Does the inflow of remittances cause environmental degradation? Empirical evidence from China

Figures & data

Figure 1. CO2 in the emerging economies.

Figure 2. The Indirect connection between Remittances and CO2 emissions. Notes: PI = Personel Income; C = Consumption; S = Savings; AD = Aggregate Demand; BD = Bank Deposits; EG = Economic Growth.

Figure 3. First stage of FSIM.

Figure 4. Second stage of FSIM.

Figure 5. Third stage of FSIM.

Figure 6. The Fourth stage of FSIM.

Figure 7. The Fifth stage of FSIM.

Figure 8. Derivation of FSIM curve.

Figure 9. Relationships between negative shocks in inflow of remittances and CO2 emissions.

Figure 10. Relationships between positive shocks in inflow of remittances and CO2 emissions.

Figure 11. Relationship between Remittances and CO2 emissions for China.

Table 1. ADF and PP unit root test.

	Level		First Difference
Variables
	ADF-GLS	PP	ADF	PP
CO2	−0.840107	0.207404	−2.206023*	−2.958129*
REM	−0.622387	−0.124514	−5.948178*	−8.350255*

Note: ^*depict significance at 5%.

Figure 12. Negative and Positive components of Remittances.

Table 2. Optimal lag and model selection criteria.

Model	AIC*	BIC	HQ	Adj. R-sq	Specification
8	−5.7984689	−5.464653	−5.691326	0.997251	ARDL(2, 2, 0)
4	−5.780946	−5.780946	−5.662817	0.997275	ARDL(2, 3,0)
7	−5.728344	−4.351159	−5.610215	0.9972127	ARDL(2. 2, 1)
3	−5.7166231	−5.291898	−5.583335	0.9971151	ARDL (2, 3, 1)

* shows an optimum selection of lag order.

Table 3. Results of non-linear ARDL equation.

Variable	Short run Coefficient	Std. Error	t-Statistic
LC(-1)	−0.173453***	0.308414	−3.630849
${\mathit{REM}}^{+}$ (-1)	0.042583***	0.014365	2.964463
${\mathit{REM}}^{-}$	0.028226***	0.014294	1.974589
D(LC(-1))	0.572122***	0.143800	3.978599
D(${\mathit{REM}}^{+}$ )	0.007791	0.014715	0.529469
D(${\mathit{REM}}^{-}$ (-1))	−0.036656	0.0147514	−2.266737
ECT(-1)	−0.173543***	0.041084	−4.224118
Variable	Long run Coefficient	Std. Error	t-Statistic
${\mathit{REM}}^{+}$	0.245377***	0.035720	6.869424
${\mathit{REM}}^{-}$	0.162644***	0.059238	2.745617
C	6.452614***	0.050902	126.7653
NARDL Bound test
$\mathrm{F}-\mathrm{statistic}\mathrm{ }\left(\mathrm{HYP}1\right)=\mathrm{ }3.946085\mathrm{ }[\mathrm{LB}=2.63,\mathrm{ }\mathrm{UB}=3.53\text{ at }10\mathrm{\%}]$ $\mathrm{ }[\mathrm{LB}=3.1,\mathrm{ }\mathrm{UB}=3.87\text{ at }5\mathrm{\%}]$

Note: ^{** *}depict significance at 1%.

Table 4. Testing hypothesis of asymmetrical effect.

Null Hypothesis	F-Statistic	Probability
Long Run Symmetries (HYP2)	5.0503	0.033
Short Run Symmetries (HYP3)	6.5541	0.0165

Figure 13. NARDL Dynamic Multiplier Graph.

Figure 14. CUSUM Test.

Figure 15. CUSUM of Square Test.

Table 5. Validation test.

Test	Test-Statistic	Probability
Normality Test	1.183440	0.553375
LM Test	1.890423	0.3886
White test	27.49186	0.3318
Ramsay Reset test	2.709920	0.1633