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Indonesia’s forest conversion moratorium assessed with an agent-based model of Land-Use Change and Ecosystem Services (LUCES)

Overview of attention for article published in Mitigation and Adaptation Strategies for Global Change, August 2016
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  • Good Attention Score compared to outputs of the same age (68th percentile)

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196 Mendeley
Title
Indonesia’s forest conversion moratorium assessed with an agent-based model of Land-Use Change and Ecosystem Services (LUCES)
Published in
Mitigation and Adaptation Strategies for Global Change, August 2016
DOI 10.1007/s11027-016-9721-0
Pubmed ID
Authors

Aritta Suwarno, Meine van Noordwijk, Hans-Peter Weikard, Desi Suyamto

Abstract

The Indonesian government recently confirmed its Intended Nationally Determined Contributions (INDCs) to mitigate global climate change. A forest moratorium policy that protects forest and peatland is a significant part of the INDCs; however, its effectiveness is unclear in the face of complex land-use and land-cover change. This study aims to assess the dynamics of land-use change and ecosystem service supply as a function of local decision-making. We developed an agent-based model, Land-Use Change and Ecosystem Services (LUCES), and used it to explore the possible effects of the forest moratorium policy on the land-use decisions of private companies and communities. Our simulations for two districts in Central Kalimantan show that the current implementation of the forest moratorium policy is not effective in reducing forest conversion and carbon emissions. This is because companies continue to invest in converting secondary forest on mineral soils and the moratorium does not affect community decision-making. A policy that combines a forest moratorium with livelihood support and increases farm-gate prices of forest and agroforestry products could increase the local communities' benefits from conservation. Forest and agroforestry areas that are profitable and competitive are more likely to be conserved and reduce potential carbon emission by about 36 %. The results for the two districts, with different pressures on local resources, suggest that appropriate additional measures require local fine-tuning. The LUCES model could be an ex ante tool to facilitate such fine-tuning and help the Indonesian government achieve its INDC goals as part of a wider sustainable development policy.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 196 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Unknown 195 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 16%
Student > Ph. D. Student 27 14%
Student > Master 25 13%
Student > Bachelor 14 7%
Student > Doctoral Student 8 4%
Other 26 13%
Unknown 65 33%
Readers by discipline Count As %
Environmental Science 43 22%
Agricultural and Biological Sciences 30 15%
Social Sciences 14 7%
Economics, Econometrics and Finance 10 5%
Earth and Planetary Sciences 6 3%
Other 21 11%
Unknown 72 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 December 2023.
All research outputs
#6,970,855
of 24,287,697 outputs
Outputs from Mitigation and Adaptation Strategies for Global Change
#447
of 698 outputs
Outputs of similar age
#110,012
of 357,200 outputs
Outputs of similar age from Mitigation and Adaptation Strategies for Global Change
#4
of 4 outputs
Altmetric has tracked 24,287,697 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 698 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.8. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 357,200 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.