Matthias Baumann | Searcharxiv

arXiv2024

Explaining the emergence of land-use frontiers

Land use expansion is linked to major sustainability concerns including climate change, food security and biodiversity loss. This expansion is largely concentrated in so-called frontiers, defined here as places experiencing marked transformations due to rapid resource exploitation. Understanding the mechanisms shaping these frontiers is crucial for sustainability. Previous work focused mainly on explaining how active frontiers advance, in particular into tropical forests. Comparatively, our understanding of how frontiers emerge in territories considered marginal in terms of agricultural productivity and global market integration remains weak. We synthesize conceptual tools explaining resource and land-use frontiers, including theories of land rent and agglomeration economies, of frontiers as successive waves, spaces of territorialization, friction, and opportunities, anticipation and expectation. We then propose a new theory of frontier emergence, which identifies exogenous pushes, legacies of past waves, and actors anticipations as key mechanisms by which frontiers emerge. Processes of abnormal rent creation and capture and the built-up of agglomeration economies then constitute key mechanisms sustaining active frontiers. Finally, we discuss five implications for the governance of frontiers for sustainability. Our theory focuses on agriculture and deforestation frontiers in the tropics, but can be inspirational for other frontier processes including for extractive resources, such as minerals.

econ.GN↗

arXiv2022

Towards Causal Inference for Spatio-Temporal Data: Conflict and Forest Loss in Colombia

In many data scientific problems, we are interested in inferring causal relationships in the data generating mechanism. Here, we consider the following real-world question: how has the Colombian conflict influenced tropical forest loss? There is evidence for both enhancing and reducing impacts. Answering such questions requires the use of causal models. In this work, we propose a class of causal models for spatio-temporal stochastic processes. It allows us to formally define and quantify the causal effect of a vector of covariates $X$ on a real-valued response $Y$, even if the causal background knowledge is incomplete. We introduce a procedure for estimating causal effects, and a non-parametric hypothesis test for these effects being zero. The proposed methods do not make strong distributional assumptions, and allow for arbitrarily many latent confounders, given that these confounders do not vary across time (or, alternatively, they do not vary across space). When applying our causal methodology to the problem of conflict and forest loss, using data from 2000 to 2018, we find a reducing but insignificant causal effect of conflict on forest loss. Regionally, both enhancing and reducing effects can be identified. Our theoretical findings are supported by simulations, and code is available online.

stat.ME↗