IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v7y2018i3p105-d168895.html
   My bibliography  Save this article

Criteria to Confirm Models that Simulate Deforestation and Carbon Disturbance

Author

Listed:
  • Robert Gilmore Pontius

    (School of Geography, Clark University, Worcester, MA 01610, USA)

Abstract

The Verified Carbon Standard (VCS) recommends the Figure of Merit (FOM) as a possible metric to confirm models that simulate deforestation baselines for Reducing Emissions from Deforestation and forest Degradation (REDD). The FOM ranges from 0% to 100%, where larger FOMs indicate more-accurate simulations. VCS requires that simulation models achieve a FOM greater than or equal to the percentage deforestation during the calibration period. This article analyses FOM’s mathematical properties and illustrates FOM’s empirical behavior by comparing various models that simulate deforestation and the resulting carbon disturbance in Bolivia during 2010–2014. The Total Operating Characteristic frames FOM’s mathematical properties as a function of the quantity and allocation of simulated deforestation. A leaf graph shows how deforestation’s quantity can be more influential than its allocation when simulating carbon disturbance. Results expose how current versions of the VCS methodologies could conceivably permit models that are less accurate than a random allocation of deforestation, while simultaneously prohibit models that are accurate concerning carbon disturbance. Conclusions give specific recommendations to improve the next version of the VCS methodology concerning three concepts: the simulated deforestation quantity, the required minimum FOM, and the simulated carbon disturbance.

