AI for Optimal and Sustainable Forest Management

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School of Business | Doctoral thesis (article-based) | Defence date: 2022-09-30
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Authors

Date

2022

Department

Tieto ja palvelujohtamisen laitos
Department of Information and Service Management

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Mcode

Degree programme

Language

en

Pages

48 + app. 50

Series

Aalto University publication series DOCTORAL THESES, 112/2022

Abstract

Forests are precious multi-use resources with high economic, ecologic, and societal value. Forests not only produce wood - a renewable resource that is increasingly replacing fossil-based materials - but also preserve biodiversity and sequestrate CO2 from the atmosphere. Optimal forest management is therefore crucial for combating climate change and for reaching several of the United Nations' (UN) Sustainability Development Goals. However, determining optimal harvest timings and intensities is one of the oldest - and still unsolved - problems in forestry. Optimizing forest management operations presents a complex, dynamic, discrete-time control problem. Complications arise from discontinuities, nonconvexities, a large number of decision variables, a hybrid action space, and a long planning horizon. Conflicting stakeholder interests and uncertainty - for example in forest growth dynamics, timber prices, currency exchange rates, or natural disasters - further complicate the problem. Existing forestry optimization methods need to either simplify the problem to remain feasible or they require days or even weeks to find an approximate solution. This leads to sub-optimal forest management decisions, which in turn lead to economic losses and unnecessary environmental destruction. Against this backdrop, this doctoral dissertation contributes novel methods and insights on optimal and sustainable forest management by applying AI-based optimization techniques that have not been previously used in economic forest research. In Article I, we use multi-objective evolutionary algorithms to compute and evaluate multi-objective forestry strategies, without the need for policy makers to assign preferences a priori. Article II marks a methodological shift and explores the necessary preconditions for successful real-world application of reinforcement learning. In Article III, we then use reinforcement learning to solve a high-dimensional optimal harvesting problem that correctly includes stochasticity in forest growth and in the occurrence of natural disasters. Our method is the first to simultaneously consider both clear-cutting and continuous cover forest management, and to calculate near-optimal harvesting schedules purely based on the long-term goals of forest owners. We find that multi-species continuous cover forestry is often more profitable and sustainable than current single-species clear-cut practices; especially when including the risk of natural disasters. Moreover, our work helps to navigate conflicting goals (economic profit vs. carbon storage vs. biodiversity). Finally, we establishes forest management as a multi-disciplinary research area by bridging economic forest research with AI research. In summary, this thesis contributes novel methods and practical insights on optimal and sustainable forest management, with far-reaching implications for forest owners, policy makers, asset managers, ESG investors, and for reaching several UN Sustainability Development Goals.

Description

Supervising professor

Malo, Pekka, Prof., Aalto University, Department of Information and Service Management, Finland

Thesis advisor

Rossi, Matti, Prof., Aalto University, Department Information and Service Management, Finland

Keywords

AI, forest management, optimization, evolutionary computation, reinforcement learning

Other note

Parts

  • [Publication 1]: Philipp Back, Antti Suominen, Pekka Malo, Olli Tahvonen, Julian Blank, Kalyanmoy Deb. Towards Sustainable Forest Management Strategies with MOEAs. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference, CancĂșn, Mexico, 1046-1054, July 2020.
    DOI: 10.1145/3377930.3389837 View at publisher
  • [Publication 2]: Philipp Back. Real-World Reinforcement Learning: Observations from Two Successful Cases. In Proceedings of the 34th Bled eConference: Digital Support from Crisis to Progressive Change, Maribor, Slovenia, 273-285, June 2021.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202108258441
    DOI: 10.18690/978-961-286-485-9.20 View at publisher
  • [Publication 3]: Pekka Malo, Olli Tahvonen, Antti Suominen, Philipp Back, Lauri Viitasaari. Reinforcement Learning in Optimizing Forest Management. Canadian Journal of Forest Research, 51, 10, 1393-1409, October 2021.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202104216310
    DOI: 10.1139/cjfr-2020-0447 View at publisher

Citation

Permanent link to this item

https://urn.fi/URN:ISBN:978-952-64-0902-3

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