·In twenty cases, the ambition of RTD&D measures targeting electro-mobility in the NIRs could be determined, and among these 18 measure sets show an increased ambition compared to the NPF situation.

·The importance given to this pair is revealed also by its presence in all the 25 MSs NIRs assessed and the good score obtained for the assessment of the policy and deployment & manufacturing support measures in relation to reaching the MSs electro-mobility objectives (13 MSs sets of measures obtained a high score and the rest a medium score). Among the MSs with a high score, there are front-running countries in the field (e.g. DE, FR and LU, which are distinguished by the consistency of their policies in electro-mobility development) but also late-starting countries (e.g. HU and LT). An expression of the serious commitment in the deployment of electro-mobility is represented by the fact that 23 MSs have measure sets for this field that were assessed to be comprehensive. In terms of expected impact of these measures to support the realisation of the AFV/AFI objectives as presented in the NPF and revised in the NIR, 12 MSs are assessed having a high level impact (of which one medium/high) and 13 a medium level. Regarding the ambition in the NIR vs. NPF, the measure sets of 21 MSs have an increased level and none a decreasing one.

·The legal measures’ level of ambition in the NIRs vs. NPFs has increased for 21 MSs out of the 24 NIRs assessed .

The pair electricity/road is the most numerous in terms of dedicated measures by the majority of MSs.

From these average sufficiency indexes one would deduct that EU is clearly moving from an adequate balance between EV and recharging points in 2018 to a progressively increasing inadequate balance in 2030. However, it is worth to note that, if the extreme MS (IE) is excluded, the EU averages are both below 12, which can be considered as an adequate sufficiency index.

Looking at these data from an EU-wide point of view, the obtained results are shown in the following table:

Figure 4.1.1‑9 Estimated ratio AFV/AFI (sufficiency index) for electricity/road (from the NIRs) for 2025 (left map) and for 2030 (right map)

Figure 4.1.1‑8 Ratio AFV/AFI (sufficiency index) for electricity/road in 2018 (left map) and estimated for 2020 (from the NIRs) (right map)

The maps in Figure 4.1.1-8 and Figure 4.1.1 ‑9 show the evolution of the sufficiency index for electricity/road in 2018, 2020, 2025 and 2030 (according to the estimates provided in the NIRs). For the 2020-2030 decade, NIR data allows computing 53 ratios while NPF data had allowed computing only 30. For 2020, the ratio can be computed for 21 MSs and ranges from 1.69 (Cyprus) to 62.34 (Ireland), with 4 MSs situated below 5, 10 MSs in between 5 and 15, and 7 MSs above 15. For 2025, the ratio can be computed for 15 MSs and ranges from 1.73 (Cyprus) to 166.24 (Lithuania), with 2 MSs situated below 5, 8 MSs in between 5 and 15, and 5 MSs above 15. For 2030, the 15 computable ratios range from 1.50 (Greece) to 780.30 (Ireland), with 1 MS situated below 5, 8 MSs in between 5 and 15, and 6 MSs above 15.

From NIR data, it is possible to compute the ratio (sufficiency index) of electric vehicles and publicly accessible recharging points for 23 MSs (with the exception of Estonia and Croatia). Concerning the adequacy of this ratio (the Directive has indicated a value of 10), it has to be considered that there are other elements influencing it, which were not included when setting the value of 10 (e.g. the share of high power (>22kW) publicly accessible recharging points and the number of private recharging points).

Figure 4.1.1‑7 Estimated density of publicly accessible recharging points (from the NIRs) in 2025 (left map) and in 2030 (right map)

Figure 4.1.1‑6 Density of publicly accessible recharging points in 2018 (left map) and estimated for 2020 (from the NIRs) (right map)

The maps in Figure 4.1.1 ‑6 and Figure 4.1.1 ‑7 show the evolution of the density of publicly accessible recharging points (number of recharging points normalized by the total length of roads) per Member State in 2018, 2020, 2025 and in 2030 (according to the estimates provided in the NIRs). Also in this case, very big differences can be seen among MSs. To be noted is the group of central European MSs (Belgium, Netherlands, Luxembourg and Germany). In 2018, their densities exceeded the value of 5 recharging points per 100 km. In 2020, their densities are foreseen to exceed the value of 10 for this parameter, while in 2030, their densities are foreseen to be well above 100 recharging points per 100 km (or more than 1 recharging point per km).

-(Growth rate) The average annual growth rate characterising the foreseen evolution of recharging points for the next decade ranges from 3% (Ireland) to 111% (Poland). Out of the 24 computed annual growth rates, 6 are below 20%, 10 are in between 20% and 40%, 6 are in between 40% and 60% while 2 are above 60% (Lithuania and Poland).

-(Progress) Considering the 24 MSs that provided at least one target for the 2020-2030 decade in their NIR or NPF, and comparing the 2018 situation with their foreseen recharging points infrastructure evolution, 6 MSs result to progress fast, 11 adequately and 7 slowly.

The values shown for 2025 and 2030 should be considered with more caution, due to the limited numbers of NIRs available to calculate the EU averages.

-(Attainment) The 2018 attainment of the MSs foreseen recharging points targets ranges significantly across EU, from 6.57% (Greece) to 119.37% (Austria) for 2020, from 1.15% (Greece) to 73.27% (Ireland) for 2025, and from 0.46% (Greece) to 92.54% (Romania) for 2030.

The average situation from a EU-wide perspective is reported in the following table. In this case, only the 2020 averages have been computed as EU-wide representative.

-(Change NIR vs NPF) Considering the 35 cases where a change could be computed (recharging infrastructure targets provided both in the NPF and NIR), a decrease of ambition is noticeable in 7 cases, a similar ambition in 18 cases and an increase of ambition in 10 cases. The changes range from -88.00% (Bulgaria) to +198.00% (Lithuania) in 2020; from -66.67% (Bulgaria) to +210.00% (Lithuania) in 2025; from -44.44% (Bulgaria) to +375.58% (Lithuania) in 2030. In other 14 cases, a target was provided only in the NIR and the changes could not be computed.

-(Coverage) Twenty-four MSs NIRs out of the total 25 assessed (96%) have provided at least some historical data (2016-2018) and 22 at least one target for the decade 2020-2030. For the three years of the next decade when targets were requested, the number of provided targets is higher in the NIRs than in the NPFs (49 vs 45).

Figure 4.1.1‑5 Shares of estimated electric vehicles (from the NIRs) normalized by population (EV motorisation) in 2025 (left map) and in 2030 (right map)

Figure 4.1.1‑4 Shares of electric vehicles normalized by population (EV motorisation) in 2018 (left map) and estimated for 2020 (from the NIRs) (right map)

The maps in Figure 4.1.1 ‑4 and Figure 4.1.1 ‑5 show the evolution of the shares of alternative fuels vehicles normalized by population (that can be named as AFV motorisation) per Member State in 2018, 2020, 2025 and 2030 (according to the estimates provided in the NIRs) for the pair electricity/road. In 2018, there were 5 MSs having more than 3 EVs per 1,000 inhabitants, and the leader is Netherlands with the value of 8.52 for this parameter. In 2020, there are 5 MSs foreseen to have more than 10 EVs per 1,000 inhabitants. In 2025, there are 8 MSs foreseen to have values above 30 EVs per 1,000 inhabitants, while in 2030, there are 8 MSs foreseen to exceed the value of 100 for this parameter. In all estimated future cases, Luxembourg leads with the values of 17 in 2020, 168 in 2025, and 337 in 2030.

Also for the EV share, the EU-wide average situation is shown in the following table:

Figure 4.1.1‑3 Shares of estimated electric vehicles (from the NIRs) in 2025 (left map) and in 2030 (right map)

Figure 4.1.1‑2 Shares of electric vehicles in use in 2018 (left map) and estimated for 2020 (from the NIRs) (right map)

-(EV share) The maps in Figure 4.1.1-2 and Figure 4.1.1-3 show the evolution of the shares of electric vehicles in 2018, 2020, 2025 and 2030 (according to the estimates provided in the NIRs), allowing a comparison among MSs since the absolute numbers shown in Figure 4.1.1 ‑1 are normalised by the existing (2018) or estimated (2020, 2025 and 2030) total fleet of each MS in the reference year. The share of electric vehicles in the total vehicle fleet (excluding PTWs) varies significantly across MSs ranging:

-(Growth rate) The average annual growth rate characterising the foreseen evolution of electric vehicles for the next decade ranges from 13% (Romania) to 96% (Czechia). Out of the 23 computed annual growth rates, 1 is below 20%, 10 are in between 20% and 40%, 9 are in between 40% and 60% while 3 are above 60%.

