Discovering the factors driving regional competitiveness in the face of climate change

Karman, Agnieszka; Miszczuk, Andrzej; Bronisz, Urszula

doi:10.2478/mgrsd-2023-0009

Figures & Tables

Research model
Source: Own elaboration — Research model Source: Own elaboration

Spatial distribution of the RCCCI index Source: Karman et al. (2022)

Research procedure Source: Own elaboration

The scatter plot for linearity Source: Own elaboration

The P–P plot of normality test Source: Own elaboration

Breusch-Pagan test

Variable	BP statistic value	P-value
RCCCI	13.069	0.022^**

Local estimated parameters

Parameter	Min	Max	Average
Intercept	11.08	12.32	11.98
β₁	−0.51	0.23	−0.28
β₂	−2.24	1.90	−0.35
β₃	−1.90	1.15	−0.13
β₅	0.076	0.173	0.132
β₆	0.207	0.249	0.228
β₇	0.337	0.635	0.418
β₉	0.182	0.496	0.361
β₁₀	0.095	0.342	0.211
β₁₁	0.006	0.07	0.033
β₁₂	−0.374	0.110	−0.096
β₁₃	−0.073	0.030	−0.019
β₁₅	−0.037	0.025	−0.058
β₁₆	−1.159	1.06	−0.182
β₁₈	0.080	0.216	0.147
β₁₉	0.104	0.358	0.279

Correlation analysis results

	RCCCI	D	ES	ECO₂	FI	AFI	RDE	VA	PUB	BAN	EB	TRY	PAR	MD	P	M	U	DIG	MAN	ATE
RCCCI	1.00	−0.21	−0.52	−0.04	−0.36	−0.08	0.55	0.49	0.63	0.84	−0.11	0.13	−0.28	−0.37	0.46	0.37	0.01	0.94	0.90	0.50
D	−0.21	1.00	−0.06	−0.08	0.04	−0.02	0.22	−0.29	−0.11	0.10	0.06	−0.01	−0.34	−0.19	0.03	−0.16	−0.17	−0.12	−0.03	0.08
ES	−0.53	−0.06	1.00	0.59	0.20	0.44	−0.21	0.05	−0.31	−0.55	0.20	0.02	0.33	0.47	−0.11	−0.04	0.59	−0.57	−0.62	−0.38
ECO₂	−0.04	−0.08	0.59	1.00	0.18	0.19	0.23	0.48	0.10	−0.00	0.21	0.06	0.13	0.32	0.36	0.28	0.84	0.01	−0.04	0.01
FI	−0.36	0.04	0.20	0.18	1.00	−0.33	−0.16	−0.03	−0.15	−0.28	0.15	−0.16	0.30	0.36	−0.22	−0.08	0.25	−0.34	−0.29	−0.01
AFI	−0.08	−0.02	0.44	0.19	−0.33	1.00	−0.12	−0.14	−0.30	0.01	0.44	0.48	−0.27	−0.15	−0.02	−0.02	0.07	−0.02	−0.15	−0.21
RDE	0.55	0.22	−0.21	0.23	−0.16	−0.12	1.00	0.44	0.53	0.52	−0.16	−0.04	−0.43	−0.32	0.55	0.30	0.17	0.48	0.56	0.51
VA	0.49	−0.25	0.05	0.48	−0.03	−0.14	0.44	1.00	0.62	0.27	−0.09	−0.20	−0.01	0.11	0.48	0.45	0.60	0.32	0.35	0.10
PUB	0.63	−0.11	−0.31	0.10	−0.15	−0.30	0.53	0.62	1.00	0.37	−0.40	−0.10	−0.15	−0.17	0.43	0.36	0.23	0.47	0.53	0.35
BAN	0.84	0.10	−0.55	−0.00	−0.28	0.01	0.52	0.27	0.37	1.00	0.13	0.25	−0.36	−0.37	0.42	0.16	−0.11	0.92	0.89	0.53
EB	−0.11	0.06	0.20	0.21	0.15	0.44	−0.16	−0.09	−0.40	0.13	1.00	0.32	−0.02	0.19	0.11	0.02	0.18	0.06	−0.08	0.15
TRY	0.13	−0.00	0.02	0.06	−0.16	0.48	−0.04	−0.20	−0.10	0.25	0.32	1.00	−0.13	−0.12	−0.05	0.03	0.01	0.21	0.06	0.14
PAR	−0.28	−0.34	0.33	0.13	0.30	−0.27	−0.43	−0.01	−0.15	−0.36	−0.02	−0.13	1.00	0.73	−0.27	−0.19	0.20	−0.28	−0.32	−0.28
MD	−0.37	−0.19	0.47	0.32	0.36	−0.15	−0.32	0.11	−0.17	−0.37	0.19	−0.12	0.73	1.00	−0.12	−0.06	0.41	−0.40	−0.37	−0.24
P	0.46	0.03	−0.11	0.36	−0.22	−0.02	0.55	0.48	0.43	0.42	0.11	−0.05	−0.27	−0.12	1.00	0.46	0.37	0.38	0.48	0.22
M	0.37	−0.16	−0.04	0.28	−0.08	−0.02	0.30	0.45	0.36	0.16	0.02	0.03	−0.19	−0.06	0.46	1.00	0.36	0.24	0.33	0.15
U	0.01	−0.17	0.59	0.84	0.25	0.07	0.17	0.60	0.23	−0.11	0.18	0.01	0.20	0.41	0.37	0.36	1.00	−0.09	−0.07	0.04
DIG	0.94	−0.12	−0.57	0.00	−0.34	−0.02	0.48	0.32	0.47	0.92	0.06	0.21	−0.28	−0.40	0.38	0.24	−0.09	1.00	0.91	0.58
MAN	0.90	−0.03	−0.62	−0.04	−0.29	−0.15	0.56	0.35	0.53	0.89	−0.08	0.06	−0.32	−0.37	0.48	0.33	−0.07	0.91	1.00	0.48
ATE	0.50	0.08	−0.38	0.01	−0.01	−0.21	0.51	0.10	0.35	0.53	0.15	0.14	−0.28	−0.24	0.22	0.15	0.04	0.58	0.48	1.00

