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Plant trees for the planet: the potential of forests for climate change mitigation and the major drivers of national forest area

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  • Sebastian Mader

    (University of Bern)

Abstract

Forests are one of the most cost-effective ways to sequester carbon today. Here, I estimate the world’s land share under forests required to prevent dangerous climate change. For this, I combine newest longitudinal data of FLUXNET on forests’ net ecosystem exchange of carbon (NEE) from 78 forest sites (N = 607) with countries’ mean temperature and forest area. This straightforward approach indicates that the world’s forests sequester 8.3 GtCO2year−1. For the 2 °C climate target, the current forest land share has to be doubled to 60.0% to sequester an additional 7.8 GtCO2year−1, which demands less red meat consumption. This afforestation/reforestation (AR) challenge is achievable, as the estimated global biophysical potential of AR is 8.0 GtCO2year−1 safeguarding food supply for 10 billion people. Climate-responsible countries have the highest AR potential. For effective climate policies, knowledge on the major drivers of forest area is crucial. Enhancing information here, I analyze forest land share data of 98 countries from 1990 to 2015 applying causal inference (N = 2494). The results highlight that population growth, industrialization, and increasing temperature reduce forest land share, while more protected forest and economic growth generally increase it. In all, this study confirms the potential of AR for climate change mitigation with a straightforward approach based on the direct measurement of NEE. This might provide a more valid picture given the shortcomings of indirect carbon stock-based inventories. The analysis identifies future regional hotspots for the AR potential and informs the need for fast and forceful action to prevent dangerous climate change.

Suggested Citation

  • Sebastian Mader, 2020. "Plant trees for the planet: the potential of forests for climate change mitigation and the major drivers of national forest area," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(4), pages 519-536, April.
  • Handle: RePEc:spr:masfgc:v:25:y:2020:i:4:d:10.1007_s11027-019-09875-4
    DOI: 10.1007/s11027-019-09875-4
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    References listed on IDEAS

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    1. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    2. Thorsten Mauritsen & Robert Pincus, 2017. "Committed warming inferred from observations," Nature Climate Change, Nature, vol. 7(9), pages 652-655, September.
    3. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, November.
    4. Jeff Tollefson, 2017. "The wooden skyscrapers that could help to cool the planet," Nature, Nature, vol. 545(7654), pages 280-282, May.
    5. Molly Hawes, 2018. "Planting carbon storage," Nature Climate Change, Nature, vol. 8(7), pages 556-558, July.
    6. Malte Meinshausen & Nicolai Meinshausen & William Hare & Sarah C. B. Raper & Katja Frieler & Reto Knutti & David J. Frame & Myles R. Allen, 2009. "Greenhouse-gas emission targets for limiting global warming to 2 °C," Nature, Nature, vol. 458(7242), pages 1158-1162, April.
    7. Jonah Busch & Kalifi Ferretti-Gallon, 2017. "What Drives Deforestation and What Stops It? A Meta-Analysis," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 11(1), pages 3-23.
    8. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, November.
    9. Katharine Ricke & Laurent Drouet & Ken Caldeira & Massimo Tavoni, 2018. "Country-level social cost of carbon," Nature Climate Change, Nature, vol. 8(10), pages 895-900, October.
    10. Leblois, Antoine & Damette, Olivier & Wolfersberger, Julien, 2017. "What has Driven Deforestation in Developing Countries Since the 2000s? Evidence from New Remote-Sensing Data," World Development, Elsevier, vol. 92(C), pages 82-102.
    11. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
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    Cited by:

    1. Stella Manes & Igor Rodrigues Henud & Kenny Tanizaki-Fonseca, 2022. "Climate change mitigation potential of Atlantic Forest reforestations," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(5), pages 1-15, June.
    2. Stella Manes & Igor Rodrigues Henud & Kenny Tanizaki-Fonseca, 2022. "Climate change mitigation potential of Atlantic Forest reforestations," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(6), pages 1-15, August.

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