-(Progress) Considering the 23 MSs that provided at least one estimate for the decade 2020-2030, and comparing the 2018 situation with their foreseen EV fleet evolution, 1 MS results to progress fast, 20 adequately and 2 slowly.

-(Attainment) The 2018 attainment of the foreseen EV estimates ranges significantly across EU, from 4.34% (Poland) to 92.80% (Netherlands) for 2020, from 0.32% (Poland) to 57.15% (Romania) for 2025, and from 0.18% (Poland) to 42.87% (Romania) for 2030. It is worth reminding that a low 2018 attainment could be due to either a slow uptake of EVs in 2018 and/or to a very ambitious estimate for the target year compared to 2018.

Considering the changes from a EU-wide point of view, the unweighted average (UWA) and the population weighted average (PWA) are reported in the following table. As mentioned earlier, the values shown for 2025 and 2030 should be considered with more caution (especially the latter), due to the limited numbers of MSs available to calculate the EU averages. To be noted that the 2020 change is small, even negative (estimates are decreasing in value in NIR vs NPF), and for 2025 and 2030, the estimations are increasing. The very high values for the EU-wide 2030 change are influenced by Spain’s significant change (1,823%), due to its EV estimation of 5,000,000 for 2030, and by the relevance of Spain population compared to the other ten MSs concurring to the 2030 averages.

-(Change NIR vs NPF) Considering the 42 cases where a change could be computed (EV estimates provided both in the NPF and NIR), a decrease of ambition is noticeable in 11 cases, a similar ambition in 19 cases and an increase of ambition in 12 cases. The changes range from -78.20% (Bulgaria) to +150.92% (Lithuania) in 2020; from -63.50% (Bulgaria) to +137.38 (Hungary) in 2025; from -49.08% (Bulgaria) to +1,823.08% (Spain) in 2030. In other 25 cases, an estimate was provided only in the NIR and the changes could not be computed.

-(Coverage) Twenty-three MSs NIRs out of the total 25 assessed (92%) have provided at least some historical data (2016-2018) and at least one estimate for the decade 2020-2030 . For the three years of the next decade when estimates were requested, the NIRs contained globally 24 more inputs than the NPFs, showing the improving MSs’ strategies regarding electro-mobility (in particular, one more estimate in 2020, 11 more estimates in 2025 and 12 more estimates in 2030 compared to the NPF).

Figure 4.1.1-1 summarises the information for the EV estimates and targeted publicly accessible recharging points as provided in the NIRs for the next decade as well as the 2016 and 2018 situation. It is worth noting that a logarithmic scale is used for these diagrams, in order to have readable data for all MSs, however there are big absolute differences among them.

Of all the alternative fuel and transport mode combinations, the pair electricity/road is the best covered as electro-mobility seems to be a priority for most Member States.

In addition to the analysis and assessment provided in each assessment report in Chapter 5 (where the measures for all the pairs AF/transport mode are presented), a paragraph summarising the total EU-wide number of measures and their impact is also presented for road transport.

For waterborne transport, only individual AFI data (shore-side supply points and LNG refuelling points) will be shown in diagrams, while for rail and air transport only a mention of the number of MSs that have delivered data will be provided.

For the other road-related fuels (LNG, hydrogen, LPG), only the diagrams with the individual numerical values of the MSs will be shown (both AFV and AFI), because the amount of available data does not allow to calculate representative EU average results.

It is considered that both averages are important and indicative. The first gives a better vision of the average strategy of all the MSs and provides a more balanced geographical representation of the situation. The second average provides holistic EU numbers, which are closer to the real situation from an EU-citizens point of view. The PWA can be used for example to check the current or foreseen EU state of play versus the objectives of the Green Deal, or when comparing Europe to the other major actors in the field. There is however an important caveat when considering the PWA as representative of the whole EU situation. The sum of the populations of the 25 assessed MSs is about 75% of the total EU-28 population. Thus, a PWA calculated from 25 MSs represents an approximation of the real EU population weighted average. This approximation deteriorates as the number of MSs becomes lower than 25. When the number of MSs was lower than 10, the EU-wide averages were not even calculated.

Secondly, an analysis and interpretation of data from an EU-wide perspective will be presented. Concerning numerical values that can be summed up, this will simply consist in adding the individual values from the MS to the total sum to represent the EU value (for example the total number of electric vehicles in 2018, 2020, 2025 and 2030, or the total number of recharging points). Instead, for those parameters that cannot be summed up (for example the “change NIR vs. NPF”), two averaged European values will be shown:

For the two best-covered pairs, namely electricity/road and CNG/road, the results will be presented from two points of view. First, the numerical values of AFV/AFI in 2016 and 2018, and the objectives for 2020, 2025 and 2030 will be shown on diagrams including all the 25 Member States. Then, in addition to the summary tables presented in Chapter 3, some considerations regarding “coverage”, “change NIR vs. NPF”, “attainment”, “progress”, “average growth rate” and “sufficiency index” will be provided, according to the results of the assessment methodology described in Chapter 2. Finally, the calculated shares of electric vehicles and CNG vehicles in 2018, and the estimated shares in 2020, 2025 and 2030, will be shown in EU maps with the use of different colours.

This section provides an EU-wide overview of the AFV-estimates / AFI-targets and the results of the measure assessment for the most relevant alternative fuels and transport modes.

Table 3.2.2 ‑2 displays that some information on LNG supply for vessels in maritime ports to compute at least one of these metrics was available in sixteen of the assessed NIRs. Due to the limited information provided, it was possible to compute the impact only for nine of the assessed NIRs.

Table 3.2.2 ‑1 displays that some information on shore-side electricity for vessels in maritime ports to compute at least one of these metrics was available in fourteen of the assessed NIRs. Due to the limited information provided, it was possible to compute the impact only for seven of the assessed NIRs.

The following tables give an overview of shore-side electricity ( Table 3.2.2 ‑1 ) and LNG supply ( Table 3.2.2 ‑2 ) for vessels in TEN-T Core maritime ports in each country. For five of the Member States, this requirement was not applicable since they have no TEN-T Core maritime ports. The tables show the results of the assessable data provided in the NIRs in terms of Changes, Attainment, Progress and measures.

Table 3.2.1 ‑2 displays that some information on LNG supply for vessels in inland ports to compute at least one of these metrics was available in only ten of the assessed NIRs. Due to the limited information provided, it was possible to compute the impact only for five NIRs in the case of LNG/water (inland).

Table 3.2.1 ‑1 displays that some information on shore-side electricity for vessels in inland ports to compute at least one of these metrics was available in only eight of the assessed NIRs. Due to the limited information provided, it was possible to compute the impact only for four of the assessed NIRs in the case of electricity/water (inland).

The following tables give an overview of shore-side electricity ( Table 3.2.1 ‑1 ) and LNG supply ( Table 3.2.1 ‑2 ) for vessels in TEN-T Core inland ports in each country. For nine of the Member States, this requirement was not applicable since they have no TEN-T Core inland ports. The tables show the results of the assessable data provided in the NIRs in terms of Changes, Attainment, Progress and measures.

Table 3.1.6 ‑1 provides an overview of the measure assessment results for the pairs LNG/road and hydrogen/road for each country. As it can be seen and due to the limited information provided, it was not possible to compute the impact for eight of the assessed NIRs in the case of LNG/road and for four in the case of hydrogen/road.

Table 3.1.5 ‑1 gives an overview of the results for hydrogen vehicles and the corresponding publicly accessible refuelling points in each country. Also in this case the level of coverage is quite scarce, although it is worth reminding that, according to the Directive, it was not compulsory to include hydrogen in the NPF (hence in the NIR).

Table 3.1.4 ‑1 gives an overview of the results for LNG vehicles and the corresponding publicly accessible refuelling points in each country. As it can be seen, the level of coverage in terms of assessable information is quite sparse.

Table 3.1.3 ‑1 provides an overview of the measure assessment results for the pairs electricity/road and CNG/road for each country. As it can be seen, for the latter pair it was not possible to compute the impact for three of the assessed NIRs, due to the limited information provided.

Table 3.1.2 ‑2 gives an overview of the results for publicly accessible CNG refuelling points in each country. Due to the limited information provided, it was not possible to compute several parameters for six of the assessed NIRs.

Table 3.1.2 ‑1 gives an overview of the results for CNG vehicles in each country. Due to the limited information provided, for nine of the assessed NIRs it was possible to compute only one parameter.

