Climate finance, institutions and innovation systems in Sub-Saharan Africa

Adu, Frank; Ramfol, Roshelle

doi:10.3326/pse.49.2.6.

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Climate finance, institutions and innovation systems in Sub-Saharan Africa

Frank Adu*

Frank Adu

Affiliation: College of Accounting Sciences, University of South Africa, Pretoria, South Africa

0000-0003-0072-882X

Correspondence
frankadu64@gmail.com

Roshelle Ramfol*

Roshelle Ramfol

Affiliation: College of Accounting Sciences, University of South Africa, Pretoria, South Africa

0000-0002-4682-2558

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https://doi.org/10.3326/pse.49.2.6

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Climate finance, institutions and innovation systems in Sub-Saharan Africa

Frank Adu, Roshelle Ramfol

https://doi.org/10.3326/pse.49.2.6 | Published online: June 7, 2025

Figure 1
Trend analysis of climate finance among SSA countries

Table 1
Variable description

Variable	Notation	Measurement	Source
Dependent variable
Innovation	INNOV	Innovation output sub-index (score 0–100)	WIPO
Independent variable
Climate finance	CF	Climate-related development finance Commitment (Current USD thousand)	OECD
Mediating variable
Institutional quality index	IQ	It is computed as an average of Kaufmann’s six indicators of institutional quality (Regulatory quality, government effectiveness, rule of law, control of corruption, voice and accountability, political stability, and lack of violence)	WDI
Control variables
Human capital	HC	School enrolment, tertiary (% gross)	WDI
GDP per capita	GDPPC	GDP per capita (constant 2015 US$)	WDI
Government expenditure	GE	General government final consumption expenditure (% of GDP)	WDI
Digital infrastructure	DIFRA	Mobile cellular subscriptions (per 100 people), Individuals using the Internet (% of the population), Fixed telephone subscriptions (per 100 people) and Fixed broadband subscriptions (per 100 people)	WDI
Financial development		Domestic credit to private sector (% of GDP)	WDI

Note: WIPO, OECD and WDI represent the World Intellectual Property Organization, Organization for Economic Co-operation and Development and World Governance Indicators, respectively.

Source: Authors.

Table 2
Descriptive statistics

Variable	Obs.	Mean	Std. dev.	Min.	Max.	Measurement unit
INNO	276	22.152	12.735	0.300	71.800	Index score 0–100
CF	275	43.724	33.943	11.000	209.102	Current USD thousand
IQ	276	0.502	0.310	0.000	1.000	Index score 0-1
GDPPC	276	5,669.880	17,113.00	262.185	90,057.03	US$
HC	276	89.880	72.415	1.000	222	%
IFRAI	276	0.525	0.236	0.000	0.992	Index score 0-1
GE	276	15.233	5.837	6.697	36.143	%
FD	276	26.672	27.283	0.000	128.838	%

Note: INNOV, CF, IQ, GDPPC, HC, IFRAD, GE and FD indicate innovation, climate finance, institutional quality index, gross domestic product per capita, human capital, infrastructure development, government expenditure and financial development.

Source: Authors.

Figure 2
Scatter plot between climate finance, institutional quality and innovation in SSA

Table 3
Pairwise correlation among the variables

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
(1) INNO	1.000
(2) CF	0.031	1.000
(3) IQ	0.056	-0.036	1.000
(4) GDPPC	0.696	0.082	-0.114	1.000
(5) HC	0.283	0.036	0.029	0.285	1.000
(6) IFRAI	0.022	0.023	-0.101	0.157	0.168	1.000
(7) GE	-0.069	0.026	-0.060	-0.086	0.085	0.050	1.000
(8) FD	0.207	0.041	0.052	0.060	0.042	0.154	0.307	1.000

Source: Authors.

Table 4
Weakly cross-sectional dependency test

Variable	CD test	Prob. value
INNOV	16.921	0.000
CF	9.680	0.000
IQ	2.348	0.019
GDPPC	21.806	0.000
HC	4.205	0.000
INFRAI	11.204	0.000
GE	2.088	0.037
FD	8.552	0.000

Note: The null hypothesis is that errors are weakly cross-sectionally dependent and the alternative hypothesis is that errors are strongly cross-sectionally dependent.

Source: Authors.

Table 5
Unit root test results

	CIPS test		CADF test
Variable	I(0)	I(1)	I(0)	I(1)
INNOV	-1.771	-2.692***	-1.012	-2.610***
CF	-2.455**	-6.279***	-1.925*	-2.273**
IQ	-2.311**	-3.044***	-1.954*	-2.740***
GDPPC	-1.545	-2.267**	-2.081**	-2.610***
HC	-1.493	-2.938**	-0.992	-2.610***
INFRAI	-2.645***	-4.061***	-2.034**	-2.475***
GE	-2.129*	-3.533***	-1.450	-2.394***
FD	-0.885	-2.180**	-0.702	-2.611***

Note: ***, ** and * represent significant at 1%, 5% and 10% significance level. *, **, *** represent stationarity.

Source: Authors.

