paths have destroyed all their accessible forests, but this is improbable. Even Thailand, which exhibited extremely rapid rates of deforestation for most years of the study only to see forest cover stabilize

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in the 1980s, still has as much as 28% of its land area in forest. Each one of these categories can be conceptualized as a country deforestation trajectory. When the dependent variable is defined as a set of alternatives, then multiple regression techniques of the sort that have been widely used in cross-country deforestation studies of tropical deforestation (e.g. Palo et al., 1994; Tole, 1998) are inappropriate. Hence, we use logit analysis. Logit models are a type of qualitative choice model. All such models are concerned with modelling choices between a set of alternatives. Logit models have been applied in a variety of disciplines, including environmental economics, where they have been used extensively to model demand for environmental quality, transportation and recreation (see, e.g. Caulkins et al., 1986; Bockstael et al., 1987; Parsons and Needelman, 1992; Train, 1993; Ben-Akiva and Lerman, 1997). Simple logit models involve a single choice between two categories. The extension to the case where many categories exist is referred to as multinomial logit. In this paper we use multinomial logit techniques as we have four categories. See Ben-Akiva and Lerman (1997) and Train (1993) for more information on qualitative choice analysis. Countries do not choose their deforestation paths the way a consumer chooses among a range of product types or users value a recreational amenity. However, it is nonetheless useful to borrow the terminology of this literature to conceptualize deforestation rates as a ‘choice’ among a set of alternative forest outcomes. Deforestation rates in every country will depend on a host of factors (population density and growth, tenure relations, level of economic prosperity, macroeconomic policies, institutions, cultural norms). These factors in turn will depend on the collective choices (not necessarily voluntary) made by individuals (e.g. as cattle ranchers, fuelwood collectors, agro-industrial farmers) to use land and other natural resources in one way as opposed to another. Accordingly, this study defines the dependent variable in terms of one of the above four categories or deforestation paths (what we figuratively refer to as ‘choices’). Defining the dependent variable in this way allows researchers to exploit the time series dimension of the Production Series data in a meaningful way. It also avoids the problem with cross-sectional studies that measure deforestation as an average. This advantage can be seen by noting that a deforestation rate, say, of 0Ð5% per year for country X, could conceivably be consistent with any one of scenarios 2, 3 or 4. In a cross-sectional

analysis, a researcher would assume 0Ð5% is the deforestation rate for country X, regardless of which scenario were true. For instance, assume country X were actually losing forest cover at a constant rate of 0Ð5% per year (Scenario 2) and country Y at an average rate of 0Ð5% per year, with 1% of this loss occurring in the first half of the time period and 0% in the last half (scenario 4, where deforestation levels off). A cross-sectional approach treats X and Y as sharing identical deforestation trajectories, which is clearly inappropriate. Moreover, given the probable measurement error in the data, the average deforestation rate may be inaccurate even if deforestation rates are in reality constant over time. Multinomial logit models can be used to estimate the effect of independent variables on the probability that a decision-maker (in this case the ‘country’) will choose one deforestation path over another. In this paper, the dependent variable is defined in terms of one of four deforestation paths. A logit model is used to analyze why these different countries ‘choose’ different trajectories. It estimates the effect of certain socio-economic and land use characteristics of each country on the probability that it will follow one deforestation path vs. another (i.e. fall into one of the four defined forest scenario categories). To provide more intuition on the interpretation of empirical results from a multinomial logit analysis, consider a country experiencing rapid expansion in the amount of land area devoted to pasture. Such a country is more likely to see its forest cover disappearing (Path 3) than stabilising (Path 1). The multinomial logit model reveals such information by a coefficient on pastureland reflecting a greater probability of choosing Path 1 over Path 3. In this case, the coefficient will be negative, for if pastureland increases, the probability of choosing Path 1 falls, which implies a negative coefficient on pastureland. Similarly, if the probability is greater that rich countries choose Path 1 over Path 2, the coefficient on GDP will have a positive sign. If this country characteristic is a strong factor influencing whether a country follows this path relative to path 2, it will also be significant, according to standard statistical tests. Formally, the multinomial logit model has a set of coefficients on b, for each pair of choices. In the standard OLS regression model, b measures the influence or marginal effect of an independent variable (x) on the dependent variable (y). In the multinomial model, b measures the influence of x on the probability that a decision maker will choose one alternative over another. Thus in the