Table 3.1.1 ‑2 gives an overview of the results for publicly accessible recharging points in each country. Due to the limited information provided, for one of the assessed NIRs it was possible to compute only one parameter.

Table 3.1.1 ‑1 gives an overview of the results for electric vehicles in each country. When a cell is empty in the columns under Changes, it means that the NPF had not provided any estimate for that year. However, if the cell is highlighted in yellow, it indicates that the NIR has provided an estimate (but the Change could not be calculated). Overall, a good coverage in terms of assessable information can be seen for the large majority of the NIRs. Only for two of the assessed NIRs, due to the limited information provided, it was possible to compute only one parameter.

For the other combinations of alternative fuel/transport mode, only some of the above results could be computed and are shown.

After that, there is a table showing the summary of the measure assessments, i.e.:

üLevel of Attainment in 2018 with respect to the infrastructure targets for the years 2020, 2025 and 2030;

üLevel of Attainment in 2018 with respect to the vehicle estimates for the years 2020, 2025 and 2030;

For the most complete alternative fuel/transport mode pairs (i.e. electricity/road and CNG/road) there are tables showing the results by AFV and AFI. In particular, for AFV:

This section presents an overview of the outcome of the assessment of the 25 NIRs. When considering all these results together, it is necessary to keep in mind two important points. First, the amount and quality of data and information provided by the Member States for each alternative fuel/transport mode combination differ quite considerably. Second, for a given combination of alternative fuel/transport mode, there can be important differences among MS in terms of details and type of data (for example recharging/refuelling points versus recharging/refuelling stations). Therefore, although the results of the NIR assessments are shown here all together for the 25 MSs, they should not be read and interpreted on a comparative basis, but more by single MS. The opposite could lead to wrong conclusions.

For example, it is possible that a Member State defines a very comprehensive package of measures for a certain pair alternative fuel/transport mode, but this cluster has a low total score. In this case the resulting impact of the measures for this pair is assessed as low.

Once the cluster’s score and comprehensiveness have been assigned, the expected impact to support the uptake of the AFV-estimates / AFI-targets as presented in the NPF and revised in the NIR is calculated. If the cluster of measures for a given pair has a high score and is comprehensive, then the impact is considered high. In all other cases, the impact is considered either medium or low, as shown in Table 2.2.7 ‑1 .

Comprehensiveness indicates to which extent the totality of measures for a given fuel and transport mode addresses various deployment barriers. It will take into account whether both infrastructure and vehicles are addressed or just one of them, what part of the vehicle population is addressed (e.g. for vehicles, whether private cars, company cars, light commercial vehicles or several groups are subject to measures), and if financial as well as nonfinancial incentives are provided within a cluster. The score for comprehensiveness is binary: comprehensive/not comprehensive. The comprehensiveness assessment is independent of the measure score.

For each cluster, the general rule is that the maximum score from all individual measure scores is taken as the cluster score. Consequently, if a Member State has defined for a given fuel/transport mode cluster at least one measure with a high adoption status, high coverage, and high effect, the total score for the cluster would also be high.

For a given fuel/transport mode pair, all the relevant measures are clustered and the resulting cluster receives an overall assessment regarding its score, comprehensiveness, and on its impact to support the uptake of the AFV-estimates / AFI-targets as presented in the NPF and revised in the NIR. The cluster’s impact can be low, medium or high based on the score and comprehensiveness, as explained below.

These measures, defined by a Member State in its NIR, are assessed individually in terms of their adoption status, coverage and effect and they receive a score (as described in subsection 2.2.6.5).

The assessment of the policy measures and AFI deployment and manufacturing support measures includes also an analysis of whether these existing or planned support actions or measures seem sufficient and are coherent with the vehicle estimates and infrastructure targets.

For each measure where a score could be assigned to status, coverage and effect, its overall score is assessed based on these three evaluations, as shown in Table 2.2.6 ‑2 . Following the precautionary principle, the overall measure score is determined by the lowest evaluation the measure has received regarding its three attributes of status, coverage and effect. For example, if the measure has a high (H) coverage and effect but is only under consideration (thus low (L) adoption status), the overall measure assessment score will be low (L) because it cannot be guaranteed that it will ever come into effect. Likewise, if its status is H (measure in effect) but the measure covers only few infrastructure items or has a low effect, its overall assessment score will also be low. The overall measure assessment score will be H only if all three attributes are evaluated as high.

Number/share of eligible vehicles versus the total AFV estimate foreseen in the period of the application of the measure

Number of eligible recharging/refuelling points versus the total AFI target foreseen in the period of the application of the measure

The scope of a measure is evaluated against two dimensions, coverage and effect, as summarised in Table 2.2.6 ‑1 .

For Policy and AFI Deployment & Manufacturing support measures the status is characterised as in paragraph 2.2.5.2, but depending on the status level identified a score is assigned:

For the individual measures belonging to the Policy and AFI Deployment & Manufacturing support categories, the assessment methodology includes more steps and the introduction of a scoring for some parameters (similarly to the NPF assessment [EC17]). This is explained below.

As explained earlier, the four groups of measures are not assessed in the same way. Legal measures and RTD&D measures are analysed and described qualitatively. In addition, the three characterisations presented in paragraphs 2.2.6.1, 2.2.6.2 and 2.2.6.3 are carried out and, where possible, some considerations on the ambition level are provided.

When the assessment involves research, technological development and demonstration (RTD&D) measures, the determination of the funding available is carried out.

In general, the level of ambition is assessed to be decreasing when a measure appeared only in the NPF and to be increasing when a new measure appears only in the NIR.

Another common activity to all four types of measures regards the determination of the level of ambition of each measure in the NIR compared to the NPF. The level of ambition is a parameter that describes qualitatively the characteristics of the measure in the NIR compared to NPF from the perspective of the influence to reaching the MS alternative fuels related targets and objectives. The determination of this parameter takes into account whether there is a new measure in the NIR that was not in the NPF, or the opposite, or any change in the measure’s characteristics (e.g. the degree of coverage, the duration of application, the amount of a subsidy/tax, the funding level, etc.). For each measure, the comparative analysis of its ambition in the NIR versus NPF can have three types of results expressed by the following symbols:

The adoption status determination of a measure represents another common activity to all four types of measures. This parameter can be assigned to one of the following five categories:

An assessment activity that is common to all four types of measures in the NIR is represented by the verification of the existence in the NPF of a similar measure. In a positive case, a comparative analysis of the two measures from the NIR and NPF is performed to determine if their descriptions coincide or contain differences. This activity has four possible results:

Once the identification and classification of all the measures contained in the NIR is concluded, the assessment continues with the analysis of all the measures, one by one. The methodology employed to assess each individual measure involves several activities that are described in detail in the following subsections. Some assessment activities are common to all four types of measures while others are dedicated only to some types of measures.

The activities of research, technological development and demonstration (RTD&D) are identified and classified according to the alternative fuel, transport mode and to the application field (AF, AFI, AFV or combination).

Annex I of the Directive requires the reporting of “annual public budget allocated to support alternative fuels RTD&D, broken down by fuel and by transport mode”.

Therefore, the deployment and manufacturing support measures provided by MSs in their NIRs are identified and classified using these proposed categories.

According to the support guidance (European Commission, 2018) concerning the NIR content prepared for the MSs, it is possible to classify the deployment and manufacturing support in two categories: a) AFI deployment and b) support of manufacturing plants for alternative fuels technologies. Manufacturing plants for AF technologies include both production sites and research facilities for any of the key components of AF technologies such as connectors of charging systems, batteries, fuel cell systems or hydrogen storage tanks.

According to the support guidance (European Commission, 2018) concerning the NIR content, it is possible to classify the policy measures in these three categories which are further disaggregated by type and indicator as it can be observed in Figure 2.2.3 ‑1 . Therefore, the policy measures provided by MSs in their NIRs are identified and classified using these proposed parameters.

• Measures to encourage and facilitate the deployment of recharging points not accessible to the public (Art. 4 (3))

The policy measures are supporting the implementation of the national policy framework. The proposed categorisation of policy measures derives from Article 3(1) and Article 4(3) of the Directive. Three categories are available for policy measures:

Therefore, the legal measures provided by a MS in their NIR are identified and classified using these proposed categories.

According to the support guidance (European Commission, 2018) concerning the NIR content, it is possible to classify the legal measures in two categories which are disaggregated by type as it can be observed in Figure 2.2.2 ‑1 .

