Drivers of climate change in selected emerging countries: the ecological effects of monetary restrictions and expansions

Figures & data

Figure 1. Mechanism through which monetary policy could affect climate change (CO₂ emissions).

Sources: Qingquan et al. (2020)

Table 1. Summary of related empirical evidence on the ecological effect of monetary policy

No.	Relation	Authors	Countries	Period	Methodology	Findings
1	The financial and environmental consequences of a green quantitative easing (QE) scheme	Dafermos et al. (2018)	Global scope	Simulated period of 2016–2120	DEFINE model	Implementing a green corporate QE program could lessen climate-induced financial instability while also limiting global warming.
2	Interaction of monetary policy and climate change on macro-economic activities	McKibbin et al. (2020)	United States	Simulated decade	G-Cubed simulations	In order to respond to the impact of climate change policy on inflation and output, the outperformed nominal income targeting could be utilized to stabilize economic activities.
3	CO2 emissions and both regimes of monetary policy	Qingquan et al. (2020)	Asian economies	1990–2014	Panel fully-modified (PFM-LS) and panel dynamic least squares (PD-LS)	The findings reveal a significant long-run positive link between monetary policy loosening and CO2 emissions; meanwhile, monetary policy tightening could serve as an effective CO2 emissions mitigation mechanism.
4	CO2 emissions and monetary policy	Shobande (2021)	East African Community	1990–2019	Panel VAR/VEC Granger causality	Monetary policy via the credit and interest rate channels could facilitate the transition to a low-carbon economy.
5	Influence of fiscal and monetary policy on CO2 emissions	Chishti et al. (2021)	BRICs countries	1985–2014	OLS, DOLS, FM-OLS and PMG-ARDL	Monetary policies that are expansionary or contractionary could worsen and improve environmental quality, respectively.

Figure 2. The overall picture of per capita CO₂ emissions for 14 selected emerging countries covering the period of 1998–2018.

Table 2. Data description

Variables	Symbols	Definition	Source
Climate change	CO2	CO2 emissions by metric tons per capita	WDI (World Bank)
Monetary policy	RIR	Real interest rate (%)	WDI (World Bank)
Financial development	CREDIT	Domestic credit to private sector by banks (% of GDP)	WDI (World Bank)
Trade openness	TRADE	Trade (% of GDP)	WDI (World Bank)
GDP per capita	GDPPCC	GDP per capita (constant 2010 US$)	WDI (World Bank)
Aggregate domestic consumption spending per capita	ADCSP	Gross domestic product + exports—imports (% total population)	Author’s calculation from database of WDI (World Bank)

Table 3. Descriptive statistics

Variables	Mean	Skewness	Kurtosis	Jarque-Bera	Prob.
CO2	1.199	0.051	1.850	16.334***	0.000
RIR	7.607	2.641	12.517	1451.456***	0.000
CREDIT	56.527	0.962	2.881	45.548***	0.000
GDPPCC	8.851	−0.228	2.135	11.719***	0.003
ADCSP	14.176	0.394	2.042	18.848***	0.000
TRADE	79.483	1.047	3.297	54.833***	0.000

Table 4. LLC-based and IPS-based unit root test of stationary

Variables	LLC		IPS
	Original level	First-difference	Original level	First-difference
CO2	−1.573*	−6.053***	1.463	−5.876***
RIR	−6.433*	−14.944***	−7.824	−14.454***
CREDIT	1.002	−4.480***	0.946	−5.544***
GDPPCC	−0.446	−2.806***	2.712	−3.749***
ADCSP	−2.078*	−5.734***	1.415	−5.375***
GCONEX	−1.340*	−8.077***	0.010	−8.173***
TRADE	−1.659*	−6.616***	−0.783	−7.268***

Notes: The symbols ***, **, and * reflect the significance at 1%, 5%, and 10% levels, respectively.

Table 5. Panel co-integration tests by Pedroni (1999)

	Statistic	Prob.
Panel v-Statistic	−1.995	0.977
Panel rho-Statistic	4.581	1.000
Panel PP-Statistic	−1.555*	0.060
Panel ADF-Statistic	−3.061***	0.001
Group rho-Statistic	5.795	1.000
Group PP-Statistic	−5.696***	0.000
Group ADF-Statistic	−3.269***	0.001

Notes: The symbols ***, **, and * reflect the significance at 1%, 5%, and 10% levels, respectively. Null hypothesis reflects the absence of co-integration among variables.

Table 6. Panel co-integration tests by Kao (1999)

	t-Statistic	Prob.
ADF	−2.713***	0.003
Residual variance	0.00165
HAC variance	0.00139

Notes: The symbols ***, **, and * reflect the significance at 1%, 5%, and 10% levels, respectively. Null hypothesis reflects the absence of co-integration among variables.

