Assessment of country institutional factor on sustainable energy target achievement in European Union

Figures & data

Table 1. Description of variables considered in the model.

Variable	Name	Description	Source of data
Energy efficiency	EES	Distance measured in absolute terms (Mtoe), calculated as a difference between the observed energy consumption in a given year to the absolute primary and final energy consumption targets in 2020 or 2030 according to Directives 2012/27/EU	Eurostat (nrg_ind_eff)
Country energy efficiency score	EES	One of the four composite scores calculated to measure country sustainable development, incorporating various elements defining the national framework ensuring and monitoring for energy efficiency, shortly described by 13 pillars of energy efficiency institutional drivers, respectively: national energy efficiency planning, energy efficiency entities, information provided to consumer about electricity usage, energy efficiency incentives from electricity rate structures, incentives & mandates: industrial and commercial end users; incentives & mandates: public sector, incentives & mandates: utilities, financing mechanisms for energy efficiency, minimum energy efficiency performance standards, energy labeling systems, building energy codes, transport sector, carbon pricing and monitoring	RISE databse
Greenhouse gas emissions	GGS	Data integrating information about gasses that genrate the greenhouse effect, such as: water vapor (H2O), carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4) and ozone (O3) seen as primary greenhouse gases, or more human-made gases, such as sulfur hexafluoride (SF6), hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs)	UNCTAD database
Energy dependence	ED	Extent to which an economy relies upon imports in order to meet its energy needs. It is calculated as net imports divided by gross available energy.	Eurostat (t2020_rd320)
Global competitive ness index	GCI	Annual score calculated for each country, as a weighted average of various components descrbing different essential aspects of country competitiveness, grouped into 12 pillars of competitiveness, respectively: institutions, infrastructure, macroeconomic environment, health and primary education, higher education and training, goods market efficiency, labor market efficiency, financial market development, technological readiness, market size, business sophistication and innovation	Global Competitive ness Report
Firm policy efficiency	FPE	Data that reflect the score calculated by Thomson Reuters for each firm, on an annual basis, refering to how reliable and consistent are firm policies adressing energy efficiency organsational requirements	Reifinitiv Eikon database

Figure 1. Evolution in time on the endogenous variables.

Source: Authors’ projection

Figure 2. Country profile based on average of endogenous variables.

Source: Authors’ projection

Table 2. Descriptive statistics.

	Energy dependence	Energy efficiency	Country energy efficiency score	Firm policy efficiency	Global competitiveness index	Greenhouse gas emissions
Mean	51.40	73.16	66.06	62.58	18.44	211.9
Median	48.95	32.97	67.50	86.74	5.130	100.6
Maximum	91.78	315.15	92.00	93.61	82.84	966.3
Minimum	−15.97	7.600	15.93	0.000	3.860	23.85
Std. Dev.	20.73	80.00	16.54	39.72	27.57	229.4
Jarque-Bera	3.629	104.4	10.51	41.69	85.81	136.3
Probability	0.163	0.000	0.005	0.000	0.000	0.000

Source: Authors’ calculation.

Table 3. Correlation matrix.

	Energy dependence	Energy efficiency	Country energy efficiency score	Firm policy efficiency	Global competitiveness index	Greenhouse gas emissions
ED	1
EE	0.136*	1
EES	0.150*	−0.058	1
FPE	0.036	0.078	−0.031	1
GCI	0.064	0.021	0.416*	0.025	1
GGS	0.146*	0.963*	−0.070	0.026	0.000	1

Note: Energy efficiency (EE), RISE energy efficiency score (EES), Energy dependency (ED), Firm policy energy efficiency (FPE), Global competitiveness index (GCI), Greenhouse gas emissions (GGS).

Source: Authors’ calculation.

* Significance level of correlation for 1%.

Table 4. Panel unit root test results.

Variable	Method	Individual intercept (level)
		Statistic	P-value**
Energy Efficiency (EE)	Levin, Lin & Chu t*	−4.260	0.000
	Im, Pesaran and Shin W-stat	−1.402	0.081
	ADF - Fisher Chi-square	51.99	0.097
	PP - Fisher Chi-square	112.3	0.000
Energy Efficiency Score (EES)	Levin, Lin & Chu t*	−12.51	0.000
	Im, Pesaran and Shin W-stat	−2.740	0.003
	ADF - Fisher Chi-square	76.82	0.001
	PP - Fisher Chi-square	61.96	0.024
Greenhouse Gas Emissions (GHC)	Levin, Lin & Chu t*	−5.306	0.000
	Im, Pesaran and Shin W-stat	−1.680	0.047
	ADF - Fisher Chi-square	65.77	0.011
	PP - Fisher Chi-square	95.54	0.000

**Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution. All other tests assume asymptotic normality.

Source: Authors’ calculation.