The first step of the measure assessment is the identification and classification of the measures as presented by the MSs in their NIRs. In doing so, it can happen that a measure placed by a MS in a category (ex. Legal measures) is moved to another category (ex. Deployment & Manufacturing) because it fits better with the classification presented in the support guidance (European Commission, 2018) and the assessment methodology. However, all the NIR measures, either confirmed or moved, are analysed and assessed.

The flowchart in Figure 2.2.1 ‑1 delivers an overview of the assessment performed for the measures present in the NIR by presenting the main activities (rectangles) that are performed and their different inputs and outputs (parallelograms). In the following sections, the methodology employed to perform the listed activities on the four types of measures included in the NIR is presented.

A key aspect of the Directive is that the Member States are asked to plan and adopt measures to support the achievement of the alternative fuels infrastructure and transport system targets and objectives of their NPF. The Annex I of the Directive mentions that the implementation report of the NPF of each MS required by Article 10(1) of the Directive “shall include at least the following elements:

For the CNG/road pair, the adequacy assessment is based on the indicative value of 600 as considered in (European Commission, 2019). For all the other AFs/road pairs, only the sufficiency index is shown, without any comment.

Where applicable, the evolution trend of the sufficiency index will be analysed and considerations will be made on the value of the index. In particular, for the electricity/road pair, an assessment is provided concerning the adequacy of the ratio between the number of vehicles and the number of recharging points. The adequacy assessment is based mainly on the indicative threshold value of 10, as mentioned by the Directive, however, the share of high power (>22kW) recharging points in the total number of recharging points is also taken in consideration.

For road transport, where the number of AFV and AFI can be relevant in almost all the MSs, the adequacy between AFV and publicly accessible AFI at MS level is monitored by calculating the sufficiency index that is the ratio between AFV estimates and AFI targets for each of the reference years for which the MSs provided values in their NIR. When an AFV estimate or an AFI target is not provided for a certain year, the corresponding ratio cannot be calculated. An example of this is shown in the table below:

We mention that attainment can be > 100% if the 2018 value is higher than the 2020 one, illustrating a foreseen descending tendency. This is shown in the table below for the CNG AFV evolution in one MS.

Because of the significant increase of the EV fleet foreseen by this MS during the period 2016-2030, the corresponding 2018 attainment of future estimates has reduced values and varies moderately, from 12.60% for 2020 to 0.80% for 2030.

As an example, the results for AFV electricity/road for one MS are presented in the table below:

The level of AFV-estimates / AFI-targets attainment can be expressed by the percentage represented by the number of AFI/AFV existent in 2018 over the total number of AFV/AFI foreseen to exist in 2020/2025/2030. The level of attainment corresponding to a certain future year (2020/2025/2030) is calculated according to the formula:

In the case of foreseen CNG AFI evolution, the good exponential fitting (R2 = 0.9899) leads to an annual growth rate of 14.35%.

In the case of foreseen EV evolution, the exponential function fitting the 2016, 2020, 2025 and 2030 data has a good R2 and an annual growth rate of 25.52% is obtained.

Two examples for the determination of the average annual growth rate from the exponential function fitting the four points corresponding to the foreseen AFV-estimates / AFI-targets evolutions are illustrated in Figure 2.1.3 ‑6 and Fig ure 2.1.3 ‑7 in the cases of EVs for one MS and of CNG AFI for another MS.

2.1.3.4.1Examples of the methodology application for the evaluation of the annual growth rate for AFV-estimates / AFI-targets

Thus, the characterisation of the AFV-estimates / AFI-targets of the foreseen evolution at MS level can be performed by providing a parameter given by the best fitting exponential function, namely the growth rate (r). This parameter describes how quickly the MS envisioned its development of the corresponding AFV/AFI for the AF/transport mode pair under analysis between 2016 and 2030 by showing the corresponding annual percentage of growth.

In order to better understand the influence of the parameters constant of proportionality (k) and growth rate (r), Figure 2.1.3 ‑5 provides a graph containing 10 exponential functions (k=0.1, 0.2…1 and a=1) with corresponding growth rates displayed.

For each MS, the fitted exponential curve will have different parameters (a, k and r).

k=constant of proportionality (if k > 0 - the amount is increasing (growing); if k < 0 - the amount is decreasing (decaying))

Since the best fitting curve is an exponential curve, the normal exponential function under its form f(x) = aekx (which is employed in situations of continuous growth or decay) is used. If a quantity grows continuously by a fixed percentage (growth rate), the pattern can be depicted by this type of function.

Having selected the exponential curve as best fit for the electricity and CNG AFV-estimates / AFI-targets values both at MS and at EU level, for each MS a specific exponential curve is obtained, which fits all its provided data as reported in the NIR. In order to characterise mathematically the exponential evolution for AFV-estimates / AFI-targets, a calculation of its growth rate is carried out as explained below.

and the Progress result, in this case, is obtained by the formula shown in section 2.1.3 B):

As described in subsection 0 B of the assessment methodology, for all the other pairs than electricity/road and CNG road, the progress is obtained in a different way. In the following example the progress obtained from 2016 until 2018 by a MS for LNG vehicles deployment is 0.79% of the overall planned deployment during the period 2016-2030. The AFV LNG/road estimates used in the calculations are presented below:

Concerning the straight line connecting the 2016 data point to the 2030 (or latest available) data point, we obtain:

Figure 2.1.3 ‑4 displays the determination of the progress type for the case of recharging points in another MS. The recorded value of recharging points in 2018 communicated by this MS is:

Concerning the straight line connecting the 2016 data point to the 2030 (or latest available) data point, we obtain:

Figure 2.1.3 ‑3 displays the determination of the progress type for the case of CNG vehicles in one MS. The recorded EV value of fleet in 2018 is:

In this subsection, three detailed examples of the computation of 2018 progress are provided for AFV and AFI, different pairs AF/transport mode and different Member States.

In other words, when the 2018 data point of the MS is in between the exponential curve and the straight line, this is considered as an adequate progress. If the 2018 data point is below the exponential curve, this is considered as a slow progress. If the 2018 data point is above the straight line, this is considered as a fast progress. This is further explained in the examples in the following subsection.

Thus, it is proposed to classify the 2018 progress of the MSs for AFV-estimates / AFI-targets depending on its position with respect to one of the three areas:

To characterise the progress in 2018 of a certain MS, the position of 2018 AFV-estimates / AFI-targets value (called “MS_situation_2018”), as reported by the MS in its implementation report, is considered with respect to the three areas described above.

·The area above the linear function is considered as area of fast progress; the area below the exponential curve is considered as area of slow progress (see figures Figure 2.1.3 ‑3 and Figure 2.1.3 ‑4 )

·Next, a linear function connecting the AFV-estimates / AFI-targets values for 2016 and 2030 is added to the diagram; this straight line is called “lin_2016_2030(x)” and constitutes the upper boundary of the area of adequate progress.

·First, a curve of exponential type is applied to the AFV-estimates / AFI-targets values of the MS from 2016 and 2030; this curve is called “best_fit_2016_2030(x)” and constitutes the lower boundary of the area of adequate progress.

Before characterising the progress of a MS, it is necessary to define the areas of slow progress, adequate progress and fast progress. This is obtained as follows:

Following the same approach adopted for the electric vehicles and recharging infrastructure, best_fit = exponential is also chosen for CNG refuelling points.

Considering the highest coefficient of determination R2 as criterion to establish the best fit curve, also in this case the result is that best_fit = exponential (R2 = 0.9233).

Using past CNG vehicle data (2008–2018) from EAFO and 2020 CNG vehicle estimates from the NPFs, the results depicted in Figure 2.1.3 ‑2 are obtained at EU level:

- the AFI Directive foresees the electric vehicles and infrastructure having a synchronised development, meaning the same evolution type as it recommends a ratio of 10 between electric vehicles and infrastructure.

- it is assumed that vehicles are the driving force for the uptake of AF transport systems and infrastructure will follow;

Considering the highest coefficient of determination R2 as criterion to establish the best fitting curve, the result is that best_fit = exponential (R2 = 0.9785).

Using past data (2008–2018) regarding electric vehicles (EVs) from EAFO and 2020 EV estimates from the NPFs, the results depicted in Figure 2.1.3 ‑1 are obtained at EU level:

For the electricity/road and CNG/road pairs, the type of evolution at EU level was determined from available past data from the European Alternative Fuels Observatory (EAFO) and future 2020 AFV-estimates / AFI-targets provided by the MSs in their NPFs. The highest coefficient of determination R2 that described the goodness of the fit was used as criterion to establish the best fitting curve to these data (exponential, linear, logarithmic or power). This identified curve type provided the type of evolution at EU level that was then considered to perform the MS level progress assessment.