The Breusch-Pagan Test combination for the determination of variables

Variable	BP test	P-value	Note
Y dengan X₁ (D)	28.21	0.03^*	Local
Y dengan X₂ (ECO₂)	139.01	0.00^*	Local
Y dengan X₃ (ES)	64.18	0.00^*	Local
Y dengan X₄ (FI)	0.61	0.89	Global
Y dengan X₅ (AFI)	18.90	0.03^*	Local
Y dengan X₆ (RDE)	26.08	0.03^*	Local
Y dengan X₇ (VA)	37.65	0.02^*	Local
Y dengan X₈ (PUB)	1.56	0.35	Global
Y dengan X₉ (BAN)	44.72	0.01^*	Local
Y dengan X₁₀ (EB)	25.25	0.02^*	Local
Y dengan X₁₁ (TRY)	20.56	0.00^*	Local
Y dengan X₁₂ (PAR)	16.30	0.00^*	Local
Y dengan X₁₃ (MD)	14.78	0.05^*	Local
Y dengan X₁₄ (M)	3.74	0.18	Global
Y dengan X₁₅ (P)	14.23	0.00^*	Local
Y dengan X₁₆ (U)	36.83	0.00^*	Local
Y dengan X₁₇ (MAN)	3.84	0.32	Global
Y dengan X₁₈ (ATE)	40.98	0.00^*	Local
Y dengan X₁₉ (DIG)	20.23	0.05^*	Local

Model summary

	b^*	SE z b^*	B std	T	p
Intercept	−0.092	0.007		−12.506	0.000
X₁ (D)	−0.080	0.015	−0.125	−5.347	0.000
X₂ (ECO₂)	−0.137	0.013	−0.140	−8.379	0.000
X₇ (VA)	0.066	0.012	0.164	5.688	0.000
X₈ (PUB)	0.026	0.011	0.077	2.284	0.025
X₁₀ (EB)	0.081	0.009	0.217	8.863	0.000
X₁₂ (PAR)	−0.036	0.007	−0.116	−4.862	0.000
X₁₄ (M)	0.018	0.009	0.049	2.045	0.044
X₁₉ (DIG)	0.227	0.008	0.770	30.003	0.000

PCA analysis results

Variable	Strength	R2	Variable	Strength	R2
ES	0.681	0.467	VA	0.773	0.406
ECO₂	0.670		RDE	0.521
D	0.011		FI	0.312
BAN	0.763	0.616	AFI	0.106
PUB	0.551		PAR	0.910	0.440
EB	0.174		MD	0.852
TRY	0.162		P	0.718
DIG	0.841	0.730	M	0.032
MAN	0.811		U	0.008
ATE	0.575

