AGRODEP MEMBERS
Identification and financing of green projects: is climate change
budget tagging the panacea for enticing climate finance and
reducing GHG emissions in developing countries?
Carren Pindiriri & Marko Kwaramba

#2023 AGRODEP CONFERENCE
Outline of the presentation
• Introduction
• Research issue and the objectives
• The framework (theory of change)
• Methods
• Data (sample determination and outcome variables’ stylized facts)
• Findings
• Conclusion

#2023 AGRODEP CONFERENCE
Introduction
• Climate change has unanimously become the new global challenge
• Developing countries that rely on climate-sensitive sectors will be the worst affected (Lalthapersad-
Pillay & Udjo, 2014 and World Bank, 2010)
• There has been increased interest in green recovery (UNDP, 2021 & Agrawala et al., 2020)
• However, effort to take climate action in developing countries has remained subdued due to limited
resources (UN, 2019)
• Failure to attract climate finance due to the identification problem (Ankomah et al., 2015)
• Climate related projects are not clearly separated from the usual developmental projects
• The initiative by the World Bank and UN to redefine the budgeting process (CBT program)
• Climate budget tagging is a government-led process to identify, measure and monitor climate-related
project expenditures (World Bank, 2012).
• One of its objective is to separate climate related projects and expenditures from usual developmental
projects for easier identification (use of the Rio climate change markers) to attract climate finance

#2023 AGRODEP CONFERENCE
Research issue and objectives
• Subdued uptake of the CBT program in developing countries despite difficulties in climate resource
mobilisation
• For instance, only a handful of African countries, Ghana, Kenya, Ethiopia and Uganda, began to
implement climate change budget tagging
• About 7 of the 19 countries implementing CBT were from Asia in 2019
• Countries usually adopt these programmes based on perceived benefits
• Yet, no evaluation has been done to assess the impact of CBT on climate finance mobilisation and
emissions reduction in these resource-constraint countries
• The main objective of this study is therefore to evaluate the impact of climate change budget
tagging on mobilization of external climate finance and GHG emissions
• Measuring the impact does not only demonstrate intervention success or failure, but it also
provides accountability to funders and beneficiaries (World Bank, 2015)

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The framework (theory of change)
The identification problem
Budget tagging
Successfully Identifies
climate-related projects
Outputs
 Climate finance inflows
 Reduced GHG emissions
Unsuccessfully identifies
climate-related projects
No climate finance
No budget tagging

#2023 AGRODEP CONFERENCE
Methods
• Applied DID
• It is more appropriate for non-experimental designs (Imbens and Wooldridge, 2009; Wooldridge,
2012; White & Raitzer, 2017 and Abadie & Cattaneo, 2018)
• Treatment and control; pre- and post-treatment
• 𝑄𝑖𝑡 = 𝛾0 + 𝛾1𝑝𝑜𝑠𝑡𝑡 + 𝛾2𝑡𝑟𝑒𝑎𝑡𝑖𝑡 + 𝛾3𝑝𝑜𝑠𝑡𝑡 ∗ 𝑡𝑟𝑒𝑎𝑡𝑖𝑡 + 𝜇𝑖 + 𝑒𝑖𝑡 , for 𝑡 = 1, ⋯ , 𝑇 and 𝑁 = 1, ⋯ , 𝑁
• Outcomes are inflow of climate related development finance and per capita 𝐂𝐎𝟐 emissions
• DID estimator
Pre-treatment
(2005-2012)
Post-treatment
(2013-2020)
Difference (𝑷𝒐𝒔𝒕 − 𝑷𝒓𝒆)
Treatment (𝑻) 𝑄0
𝑇
𝑄1
𝑇
𝑄1
𝑇
− 𝑄0
𝑇
Control (𝑪) 𝑄0
𝐶
𝑄1
𝐶
𝑄1
𝐶
− 𝑄0
𝐶
Difference (𝑻 − 𝑪) 𝑄0
𝑇
−𝑄0
𝐶
𝑄1
𝑇
− 𝑄1
𝐶
𝛾3 = 𝑄1
𝑇
− 𝑄1
𝐶
− (𝑄0
𝑇
− 𝑄0
𝐶
)

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Data – sample determination
• World Bank – all implementing countries
• Made use of early adopters (2012/13) and did robust checks using medium adopters (2017)
• Study sample
• Pre-treatment (2005 – 2012); post-treatment (2013-2020)
• Selection of comparators (neighbours with similar economic characteristics)
• Symmetric approach applied in the determination of both pre-treatment and control group
• Using the same approach, 4 non-Asian countries, Colombia, Ethiopia, Honduras and Kenya and
their comparators, Venezuela, South Sudan, Guatemala and Tanzania over 2013 to 2020 were used
to check robustness of the findings
Treated country Non-treated comparator
Cambodia Laos
Indonesia Thailand
Nepal Bhutan

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Data – outcome variables’ stylized facts
• OECD and World Bank – Climate related development finance and CO2 emissions
• * Climate related development finance * CO2 emissions per capita
0
1000
2000
3000
0
1000
2000
3000
2005
2010
2015
2020
2005
2010
2015
2020
2005
2010
2015
2020
Bhutan Cambodia Indonesia
Laos Nepal Thailand
year
0
1
2
3
4
0
1
2
3
4
2
0
0
5
2
0
1
0
2
0
1
5
2
0
2
0
2
0
0
5
2
0
1
0
2
0
1
5
2
0
2
0
2
0
0
5
2
0
1
0
2
0
1
5
2
0
2
0
Bhutan Cambodia Indonesia
Laos Nepal Thailand
Per
capita
CO2
emissions
(metric
tons)
year

