Getting Started: WiNDC 1.0
Note that this page refers to WiNDC Version 1.0, released in December 2018. For details of the current version, see Getting Started: WiNDC Build Stream.
Utilities and Dataset Description
The WiNDC build routine is a collection of GAMS programs for producing subnational economic accounts for input-output or computable general equilibrium models of the United States economy. All code and data necessary for producing subnational accounts are provided in this repository. Currently, the routine can produce state level accounts.
We begin with the national input-output table of the Bureau of Economic Analysis' (BEA) and downscale to the regional level using publicly available economic statistics from governmental agencies. We use additional data from the BEA on regional gross product and consumer expenditures and data from the Census Bureau on foreign trade, bilateral trade and state government expenditures. For illustrative purposes, we show how to complement the core input-output tables with physical energy quantities and energy prices from the State Energy Data System (SEDS) of the Energy Information Administration (EIA) of the Department of Energy.
The energy-environment module of WiNDC is called blueNOTE.
Downloads
-
The WiNDC build stream package including the datasources files and the GAMS file
run.gms
that generates the WiNDC database: windc_build-1.0.zip (13.8 MB).
Note that the files are described in the paper. - The pre-compiled core WiNDC database: in GDX format (39.8 MB) and JSON format (69.2 MB).
- The datasources files (157.9 MB). Note that the datasources files in GDX format are part of the WiNDC build stream package.
Step-By-Step Instructions
Currently, the datasets and build routine are available for the energy-environment module, called blueNOTE, and a basic agricultural model, labeled "nass".
If you are interested only in the core WiNDC database, you can download it in GDX format (39.8 MB) and JSON format (69.2 MB). If you are interested in details of the build and the source datasets, please follow the steps outlined below.
- Download the WiNDC build stream package: windc.zip (84.7 MB).
-
Unzipping the file will create a directory called
windc
with the subdirectoriesbuild
andgdx-in
, and the GAMS filerun.gms
. The directorybuild
contains all subroutines and the directorygdx-in
contains the datasources in GDX format. -
If you have a local version of GAMS and have access to the relevant licenses,
navigate in your command line or terminal to the
windc
directory and run the GAMS filerun.gms
by simply typing the following command:gams run.gms
-
The core WiNDC database,
WiNDCdatabase.gdx
, will be generated locally in a directory calleddatasets
. It contains data for all US states and 71 (summary) sectors from 1997-2014. - If you don't have access to a GAMS license including needed solver licenses, you can generate the database locally by using NEOS. Please contact us for detailed instructions.
- Once the database is created, it can be loaded into a general equilibrium model in GAMS. Note that accounting models are included in the build distribution which can be used as starting points for model formulation. If a dataset is generated without any optional recalibration, it includes data for all years. Here is an example of how to read the core data file into GAMS in a simple general equilibrium model and verify benchmark consistency in the underlying dataset. Note that this model contains no customizations aside from sectoral aggregation.
Notes
-
You can modify the build stream to produce a specific module or select
only data for a specific year.
For example, the following command will generate a dataset consistent with the blueNOTE module:gams run.gms --satdata=bluenote
--satdata=nass
will generate the dataset for the agricultural module.
To select a specific year, use the following command:gams run.gams --year=2014
-
In addition, you can modify the build stream to produce a disaggregated
dataset. After generating or downloading the core WiNDC database, choose one of
the following three options:
gams run.gams --aggr=389
gams run.gams --aggr=bluenote
satdata=bluenote
, which recalibrates the data).gams run.gams --aggr=nass
satdata=nass
, which recalibrates the data).