Country ‘Choices’ or deforestation paths

categorization of country trajectories described in the beginning of this section, each independent variable will have a set of coefficients or bs that measure the probability that a country will follow one path relative to other alternatives. Specifically, there will be a b for each possible pairwise comparison: (a) Path 1 (stable/expanding) vs. Path 2 (moderate); (b) Path 1 vs. Path 3 (extreme/worsening); (c) Path 2 vs. Path 4 (improving); (d) Path 2 vs. Path 3; (e) Path 2 vs. Path 4; and (f) Path 3 vs. Path 4. Collectively, these choices exhaust the possibilities for this set of deforestation categories (i.e. the comparison of Path 1 vs. 2 contains the same information as a comparison of Path 2 with Path 1 so the latter is redundant. A positive value for 1 vs. 2 would imply a negative value for 2 vs 1). Since the multinomial logit model estimates a greater number of coefficients, significant results are more difficult to obtain than in the case of the standard or linear regression model. For this reason, and in view of the fact that the data doubtless suffers from a fair degree of measurement error which tends to lower significance, we report results as significant at the 20% level in addition to the standard 10% and 5%. Further, unlike the standard regression case where the dependent variable is a continuous measure, the magnitudes of the coefficients do not have simple interpretations. The most important information they reveal is the sign and significance of the effect of the independent variable. Hence only the sign and significance of coefficients are presented in this study. Previous cross-country studies have used regression techniques to investigate the effects of key socio-economic and land use indicators on deforestation. In this paper, we compare our multinomial logit results to a standard OLS cross-sectional regression approach. In the latter case, the dependent variable is measured as the annual Table 2.

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average percentage loss of forest cover, expressed as the negative of the numbers in Table 1. We now turn to a brief discussion of data issues. The FAO definition of forest loss encompasses a wide range of forest types, from closed tropical forests to woodlands to logged over areas, and is derived from the Production Series agrostat data (FAO, 1993). Formally, forests are defined as ‘land under natural or planted stands of trees, whether productive or not, and includes land from which forests have been cleared but that will be reforested in the foreseeable future’. As independent variables, we use agricultural expansion (pasture and cropland), timber/fuelwood production, national indebtedness, a welfare index and GDP level and growth. Independent variables, along with their definitions, acronyms, and data sources are presented in Table 2. It is important to note that many of the independent variables are based on consistent definitions and reporting procedures. However, they are also themselves subject to country-specific interpretations, definitions and modes of collection, and thus have varying levels of reliability. For example, roundwood production estimates do not always fully capture the amount of timber produced in each country, particularly that produced through illegal felling, removal and trading. Thus, they should clearly be viewed as incomplete. Consequently, results presented in this paper should always be viewed within the context of the limitations of these data sets and the difficulties many developing countries face in general in providing comprehensive and reliable data for a wide range of land use and socio-economic indicators. It should be stressed that the techniques adopted in this paper (and those adopted in other regression based cross-country studies of deforestation (e.g. Palo, 1994; Tole, 1998) identify broad patterns in the data. For instance, a finding that the coefficient

Variable definitions, data sources and acronyms

Acronym CHINDRND CHFUEL CHPAST CHCROP GDP CHGDP DEBT POP WELFARE

Name of variable % change in industrial roundwood production 1979/81 to 1989/91 % change in fuelwood & charcoal production 1979/81–1989/91 % change in permanent pasture, ’000 hectares 1979/81–1991 % change cropland, ’000 hectares 1979/81–1991 real GDP per capita % change in real per capita GDP, 1980–88 average % ratio of debt service to GNP, 1985 population density people,’000 hectares, various years, 1980s relative welfare index1 various years, 1980s

Source WRI, 1994/5 WRI, 1994/5 WRI, 1994/5 WRI, 1994/5 Summers and Heston, 1988 Summers and Heston, 1988 WDR, 1987 WRI, 1994/5 IFAD, 1992

1 Measured by country performance on a wide range of welfare indicators (e.g. education, health, food self-sufficiency). The index takes values between zero and one; the closer the value is to one, the higher the welfare status of the country. See IFAD (1992) for more details.

