Now that Rio+20 has come to a close, two things are clear: 1.) little was done to systematically track the progress of aid commitments for sustainable development and the environment since the first Rio Earth Summit in 1992; and 2.) following Rio+20, it seems that yet again it is unlikely that this process will be any different moving forward.
We’ve added another resource to the AidData Research Release repository that will assist with the effort to track the likely impact of aid projects on the environment: PLAID 1.9 with Environmental Codes. This dataset has its origins in the Greening Aid?book. For Greening Aid?, researchers at the College of William and Mary assessed the likely environmental impact of projects allocated through 1999. In 2009, AidData postdoctoral fellow Chris Marcoux led a team to extend this coding. The release of PLAID 1.9 with Environmental Codes represents the full assessment of all projects through 2008.
The environmental impact variable evaluates the likely environmental impact of a project, as well as the scope of environmental benefit (if applicable). It has 8 possible values:
- Dirty, strictly defined – Projects that cause significant and immediate environmental harm (e.g., resource extraction & heavy industry)
- Dirty, broadly defined – Projects that will cause moderate environmental harm over the long term (e.g., electricity distribution, hydro power)
- Environmental, broadly defined (Brown) – Projects that are preventive in nature or that produce less immediate, more long term environmental benefits. Brown projects produce local environmental benefits (e.g., disaster prevention)
- Environmental, broadly defined (Green) – Projects that are preventive in nature or that produce less immediate, more long term environmental benefits. Green projects produce regional or global environmental benefits (e.g., genetic diversity & industrial reforestation)
- Environmental, strictly defined (Brown) – Projects expected to produce significant, immediate environmental benefits. Brown projects produce local environmental benefits (e.g., water treatment & sewer systems)
- Environmental, strictly defined (Green) – Projects expected to produce significant, immediate environmental benefits. Green projects produce regional/global benefits (e.g., mitigation of transboundary air pollution)
- Neutral – Projects that are not likely to have an immediate or significant environmental impact (e.g., education, health, SME support)
- Unsure – Project description did not provide sufficient information for environmental coding
These variables were assigned to the PLAID 1.9 research release, a precursor to the current AidData web portal and AidData 2.0 research release. What this means for users is:
- Donor names have been harmonized, but may not match current AidData donor names
- Recipient names are not harmonized and some core contributions to multilateral institutions are included, with the multilateral institution listed as recipient
- Sector code coverage is not as extensive as AidData 2.0
- Not all projects have unique AidData IDs, but do have a unique dataset-specific ID (temp_plaid_id)
- “Constant USD” is USD-2000, unlike AidData 2.0’s USD-2009
We’re in the process of matching these environment codes with database records in the AidData web portal. Those interested in these data should stay tuned because when this process is finished users will be able to export environmental codes directly from the AidData web portal (bringing in better sector coding coverage, current donor and recipient names, project amounts in constant 2009 USD, etc.).
Also, a quick note for researchers: As we improve the infrastructure of our internal database, we’ll be adding new variables, such as World Bank IEG evaluation data and AidData-generated geocodes. If you have a created a project-level variable (even if it's specific to a particular donor, recipient, sector, etc.) that you would like us to make available through the AidData web portal, contact us at info@aiddata.org.
PLAID 1.9 with Environment Codes is available in 5 formats (third dataset from the top of the page).