TY - JOUR

T1 - Agriculture-driven deforestation in the tropics from 1990-2015

T2 - emissions, trends and uncertainties

AU - Carter, Sarah

AU - Herold, Martin

AU - Avitabile, Valerio

AU - de Bruin, Sytze

AU - De Sy, Veronique

AU - Kooistra, Lammert

AU - Rufino, Mariana C.

PY - 2018/1

Y1 - 2018/1

N2 - Limited data exists on emissions from agriculture-driven deforestation, and available data are typically uncertain. In this paper, we provide comparable estimates of emissions from both all deforestation and agriculture-driven deforestation, with uncertainties for 91 countries across the tropics between 1990 and 2015. Uncertainties associated with input datasets (activity data and emissions factors) were used to combine the datasets, where most certain datasets contribute the most. This method utilizes all the input data, while minimizing the uncertainty of the emissions estimate. The uncertainty of input datasets was influenced by the quality of the data, the sample size (for sample-based datasets), and the extent to which the timeframe of the data matches the period of interest. Area of deforestation, and the agriculture-driver factor (extent to which agriculture drives deforestation), were the most uncertain components of the emissions estimates, thus improvement in the uncertainties related to these estimates will provide the greatest reductions in uncertainties of emissions estimates. Over the period of the study, Latin America had the highest proportion of deforestation driven by agriculture (78%), and Africa had the lowest (62%). Latin America had the highest emissions from agriculture-driven deforestation, and these peaked at 974 +/- 148 Mt CO2 yr(-1) in 2000-2005. Africa saw a continuous increase in emissions between 1990 and 2015 (from 154 +/- 21-412 +/- 75 Mt CO2 yr(-1)), so mitigation initiatives could be prioritized there. Uncertainties for emissions from agriculture-driven deforestation are +/- 62.4% (average over 1990-2015), and uncertainties were highest in Asia and lowest in Latin America. Uncertainty information is crucial for transparency when reporting, and gives credibility to related mitigation initiatives. We demonstrate that uncertainty data can also be useful when combining multiple open datasets, so we recommend new data providers to include this information.

AB - Limited data exists on emissions from agriculture-driven deforestation, and available data are typically uncertain. In this paper, we provide comparable estimates of emissions from both all deforestation and agriculture-driven deforestation, with uncertainties for 91 countries across the tropics between 1990 and 2015. Uncertainties associated with input datasets (activity data and emissions factors) were used to combine the datasets, where most certain datasets contribute the most. This method utilizes all the input data, while minimizing the uncertainty of the emissions estimate. The uncertainty of input datasets was influenced by the quality of the data, the sample size (for sample-based datasets), and the extent to which the timeframe of the data matches the period of interest. Area of deforestation, and the agriculture-driver factor (extent to which agriculture drives deforestation), were the most uncertain components of the emissions estimates, thus improvement in the uncertainties related to these estimates will provide the greatest reductions in uncertainties of emissions estimates. Over the period of the study, Latin America had the highest proportion of deforestation driven by agriculture (78%), and Africa had the lowest (62%). Latin America had the highest emissions from agriculture-driven deforestation, and these peaked at 974 +/- 148 Mt CO2 yr(-1) in 2000-2005. Africa saw a continuous increase in emissions between 1990 and 2015 (from 154 +/- 21-412 +/- 75 Mt CO2 yr(-1)), so mitigation initiatives could be prioritized there. Uncertainties for emissions from agriculture-driven deforestation are +/- 62.4% (average over 1990-2015), and uncertainties were highest in Asia and lowest in Latin America. Uncertainty information is crucial for transparency when reporting, and gives credibility to related mitigation initiatives. We demonstrate that uncertainty data can also be useful when combining multiple open datasets, so we recommend new data providers to include this information.

KW - error propagation

KW - forest biomass

KW - land use change

KW - land cover change

KW - REDD

KW - CARBON EMISSIONS

KW - COVER CHANGE

KW - FOREST AREA

KW - LAND-USE

KW - MAP

KW - REGIONS

KW - BUDGET

U2 - 10.1088/1748-9326/aa9ea4

DO - 10.1088/1748-9326/aa9ea4

M3 - Journal article

VL - 13

JO - Environmental Research Letters

JF - Environmental Research Letters

SN - 1748-9326

IS - 1

M1 - 014002

ER -