B)For all the other AF/transport mode pairs, for which a clear evolution type could not be identified, the 2018 progress of a MS for the corresponding transport systems or infrastructure deployment is obtained by dividing the achievement in the period 2016-2018 by the overall planned deployment during the period 2016-2030. Thus, the progress determination in these cases is based on the formula:

For these two AF/transport mode pairs, the progress type information is complemented by an evaluation of the AFV-estimates / AFI-targets growth rate of the foreseen evolution (see subsection 2.1.3.4).

•Characterisation of the 2018 progress at MS level for AFV-estimates / AFI-targets deployment as fast, adequate or slow progress (see subsection 2.1.3.2).

•Determination of the type of evolution at EU level for each of the two AFs from past available data until 2018, and 2020 foreseen situation in the NPF (see subsection 2.1.3.1);

A)For the electricity/road and CNG/road pairs, where a clear evolution type could be identified in many MSs and at EU level, the progress is assessed following the next steps, which are described in detail in the mentioned subsections:

The progress assessment evaluates what has been achieved at MS level between 2016 and 2018 regarding alternative fuels transport systems and infrastructure compared to the overall planned evolution in the NPF/NIR for the period 2016-2030. Therefore, the progress is not influenced by the initial situation in 2016. Due to the different level of development of the various AF/AFV/AFI, both at MS level and at EU level, two separate cases have been identified and are treated differently.

When for a given column in Table 2.1.2-1 the NIR value is present and the corresponding cell “Change NIR vs NPF [%]” is empty, it means that the NPF did not contain the AFV estimates or AFI targets.

It should be noted that the values shown in Table 2.1.2 ‑1 are those in the NIR, however the corresponding values in the NPF can be easily calculated from the NIR values and the percentage changes versus NPF:

In the assessment of each MS’ NIR, these changes are computed for all the pairs alternative fuel/transport mode for which data are available and are displayed under tabular format using the colour coding described above. Table 2.1.2 ‑1 presents an example from one MS’ NIR.

The changes presented in the NIR in comparison with the NPF concerning AFV-estimates / AFI-targets are computed for a given year according to this formula:

The flowchart in Figure 2.1.1 ‑1 gives an overview of the AFV estimates and AFI targets assessment by presenting the main activities (rectangles) that are performed and their different inputs and outputs (parallelograms). The following sections present in detail the methodology employed to perform the listed activities:

The assessment of the Implementation Reports at Member State level is performed along two main directions: on the one hand, it evaluates the AFV estimates and AFI targets, on the other hand, the support measures. Concerning the former, the assessment analyses the MSs situation in 2018 towards reaching the AFV-estimates / AFI-targets and characterises the modifications reported in the NIR versus the NPF. As for the latter, by applying the assessment methodology, it examines whether the existing or planned support actions or measures from the NIR are coherent with the AFV-estimates / AFI-targets set in the NIR, and provides an evaluation of the possible impact of such measures to achieve the objectives.

The analysis of the national implementation reports (NIR) has been carried out in two stages: first, an assessment at member state level according to the methodology described in the following sections. After that, an analysis at EU level has been performed on the basis of the 25 NIRs received (out of 28), which also includes some considerations on the economic and social impact of the implementation of the Directive.

When considering the individual assessment reports and the EU-wide analysis it is important to keep in mind that the information provided in the NIRs and assessed by the Commission constitutes a snapshot of the situation in 2018. Almost two years have passed since then and in several cases the position and strategies of the MSs concerning alternative fuels might have changed quite considerably. Nevertheless, this SWD provides an impressive amount of data, information and analysis on the level of implementation of the Directive three years after the submission of the NPFs by the MSs.

Chapter 5 contains the detailed assessment reports of the 25 Member States that have delivered their NIRs within the latest deadline granted by the Commission. These 25 assessment reports are preceded by a section that explains the structure and content of each part of the assessment.

The second section of Chapter 4 presents an update of the simplified analysis regarding the economic and social impact of the Directive, based on the NIRs.

Chapter 4 is divided in two sections. The first section provides some considerations and additional information on the results shown in Chapter 3, but from an EU-wide perspective. Where possible, the results from the individual NIRs and MSs are combined to provide EU averages that, although with some limitations related to the availability of data and the number of Member States assessed, give an indication of the EU-wide state of play in 2018 and of the outlook until 2030.

Chapter 3 provides for all the MSs a tabularised synthesis of the most relevant assessment results, both concerning the numerical values provided in the NIRs in relation to alternative fuels vehicles and related infrastructure, and concerning the outcome of the analysis and assessment of the measures.

The SWD is structured as follows: after the introduction, Chapter 2 describes the methodology adopted for the assessment of the NIRs and presents the new parameters and calculations carried out in this SWD, which were not present in the Commission SWD on the National Policy Frameworks.

The present Commission Staff Working Document (hereinafter “SWD”) responds to the above request of the Directive. It covers 25 Member States instead of 28, because Italy, Portugal and UK did not deliver their NIRs within the latest possible deadline agreed between the Commission and the Member States to have their NIRs included in this SWD.

Point 3 of Article 10 of the Directive prescribes also that “The Commission shall submit a report on the application of this Directive to the European Parliament and to the Council every three years with effect from 18 November 2020”.

Under Article 10 of the Directive, Member States have to submit a report on the implementation of their national policy framework to the Commission by 18 November 2019, and every three years thereafter. The national implementation report (hereinafter “NIR”) has to cover the information listed in Annex I of the Directive and, where appropriate, to include a relevant justification regarding the level of attainment of the national targets and objectives referred to in Article (3)(1).

·By 31 December 2025, establish an adequate number of publicly accessible LNG refuelling points in the existing TEN-T Core Network at least to ensure that LNG-powered heavy-duty vehicles can circulate throughout the EU on demand unless costs are disproportionate in relation to the benefits, including environmental benefits.

·by 31 December 2025, establish an adequate number of LNG refuelling points in ports to ensure the circulation of LNG inland waterway vessels or seagoing vessels throughout the TEN-T Core Network;

·by 31 December 2025, establish an adequate number of publicly accessible CNG fuelling points at least in the existing TEN-T Core Network in order to ensure that CNG vehicles can circulate throughout the European Union (hereinafter "EU”);

·by 31 December 2020, establish an adequate number of publicly accessible refuelling points to ensure that CNG vehicles can circulate in urban/suburban agglomerations and other densely populated areas and, where appropriate, on networks designated by Member States;

·by 31 December 2020, establish an adequate number of publicly accessible recharging points to ensure that electric vehicles (hereinafter “EV”) can circulate at least in urban/suburban agglomerations and other densely populated areas;

The Directive requires Member States to set up long-term National Policy Frameworks (NPFs) for the development of the alternative fuels market and the planning of the deployment of relevant alternative fuels infrastructure. The Directive also sets requirements for rollout of alternative fuels infrastructure along the core network of the Trans-European Transport Network (TEN-T) and its urban areas - with different milestones for 2020, 2025 and 2030 for different alternative fuels. Finally, the Directive prescribes common technical specifications for recharging and refuelling points and for consumer information.

To minimise transport dependence on oil and to reduce the environmental impact of transport, Directive 2014/94/EU of the European Parliament and of the Council on the deployment of alternative fuels infrastructure (hereinafter the "Directive”), of 22 October 2014, provides a common approach for the development of alternative fuel infrastructure as well as common technical specifications.

Detailed Assessment of the Member States Implementation Reports on the National Policy Frameworks for the development of the market as regards alternative fuels in the transport sector and the deployment of the relevant infrastructure. Implementation of Art 10 (3) of Directive 2014/94/EU

The total effect on labour demand amounts to roughly 247,000 person-years until 2030, again with higher effects of around 40,000 persons per year during the last period 2026-2030. Additional employment effects could be triggered by the substantial deployment of private recharging points that several MSs refer to in their NPF/NIR, but are not considered in this analysis. In conclusion, a consistent EU-wide build-up of alternative fuels infrastructure could trigger a sustained positive employment effect, and could contribute to translating the temporary extra labour demand resulting from NPFs and NIRs into permanent jobs. Moreover, the respective qualification of workforce, which is more likely to occur in the presence of longer-term targets, can support the maintenance or increase of domestic shares in AFI production and installation. This again can have a positive impact on the EU sector's competitiveness.