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on GDP is negative in a regression of deforestation on GDP indicates that countries with a high GDP tend to have low deforestation rates and countries with low GDP tend to have high deforestation rates. In reality, the relationships between independent variables are undoubtedly complex and subject to multiple feedback effects. One way to measure such complex, non-linear relationships is through a systems modelling approach (see Kaimowitz and Angelsen, 1998 for a description of one form of such models, Computable General Equilibrium (CGE) models, in tropical deforestation studies)). However, such an approach is not possible with the Production Series data for several reasons. First, the existence of measurement error in the FAO data and lack of sufficient quality data on many potential independent variables suggests a statistical approach. Second, the nature (i.e. magnitude and direction of influence) of the relationships between socio-economic causes and deforestation is both unknown and country-specific. Estimating such relationships will first require that researchers construct a plausible model of human-forest interactions for each country, a difficult task given that these relationships are poorly understood and vary from country to country. Existing country-specific models of tropical deforestation in the literature are themselves very simplistic, and mainly focus on the measurement of macroeconomic factors (e.g. Barbier and Burgess, 1996; Persson and Munasinghe, 1995). Even if such relationships were understood and could be modelled accurately, it would be impossible to find data on all the socio-economic variables influencing them. Such an approach would also not provide the comparative insights that are obtainable from a global analysis. Given the measurement problems in the FAO data, the poor understanding of the nature of these relationships, and lack of suitable data, our position is that the best approach is to search for general patterns or stylized facts in the data. These stylized facts, Table 3.

Empirical results Preliminary remarks Table 3 presents estimates of the influence that individual independent variables have on deforestation. The column labelled ‘Simple correlation’ calculates the simple correlation between the relevant independent variable and the annual average percentage rate of forest loss, while the other columns are identified by labels such as ‘1/2’. These latter columns contain results from a multinomial logit model with one independent variable. Signs in the columns with the interpretation ‘1/2’ as ‘C’, for example, mean that higher (lower) values of that independent variable are associated with greater (lesser) probability of a country’s choosing Path 1 over Path 2. Likewise, ‘1/2’ being ‘ ’ means that higher (lower) values of that independent variable are associated with lesser (greater) probability of a country’s choosing Path 1 over Path 2. Table 4 presents similar information, except that results are based on the inclusion of all independent variables in either the standard regression or the multinomial logit model, rather than just one variable. Note that the R2 for the standard or linear regression model is 0Ð50 and the adjusted R2 is 0Ð40. The multinomial logit results with all the independent variables included are no doubt of greater interest since they present the effect of each independent variable after controlling for all others. As

Results using one independent variable at a time Simple correlation

CHINDRD CHFUEL CHPAST CHCROP GDP CHGDP DEBT POPDEN WELFARE

while admittedly simplistic, do have value. They can highlight possible areas for intervention by social policymakers and planners concerned with combating deforestation on the ground. They may also provide the information about such relationships needed to build more sophisticated models of human-forest interactions at the global level.

0Ð40ŁŁŁ 0Ð09 0Ð41ŁŁŁ 0Ð18Ł 0Ð14 0Ð39ŁŁŁ 0Ð10 0Ð00 0Ð24ŁŁŁ

1/2

1/3

1/4

2/3

2/4

C C

CŁ

CŁ

C

CŁ

CŁŁ

CŁ C C C C

CŁŁŁ

ŁŁ

Ł Ł

C

ŁŁŁ

ŁŁ

C

ŁŁŁ

C

C C CŁŁŁ Ł

Ł

C

Significant at Ł 20% level, ŁŁ 10% level, ŁŁŁ 5% level.

3/4

ŁŁ

C Ł

ŁŁŁ

ŁŁ

Ł

Country ‘Choices’ or deforestation paths Table 4.

141

Results using all independent variables Standard cross-sectional regression coefficients 0Ð01ŁŁŁ 3Ð0ð10 4 0.03ŁŁŁ 0Ð01 2Ð0ð10 4ŁŁŁ 0Ð15ŁŁŁ 0Ð07ŁŁŁ 5Ð9ð10 5 1Ð53Ł

CHINDRND CHFUEL CHPAST CHCROP GDP CHGDP DEBT POPDEN WELFARE Significant at

Ł

20% level,

ŁŁ

10% level,

1/2

1/3

1/4

2/3

2/4

3/4

C

C

C

C CŁ

C C

C

Ł Ł ŁŁŁ

CŁŁŁ C Ł ŁŁŁ

ŁŁŁ Ł

C CŁŁŁ

C CŁŁŁ

CŁ CŁŁ C

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discussed in the previous section, the multinomial logit results tend to be less significant (i.e. have lower p-values, a common measure of significance) than do the standard regression results to the larger number of coefficients estimated with multinomial logit. Note that a joint test for the significance of the independent variables in the multinomial logit model rejects the hypothesis of ‘no significance’ at the 0Ð001 level. Another measure of the fit of the multinomial logit model is the number of choices it can correctly predict. The specification of Table 4 correctly predicted 66% of the time, which is quite high for a qualitative choice model with a large number of choices.

