Sentence examples for baselines of deforestation from inspiring English sources

Sophisticated prognostic models of land competition that could be employed to provide forward-looking baselines of deforestation pressures (Obersteiner, M. et al.: Avoiding REDD hot air – an IIASA proposal for generating standardized and globally consistent national reference scenarios that maximize sustainability, submitted) require a lot of data streams to be assimilated in these models.

In this paper we illustrate how one could use LULC change projections to establish a baseline of deforestation to set eligibility and cap requirements in a global REDD-like program instead of relying on historic deforestation rates [88].

Depending on the baseline levels of deforestation and carbon storage rates, Kohl et al. [4] found in a simulation study that many countries would generate no economic benefit with total errors in carbon estimates exceeding 5%% unless baseline deforestation rates are very high.

The focus on opportunity costs of REDD+ is only one crucial part of REDD+ economics providing information on the economic baseline situation of deforestation and forest degradation agents and on the required economic incentives to halt unsustainable land use.

The assessment of opportunity costs is a crucial first step to provide information on the economic baseline situation of deforestation and forest degradation agents and on the economic incentives required to halt unsustainable land use.

These may come from inflated national baselines for deforestation, or allowances that permit some sorts of tree-felling to be ignored.

To address the issue of potential information asymmetries inherent in the estimation of deforestation baselines required by the current Reducing Emissions from Deforestation and Forest Degradation+ (REDD+) scheme, we offer a theoretical analysis of an extended scheme relying on the theory of incentives.

In addition to reducing source data uncertainties, reanalysis of historic changes in forest area can also facilitate sub-national allocation of deforestation baselines.

This can lead to an overestimation of baseline deforestation emissions and thus violate the environmental and economic effectiveness of a REDD regime.

Allocation of baseline deforestation information to Amazon states can be accomplished using PRODES statistics (Approach 2), but below the state scale, regional or project-scale activities may require a new analysis of historic deforestation and forest degradation to provide Approach 3 data for all years during the baseline period.

The apparent similarity in scale of deforestation across states conceals relevant baseline differences between the agents engaging in forest clearing in each locality.