Corriente de los efectos y pánicos mediáticos

Globally, carbon dioxide emissions from fossil fuel combustion increase under Baseline conditions from 7.3 GtC in 2000 to 12.5 GtC in 2030 and 14.7 GtC in 2050. Among the energy-related emissions, those from electric power generation and transport are the largest and also increase the most over the Outlook period. Per capita emissions in OECD countries remain much higher than for most non-OECD countries. Total global greenhouse gas emissions amount to 11.5 Gt C-equivalent in 2000 and are projected to be 17.5 Gt C-equivalent in 2030.Whereas emissions from OECD increase by nearly one-third (1.4 GtC) from 2000 to 2030, emissions from BRIC and Rest of the World nearly double over the same period and their share in the global emissions increases from 57% to 64%. These Baseline emissions would lead to a temperature increase of nearly 1.9 degrees Celsius above pre-industrial level

temperature pattern, the effect is very unevenly distributed. In already water-stressed areas such as southern Europe and India, the negative impact on agriculture and human settlements can be substantial. Areas with substantial increases over already high levels in 2000 are more susceptible to run into water drainage or flooding problems. In general, all areas facing considerable changes in surplus will have to adapt to cope with these changes, including through adjustments in water management practices and/or infrastructure.

Box 3.1: The IMAGE framework of models: Land use and land cover (source: OECD 2008)

Agricultural land supply and use: LEITAP

The LEITAP model, named after the Agricultural Economics Institute (LEI) that developed and applies it, is an extended version of the GTAP model developed at Purdue University. A more detailed description of LEITAP is included in the background report to the OECD Environmental

Outlook (Bakkes & Bosch, 2008); an example of a stand-alone application can be found in

Francois et al. (2005).

The base version of GTAP represents land allocation in a structure of constant elasticities of transformation, assuming that the various types of land use are imperfectly substitutable, but the substitutability is equal among all land use types. LEITAP extends the land use allocation structure by taking into account the fact that the degree of substitutability of types of land differs between types (Huang et al., 2004). It uses the more detailed OECD’s Policy Evaluation Model (OECD, 2003) structure. This structure reflects the fact that it is easier to shift land between producing crops like wheat, coarse grains and oilseeds, than between land uses like pasture, sugarcane or, even more so, horticulture. The values of the elasticities are taken from OECD (2003).

In the standard GTAP model the total land supply is exogenous. In LEITAP the total agricultural land supply is modelled using a land supply curve which specifies the relationship between land supply and a land rental rate in each region. Land supply to agriculture can be adjusted as a result of idling of agricultural land, conversion of non-agricultural land to agriculture, conversion of agricultural land to urban use and agricultural land abandonment. The concept of a land supply curve has been based on Abler (2003). The general idea underlying the land supply curve specification is that the most productive land is first taken into production. However, the potential for bringing additional land into agriculture is limited. If the gap between potentially available agricultural land and land used in the agricultural sector is large, the increase in demand for agricultural land will lead to land conversion to agricultural land and a modest increase in rental rates to compensate for the cost of bringing this land into production.

The land supply curve is derived using biophysical data from the IMAGE modelling framework, described below. In the IMAGE model, climate and soil conditions determine the crop productivity on a grid scale of 0.5 by 0.5 degrees longitude-latitude. This allows spatially heterogeneous information on land productivity to be fed into the agro-economic model with LEITAP. In practice, land use change projections are iterated between LEITAP and the IMAGE until a stable solution is reached — typically one iteration is enough. Land supply functions differ between region according to survey results on land type supply constraints.

Land use and land cover from an environmental point of view: IMAGE

The IMAGE model is geographically explicit in the description of land-use and land-cover change. The model distinguishes 14 natural and forest land-cover types and 6 man-made land-cover types. The land use model describes both crop and livestock systems on the basis of agricultural demand, demand for food and feed crops, animal products and energy crops. A crop module based on the FAO agro-ecological zones approach (FAO, 1978-1981) computes the spatially explicit yields of the different crop groups and the grass, and the areas used for their production, as determined by climate and soil quality. Where expansion of agricultural land is required, a rule-based “suitability map” determines the grid cells selected (on the basis of the grid cell’s potential crop yield, its proximity to

other agricultural areas and to water bodies). An initial land-use map for 1970 is incorporated on the basis of satellite observations combined with statistical information. For the period 1970-2000, the model is calibrated to be fully consistent with FAO statistics. From 2000 onwards, agricultural production is driven by the production of agricultural products as determined by LEITAP and demand for bio-energy crops from the TIMER model. Changes in natural vegetation cover are simulated in IMAGE 2.4 on the basis of a modified version of the BIOME natural vegetation model (BIOME, Prentice, 1992). This model computes changes in potential vegetation for 14 biome types on the basis of climate characteristics. The potential vegetation is the equilibrium vegetation that should eventually develop under a given climate (Bouwman et al., 2006).

Modelling of change in agricultural land use