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Quickstart.md

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Getting Started

  1. If you don't already have python installed, you should first go here and install the anaconda distribution.
  2. Download the model to your own computer. You can do this using the github desktop software, or just as a simple zip file.
  3. We have tried to avoid using python libraries that are not already included in the distribution so you should not need to install anything else. There is a list of module requirements in dependencies.md
  4. The model was designed and implemented using the spyder IDE (which comes bundled with anaconda) and while it should operate using alternatives, it has not been tested under different frameworks.
  5. Spyder allows you to run specific code at the beginning of each session (you can find this under tools>preferences>ipython console>startup) You need to use this to make sure that python can recognise where the model is located. Set up a new python file with code similar to setup.py(in this repository) and add it to the "run a file" option of the spyder startup tab.
  6. While we see no barrier to the code working on a variety of operating systems, it has only been tested in windows (so other operating systems may require modifications to these instructions.)

Model introduction

Core Model

The main body of the model is found in the Core_Model folder. This contains four files:

  1. formatting.py: This contains two core functions, 1) a matplotlib routine to make sure that the fonts on all the graphs produced match and 2) a simple lookup function to translate site codes into proper english text (e.g. NE_MBB becomes "NE maple \ beech \ birch")

  2. functions.py: All of the functions used by the model:

    • all of the equations for handling growth, supply chains and emission calculations
    • functions for calculating payback when compared to counterfactuals within the model.

  3. sbcm.py: The "Simple Biomass Comparison Model" (sbcm). This contains the Scenario object used to calculate emissions and changes to carbon pools over time. The Scenario object contains a number of functions:

    • initialise sets up the model once the key variables have been set.
    • fell simulates the felling of a forest site within the model.
    • runstep simulates a year's regrowth of the forest site.
    • report gathers all the important results into a pandas DataFrame and returns a payback period.
    • spinup increments the forest management by a number of rotations to ensure that forest and soil carbon are at equilibrium levels.
    • describe prints a brief description of the scenario object.

  4. variables.py contains all the variables as needed to run the model using default settings:

    • SPP: a list of species codes
    • f_value the multiplier for carbon fertilisation (used in functions.py)
    • original data USDA data for carbon by species (x and y for forest and soil carbon tC/ha)
    • forest variables variables as used in the default (Sterman et al.) parameterisation of the model.
    • forest_use / coal_use supply chain parameterisations as used in the default version of the model.

Model Runs

The calculations used to write the paper are all contained here. They are all numbered and should be run in sequence (as some calculations rely on results from earlier calculations) A python script (all.py) runs everything but takes about 10 minutes to do it: this is not particularly efficient code... Each .py file relates to a corresponding results folder and saves calculated values as .csv files. Within each of these folders, further .py files exist to produce figures and charts from the paper.