0597-C5
Sen Wang 1
The theory of the Environmental Kuznets Curve contends that a U-shaped relationship exists between environmental quality and economic development. This paper examines two questions: (1) Is forest cover a meaningful benchmark indicator of environmental quality? (2) Is forest cover related to income levels? Findings reveal that forests serve as an important indicator of the soundness of the natural environment, but the correlation between forest cover and economic well-being measured by per capita GDP is tenuous, based on a statistical analysis of cross-country data. The discrepancy between theory and empirical evidence is explored, with a pointer to other important factors worthy of further investigation.
Given that sustainability is, essentially, about the ability of a system to maintain its processes and functions over time, there is an apparent need for a parameter measure to assess the condition of the system against a set of established criteria or objectives. The natural environment is such a system, which is viewed as a source of human well-being (Dasgupta 2001). Ideally, an index may be constructed to serve as a measure that informs about the state and trends of the environment. However, over the past decade or so, efforts to design credible indicators of environmental quality have been wrought with difficulty for the simple fact that a widely accepted set of indicators capable of capturing the state of the environment is hard to come by. At the level of a jurisdiction, forest cover is often used to indicate the soundness of the natural environment. But, this indicator alone may be inadequate.
This paper seeks to answer two questions: (1) Is forest cover a meaningful benchmark indicator of environmental quality? (2) Is forest cover related to income levels? The next section briefly describes the role of forests in the natural environment. It is followed by an overview of the theory of environmental Kuznets curve (EKC), which contends that a U-shaped relationship exists between environmental quality and economic development. Then, cross-sectional data are analyzed in the hope of ascertaining whether there exists an empirical relationship between forest cover and economic well-being measured by per capita gross domestic product (GDP). The findings are discussed, and a few concluding remarks are presented.
Forests have been a source of life from time immemorial. Apart from being the basis for a variety of wood and non-wood products and services, forests are home to many forms of life and play an essential role environmentally, including climatic regulation, carbon cycling, biodiversity preservation, and soil and water conservation.
According to a recent report of the Intergovernmental Panel on Climate Change (IPCC 2000), although forests represent 28 percent of the world's biome, they account for 46 percent of the global carbon stocks. In terms of carbon pools contained in vegetation and soil down to a depth of 1 metre, forests vary from one type to another. Specifically, tropical forests play a highly significant role in terms of carbon pools thanks to their dense vegetative cover. In comparison, boreal forests contain enormous carbon stocks in the soil.
Biodiversity is widely recognized as a major source of sustainability. Indicators may be identified to help detect human impact on Nature, including the health of ecosystems, the functionality of watersheds, and so on. The most commonly used indicators include endangered species, protected areas, and habitat loss for key ecosystems. Tropical forests are home to upwards of 50 percent of the 10-30 million species on Earth (Reid 1992). There is a general consensus in the international scientific community that forest degradation and fragmentation diminish biodiversity, although debates persist over the number of species that deforestation claims. While protected area serves as a biodiversity indicator, fragmentation is an indicator that shows the threats to biodiversity. Specifically, road density on forestland used to be an indicator of logging industry development, but nowadays it is sometimes cited as an indicator of fragmentation. Nevertheless, easy access to forests and nature parks has become a commonly used measure of community wealth, economic health, and quality of life.
Given the important and diversified role that forests play, the degree of forest cover has become an indicator of the natural environment. By definition, forest cover is the ratio of forested land against a geographical unit's total land area. It is used to indicate the extent of forests in a jurisdiction. However, this statistic involves at least two issues. First, forest cover is a quantity indicator, not a quality indicator. Second, given that it is a ratio statistic, it does not inform about the structure and distribution of the forests, thus presenting a major challenge for a large and diverse country such as Canada.
On the basis of a framework that Kuznets (1955) originally developed for a different context, a proposition has gained popularity. The proposition asserts that economic growth may be harmful to the environment before reaching a certain stage but becomes conducive afterwards. Thus, the relationship assumes a U shape (Arrow et al. 1995).