Simple correlation and multiple regression results Simple correlation and regression results presented in the first column of Tables 3 and 4, respectively, indicate that expansion in pasture, improvements in welfare and high levels of indebtedness are significantly associated with higher rates of deforestation. Expansion in industrial roundwood production and faster GDP growth are significantly associated with lower rates of deforestation. A further puzzle, given its strong significance in many studies (e.g. Inman, 1993; Capistrano, 1994; Palo, 1994; Tole, 1998), is why cropland expansion is not more significantly associated with deforestation. Correlation and regression results also raise important concerns about the relationship between development and deforestation. One measure of the level of development (WELFARE) is positively correlated with deforestation. Another, GDP, is negatively correlated. Further, the study’s measure of the pace

of economic development, GDP growth, is strongly negatively correlated with deforestation.

Multinomial logit results The multinomial logit and regression results are quite similar, but the multinomial logit provides additional insights into the relationships between socio-economic causes and deforestation. We consider the effect of each independent variable on forest loss in turn. Standard regression results for CHINDRD (change in industrial roundwood production) indicate, counter-intuitively, that its coefficient is highly significant and negative. This suggests that countries that have experienced large expansion in industrial roundwood production have had lower rates of deforestation. Logit results relating to Path 1 are consistent with the regression result. For instance, larger values of CHINDRD are associated with countries choosing Path 1 (a stable/expanding path) over any of the other paths. One possible explanation of this finding is that countries in which wood production is expanding fastest may be more likely to reserve permanent forest area and manage it as a sustainable asset. Hence, in these countries expanding roundwood production is linked with generally moderate and even negative rates of deforestation. However, another interesting finding is that CHINDRD is negatively correlated with a choice of Path 4. In other words, countries in which roundwood production has expanded the fastest are least likely to be on Path 4 (the improving path). This suggests that a contraction in roundwood production is correlated with countries following an

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improving deforestation path in recent years (e.g. small values of CHINDRD are associated with choosing Path 4 over Path 3). This path choice includes countries such as Thailand and Costa Rica. Both have witnessed a reduction in their deforestation rates over time, partly as a result of structural changes in their economies which have reduced dependency on primary-resource based extraction and shifted output to manufacturing and service sectors. Some countries (e.g. Thailand, Costa Rica, Malaysia) also placed various restrictions on logging during the early 1990s. In sum, the standard regression result implies that logging will have a protective effect on forests since the coefficient of CHINDRND is negative. However, the logit results suggest a more complex relationship. Although not significant, results imply that a contraction in logging is correlated with the choice of a more beneficial forest path (i.e. Path 4, improving deforestation) even when the alternative choice involves the worst outcome (i.e. Path 3). Multiple regression analysis suggests that an expansion in fuelwood production is associated with increasing rather than decreasing forest cover; this result accords with other studies (e.g. Allen and Barnes, 1985; Tole, 1998). However, this result is not significant. In contrast, the multinomial logit results suggest that the influence of increasing fuelwood production on forest cover will differ according to the deforestation path in question. A comparison of Path 1 with Path 2 indicates that expanding fuelwood production is significantly correlated with countries where deforestation rates have been moderate rather than stable or expanding. Comparing Path 2 with Path 3, we note that the expansion in fuelwood production is significantly correlated with an increase in the probability that a country will follow a moderate rather an extreme deforestation path. For those countries with extreme deforestation rates, there are doubtless other factors that are swamping the minor role played by fuelwood production in forest loss. The standard regression result for the pastureland expansion variable is highly significant and positively associated with declining forest cover. However, the multinomial logit results shed more light on the nature and magnitude of this relationship. From Table 4 we note that the effect of expanding pasture land (CHPAST) on the probability that a country will choose one path vs. another suggests that this variable is more correlated with a country choice of path 3 over any other. The

