Enciclopédia da Energia Natural   CPMA.COMUNIDADES.NET
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Enciclopédia da Energia Natural CPMA.COMUNIDADES.NET


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as the relative probabilities of
estimated global average warming from several different AOGCM and Earth System Model of Intermediate Complexity studies for the same periods. Some
studies present results only for a subset of the SRES scenarios, or for variousmodel versions. Therefore the difference in the number of curves shown in the left-
hand panels is due only to differences in the availability of results. Reproduced from IPCC (2007) Summary for policymakers. In: Solomon S, Qin D, Manning
M, Chen Z, Marquis M, Averyt KB, et al. (eds.) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press, http://www.ipcc.ch/pub/un/syreng/spm.pdf.
Climate Change and Policy | Climate Change and Food Situation 25
separately. Crop models alone are thus only a first step toward
assessing the impact of climate change on food production.
The use of statistical methods to quantify the influence of
climatic factors on crop yields date back to the beginning of the
twentieth century. Advances in computational techniques have
greatly assisted the application of regression techniques but
their popularity is mainly due to their ability to distinguish
the impact of weather on crop yields from other factors. Em-
pirical studies have been based on time series, cross section,
and panel (combined time series and cross section) data with
observations at the farm level or aggregated at the regional
level. Regression analysis allows the quantification of the past
effect of one factor (e.g., the effects of weather variation) on
crop yields in an actual cropping context. Therefore, in addi-
tion to facilitating impact assessment of past climate on crop
yields, regression analysis reflects the actual response of
farmers to weather.
Shortcomings relating to the use of regression analysis to
analyze crop yields are well known. Firstly, misspecification of
the functional form can lead to incorrect estimates of the
impact of climate change and emphasizes the need to model
nonlinear relationships properly. The omission of some im-
portant variables might also lead to biased coefficients in the
model. Furthermore, the correlation between different agro-
climatic determinants makes it difficult for multiple regression
analyses to identify the exact relationship between the depen-
dent variable and each explanatory variable. This correlation
might lead to incorrect signs and/or increased variability of the
coefficient estimates. Besides estimation issues, estimated co-
efficients may not necessarily be valid for prediction as coeffi-
cients represent the impact of past climate conditions on
agricultural outcomes. If future climate values are not within
the range of previously observed values, and if there are non-
linearities in the production function that are not apparent in
the historical range of climate data, then the relationship be-
tween the dependent variable and climate conditions may
change. A related problem is the use of experimental site esti-
mates from which regional studies are derived. Within a re-
gion, differences can be observed across agricultural systems
and zones and the ability of field experiments to represent a
region is therefore questionable. Finally, data requirements
place a major constraint on regression analyses. Data are not
always accurately measured or even available. The use of ex-
perimental data addresses the former problem but such data
are costly. Limited data availability may also lead to omitted
variable bias. Finally, it has been difficult to identify the effect
of climate and weather \u2013 it has been argued that cross-section
data may better represent the effect of climate because farmers
in different climate zones have had time to adapt whereas
annual weather events offer much less opportunity for
adaptation.
By modifying the level of production, crop yield changes
influence revenues for farmers and ultimately affect countries\u2019
economies. Two main approaches are used to estimate
the economic impact of climate change: (1) the Ricardian
approach, which considers a proxy for land productivity and
(2) integrated assessments combining the results of crop yield
models with economic models.
The Ricardian method is often used to estimate the impact
of climate change at the farm level. This technique, also
referred to as the hedonic approach, draws on Ricardo\u2019s notion
that land values reflect land productivity (determined by its
intrinsic characteristics). Farmers are willing to pay more for
land producing higher yields since the selling price of crop is
the same whether the crop comes from productive or unpro-
ductive land. The productivity of land can be represented either
by the land sale value (market value) or by the land rental
value. The land market value is either the registered sales value
or, when data are not available, the value estimated by farmers.
The market value corresponds to the discounted aggregate of
future land profits or rents. The rental value is defined by
Ricardo as that portion of the produce of the earth, which is paid
to the landlord for the use of the original and indestructible powers
of the soil. As land is a fixed factor, it can be represented by
farm net revenues (total revenues minus total costs), assuming
a perfectly competitive land market (i.e., farmers make zero
profit).
Generally using cross-sectional data, Ricardian analyses re-
gress the chosen productivity proxy (land value or net reve-
nues) on climatic, agronomic, and input variables to quantify
the impact of climate change. It is assumed that farmers choose
agricultural activities in order to maximize revenue given the
environmental conditions. \u2018Traditional\u2019 Ricardian analyses im-
plicitly account for contemporaneous farm level adaptations.
That is, Ricardian analyses explain land values or net revenues
that reflect the costs and benefits associated with each farming
practice, including adaptation measures. However, they do not
provide estimates of the effect of each adaptation. \u2018Structural\u2019
Ricardian analyses address this shortcoming by modeling and
measuring the effect of different adaptive measures.
Despite the popularity of the Ricardian approach there are
several limitations associated with this technique. Ricardian
studies are often based only on a single year\u2019s data and this
year might not be representative of other years. Any excep-
tional climatic, agronomic, or economic conditions during
the year considered could easily bias the results. Some authors
address this issue by ensuring the \u2018normality\u2019 of the year con-
sidered or by repeating the estimation over 2 years to ascertain
the similarity of the results. The location of the data is another
concern. Using land values data for farms located around
urban areas can be problematic as land could be used for
purposes other than agriculture, so its value may reflect factors
other than soil productivity. More important criticisms relate
to assumptions underpinning the Ricardian technique. For
instance, the theoretical assumption of factor price long run
equilibrium does not always hold in developing countries
where markets are not integrated. The use of land values,
which are market prices, are therefore not appropriate. Further-
more, the hypothesis of constant prices biases estimated im-
pacts as it does not account for supply-induced price changes.
If price increases following a decrease in productivity were
considered, consumers should be negatively impacted as de-
mand for food is not price elastic. Thus, when consumer sur-
plus loss is not accounted for,