Table 7. Long-run coefficient by OLS, DOLS, and FMOLS (Y = CO₂)

Estimation method	OLS		DOLS		FMOLS
Variables	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic
CMP	−0.0105	[−2.640]	−0.0257**	[−2.133]	−0.0283*	[−1.741]
EMP	0.0218***	[3.659]	0.0348***	[2.724]	0.0391**	[2.120]
CREDIT	0.0049***	[6.762]	0.0076***	[3.757]	0.0077***	[4.185]
GDPPCC	0.6822***	[0.130]	0.0024	[0.081]	0.0114	[0.377]
ADCSP	0.0044	[4.384]	0.0427***	[2.819]	0.0366**	[2.253]
TRADE	0.0032***	[4.046]	0.0034***	[2.705]	0.0042***	[2.996]

Notes: The symbols ***, **, and * reflect the significance at 1%, 5%, and 10% levels, respectively. t-statistics of the corresponding parameters are reported in square brackets.

Table 8. Quantile panel regression (Y = CO₂)

	Q10	Q25	Q50	Q75	Q90
CMP	−0.026	−0.0064	−0.0202*	−0.0358**	−0.0245**
	[−1.647]	[−0.748]	[−1.864]	[−2.346]	[−2.201]
EMP	0.0199	0.0112	0.0231*	0.0449***	0.0336***
	[1.15]	[1.209]	[1.691]	[4.065]	[3.017]
CREDIT	0.0109**	0.0042	0.0101***	0.0065***	0.0053***
	[2.383]	[1.622]	[3.698]	[6.306]	[6.354]
GDPPCC	−0.1323***	−0.0498**	−0.0046	0.062**	0.0702***
	[−3.44]	[−2.191]	[−0.143]	[1.998]	[3.548]
ADCSP	0.0729***	0.0408***	0.0353*	0.0519***	0.0681***
	[2.945]	[3.782]	[1.751]	[3.767]	[5.094]
TRADE	0.0005	0.0055***	0.003**	0.0024*	0.0014
	[0.153]	[4.019]	[1.985]	[1.916]	[1.058]

Notes: The symbols ***, **, and * reflect the significance at 1%, 5%, and 10% levels, respectively. t-statistics of the corresponding parameters are reported in square brackets.

Figure 3. Quantile regression estimates with 90% confidence intervals for the impacts of climate change determinants.

Table 9. Two-step system GMM estimation (Y =CO₂)

	(1)	(2)	(3)	(4)	(5)
Lag.CO2	0.989***	0.980***	0.977***	0.985***	0.984***
	[838.81]	[269.69]	[471.23]	[175.89]	[121.81]
CMP	−0.001***	−0.001***	−0.001***	−0.001***	−0.001**
	[−8.81]	[−6.05]	[−5.04]	[−3.86]	[−2.88]
EMP	0.002***	0.002***	0.002***	0.001**	0.002**
	[6.55]	[4.64]	[5.18]	[2.44]	[2.68]
CREDIT		0.000***	0.000***	0.000	0.000*
		[8.52]	[6.46]	[1.65]	[2.11]
GDPPCC			0.006*	0.008	0.007
			[1.92]	[1.42]	[0.92]
ADCSP				0.003***	0.003***
				[3.80]	[3.30]
TRADE					0.000
					[0.63]
CONS	0.030***	0.023***	−0.028	−0.008	−0.005
	[9.47]	[5.42]	[−0.90]	[−0.17]	[−0.08]
AR(1)	0.001	0.001	0.001	0.001	0.001
AR(2)	0.273	0.285	0.288	0.301	0.285
Sargan	0.782	0.773	0.751	0.732	0.720
Hansen	0.358	0.327	0.877	0.895	0.788

Notes: The symbols ***, **, and * reflect the significance at 1%, 5%, and 10% levels, respectively. t-statistics of the corresponding parameters are reported in square brackets.

Figure 4. Estimation outcomes of OLS, DOLS, FMOLS, GMM and panel quantile regression.

Notes: + and—signify positive and negative impact in OLS, DOLS, FMOLS, GMM and Panel Quantile estimation, respectively. * denotes the insignificant impact of this factor on CO₂ emissions based on OLS, DOLS, FMOLS, and GMM; however, we discover both negative and positive impact of economic growth when using Panel Quantile estimation.

Table A1. Abbreviations and its definitions

Abbreviations	Definitions
VECM	Vector Error Correction Model
FDI	Foreign Direct Investment
BRICS	The World’s Leading Emerging Market Economies such as Brazil, Russia, India, China And South Africa
ARDL	Autoregressive Distributed Lag
PVAR	Panel Vector Autoregressive
PMG-ARDL	Pooled Mean Group Autoregressive Distributed Lag
CO2	Carbon Emissions
VAR	Vector Autoregressive
PVAR	Panel Vector Autoregressive
EMP	Monetary Expansions
CMP	Monetary Restrictions
OLS	Ordinary Least Squares
DOLS	Dynamic OLS
FMOLS	Fully-Modified OLS
MSCI	Morgan Stanley Capital International
EKC	Environmental Kuznets Curve
GMM	Generalized Method of Moments
LLC	Levin-Lin-Chu
IPS	Im-Pearsan-Shin
WDI	World Development Indicators

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