Figure 1: The U-shaped environmental Kuznets curve
The environmental Kuznets curve is illustrated in Figure 1, where EQ stands for environmental quality, EW refers to economic well-being, and X* is the turning point. The curve indicates that, as the economy grows, environmental degradation increases up to X*, after which environmental quality improves. This means that, at low-income levels, environmental quality tends to decline along with economic growth, but ultimately improves as income levels rise beyond a threshold. In other words, in early stages of economic development, increased environmental degradation-such as atmospheric and water pollution, loss of wetland and deforestation-is a side effect of economic growth. When a country has attained a sufficiently high standard of living, people give more attention to environmental improvement and make efforts to introduce environmental legislation and new institutions for the protection of the environment, and so forth (Arrow et al. 1995). After all, thanks to successful economic development, societies can afford to place greater emphasis on environmental amenities, because they have the funds to meet desirable environmental goals.
Hence, the U-shaped relationship is dictated by the ability to spend on environmental amenities, implying that wealthy countries have lower levels of environmental damage because they can afford to pay for environmental improvement, whereas poor countries cannot afford to emphasize amenities over material well-being. As far as forests are concerned, a question of considerable interest to researchers and policy makers is whether or not a relationship exists between the extent of forests and income levels. This question is examined in the section below.
Human beings depend on forests for a variety of purposes. Population growth results in higher demand for forest-based products and services. Therefore, it is reasonable to postulate that population increase is a fundamental driving force of change in forest cover. Mather et al. (1999) suggest that there is a theoretical basis for linking long-term trends in forest use with economic development, including the emergence of forest transitions as society's income rises. Nevertheless, research efforts have failed to uncover, in a statistically significant manner, empirical evidence to confirm the relationship of forest cover with population growth and income levels (see Mather and Needle 2000). The Food and Agriculture Organization of the United Nations (FAO 2001, pp.14-15) contends that relatively weak correlations exist at the national level between forest area change rate, demographic parameters, and per capita gross national product. This finding is an important reference regarding the limitations of the population-driven deforestation model used in the FAO Forest Resources Assessment 1990 (FAO 1995). FAO concludes that, given the complexity of forest-change processes, it is extremely difficult to disentangle the effects of population from other factors when examining changes in forest cover.
To enhance our understanding of the determinants of forest cover, this section examines the relationship between forest cover and several selected variables. Cross-sectional data are gathered from State of the World's Forests 2001 (FAO 2002) and 2001 Human Development Report (UNDP 2002). The variables that are deemed highly relevant include: population density (persons per km2), economic well-being measured by per capita GDP in purchasing-power parity dollars, and proportions of rural population. In addition, countries are classified by their geographical location in or outside the tropical region. Due to the fact that the data are reported at the level of countries and economies, missing data have occurred for a number of jurisdictions on one or more of the identified variables, and this reduces the number of usable observations down to 168. It must be noted that this reduction may cause a bias, especially to the disadvantage of small countries.
Table 1: Correlation matrix of selected variables
Forest cover (foc) |
1.000 |
1.000 |
1.000 |
1.000 |
1.000 |
Foc |
pod |
rup |
tro |
gdp |
As shown in Table 1, among the selected variables, forest cover is correlated to a greater extent with population density and tropical location than with the other variables; GDP is highly correlated with the extent of urbanization. Ordinary least square (OLS) techniques are employed in analyzing the data. Two models are separately estimated, and the dependent variable in the regressions is forest cover. The first model has two explanatory variables, namely, population density and a tropical country dummy; the second model has three explanatory variables, i.e., the proportions of a country's population in rural areas, per-capita GDP, and per-capita GDP squared, with the latter variable being included to test whether or not a nonlinear relation exists between forest cover and income levels.
The regression results in Table 2 indicate that there is a statistically significant, negative relationship between forest cover and population density. This suggests that as population pressure builds up, forest cover has a tendency to go down. Indeed, forest cover is higher in the tropical region. Forest cover does not have a significant relationship with the proportions of rural population. Economic well-being does not appear to have much explanatory power in the extent of forests at a country level, as the relationship between income and forest cover is statistically insignificant. Perhaps, there is some weak evidence suggesting that forest cover may increase as income level rises, but the rate of increase may occur at a decreasing rate.