finding that countries which have experienced a large expansion in pastureland are also experiencing extreme rates of deforestation accords with the highly extensive nature of much cattle ranching in the tropics. An examination of another land use variable, CHCROP (change in cropland) indicates that the standard regression result for this variable using a simple average deforestation rate is very weak and insignificant. In contrast, multinomial logit results are stronger and provide additional information on the nature of the relationship between this variable and forest loss. In particular, expanding cropland is correlated with a highly significant likelihood (p D 0Ð05) that a country will be on a moderate as opposed to a stable or expanding forest cover trajectory. There is also a marginally significant probability that a country will be on an improving rather than a stable or expanding forest cover path. Thus, cropland expansion, while destructive of forest cover, is not as destructive as pastureland expansion, which is more extensive and less restricted by such factors as soil, slope and water availability. The OLS regression result for income level (GDP) indicates a highly significant negative association with deforestation; that is, the higher the GDP, the less deforestation. In contrast, the multinomial logit results suggest that GDP will significantly increase the probability that a country will be on a moderate as opposed to an extreme deforestation path. Thus, a high GDP will significantly reduce the probability that a country will follow a path of extreme deforestation; but beyond this it appears to have no significant effect. In contrast, the change in GDP (CHGDP) variable has a strongly significant relationship with deforestation, in both the standard regression and the multinomial logit model. The coefficient for the standard regression model using an average percentage change in forest cover is highly significant and negatively correlated with deforestation. This suggests that the more expansion in GDP the less deforestation. The multinomial logit results provide added insight into the nature of this relationship, indicating that countries undergoing rapid increases in GDP are also highly significantly more likely to be on a stable or expanding forest cover path. National indebtedness (DEBT) appears to be significantly and positively correlated with deforestation. This finding is also confirmed by the result for the standard regression case in column 1. However, the multinomial logit results suggest that level of national debt is a less strong determinant of

Country ‘Choices’ or deforestation paths

deforestation than the standard regression model indicates. In only one case is it significantly associated with a country following one deforestation path rather than another. That is, it is significantly probable that a country with high levels of debt will be on an improving rather than a moderate deforestation path. As reported in Table 4, population density (POPDEN) plays no role in explaining observed variations in deforestation in the standard regression case. Although only weakly significant, the multinomial logit results suggest that higher population densities are correlated with a greater probability that a country will be on an extreme as opposed to a stable or expanding forest trajectory (Path 1) or a moderate forest loss trajectory (Path 2). However, countries are also more likely to follow an improving as opposed to a stable or expanding forest path. Welfare level (WELFARE) is only marginally significant in the standard regression case. Its positive coefficient suggests the counter-intuitive finding that the higher the level of welfare, the more deforestation. This suggests that those countries that have improved welfare levels have done so at the expense of forest cover. An examination of the multinomial logit results suggests that countries with high levels of welfare are significantly more likely to choose an extreme deforestation path over a stable or expanding one. However, the multinomial results also indicate that it is marginally more probable that such countries will follow a moderate or an improving deforestation path rather than a stable or expanding forest cover path. Although only weakly significant, this finding suggests that higher levels of welfare are associated with an eventual deceleration in the rate of deforestation. Table 5 summarizes the main findings of significance of the multinomial logit results.

Discussion The logit model results indicate that the relationships between land use practices, socio-economic indicators and forest loss rates are more complex than standard regression results indicate. For example, although the standard regression results presented here and in other studies (e.g. Tole, 1998) indicate that industrial logging of tropical forests is correlated strongly with less deforestation, the logit results for this variable suggest that this relationship is more complex. Although not significant in the multinomial analysis, the signs of

Table 5.

Summary of main findings of multinomial logit

Variable CHINDRND CHFUEL CHPAST CHCROP GDP CHGDP DEBT POPDEN WELFARE

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Major country deforestation path choice among alternatives Stable/expanding forest cover/improving deforestation over Extreme Moderate deforestation over Stable/expanding or Extreme Extreme deforestation over Stable/expanding or Extreme Moderate/Improving deforestation over Stable/expanding or Extreme Moderate deforestation over Extreme Stable/expanding forest cover/improving deforestation over Moderate or Extreme Improving deforestation over Moderate Extreme/improving deforestation over Stable/expanding forest cover or moderate deforestation Extreme/moderate/improving deforestation over stable/expanding forest cover