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Findings – mean differences
• CRDF mean and differences (US$m)
• CO2 mean and differences (metric tons)
Pre-treatment
(2005-2012)
Post-treatment
(2013-2020)
Difference (𝑷𝒐𝒔𝒕 − 𝑷𝒓𝒆)
Treatment (𝑻) 𝑄0
𝑇
=228.64 𝑄1
𝑇
=937.47 𝑄1
𝑇
− 𝑄0
𝑇
=708.83
Control (𝑪) 𝑄0
𝐶
= 84.45 𝑄1
𝐶
= 200.21 𝑄1
𝐶
− 𝑄0
𝐶
=115.76
Difference (𝑻 − 𝑪) 𝑄0
𝑇
−𝑄0
𝐶
= 144.19 𝑄1
𝑇
− 𝑄1
𝐶
=737.26 𝛾3 = 𝑄1
𝑇
− 𝑄1
𝐶
− 𝑄0
𝑇
− 𝑄0
𝐶
= 𝟓𝟗𝟑. 𝟏
Pre-treatment
(2005-2012)
Post-treatment
(2013-2020)
Difference (𝑷𝒐𝒔𝒕 − 𝑷𝒓𝒆)
Treatment (𝑻) 𝑄0
𝑇
=0.69 𝑄1
𝑇
=1.01 𝑄1
𝑇
− 𝑄0
𝑇
=0.32
Control (𝑪) 𝑄0
𝐶
= 1.41 𝑄1
𝐶
= 2.19 𝑄1
𝐶
− 𝑄0
𝐶
=0.78
Difference (𝑻 − 𝑪) 𝑄0
𝑇
−𝑄0
𝐶
= −0.72 𝑄1
𝑇
− 𝑄1
𝐶
=-1.18 𝛾3 = 𝑄1
𝑇
− 𝑄1
𝐶
− 𝑄0
𝑇
− 𝑄0
𝐶
= −𝟎. 𝟒𝟔

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Findings – CRDF DID estimator
• Climate related development finance
Outcome
variable
𝒄𝒓𝒅𝒇 S. Err. |t| P>|t|
Pre-treatment
Control 84.45
Treated 228.64
Difference 144.18 143.35 1.01 0.317
Post-treatment
Control 200.21
Treated 937.47
Difference 737.26 143.35 5.14 0.000***
Post*treat 593.08 202.72 2.93 0.004***
R-squared 0.32

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Findings – CO2 DID estimator
• CO2 emissions per capita
Outcome variable 𝒑𝒄_𝒄𝒐𝟐 S. Err. |t| P>|t|
Pre-treatment
Control 1.41
Treated 0.69
Difference -0.73 0.31 -2.38 0.020**
Post-treatment
Control 2.19
Treated 1.01
Difference -1.18 0.31 3.85 0.000***
Post*treat -0.45 0.43 1.04 0.300
R-squared 0.23

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Findings – Panel DID estimator
• CO2 emissions per capita
(OLS) (OLS) (RE) (FE) (RE) (FE)
Variables 𝑐𝑟𝑑𝑓 𝑝𝑐_𝑐𝑜2 𝑐𝑟𝑑𝑓 𝑐𝑟𝑑𝑓 𝑝𝑐_𝑐𝑜2 𝑝𝑐_𝑐𝑜2
𝑡𝑟𝑒𝑎𝑡 144.2** -0.726** 144.2 -0.726
(69.74) (0.306) (332.2) (0.984)
𝑝𝑜𝑠𝑡 115.8 0.775** 115.8 115.8 0.775*** 0.775***
(74.19) (0.306) (109.5) (109.5) (0.101) (0.101)
𝑝𝑜𝑠𝑡 ∗ 𝑡𝑟𝑒𝑎𝑡 593.1*** -0.451 593.1*** 593.1*** -0.451*** -0.451***
(202.7) (0.432) (154.9) (154.9) (0.143) (0.143)
constant 84.45** 1.414*** 84.45 156.5*** 1.414** 1.050***
(34.49) (0.216) (234.9) (54.76) (0.696) (0.0505)
Observations 96 96 96 96 96 96
R-squared 0.324 0.226 0.328 0.328 0.440 0.440
F-statistics 8.43*** 9.53*** na 21.5*** na 34.62***
Wald statistic na na 44.86*** na 70.17*** na

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Findings – DID graphs with panel data
0
500
1000
1500
2005 2010 2015 2020
year
Treated Control
.5
1
1.5
2
2.5
Mean
per
capita
CO2
emissions
(metric
tons)
2005 2010 2015 2020
year
Treated Control

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Findings – alternative sample
• Results using a non-Asian sample (South America and Africa)
Outcome
variable
𝒄𝒓𝒅𝒇 S. Err. |t| P>|t|
Pre-treatment
Control 192.52
Treated 558.01
Difference 365.49 138.88 2.63 0.011**
Post-treatment
Control 247.51
Treated 1130.98
Difference 883.47 138.88 6.36 0.000***
Post*treat 517.98 196.41 2.64 0.011**
R-squared 0.49

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Conclusion
• CBT can be used as a useful tool for climate finance mobilisation in developing countries.
• The results show significant benefits of CBT in enticing climate related development
finance.
• The other conclusion from the findings is that it is not in all countries that CBT reduces
CO2 emissions (country-specific) – the need to identify more effective sectors for
targeting.
• The findings are however indicative only – the sample size is still small despite improved
efficiency from panel data.
• Processing of the results to be continued as more observations trickle in with time.

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