Suggested Citation

  • Robert Gilmore Pontius, 2018. "Criteria to Confirm Models that Simulate Deforestation and Carbon Disturbance," Land, MDPI, vol. 7(3), pages 1-14, September.
  • Handle: RePEc:gam:jlands:v:7:y:2018:i:3:p:105-:d:168895
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/7/3/105/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/7/3/105/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Robert Pontius & Wideke Boersma & Jean-Christophe Castella & Keith Clarke & Ton Nijs & Charles Dietzel & Zengqiang Duan & Eric Fotsing & Noah Goldstein & Kasper Kok & Eric Koomen & Christopher Lippitt, 2008. "Comparing the input, output, and validation maps for several models of land change," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(1), pages 11-37, March.
    2. Yan Liu & Yongjiu Feng & Robert Gilmore Pontius, 2014. "Spatially-Explicit Simulation of Urban Growth through Self-Adaptive Genetic Algorithm and Cellular Automata Modelling," Land, MDPI, vol. 3(3), pages 1-20, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Claudia P. Romero & Alicia García-Arias & Celine Dondeynaz & Félix Francés, 2020. "Assessing Anthropogenic Dynamics in Megacities from the Characterization of Land Use/Land Cover Changes: The Bogotá Study Case," Sustainability, MDPI, vol. 12(9), pages 1-21, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yongjiu Feng & Jiafeng Wang & Xiaohua Tong & Yang Liu & Zhenkun Lei & Chen Gao & Shurui Chen, 2018. "The Effect of Observation Scale on Urban Growth Simulation Using Particle Swarm Optimization-Based CA Models," Sustainability, MDPI, vol. 10(11), pages 1-20, November.
    2. Yang, Yuanyuan & Bao, Wenkai & Liu, Yansui, 2020. "Scenario simulation of land system change in the Beijing-Tianjin-Hebei region," Land Use Policy, Elsevier, vol. 96(C).
    3. Youjung Kim & Galen Newman, 2019. "Climate Change Preparedness: Comparing Future Urban Growth and Flood Risk in Amsterdam and Houston," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
    4. Aritta Suwarno & Meine van Noordwijk & Hans-Peter Weikard & Desi Suyamto, 2018. "Indonesia’s forest conversion moratorium assessed with an agent-based model of Land-Use Change and Ecosystem Services (LUCES)," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(2), pages 211-229, February.
    5. Yuanyuan Yang & Shuwen Zhang & Jiuchun Yang & Xiaoshi Xing & Dongyan Wang, 2015. "Using a Cellular Automata-Markov Model to Reconstruct Spatial Land-Use Patterns in Zhenlai County, Northeast China," Energies, MDPI, vol. 8(5), pages 1-21, May.
    6. Bonoua Faye & Guoming Du & Edmée Mbaye & Chang’an Liang & Tidiane Sané & Ruhao Xue, 2023. "Assessing the Spatial Agricultural Land Use Transition in Thiès Region, Senegal, and Its Potential Driving Factors," Land, MDPI, vol. 12(4), pages 1-20, March.
    7. Rifat, Shaikh Abdullah Al & Liu, Weibo, 2022. "Predicting future urban growth scenarios and potential urban flood exposure using Artificial Neural Network-Markov Chain model in Miami Metropolitan Area," Land Use Policy, Elsevier, vol. 114(C).
    8. Jing Yang & Feng Shi & Yizhong Sun & Jie Zhu, 2019. "A Cellular Automata Model Constrained by Spatiotemporal Heterogeneity of the Urban Development Strategy for Simulating Land-use Change: A Case Study in Nanjing City, China," Sustainability, MDPI, vol. 11(15), pages 1-19, July.
    9. Brian Pickard & Joshua Gray & Ross Meentemeyer, 2017. "Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models," Land, MDPI, vol. 6(3), pages 1-21, August.
    10. Ju-Sung Lee & Tatiana Filatova & Arika Ligmann-Zielinska & Behrooz Hassani-Mahmooei & Forrest Stonedahl & Iris Lorscheid & Alexey Voinov & J. Gareth Polhill & Zhanli Sun & Dawn C. Parker, 2015. "The Complexities of Agent-Based Modeling Output Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(4), pages 1-4.
    11. Yan Liu & Yongjiu Feng, 2016. "Simulating the Impact of Economic and Environmental Strategies on Future Urban Growth Scenarios in Ningbo, China," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
    12. Zhang, Yan & Chang, Xia & Liu, Yanfang & Lu, Yanchi & Wang, Yiheng & Liu, Yaolin, 2021. "Urban expansion simulation under constraint of multiple ecosystem services (MESs) based on cellular automata (CA)-Markov model: Scenario analysis and policy implications," Land Use Policy, Elsevier, vol. 108(C).
    13. Margaret Gitau & Nathaniel Bailey, 2012. "Multi-Layer Assessment of Land Use and Related Changes for Decision Support in a Coastal Zone Watershed," Land, MDPI, vol. 1(1), pages 1-27, December.
    14. Xiaoli Hu & Xin Li & Ling Lu, 2018. "Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    15. Yaya Jin & Jiahe Ding & Yue Chen & Chaozheng Zhang & Xianhui Hou & Qianqian Zhang & Qiankun Liu, 2023. "Urban Land Expansion Simulation Considering the Increasing versus Decreasing Balance Policy: A Case Study in Fenghua, China," Land, MDPI, vol. 12(12), pages 1-21, November.
    16. Charlotte Shade & Peleg Kremer, 2019. "Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies," Land, MDPI, vol. 8(2), pages 1-19, February.
    17. repec:ris:cieodp:2013_019 is not listed on IDEAS
    18. Wu, Wei & Yeager, Kevin M. & Peterson, Mark S. & Fulford, Richard S., 2015. "Neutral models as a way to evaluate the Sea Level Affecting Marshes Model (SLAMM)," Ecological Modelling, Elsevier, vol. 303(C), pages 55-69.
    19. Chengge Jiang & Lingzhi Wang & Wenhua Guo & Huiling Chen & Anqi Liang & Mingying Sun & Xinyao Li & Hichem Omrani, 2024. "Spatio-Temporal Evolution and Multi-Scenario Simulation of Non-Grain Production on Cultivated Land in Jiangsu Province, China," Land, MDPI, vol. 13(5), pages 1-21, May.
    20. Kikuko Shoyama, 2021. "Assessment of Land-Use Scenarios at a National Scale Using Intensity Analysis and Figure of Merit Components," Land, MDPI, vol. 10(4), pages 1-13, April.
    21. Yi Lu & Shawn Laffan & Chris Pettit & Min Cao, 2020. "Land use change simulation and analysis using a vector cellular automata (CA) model: A case study of Ipswich City, Queensland, Australia," Environment and Planning B, , vol. 47(9), pages 1605-1621, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:7:y:2018:i:3:p:105-:d:168895. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.