The effects of AFI infrastructure build-up were calculated using the model described above (subsection 4.2.1). The model was run using AFI build-up targets for the different road transport AFI types (recharging points, CNG, LNG and hydrogen refuelling points) for the periods 2019-2020, 2021-2025 and 2026-2030. Table 4.2.2 ‑1 shows the EU-wide value added and additional labour demand that can be achieved by fulfilling the targets for publicly accessible recharging points and CNG, LNG and hydrogen refuelling points for the next decade provided by the Member States in their NPF and revised in the NIR . The total value-added until 2030 sums up to more than 16 billion € with annual effects ranging from roughly 360 to 2,800 million €. The economic effect is strongest for the period 2026-2030, as the Member States foresee a significant increase of the number of AFI towards the end of the decade. The annual effects for the 2021-2025 period are smaller since several MSs did not provide targets for the year 2025 (in this case, if a target is provided for 2030, the model considers that all infrastructure build-up and the associated economic benefits related to the 2030 target take place in the period 2026-2030).

The model allows for running scenarios on a wide number of parameters. These include, for example:

The domestic employment effect is derived by aggregating over all sectors, and the EU-wide effect by then aggregating over all Member States.

As labour productivity varies for each Member State and sector, this calculation is done on sectoral level. Data on the number of persons employed in the production of AFI is not available, thus productivities in the sectors contributing to AFI build-up (see Table 4.2.1 ‑1 ) were used. These were derived by dividing each Member State's sectoral gross value added by the number of employed persons, both taken from Eurostat .

As shown in Figure 4.2.1 ‑1 , the employment effect of building a given type of infrastructure in each Member State is derived from domestic GVA per sector, dividing it by productivity. This yields the amount of person-years required to build the AFI envisaged in the NPF and revised in the NIR, which is assumed to translate into employment.

Summing over the sectors, the Member States' domestic GVA effect from the particular infrastructure type results. For each Member State, total sectoral GVA effect includes the domestic effect of own AFI installation and the Member States exports of preliminary products for AFI installation to other EU countries. The sum over all AFI types per Member State is the total national GVA effect from the EU-wide implementation of AFI targets as envisaged in the NPFs and revised in the NIRs, and the sum over all Member States yields the EU-wide effect. AFI maintenance costs are included via a multiplier representing annual costs as percentage of total investment per facility.

The national GVA effect resulting in the sectors C25, C26, C27 and C28 from the production is allocated completely (adjusted by preliminary imports) to the producing country. The costs of installing a recharging or refuelling point, occurring in sectors C33 and F, is divided into a GVA effect in the producing country and the country that installs the infrastructure.

For each of these sectors, the sectoral GVP is multiplied by the sectoral domestic production share, yielding the sectoral domestic GVA for each of the six sectors for the AFI type and Member State under consideration. By default, the sectoral domestic share in AFI production in each Member State is assumed to be equal to the Member State's present sectoral share of production value within the EU, which is derived from Eurostat data . The model allows reallocating domestic production shares as well as import shares from outside the EU for scenario analysis.

In a next step, the share of each Member State in the production and installation of AFI needs to be determined, and imports from outside the EU need to be deducted. As the share of imported preliminary products differs among economic sectors, the GVP is sub-split. This is done by assigning the different technological components of an AFI installation (and thus their costs) to different economic sectors, on the basis of data on the composition and prices of the different AFI types. Price information was taken from studies and industry sources (Steer Davies Gleave, 2016), (Ludwig-Bölkow-Systemtechnik, 2016), (Nationale Plattform Elektromobilität (NPE), 2015), (European Commission, 2020). AFI GVP is assigned to the following sectors (in line with Eurostat NACE Rev. 2):

Annual numbers of new AFI installed are multiplied by their net market prices to derive the Gross Value of Production (GVP). As the market price of a technology includes all value added along the value chain, it is a reasonable proxy for the calculation of gross value of production added.

Figure 4.2.1 ‑1 shows a model flowchart for the calculation of the domestic economic effects of recharging point build-up in a Member State. The calculations are intended to cover the period 2019-2030 by considering three sub-periods dictated by the years for which targets were requested by the Directive (i.e. 2019-2020, 2021-2025 and 2026-2030) and they are adapted to the AFI targets provided by the Member States. For each infrastructure type, Member State and sub-period, AFI build-up targets are derived in a first step, calculated as the target number of recharging or refuelling points for each requested year (2020, 2025 and 2030) minus the previously built or targeted number as given in the NIR (for example 2020-2018, or 2025-2020, or 2030-2025). Summed over Member States, the number of total planned AFI of each type in the EU is obtained. AFI build-up is assumed to be linear for each sub-period in the model.

A computational model was developed for calculating the value creation and employment effects resulting from AFI build-up as described in the NPFs and revised in the NIRs. It outputs Member States' domestic as well as the EU-wide effects resulting from infrastructure production and installation. Types of infrastructure covered by the model include electricity recharging points and CNG, LNG and hydrogen refuelling points for road transport.

Changes could be computed for only three MSs: Luxembourg has increased its targets and Netherlands and Austria have similar targets as in the NPF.

Eight MSs have provided in their NIRs the existing numbers of electricity supply for stationary airplanes in the period 2016-2018 and at least one estimate for the next decade.

Figure 4.1.3 ‑2 summarises the information for the LNG refuelling supply for waterborne transport (maritime and inland) as provided in the NIRs for the next decade as well as the 2016 and 2018 situation.

Around 50% of the MSs that should have reported about the LNG supply infrastructure for their maritime ships have actually done so. This percentage becomes 60% for the waterborne inland transport. The data provided by the MSs in their NIRs are summarised in the following diagram.

Figure 4.1.3 ‑1 summarises the information for the shore-side electricity supply for waterborne transport (maritime and inland) as provided in the NIRs for the next decade as well as the 2016 and 2018 situation.

Around 50% of the MSs that should have reported about the shore-side electricity supply infrastructure for their maritime ships have actually done so. This percentage becomes 60% for the waterborne inland transport. The data provided by the MSs in their NIRs are summarised in the following diagram.

Germany has reported the existence of two hydrogen locomotives in 2018, while France, the Netherlands and Slovakia have announced plans for 15, 1 and 10 hydrogen locomotives, respectively within the next decade.

Six MSs have provided in their NIRs the existing numbers of locomotives in the period 2016-2018 and at least one estimate for the next decade.

·The level of NIR’s ambition vs NPF for RTD&D measures is assessed for 3 MSs, and for two of them the level is considered increased.

·From the seven NIRs containing assessable policy and deployment & manufacturing support measures for this cluster, only ES had a medium score and a medium level of impact, the rest have low level impact. The level of ambition is similar to the one in the NPF for four NIRs.

·The level of NIR’s ambition vs NPF for legal measures is assessed for 5 MSs, and for two of them the level is increased.

- (Attainment) The 2018 attainment of the foreseen LPG refuelling points targets is computed for 6 MSs, resulting 12 attainment values. They range across EU from 25.00% (Cyprus) to 100% (Slovakia) for 2020, from 70.00% (Greece) to 99.18% (Slovakia) for 2025, and from 94.53% (Czechia) to 99.18% (Slovakia) for 2030. Three Member States (Denmark, Lithuania and Hungary) foresee a decreasing trend of their LPG refuelling infrastructure in the future decade.

- (Change) Because only four MSs had provided targets for the future in the NPF, only these MSs allow change computing. Excepting Greece that preserved in NIR the NPF targets, Spain, Cyprus and Hungary have decreased their targets. In other 13 cases, a target was provided only in the NIR and the changes could not be computed.

- (Coverage) Seventeen MSs out of 25 (68%) have provided at least some historical data (2016-2018). Nine MSs (44%) have declared at least one target for the decade 2020-2030.

- (Progress) Comparing the 2016-2018 LPG vehicles evolution with the 2016-2030 foreseen evolution, six progress values are determined. They range from -14.33% (Greece) to 69.09% (Malta). For two Member States (Denmark and France), the progress is not computed since they foresee a decreasing trend of their LPG vehicle fleets in the future.

- (Attainment) The 2018 attainment of the foreseen LPG vehicle estimates is computed for 6 MSs, resulting 18 attainment values. They range across EU from 32.05% (Slovenia) to 94.06% (Slovakia) for 2020, from 20.54% (Spain) to 85.58% (Slovakia) for 2025, and from 8.22% (Estonia) to 83.00% (Malta) for 2030. Two Member States (Denmark and France) foresee a decreasing trend of their LPG vehicle fleets in the future.

