Understanding how economic systems interact with ecosystems requires models that
include geospatial heterogeneity. Integration of economic and ecological systems is vital
for sustainable development, especially with respect to climate change, food security and
the management of common property resources. Advances in remote sensing and
geographic information systems have created a wealth of data applicable to economics,
but it is challenging to incorporate high-resolution, global data in existing economic
models. In this thesis, I integrate geospatial data with economic theory to analyze
important environmental problems.
The next three chapters describe the techniques I use for modeling geospatially-explicit
economic systems and apply them to current environmental challenges. Chapter one
addresses the tradeoffs between food production and environmental protection. I address
the question of how we can optimally feed a growing population (requiring a 100%
increase in calorie production by 2050) while minimizing the loss of carbon storage
(which is important for mitigating climate change). I use high-resolution, gridded global
data to give geospatial specificity to the results of the optimization. The framework I
present in this chapter includes only one production choice and one environmental good,
but it is more broadly applicable to multiple goods and multiple ecosystem services. This
chapter also shows how using geospatial data can increase the policy relevance of an
analysis. For example, instead of claiming that tropical forests are in general very
valuable, using geospatial data allows for the more precise claim that this specific 10
kilometer patch of forest is better kept as forest than cultivated. Spatially explicit
information like this can help construct more specific policies, such as food-for-carbon
swaps or identifying which parcels ought to be protected first given a limited
conservation budget.