the coefficient for the variable measuring change in industrial roundwood suggest that countries sharing this characteristic will, with greater probability, be on a stable or expanding forest path as opposed to any another. At the same time, results also suggest that countries in which logging activities have contracted in recent years are more likely to be on an improving forest path. That is, logging, while unsustainable in these countries in the past (i.e. when on the extreme deforestation trajectory), has since contracted (hence the negative coefficient implied by the choice of path 4 over path 3). Similarly, those countries on a stable/expanding forest trajectory may now be engaging in more sustainable logging practices. Countries in-between the stable/expanding and improving forest trajectories have yet to curtail unsustainable logging practices. This is reflected in the finding that such countries will tend to choose Path 2 over Path 4 and Path 3 over Path 4. The multinomial logit model also suggests that the impact on forest cover of increasing fuelwood use will differ depending on the forest path a country is following. This is in contrast to the standard or multiple regression results reported in this study and others (Allen and Barnes, 1985; Tole, 1998) which measure fuelwood use as an average percentage change. Multinomial logit results indicate that fuelwood use is associated with countries choosing path 2 (a moderate deforestation path) over either an extreme or a stable/expanding path. The standard regression results for pasturel and expansion indicate that this variable is highly significant and positively correlated with declining forest cover. This finding accords with that of

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other studies (Allen and Barnes, 1985; Andersen et al., 1996; Tole, 1998). However, the multinomial results suggest that the change in pasture land is only correlated significantly with an extreme deforestation trajectory. Multinomial logit results for cropland expansion suggest that this type of land use is associated with a moderate to improving deforestation path. Thus, while destructive of forest cover, cropland expansion is less so than pastureland expansion. Thus, the relationship between land use practices and forest loss rates is a complex one; more complex than standard regression results imply (Allen and Barnes, 1985; Inman, 1993; Tole, 1998). Fuelwood consumption has a significant impact in countries on a moderate deforestation path. Pasture expansion is associated with extreme rates of deforestation, and cropland expansion, with moderate or even decelerating deforestation. How can we explain these differences? Roundwood and food production are more amenable to attracting technology and other endowments that promote greater efficiency in the use of resources. Fuelwood extraction, on the other hand, requires initially few if any resource inputs to production and thus absorbs less capital. Hence it provides less incentive for land users to promote technological innovations that could eventually lead to more efficient extraction. Similarly, commercial cattle ranching in the tropics is a highly extensive enterprise that can be carried out with relatively few technological inputs. Once initial outlays of capital are made, even small farmers can raise cattle without the need for sophisticated inputs or technology to ensure their on-going sustenance. Note that countries where such activities dominate tend also to be low to lower-middle income (e.g. Haiti, Guatemala, Honduras, and many in Africa) which lack the resources to invest in less forest destructive enterprises. At this stage in their development, such activities may provide essential resources for poor populations and require less investment than more intensive land uses. Standard regression results found a link between population density and deforestation, a finding that accords with other studies (e.g. Allen and Barnes, 1985; Inman, 1993; Cropper and Griffiths, 1994; Kahn and MacDonald, 1994; Palo, 1994; Rudel, 1994; Tole, 1998). The multinomial logit analysis suggests a more complicated role for this variable in influencing deforestation rates. Countries in which deforestation is either extreme or worsening will with greater probability have higher population densities than countries in which deforestation rates have improved significantly or forest cover

has remained stable or expanded. At the same time, however, the logit model reveals that it is more probable that countries with high population densities will also follow an improving as opposed to a stable or expanding forest path. One possible interpretation of this contradictory finding is that the effects of population density are dependent on a number of mediating variables (e.g. poverty) that operate in a non-linear way. For example, heavily populated countries, which are often the poorest, face greater pressures on their forests than less populated countries due to their largely subsistence economies. However, there may also be a point after which population pressures may be correlated with an improving deforestation path. This may be the stage of development where subsistence pressures on the land base are ameliorated, the number of non-agricultural employment opportunities increases substantially, and substitution of forest produce is possible due to rising incomes. An examination of the list of country categories supports this interpretation. Some densely populated but more prosperous countries (e.g. Burundi, Sri Lanka, Thailand) are experiencing relatively stable rates of forest cover and others (e.g. Bangladesh, the Philippines, Haiti and Nigeria) extreme or worsening rates of forest loss. In contrast, countries with moderate deforestation rates or countries in which forest cover has either remained stable or expanded over the years are significantly more likely to have high per capita income levels or to have seen large increases in per capita income. Moreover, it is also marginally probable that deforestation rates have improved in these countries since the 1980s. These findings suggest a strong beneficial effect for economic growth on the forest environment and are counter to several empirical deforestation studies (e.g. Capistrano, 1994; Rudel, 1994; Shafik, 1994). Countries at low levels of economic development tend to be characterized by traditional techniques of natural resource management, and large primary resource-based industrial development typically has yet to take hold. Generally, with increasing growth, economies tend to become more diversified and less dependent on the production of primary commodities. They also have greater resources available to them for forest management. Such countries may thus eventually reach the stage where deforestation rates decline and forest cover returns. Multinomial logit results are consistent with research indicating that economic growth is correlated with improving environmental quality