Table 5. Granger causality analysis.

Variable type	Causality relation	No. lags	Lag = 2	Lag = 3	Lag = 1
Endogenous variables	EE <-- EES	F statistic	4.122*	0.011	2.598
	EES <-- EE	F statistic	6.052*	0.056	0.043
	EE <-- GGS	F statistic	3.871*	0.019	0.183
	GGS <-- EE	F statistic	3.257*	0.005	1.765
	EES <-- GHC	F statistic	4.714*	0.071	0.065
	GHC<-- EES	F statistic	0.091	0.477	0.089
Exogenous variables	EE <-- FPE	F statistic	0.254*
	EE <-- GCI	F statistic	3.701*
	EE <-- ED	F statistic	3.247*

EE, energy efficiency; EES, RISE energy efficiency score; GHC, greenhouse gas emissions; FPE, firm policy efficiency score; GCI, country global competitiveness index; ED, economic dependence.

*Rejection of the null hypothesis of no Granger causality at the 5% significance level.

Source: Authors’ calculation.

Table 6. Lag length selection results.

Lag	LogL	AIC	SC	HQ
0	−2.019	32.100	32.167	32.127
1	−1.044	16.768	17.04*	16.877
2	−1.029	16.66*	17.136	16.86*
3	−1.020	16.668	17.344	16.943
4	−1.,012	16.689	17.566	17.045

AIC, Akaike information criterion; SC, Schwarz information criterion; HQ, Hannan–Quinn information criterion.

*Lag order selected by the criterion.

Source: Authors’ calculation.

Table 7. VAR Model estimation.

Variable		Lag	energy efficiency	energy efficiency score	greenhouse gas emissions
Endogenous variables	Energy efficiency	1	0.570*	0.541	−0.967*
			0.164	0.588	0.515
		2	0.419*	−0.540	0.952*
			0.162	0.587	0.510
	Energy efficiency score	1	−0.026	0.746*	−0.036
			0.044	0.161	0.165
		2	0.051	0.075	0.048
			0.051	0.159	0.199
	Greenhouse gas emissions	1	0.076*	0.062	1.190*
			0.027	0.039	0.252
		2	−0.076*	−0.060	−0.202
			0.027	0.038	0.248
Exogenous variables	Constant		−1.329*	15.97*	−0.189
			0.664	2.228	2.571
Model validation	Adj. R-squared		0.999	0.814	0.999
	F-statistic		25269.4	117.5	19272.8
	AIC criterion		17.58
	SW criterion		17.97
Residual’s diagnostic	Joint normality test	JB-Stat	1380.2*
	Autocorrelation test	LM-Stat	10.175
		P-value	0.3365
	White heteroscedasticity test	Chi-sq	597.3*
Model stability	Maximal modulus (Characteristic polynomial roots)		0.992

Source: Authors’ calculation.

*Significance level of coefficient estimated for 1%.

Figure 3. Representation of shock effects on model endogenous variables.

Source: Authors’ projection

Figure 4. Representation of endogenous variables to a variation on energy efficiency.

Source: Authors’ projection

Table 8. Var model estimation considering exogenous variables.

Variable		Lag	Energy efficiency	Energy efficiency score	Greenhouse gas emissions
Endogenous variables	Energy efficiency	1	0.581*	0.612	−0.828
			0.169	0.595	0.506
		2	0.408*	−0.608	0.818
			0.167	0.596	0.500
	Energy efficiency score	1	−0.025	0.746*	−0.035
			0.044	0.159	0.162
		2	0.059	0.112	0.118
			0.049	0.164	0.189
	Greenhouse gas emissions	1	0.076*	0.054	1.175*
			0.029	0.034	0.246
		2	−0.075*	−0.052	−0.187
			0.029	0.033	0.242
Exogenous variables	Constant		−1.178*	15.15*	−2.378
			0.509	2.844	2.241
	Energy dependence		−0.357	−0.422	−0.536
			0.415	0.754	1.611
	Global competitiveness index		−0.008	−0.037*	−0.072*
			0.006	0.015	0.022
	Firm policy efficiency score		0.001	−0.002	0.001
			0.004	0.015	0.009
Model validation	Adj. R-squared		0.999	0.818	0.999
	F-statistic		16727.9	79.043	13221.5
	AI criterion		31.4
	Schwarz criterion		31.971
Residuals diagnostic	Joint normality test	JB-Stat	1386.2*
	Autocorrelation test	LM-Stat	7.415
		P-value	0.594
	White heteroscedasticity test	Chi-sq.	707.9*
Model stability	Maximal modulus (characteristic polynomial roots)		0.992

Source: Author’s calculation.

*Significance level of coefficient estimated for 5%.