The 1451 Review (Volume 1) 2021

1) Does vulnerability affect adaptation aid allocation? Does this effect vary
between the different dimensions of vulnerability?
2) Does implementation capacity affect adaptation aid allocation?
Methodology
In this section I will discuss the data and the empirical strategy of this research. The
relationship between adaptation aid and vulnerability to climate change will be tested
using a panel data set for 140 countries for the period 2011-2017.
Dependent Variable; Adaptation Aid per Capita
The dependent variable has been constructed using the OECD-DAC statistics on
climate related development finance commitments that specifically target adaptation
(OECD 2019a). Data is reported at the project level, and it includes both bilateral flows
straight to recipient countries as well as flows from multilateral donors to recipient
countries. Multilateral donors include both multilateral development banks (MDBs)
and Funds, where the data on MDBs is included from 2013 onwards. The OECD
Creditor Reporting system reports commitments rather than disbursements, and
although looking at disbursement data would be ideal for evaluating the outcome,
analysing commitments reflects donor priorities better. One of the prominent
drawbacks of the data is that it relies on donor self-reporting, but even then, it is the
most comprehensive and reliable data on adaptation aid to date (Betzold and Weiler;
2018).
The data employs two methods to account for adaptation related financial
flows. In 2010 the OECD introduced an adaptation Rio marker to monitor adaptation
objectives in development cooperation, having previously implemented markers for
climate change mitigation, desertification and biodiversity (OECD 2016). The aid
project will be relevant for adaptation when ‘it intends to reduce the vulnerability of
human or natural systems to the current and expected impacts of climate change,
including climate variability, by maintaining or increasing resilience, through
increased ability to adapt to, or absorb, climate change stresses, shocks and variability
and/or by helping reduce exposure to them’ (OECD 2016: 8). A project can score
either, ‘not targeted’, ‘significant’ or ‘principal’ based on whether adaptation is an
objective, and whether other objectives motivate the project as well.
The activity is marked as ‘principal’ if adaptation is the fundamental motivation
behind it, and ‘significant’ if adaptation is stated as one of the objectives albeit not the
fundamental motivation (OECD 2016). I include both principal and significant flows
into the analysis, recognising that adaptation often overlaps with other development
objectives and hence may be reported under the ‘significant’ marker. However, these
flows have been discounted by 50% to account for overstating. Firstly, donors tend to
overstate their commitments relative to the actual disbursements. Secondly,
independent assessment has found that donors overstate the relevance of their aid
projects for adaptation, especially in projects where adaptation is only a ‘significant’
objective (Carty and le Compte 2018; AdaptationWatch 2015). The discount rate was
selected to balance between overcounting and recognising that ‘significant’ flows also
target adaptation to a certain extent, following the approach in Betzold and Weiler
(2017).
Alongside the Rio Marker approach, MDBs have implemented their own
climate components approach, which has been argued to be more useful in accounting
for adaptation contributions in a more ‘mainstreamed’ approach where adaptation
actions are also carried out through regular development projects (AdaptationWatch
2015). The given approach only accounts for the share of the project that targets
adaptation directly, hence the adaptation finance contributions from MDBs are
included in the analysis in full.
The dependent variable is constructed by pooling the data to obtain a total
amount of adaptation aid received annually from both bilateral and multilateral
sources. Although the adaptation marker was introduced in 2010, I only include data
starting from 2011 since some donors had not fully implemented the marker in 2010
and therefore the data may be unreliable (Saunders 2019). After pooling the data,
population is used to construct the total annual adaptation aid per capita, assuming
that aid is allocated respective to the population of a country, consistent with what has
been found in the aid literature (McGillivray and Oczkowski 1992). The dependent
variable enters the model in a log-transformation to account for the high skew in the
data.
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