Country ‘Choices’ or deforestation paths

(in this case stable/expanding forest outcomes; Stern et al., 1996). Closely related to the issue of economic performance is social welfare. Rising prosperity makes possible investments in human capital in the form of better nutrition, shelter, health, education and other amenities. The provision of these amenities can raise the returns to sustainable land use practices, contribute to the growth of income-earning opportunities (both on and off-farm) and induce positive changes in fertility. The multinomial logit results indicate that one measure of economic development, the provision of welfare, is significantly associated with countries that are on an extreme deforestation path. It would be deeply discouraging if this relationship were a reliable indicator of future forest outcomes for low income countries. It suggests that countries wishing to improve the provision of welfare to their populations may have to do so at the expense of forest cover. However, an examination of the multinomial logit results for this variable suggests that there is room for optimism. Rising income levels appear to have a more significant effect than do high levels of welfare provision per se in determining whether countries are on an extreme or worsening deforestation path. This finding suggests that income growth (perhaps through its effects on employment creation or increased purchasing ability) can more effectively de-couple people from natural resource dependency than can simply providing cleaner water, education or better housing. However, it should be stressed that high levels of welfare were also found (albeit marginally) to be associated with a choice of a decelerating or improving deforestation scenario. Thus, there may be a point after which government investment in social welfare has beneficial effects similar to income growth (e.g. generating new opportunities to make a living off the land). These findings reflect the multi-faceted nature of welfare provision in developing countries. That is, in the early stages of development, countries tend to raise revenues through forest concessions, export crop production and other land-use activities. All of these activities are destructive of forest cover. However, as the income from these activities is invested in rural development, positive structural changes in the economy will typically result in less dependency on forest-destructive or primary resource activities. As a consequence, rural poverty will decline, population growth stabilize, and educational and employment opportunities improve, leading to an eventual decline in the overall rate of deforestation.

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A similar complex pattern holds in respect to debt. External indebtedness has been implicated in deforestation in its role in stimulating the expansion of natural resource based industries need to raise revenues for debt servicing. The finding that national debt is also significant and positively correlated with deforestation in the standard regression case is confirmed by other cross-sectional and panel data studies (e.g. Inman, 1993; Capistrano, 1994; Kahn and MacDonald, 1994; Tole, 1998). Multinomial logit results indicate that level of country debt is only significantly associated with countries being on an improved deforestation path. This finding may be due to the welfare or employment-effects of government borrowing, which may eventually exert an influence on forest depletion by lessening immiserating pressures on the land. Of course, it is also likely that countries with high levels of debt simply cannot afford investments in large forest-destructive development projects (e.g. subsidies to large cattle ranchers), thus leading to an improvement in deforestation rates as the level of indebtedness rises.

Conclusion In this study, we have noted the problems that plague the FAO Production Series data set, rendering it problematic to carry out panel data or cross-country regression studies of forest change. We argue that in view of these problems the most that the data can reveal is the extent to which countries can be said to have: (i) no real deforestation problem; (ii) a moderate deforestation problem; (iii) an extreme deforestation problem; (iv) an improving deforestation problem. We use multinomial logit techniques to estimate models where the dependent variable is defined according to one of these four trajectories. The study’s multinomial logit approach was applied in an analysis of the role of socio-economic and land use factors in deforestation. The empirical analysis was designed to demonstrate the suitability of such techniques for surmounting problems in the use of existing time series and cross-sectional forest data sets; and to show how they can provide more detailed information about the nature of human-forest impacts globally than do existing approaches to measuring these relationships. The contribution of the study to the field of global forest change analysis can be summarised as follows:

