The wild tiger population of tropical Asia has plummeted drastically in the last century, from about 100,000 to 3,500, with the Bali, Javan and South China subspecies believed to be extinct in the wild. An estimated 2,380 Bengal tigers survive, along with 340 Indochinese, 500 Malayan and 325 Sumatran tigers, with their remaining habitat being mostly the upland areas arcing from southwest India to northwest Indonesia. Long term survival of the tiger is dependent on conservation of these tiger habitats, which has prompted the World Bank to join the Global Tiger Initiative (GTI), along with the governments of the various tiger habitat countries and many civil society and private sector organizations.
Habitat conservation is primarily a development problem, and thus success for the GTI and other conservation initiatives will require program designs tailored to the economic dynamics of forest clearing in tropical forest countries.
Until recently, research on these economic dynamics has been hindered by the shortage of high-resolution time series data. Recently, David Wheeler, Dan Hammer, Robin Kraft and I used new information from FORMA (Forest Monitoring for Action), a high-resolution remote-sensing database of monthly forest clearing since 2005, to investigate habitat threats for Bengal, Indochinese, Malayan and Sumatran tigers in Bangladesh, Bhutan, Cambodia, India, Indonesian Sumatra, Lao PDR, Peninsular Malaysia, Myanmar, Nepal, Thailand and Vietnam.
Conducting an econometric analysis of habitat loss in forested areas of 100 km2 – the typical area required to support tiger breeding – we linked forest clearing across these areas to profitability calculations that are affected by market expectations, environmental conditions and evolving patterns of settlement, economic activity, infrastructure provision and regulatory activity. We estimated the model using new spatial panel estimation methods that allow for temporal and spatial autocorrelation.
Across areas, our results indicate that patterns of forest clearing are persistent – current clearing activity is significantly related to past forest clearing in 2000-2005. And over time, the spread of clearing into new or contiguous areas is reducing the number of 100 km2 forest blocks that can support breeding tigers. Clearing increases with increase in the opportunity cost of forested land, as the expected profitability of clearing land rises with its value in commercial use; clearing is also greater in areas that are relatively remote from major transport links. Over time, clearing is positively related to the expected prices of forest products (because higher prices raise the expected profitability of clearing) and expected future exchange rates (denominated in local currencies/dollar, because devaluation lowers domestic production costs relative to commodity export prices); and negatively related to rainfall (which makes clearing more difficult and costly), real interest rates (because forest clearing is an investment activity), and to the elevation of the terrain (principally because oil palm plantations have lower productivity at higher altitudes). After controlling for all these factors, we find that significant unexplained drivers remain. They have had a negative impact on forest clearing in Cambodia, Indonesian Sumatra, Bangladesh, India, Nepal and Vietnam, and a positive impact in Myanmar, Lao PDR and Peninsular Malaysia.
Figure 1: Tiger Subspecies Landscapes and Elevation
Although our results indicate that forest clearing in all the tiger habitat countries is affected by the economic variables, there are significant differences in sensitivity to these influences across countries. In the export-oriented economies of Indonesia and Malaysia, the habitat countries of Sumatran and Malayan tigers, forest clearing is highly sensitive to changes in exchange rates, real interest rates and the prices of forest products. This sensitivity compounds the vulnerability created by the small remaining numbers and limited ranges of Sumatran and Malaysian tigers. In contrast, we find significantly lower sensitivity to these variables in India, Bangladesh and Nepal -- habitat countries of the Bengal tiger.
Differences in subspecies’ habitat vulnerability also emerge in our results for protected areas, which reveal no measured effects in the Sumatran and Malayan habitat countries in general, but significant effects in the habitat countries of Bengal tigers. We believe that the latter results may reflect more consistent protection at the local level. In the former case, we do find significant protection effects in some states in Peninsular Malaysia and provinces in Indonesian Sumatra. We hope that future research will provide more insight into the sources of these differences.
Our findings highlight an important message for the conservation policy community: Changes in world forest product markets and national financial policies have significant, measurable effects on tropical forest clearing, but with variable time lags and differing degrees of responsiveness across countries. Measuring these effects and pinpointing areas at high risk can provide valuable guidance for policymakers, conservation managers, and donor institutions about the challenges to be overcome in offsetting incentives for forest clearing, and about potential responses tailored to the circumstances of different countries and habitat areas.