- (Change) Because only 4 MSs had estimates for the future in the NPF, only these MSs allow change computing. Slovenia preserves its estimates while Greece and Estonia are decreasing their ambition. Malta shows a slight increase in 2020 followed by decreases in 2025 and 2030. In other 16 cases, an estimate was provided only in the NIR and the changes could not be computed.

- (Coverage) Twenty MSs out of 25 (80%) have provided at least some historical data (2016-2018). Nine MSs (44%) have declared at least one vehicle estimate for the decade 2020-2030.

Figure 4.1.1 ‑21 summarises the information for the LPG vehicle estimates and targeted publicly accessible refuelling points as provided in the NIRs for the next decade as well as the 2016 and 2018 situation.

•For this pair, 21 NIRs contain data regarding policy, and deployment and manufacturing support measures. Three measure packages are assessed with high score (Czechia, Germany and Croatia), nine are comprehensive but only one receives a high level impact (Germany). An indicator of the increased interest is the fact that 18 NIRs from 21 have an increased ambition level in the comparison with the NPF.

•The level of NIR’s ambition vs NPF for legal and RTD&D measures is assessed for 13 MSs and for the majority of them the level is increased (10 and 11 respectively).

-(Attainment) The 2018 attainment of the foreseen hydrogen refuelling points are computed for 8 MSs and 15 attainment values. For 2020, they range across MSs from 46.15% (Sweden) to 100% (Austria), from 14.29% (Slovenia) to 46.15% (Sweden) for 2025 and from 5.00% (France) to 14.29% (Slovenia) for 2030. One Member State (Denmark) foresees a decreasing trend of its hydrogen refuelling points in the future.

-(Change) Ten MSs allow the change computing. Two MSs have decreased their targets (Bulgaria and Spain). Four of them preserve the NPF target values and therefore the change values are zero. The remaining four have shown an increase of their ambition, significant in three cases: Netherlands (in 2020) and Czechia (in 2025), both with 400% change values, and France with 233% in 2025. In other 15 cases, a target was provided only in the NIR and the changes could not be computed.

-(Coverage) Seventeen MSs (out of 25) have provided at least some historical data (2016-2018) in their NIRs. Among these, nine MSs have declared zero hydrogen infrastructure in 2018. This means that 8 out of 25 MSs have declared hydrogen infrastructure by 2018 (32%). Seventeen MSs have declared at least one target for the decade 2020-2030, but two of them have declared only targets of zero. This means that 15 out of 25 MSs have set hydrogen infrastructure targets above zero by 2030 (60%).

-Progress Comparing the 2016-2018 hydrogen vehicles evolution with the 2016-2030 foreseen evolution, five progress values are determined. They range from 0.00% (Czechia) to 1.81% (Denmark). For one Member State (Sweden), the progress is not computed since the 2030 estimated value is below the 2018 existing one.

-Attainment The 2018 attainment of the foreseen hydrogen vehicle estimates are computed for 6 MSs and 17 attainment values result. For 2020, they range significantly across MSs from 3.13% (Netherlands) to more than 100% (France), from 0.01% (Czechia) to 14% (Spain) for 2025 and from less than 0.01% (Czechia) to 8.91% (Denmark) for 2030. For one Member State (Sweden), the attainment is not computed since all the future estimates are below the 2018 value.

-(Change) The NPF and NIR data allow change computing for only 6 MSs. Two MSs have provided less ambitious AFI estimates in their NIR compared to the NPF (Bulgaria and Spain). Other three MSs have practically confirmed their initial NPF plans, while Hungary increased its ambition for 2025 and 2030. In other 20 cases, an estimate was provided only in the NIR and the changes could not be computed.

-(Coverage) Ten MSs (out of 25) have provided positive values for hydrogen vehicles in 2018 (40%). The other 13 MSs have declared zero hydrogen vehicles and 2 have not reported anything. Twelve MSs have declared at least one positive estimate for the decade 2020-2030 (48%).

Figure 4.1.1 ‑20 summarises the information for the hydrogen vehicle estimates and targeted publicly accessible refuelling points as provided in the NIRs for the next decade as well as the 2016 and 2018 situation.

·From the 17 NIRs offering assessable data regarding policy and deployment measures, only two have a high score and none of them displays a high impact. The ambition level is predominantly increased (13 MS).

·The level of NIR’s ambition vs NPF for legal and RTD&D measures is assessed for 21 and 17 MSs respectively, and for the majority of them the level is increased (14 and, 9 respectively).

-(Progress) Comparing the 2016-2018 LNG AFI evolution with their 2016-2030 foreseen evolution, four progress values are determined. They range from 3.33% (Czechia) to 47.50% (France).

-(Attainment) The 2018 attainment of the foreseen LNG AFI targets is computed for 11 MSs and 20 determined attainment values result. They range across MSs from 27.27% (Sweden) to 66.67% (Finland) For 2020, from 7.14% (Czechia) to 100% (Austria) for 2025, and from 3.33% (Czechia) to 48.78% (France) for 2030.

-(Change) The NIR and NPF data allows computing 24 changes corresponding to 2020-2030 period, for 13 MSs. There are 3 decreasing changes (1 in 2020), 14 situations with the same ambition (4 in 2020) and 7 with increased ambition (2 in 2020). In other 11 cases, a target was provided only in the NIR and the changes could not be computed.

-(Coverage) Fifteen MSs (out of 25) have provided at least some historical data (2016-2018). Among these, six MSs have declared zero LNG infrastructure in 2018. This means that 9 out of 25 MSs have declared LNG infrastructure by 2018 (36%). Nineteen MSs have declared at least one target for the decade 2020-2030 (76%).

-(Progress) Comparing the 2016-2018 LNG vehicle fleet evolution with their 2016-2030 foreseen evolution, the progress was computed for 11 MSs and it ranges from 0.00% (Slovenia) to 8.05% (Luxembourg).

-(Attainment) The 2018 attainment of the foreseen LNG vehicle estimates is computable for eight MSs, with 24 attainment values as result. They range significantly across MSs from 2.5% (Czechia) to 100% (Lithuania) for 2020, from 0.09% (Czechia) to 42.95% (Lithuania) for 2025, and from 0.03% (Czechia) to 16.53% (Lithuania) for 2030.

-(Change) In the NPFs, there were only 7 MSs having at least one target. Comparing with NIR data, it results that the change is computable for 7 MSs and only 14 changes can be determined. Out of these 14 computable changes, 3 have provided less ambitious LNG vehicle estimates in the NIR compared to the NPF (1 MS in 2020). Seven changes indicate the same ambition as NPF (2 MSs in 2020) and 4 changes show increased ambition (2 MSs in 2020). In other 15 cases, an estimate was provided only in the NIR and the changes could not be computed.

-(Coverage) Thirteen MSs (out of 25) have provided at least some historical data (2016-2018). Among these, three MSs have declared zero LNG vehicles up to 2018. This means that 10 out of 25 MSs have declared LNG vehicles in 2018 (40%). Eleven MSs have declared at least one target for the decade 2020-2030 (44%).

With LNG, the level of coverage by the MSs starts to decrease and does not allow to provide averages at EU-wide level.

Figure 4.1.1 ‑19 summarises the information for the LNG vehicle estimates and targeted publicly accessible refuelling points as provided in the NIRs for the next decade as well as the 2016 and 2018 situation.

·In seventeen cases, the ambition of RTD&D measures targeting CNG/road in the NIRs could be determined, and among these 10 measure sets show an increased ambition compared to the NPF situation.

·Concerning the policy and deployment & manufacturing support measures, 22 MSs have reported assessable clusters of measures. Of these, only 4 clusters have obtained a high overall score, 14 clusters have obtained a medium score, 2 clusters a low/medium score and 2 a low score. Ten clusters are comprehensive, while the others are not comprehensive. In terms of expected impact of these measures to support the realisation of the AFV/AFI objectives as presented in the NPF and revised in the NIR, only 2 clusters are assessed as having a high level impact (Finland and Ireland), 8 a medium level and 12 a low impact. Regarding the ambition in the NIR vs. NPF, an increased level has been found in 17 cases.

·The legal measures’ level of ambition in the NIR vs. NPF has increased for 12 MSs out of the 25 NIRs assessed.