Table 6
Effect of climate finance and institutional quality on innovation in SSA

Variable	Coefficient
Variable	System-GMM	Difference GMM
lnINNOV_t_-1	-0.306*** (0.069)	-0.184* (0.108)
lnCF	-1.003*** (0.376)	-0.438** (0.227)
IQ	3.039** (1.334)	1.430* (0.775)
lnCF_IQ	2.074** (0.757)	0.974* (0.580)
lnGDPPC	-0.980 (1.339)	1.413 (4.054)
HC	0.011** (0.004)	0.003 (0.005)
IFRAD	-1.874* (1.084)	-0.918 (1.039)
lnGE	-10.193** (4.511)	-1.441 (2.577)
lnFD	5.289 (3.899)	0.847 (3.372)
Net/marginal effect	3.039** (1.3339)	1.430* (0.775)
Constant	19.887 (12.593)
AR(2) test statistic	-0.67	-0.88
AR(2) P-value	0.505	0.377
Hansen test statistic	4.94	5.89
Hansen P-value	0.895	0.751
No. groups	22	22
No. instruments	20	20

Note: ***, ** and * represent significant at 1%, 5% and 10% significance level.

Source: Authors.

Table 7
Marginal effect of climate finance on innovation

Percentile	Percentile values	System-GMM	Difference GMM
10	0.0212	-0.959** (0.361)	-0.418 (0.316)
25	0.2612	-0.461** (0.216)	-0.184 (0.184)
50	0.509	0.053 (0.172)	0.058 (0.084)
75	0.762	0.577* (0.291)	0.304** (0.148)
90	0.922	0.910** (0.397)	0.460** (0.232)

Note: ** and * represent significance at 5% and 10%, respectively.

Source: Authors.

Table 8
Dumitrescu-Hurlin panel causality test results

Variables	W-bar-Stat.	Z-bar-Stat.	Prob. value	Conclusion
INNO ˃ CF	2.758	1.818	0.069*	↔
CF ˃ INNOV	3.836	4.403	0.000***	↔
INNO ˃ IQ	7.047	12.102	0.000***	↔
IQ ˃ INNOV	4.472	5.927	0.000***	↔
CF ˃ IQ	3.366	3.275	0.001***	↔
IQ ˃ CF	5.222	7.725	0.000***	↔

Note: *** and * denote significance at 1% and 10% levels, respectively; > denotes the direction of causality; ↔ signifies a bidirectional causality, and → denotes a one-way causality.

Source: Authors.

Table A1
Effect of climate finance and institutional quality on innovation in SSA (non-linear system-GMM results)

		Coefficient
Variable	Model 1	Model 2	Model 3
lnINNOV_t_-1	-0.253*** (0.059)	-0.273*** (0.081)	-0.005 (0.012)
SQCF	-1.439* (0.694)
IQ	1.435** (0.608)		1.541** (0.681)
lnCF		-1.099* (0.570)	-0.052 (0.749)
SQIQ		3.463* (1.871)
lnCF_IQ	0.805* (0.454)	2.2529** (1.078)
SQCF_SQIQ			-3.085** (1.095)
lnGDPPC	-1.383 (1.549)	-1.369 (1.212)	2.241** (0.788)
HC	0.010 (0.005)	0.015** (0.004)	0.005 (0.005)
IFRAD	-1.464 (1.167)	-0.889 (1.385)	0.046 (0.439)
lnGE	-9.912* (4.972)	-11.830** (4.987)	6.330*** (2.126)
lnFD	6.869* (3.368)	5.634 (3.454)	-7.057*** (1.295)
Net/marginal effect	1.435** (0.608)	3.463* (1.871)
Constant	18.732 (16.225)	25.500 (17.185)	-8.477 (7.984)
AR(2) test statistic	-1.06	-0.61	0.20
AR(2) P-value	0.290	0.545	0.845
Hansen test statistic	6.88	5.56	10.34
Hansen P-value	0.737	0.724	0.500
No. groups	22	22	22
No. instruments	20	20	20

Table A2
Effect of climate finance and institutional quality on innovation in SSA (system-GMM results)

Variable	Coefficient
Variable	Model 1 (Corruption)	Model 2 (Political stability)
lnINNOV_t_-1	-0.0224*** (0.0077)	-0.0282*** (0.0082)
lnCF	0.2341* (0.1299)	0.2872* (0.1648)
IQ	0.9704 (1.4483)	12.7816*** (4.0337)
lnCF_IQ	0.3464* (0.1786)	0.0278 (0.9146)
lnGDPPC	-2.4345 (1.5679)	-3.2546 (1.6244)
HC	-0.0007 (0.0045)	0.0045 (0.0029)
IFRAD	0.9363 (0.8260)	1.2717** (0.5709)
lnGE	-6.5124 (3.8685)	-6.6579* (3.7287)
lnFD	0.5256 (1.5001)	2.9371 (2.0951)
Constant	35.6356** (17.7762)	36.4524** (17.1689)
AR(2) Test statistic	-0.91	-0.88
AR(2) P-value	0.362	0.379
Hansen test statistic	6.43	7.04
Hansen P-value	0.696	0.633
No. groups	22	22
No. instruments	19	19

This is an Open Access article distributed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC-BY-NC) which permits non commercial use and redistribution, as long as you give appropriate credit, provide a link to the license, and indicate if changes were made.

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