Results of best subset regression

Cluster	Regions	Variables	AIC	Cp Mallows	R2
Low	SK, LV, PL, BG, EL, HU, RO	All variables except M	−711.649	-	0.9
Middle–low	UK, FR, AT, IT, ES, PT, CZ, MT	D, ECO₂, AFI, RDE, VA, PUB, BAN, EB, TRY, PAR, MD, P, M, U, ATE	-	-	0.9
Middle–high	SE, DE, DK, FIN, UK, BE, NL	D, ES, FI, RDE, BAN, EB, PAR, U, M, MAN, ATE	−166.624	10.5	0.87
High	SE, NL, DE	ECO₂, BAN, U, ATE	−129.501	−3.652	0.74

Descriptive statistics

Variable	N-significant	Mean	Minimum	Maximum	Variance	St. dev.	Skewness	Kurtosis
RCCCI	281	0.01	−0.8	0.6	9.76E−02	0.31	−0.457	−0.775
D	233	2412.10	94.7	5717.4	7.09E+05	842.19	0.055	1.457
ES	240	15.96	0.0	129.6	3.88E+02	19.70	2.494	7.555
ECO₂	270	14425.60	2.0	88498.3	2.18E+08	14767.67	2.376	7.168
FI	273	4.22	0.0	34.9	3.05E+01	5.53	2.966	11.064
AFI	278	0.81	0.5	1.5	3.91E−02	0.20	0.617	0.557
RDE	208	0.34	0.0	1.0	6.84E−02	0.26	0.953	0.196
VA	240	53109.92	1178.4	674282.8	4.51E+09	67168.07	4.424	32.094
PUB	209	0.55	0.1	1.0	5.74E−02	0.24	0.171	−0.777
BAN	281	62.06	11.3	95.0	3.59E+02	18.96	−0.731	0.373
EB	170	10.26	5.2	19.9	7.38E+00	2.72	0.384	0.595
TRY	262	2.90	2.2	4.3	6.38E−02	0.25	0.448	3.485
PAR	195	21.39	7.9	49.7	7.31E+01	8.55	1.176	0.909
MD	156	6.80	0.5	25.8	3.88E+01	6.23	1.495	1.390
P	281	489.36	3.3	11509.1	1.66E+06	1289.34	5.669	38.089
M	270	4650.19	−64908.0	48975.0	9.80E+07	9900.16	−0.172	10.768
U	280	806.32	14.0	5253.2	4.40E+05	663.49	2.610	10.968
DIG	194	0.53	0.0	1.0	6.12E−02	0.25	−0.036	−0.785
MAN	160	0.35	0.1	0.5	1.02E−02	0.10	−0.422	−1.088
ATE	260	0.65	0.5	1.5	3.14E−02	0.18	2.411	7.015

Multicollinearity results

Indicator	Tolerance	VIF
D	0.462	2.163
ES	0.116	8.642
ECO₂	0.166	6.029
FI	0.460	2.172
AFI	0.165	6.051
RDE	0.266	3.763
VA	0.189	5.281
PUB	0.279	3.588
BAN	0.057	17.621
EB	0.274	3.653
TRY	0.470	2.127
PAR	0.217	4.604
MD	0.244	4.096
P	0.339	2.947
M	0.537	1.862
U	0.102	9.831
DIG	0.033	30.705
MAN	0.061	16.413
ATE	0.241	4.150

j_mgrsd-2023-0009_tab_011

	I	Z	p-value
EB	0.22	4.12	0.00
ECO₂	0.19	4.08	0.02
ES	0.21	4.10	0.04
FI	0.24	5.07	0.01
AFI	0.36	6.43	0.00
RDE	0.18	3.55	0.04
PAR	0.05	1.47	0.06
MD	0.10	3.75	0.00
P	0.11	3.95	0.00
VA	0.48	8.72	0.00
PUB	0.03	1.30	0.00
BAN	0.27	4.62	0.03
TRY	0.17	3.15	0.02
D	0.23	4.08	0.00
M	0.10	2.21	0.06
U	0.33	6.12	0.02
DIG	0.46	7.51	0.00
MAN	0.09	3.56	0.04
ATE	0.26	4.74	0.04

Global estimated parameters

Parameter	Estimation Value
β₄	0.003
β₈	0.052
β₁₄	−0.007
β₁₇	0.031

Discovering the factors driving regional competitiveness in the face of climate change

Figures & Tables

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Breusch-Pagan test

Local estimated parameters

Correlation analysis results

The Breusch-Pagan Test combination for the determination of variables

Model summary

PCA analysis results

Results of best subset regression

Descriptive statistics

Multicollinearity results

j_mgrsd-2023-0009_tab_011

Global estimated parameters

Paradigm

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