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Category: Secondary Data

The Kyoto Protocol & Payments for Tropical Forest: An Interdisciplinary Method for Estimating Carbon-Offset Supply and Increasing the Feasibility of a Carbon Market under the CDM

Alexander Pfaff, Suzi Kerr, R. Flint Hughes, Shuguang Liu, G. Arturo Sanchez-Azofeifa, David Schimel, Joseph Tosi, Vicente Watson
Ecological Economics 35 (2000) 203–221

PDF link iconProtecting tropical forests under the Clean Development Mechanism (CDM) could reduce the cost of emissions limitations set in Kyoto. However, while society must soon decide whether or not to use tropical forest-based offsets, evidence regarding tropical carbon sinks is sparse. This paper presents a general method for constructing an integrated model (based on detailed historical, remote sensing and field data) that can produce land-use and carbon baselines, predict carbon sequestration supply to a carbon-offsets market and also help to evaluate optimal market rules. Creating such integrated models requires close collaboration between social and natural scientists. Our project combines varied disciplinary expertise (in economics, ecology and geography) with local knowledge in order to create high-quality, empirically grounded, integrated models for Costa Rica.


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From Deforestation to Reforestation in New England, USA

Alexander Pfaff
The Forest in the South and North in the Context of Global Warming (book by WIDER).

PDF link iconHistorical data from the late 19th to the early 20th century are examined for New England. From the attempt to explain the reforestation that occurred, three main land-use claims arise: 1) population clearly does not fully dictate land use (e.g., de- or re-forestation); while population may well have an independent effect on land use, that effect clearly does not dominate all others; 2) factors that affect relative land-use returns, whether “external” to a region or not, clearly do affect land use; two examples are transport costs and productivity of other regions, which affect trade; and finally, 3) long-run analysis must consider shifts even in overall framework, such as from agriculture to migration and industrialization processes involving different economic dynamics. Support for these claims comes from limited historical data alongside relevant theory concerning optimal allocation of land between the four most relevant land uses: agriculture, manufacturing, forest (for timber or as a result of abandonment), and shelter (or, more generally, land uses other than for production). Supporting the “population” claim, previous New England farm expansion flattened out post-1850 and eventual reversed itself, even as population was increasing. Regarding the “returns” claim, the breakpoint in the 1790-1930 series of within-region measures (based on county-level data) of concentration of population is very clearly at about 1830, precisely the era in which the transportation revolution involving railroads, steamships and canals started to have its effect. Concerning the “long-run” claim, given an interest in land use there are grounds for attention to shifts in regional output, such as towards manufacturing from agriculture, as there is evidence that such shifts involved significant changes, in particular concentration of population within particular counties, along rivers and in particular locations along rivers.


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What Drives Deforestation in the Brazilian Amazon? Evidence from Satellite and Socioeconomic Data

Alexander Pfaff
Journal of Environmental Economics and Management 1999 volumne 37, pp. 26-43

PDF link iconWhile previous empirical analysis of deforestation focused on population, this paper builds from a model of land use which suggests many determinants of deforestation in the Brazilian Amazon. I derive a deforestation equation from this model and test a number of those factors using county-level data for the period 1978-1988. The data include a satellite deforestation measure which allows improved within-country analysis. The major empirical finding is the significance of both land characteristics (such as soil quality and vegetation density) and factors affecting transport costs (such as distance to major markets and both own- and neighboring-county roads). Government development projects also appear to affect clearing, although credit infrastructure does not. However, as such policies themselves may be functions of other factors, estimated effects of policies must be interpreted with some caution. Finally, the population density does not have a significant effect on deforestation when many potential determinants are included. However, a quadratic specification reveals a more robust result: the first migrants to a county have greater impact than later immigrants. This implies that the distribution of population affects its impact.

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