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(1) It explicitly highlights the drawbacks for researchers of using the Production Series data in deforestation studies, and develops a categorisation of countries based on observed variations in deforestation rates. (2) It uses this categorisation to develop a way of measuring deforestation that allows for a more truthful and reliable representation of the Production Series data, the only existing time series data on global land cover change available to researchers. In addition, this categorization is able to incorporate patterns in the data in a way that is not possible when the dependent variable is measured as an average; that is, when the full-time Production Series data set or other cross-sectional data sets (e.g. the FAO 1990 Tropical Forest Assessment) are used. (3) It borrows from the qualitative choice model literature (hitherto largely used in environmental studies to model the demand for recreation and environmental quality) in order to conceptualise observed variations in forest cover as a choice between a set of competing alternative deforestation paths. (4) It applies this multinomial logit technique in an empirical study of the underlying human causes of global deforestation in order to determine if any new insights can be gained into the nature of these relationships. (5) Multinomial logit results confirm the contribution to deforestation of many socio-economic and land use factors found in other quantitative studies. However, unlike these studies, our multinomial logit model allows researchers to derive more information about the nature of these relationships. Based on the measurement of a range of independent variables, results clearly indicate that the socio-economic causes of deforestation do not exert their influence uniformly across countries. Indeed, such influences will differ depending on the particular deforestation trajectory under consideration. Some factors will significantly increase the likelihood that countries will be on a less destructive forest path, while others will significantly decrease this chance. Still others suggest a mixed influence, increasing the probability that countries will choose an extreme or worsening path but eventually, perhaps, an improving path. The study’s findings have important implications for the design of environmentally sustainable forest polices and programs. Results suggest that appropriate interventions in certain areas can

significantly increase the likelihood that a country will move from an extreme or worsening deforestation path. Such interventions can be thought of as changing the balance of probabilities that a country will choose stable, expanding or improving forest trajectories over moderate, extreme or worsening ones. Our multinomial logit results suggest that countries with high rates of pastureland expansion will have more difficulty moving to a path where forest cover will eventually stabilize or expand—or at the very least—lead to a moderate deforestation scenario. Such countries are also more likely to be on an extreme or worsening deforestation path. Similarly, high population densities will make it more unlikely that a country will follow a less destructive forest outcome (or for that matter, a moderate deforestation trajectory). Thus, measures designed at specifically lowering and perhaps even redistributing populations where possible, and promoting land uses other than animal grazing should increase the chances that a country will move onto a stable or expanding forest path. Even other land uses, such as cropland and fuelwood expansion, appear to have less of a negative impact on forest cover than does the expansion in grazing land, as they will significantly increase the chance that a country will follow a moderate or even improving path over an extreme/worsening path. Although results were not significant in the multinomial logit analysis, the signs of the coefficients suggest that expanding roundwood production has less impact on forest cover than the other land uses measured. It is overwhelmingly associated with a country’s choosing stable/expanding deforestation scenarios over all others. One of the most significant of these interventions which could move a country onto a stable or expanding forest path is to stimulate rapid economic growth. The strong significance of these findings in relation to the stable/expanding forest scenario, in particular, suggests that policies designed to encourage rapid economic growth may provide the quickest route for countries to move from an extreme/worsening deforestation path to a stable/expanding forest path (or for that matter, from any other). In this respect, governments can play a role in fostering macroeconomic and regulatory reforms that will facilitate investment in highincome enterprises (e.g. the production of manufactured goods and services) which are also less destructive of forests and labour-intensive. This could be achieved through the promotion of nonforestry and non-agricultural pursuits particularly in and around areas of remaining forest cover.

Country ‘Choices’ or deforestation paths

Although it also appears to be significantly associated with countries being on a moderate to extreme/ worsening deforestation path, the fact that our results also suggest that countries with high levels of welfare provision are also significantly more likely to be on an improving deforestation trajectory suggests that a high level of public service provision and improvements in the quality of life can eventually move countries onto this path. These factors may exert their influence through improvement in income levels. Similarly, countries with heavy demographic pressures may eventually reach the stage where deforestation improves; however unlike our welfare measure, this path is more often significantly correlated with countries choosing an extreme or worsening path over a more beneficial one (e.g. moderate or improving). Furthermore, were countries simply to reduce population growth instead and forget about economic growth, they would with less probability move to a stable or expanding path. Indeed, it would be more probable that they would choose a moderate or improving deforestation path. In each case, the deforestation rate should be assessed and significant driving forces determined with a view to making cost-efficient interventions, preferably in conjunction with technical solutions (e.g. monitoring of park boundaries). The importance of a holistic approach to the solution of the deforestation problem should not be underestimated. Tree-planting programs and better enforcement of protected areas will only be efficacious if the socio-economic causes of deforestation specific to each country are addressed as part of a long-term sustainable plan for management of forest resources. Although not explored in this paper, it is possible that each one of the paths described here represents a different stage in development. Very poor, subsistence dependent, densely populated countries may face extreme to worsening rates of deforestation in the initial stages of development. However, deforestation rates may eventually improve with successive increases in economic growth and welfare. Consequently, countries may see the need to adjust policies as they develop and the human impacts on the forest environment successively change.

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