Bio-economic modeling combines both socioeconomic and biophysical data (like soil nutrient loss, rainfall, temperature) to analyze the impact of a particular intervention that has taken place.
I have encountered (but not utlised) R packages for optimisation. There are other solutions I've heard of, but I have not migrated from GAMS.
Pyomo (https://software.sandia.gov/trac/coopr/wiki/Pyomo) looks promising. Differently from GAMS, it is open source, and it allows you to develop with a full-featured high-level programming language (Python). Depending on the complexity of the optimization problem you are dealing with, you may still need a commercial solver, in which case you need to check whether it has a Python interface.
In my case, even if there are alternatives, the cost of migration is so high that I don't even think of changing from GAMS.
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