Table 2: Regression results
Variable |
Estimates |
t value |
Model 1: |
-0.009 |
-2.678** |
Model 2: |
0.0014 |
1.337 |
| ** Significant at the 0.01 level, * significant at the 0.05 level. |
The regression results show that forest cover is negatively related with population density and is higher in countries that are located in the tropical region. But, forest cover bears statistically insignificant relationship with income levels, hence the result does not support the applicability of the environmental Kuznets curve to forest cover being taken as an indicator of environmental quality. One possible explanation for the discrepancy between the theory of environmental Kuznets curve and the empirical results lies in the lack of accurate forest-cover data, which has been raised repeatedly (see Achard et al. 2002; Stokstad 2001). There have been assertions that the estimates of forest area reported by many developing countries were assessed sometime between 1970 and 1990, although progress has been made in recent years thanks to a wider use of remote-sensing techniques (IPCC 2000, p.95).
Perhaps the regression results have two implications. At a specific level, change in the state of the forest is subject to a certain set of opportunities and constraints, and income level is merely one of them. Other important elements include availability of land and human capital, tradition and culture, institutional capacity, political commitment, and so forth. More broadly, forest cover is, by and large, a quantity statistic. It does not inform about the quality of the forest. Furthermore, it is merely one of many indicators of the state of the natural environment, so it cannot be used as a wholesale surrogate for assessing the complex interactions between economic growth and the natural environment.
Figure 2: Modified environmental Kuznets curve
This latter point deserves closer examination. Indeed, not only are environmental consequences of growing economic activity rather mixed, but environmental degradation has a variety of elements as well. The amelioration of damaged environment certainly involves activities that differ in objectives, scale, and timeline. While people will want to spend proportionately more money on improving environmental quality as their income rises, the level of expenditures varies from one program element to another. Arrow et al. (1995) observed that the U-shaped relations exist for emissions of pollutants, not resource stocks. They found that the environmental Kuznets curve is valid only for pollutants involving local short-term costs, not for the accumulation of stocks of waste or for pollutants involving long-term and more dispersed costs (such as CO2), which often are an increasing function of income. Specifically, the relationship is less likely to hold wherever the feedback effects of resource stocks are significant, such as those involving soil and its cover, forests, and other ecosystems. These findings are important in that they provide the basis for modifying the environmental Kuznets curve, as shown in Figure 2. While everything else remains the same as in Figure 1, h1 denotes the curve that corresponds to control of water pollution and the like, h2 denotes activities such as rehabilitation of forest cover and habitat, h3 denotes things like emissions, and so forth. If Figure 2 is closer to reflecting the real world, then the foregoing regression results may be interpreted to mean that wealthier countries spend proportionally less money on forest conservation than on pollution control. This would explain, at least partially, why the relationship between forest cover and income levels is statistically insignificant.
Arrow et al. (1995), who argued that economic growth may not be a panacea for environmental quality, questioned the general proposition that economic growth is conducive to the environment. What is important is the content of growth-the composition of inputs (including environmental resources) and outputs (including waste products). This content is determined by, among other things, the economic institutions within which human activities are conducted. These institutions need to be designed so that they provide the right incentives for protecting the resilience of ecological systems. Furthermore, although much of the world's biodiversity is in developing countries, for many of them, biodiversity preservation may not be the first priority, because the top priority is always to secure people's livelihood-even if this means paying high environmental costs. From the perspective of developing countries, unless the gap between global biodiversity benefits and the needs of local people is narrowed, the required economic growth will occur at the expense of much of the planet's biodiversity (Fuentes-Quezada 1996).
To answer the question posed in the title of this paper, the findings reveal that forests serve as an important indicator of the soundness of the natural environment, but the correlation between forest cover and economic well-being measured by per capita GDP is tenuous, based on a statistical analysis of cross-country data. Despite having an important bearing on environmental quality, the extent of forest cover need to be matched by a quality aspect. Further, forest managers and policy makers need to look beyond economic variables to examine a host of institutional factors to resolve forest management problems. In other words, the chain that connects forests with environmental quality and economic well-being cuts across the forestry box and beyond.
This paper suggests two areas that call for further research. First, researchers not only need to pay attention to differences between countries, but also differences within countries and sub-national regions. In other words, going beyond country-level analysis may be a more fruitful avenue of inquiry. This is challenging indeed, as data are often available only at the country level. Second, more research is needed to understand the relationship between environmental quality and economic growth. One key area is how to measure environmental quality. Again, if a highly aggregate index is too difficult to develop, examination of benchmark elements may be necessary. It may very well be that, upon closer examination of the multiple elements that comprise environmental quality, the environmental Kuznets curve is not an altogether nice-looking U shape.
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1 Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, 506 West Burnside Road, Victoria, BC, Canada V8Z 1M5. [email protected]