The pair CNG/road is the second most covered in terms of dedicated measures by the majority of MSs. Compared to the electricity/road pair, for which a full commitment and positive outlook are shown by almost all MSs (although with important differences in absolute terms), the pair CNG/road presents a more articulated and MS-differentiated scenario:

Looking at these data from an EU-wide point of view, the results shown in the following table are obtained (the results for 2020 and 2025 are clearly less representative than for 2018):

Figure 4.1.1‑18 Estimated ratio AFV/AFI (sufficiency index) for CNG/road (from the NIRs) for 2025 (left map) and for 2030 (right map)

Figure 4.1.1‑17 Ratio AFV/AFI (sufficiency index) for CNG/road in 2018 (left map) and estimated for 2020 (from the NIRs) (right map)

The maps in Figure 4.1.1 ‑17 and Figure 4.1.1 ‑18 show the evolution of the sufficiency index for CNG/road in 2018, 2020, 2025 and in 2030 (according to the estimates and targets provided in the NIRs).

From NIR data, it is possible to compute the ratio of CNG vehicles and publicly accessible CNG refuelling points for 13 MSs for the 2020-2030 decade, overall in 32 cases compared with the NPF that allowed 27. For 2020, the ratio can be computed for 11 MSs and ranges from 50.71 (Denmark) to 597.69 (Hungary), with 10 MSs situated below 400 and 1 MS in between 400 and 800. For 2025, the ratio can be computed for 12 MSs and ranges from 89.53 (Denmark) to 612.37 (Hungary), with 7 MSs situated below 400 and 5 MSs in between 400 and 800. For 2030, the 9 computable ratios range from 77.23 (Czechia) to 1,086.67 (Belgium), with 5 MSs situated below 400, 3 MSs in between 400 and 800, and 1 MS above 800.

Figure 4.1.1‑16 Estimated density of publicly accessible CNG refuelling points (from the NIRs) in 2025 (left map) and in 2030 (right map)

Figure 4.1.1‑15 Density of publicly accessible CNG refuelling points in 2018 (left map) and estimated for 2020 (from the NIRs) (right map)

The maps in Figure 4.1.1 ‑15 and Figure 4.1.1 ‑16 show the evolution of the density of publicly accessible CNG refuelling points (number of refuelling points normalized by the total length of roads) per Member State in 2018, 2020, 2025 and in 2030 (according to the estimates provided in the NIRs). In 2018, a group of 6 MSs in Central Europe had a density superior to 0.1 CNG refuelling points per 100 km of road and Belgium had the highest value for this parameter (0.77). Because of the absence of several CNG AFI targets for the decade 2020-2030, the number of MSs foreseen to have a density above 0.1 CNG refuelling points per 100 km of road is 3 for 2020, 5 for 2025 and 6 for 2030.

Looking at these data from an EU-wide point of view, the results shown in the following table are obtained.

-(Growth rate) The average annual growth rate characterising the foreseen evolution of CNG refuelling points for the next decade ranges from 1% (Ireland) to 129% (Romania). Out of the 17 computed annual growth rates, 5 are below 10%, 9 are in between 10% and 20%, 2 are in between 20% and 30% while 1 is above 60% (Romania).

-(Progress) From the 20 MSs that provided at least one target for the 2020-2030 decade in their NIR or NPF, comparing the 2018 situation with their foreseen CNG refuelling infrastructure evolution, 4 MSs results to progress fast, 7 adequately and 8 slowly (in the case of Luxembourg the progress was not computed).

Looking at these data from an EU-wide point of view, we obtain the results shown in the following table:

-(Attainment) The 2018 attainment of the foreseen CNG AFI targets ranges significantly across MSs from 5.45% (Romania) to 100% or more (Denmark, Ireland, Hungary) for 2020, from 0.00% (Cyprus) to 100% (Denmark) for 2025 and 2030. One Member State (Luxembourg) foresees a decreasing trend of its CNG refuelling points in the future. Four Member States (Denmark, Estonia, Netherlands and Slovenia) foresee a constant number of refuelling stations in the next decade (2020-2030).

Looking at these data from an EU-wide point of view, the results shown in the following table are obtained. In this case, only the 2020 and 2025 averages have been computed as EU-wide representative, it should be kept in mind that the 2025 values are less representative.

-(Change NIR vs NPF) Considering the 36 situations where a change could be computed (CNG AFI targets provided both in the NPF and NIR), a decrease of ambition is noticeable in 9 cases (6 for 2020), a similar ambition in 20 cases (9 for 2020) and an increase of ambition in 7 cases (4 for 2020). In other 14 cases, a target was provided only in the NIR and the changes could not be computed.

-(Coverage) In their NIRs, 20 MSs out of the total 25 assessed (80%) have provided at least some historical data (2016-2018) and 19 MSs (76%) at least one target for the decade 2020-2030. For the three years of the next decade when targets were requested, the number of provided targets is higher in the NIRs than in the NPFs (45 vs 41).

Figure 4.1.1‑14 Shares of estimated CNG vehicles (from the NIRs) normalized by population (CNG vehicle motorisation) in 2025 (left map) and in 2030 (right map)

Figure 4.1.1‑13 Shares of CNG vehicles normalized by population (CNG vehicle motorisation) in 2018 (left map) and estimated for 2020 (from the NIRs) (right map)

The maps in Figure 4.1.1 ‑13 and Figure 4.1.1 ‑14 show the evolution of the shares of alternative fuels vehicles normalized by population (that can be named as AFV motorisation) per Member State in 2018, 2020, 2025 and in 2030 (according to the estimates provided in the NIRs) for the pair CNG/road. In 2018, there were 5 MSs having more than 1 CNG vehicle per 1,000 inhabitants. In 2020, there are 3 MSs foreseen to have more than 2 CNG vehicles per 1,000 inhabitants. In 2025, there are 5 MSs foreseen to have values above 3 CNG vehicles per 1,000 inhabitants. In 2030, there are 9 MSs foreseen to exceed the value of 4 CNG vehicles per 1,000 inhabitants. In 2018 and 2020, Sweden presents the highest values of CNG vehicles per 1,000 inhabitants: 4.20 in 2018 and 4.18 in 2020. In 2025 and 2030, Belgium presents the highest values of CNG vehicles per 1,000 inhabitants: 13.31 in 2025 and 56.53 in 2030.

The EU-wide average situation concerning CNG vehicle shares is shown in the following table (the results for 2020, 2025 and 2030 are less statistically representative than the 2018 result, due to the lower number of MSs concurring to the calculation of the averages):

Figure 4.1.1‑12 Shares of estimated CNG vehicles (from the NIRs) in 2025 (left map) and in 2030 (right map)

Figure 4.1.1‑11 Shares of CNG vehicles in use in 2018 (left map) and estimated in 2020 (from in the NIRs) (right map)

-(CNG vehicle share) The maps in Figure 4.1.1 ‑11 and Figure 4.1.1 ‑12 show the evolution of the shares of CNG vehicles in 2018, 2020, 2025 and in 2030 (according to the estimates provided in the NIRs). The share of CNG vehicles in the total vehicle fleet (excluding PTWs) ranges from 0% (Cyprus and Malta) to 0.77% (Sweden) in 2018. This share is also foreseen to vary in the future in the 18 MSs providing CNG vehicle estimates

The average situation from an EU-wide perspective is reported in the following table (also in this case, it is acknowledged that the number of MSs is not sufficient for a robust estimation):

-(Growth rate) The average annual growth rate characterising the foreseen evolution of CNG vehicles for the next decade ranges from 4% (Sweden) to 41% (Belgium). Out of the 14 computed annual growth rates, 2 are below 10%, 3 are in between 10% and 20%, 7 are in between 20% and 30% while 2 are above 30% (Belgium and Spain).

-(Progress) Considering the 14 MSs for which progress could be calculated, comparing the 2018 situation with their foreseen CNG vehicle fleet evolution, 2 MSs result to progress fast, 6 adequately and 6 slowly.

The average situation from an EU-wide perspective is reported in the following table:

-(Attainment) The 2018 attainment of the foreseen CNG vehicle estimates ranges significantly across MSs from 15.41% (Slovenia) to more than 100% (France, Luxembourg and Sweden) for 2020, from 7.08% (Slovenia) to 78.25% (Sweden) for 2025, and from 1.82% (Belgium) to 55.22% (Sweden) for 2030. One Member State (Luxembourg) foresees a decreasing trend of its CNG vehicle fleet in the future.

The average situation from an EU-wide perspective is reported in the following table. Contrary to the case of EVs where 20 MSs concurred to the calculation of the EU averages for 2020, for CNG vehicles the number of MSs is insufficient to be considered representative. The result is shown with this caveat. The two 2020 average changes show the decreasing tendency of CNG vehicles estimates in NIR vs NPF.

-(Change NIR vs NPF) Considering the 26 cases where a change could be computed (CNG estimates provided both in the NPF and NIR), a decrease of ambition is noticeable in 10 cases (3 in 2020), a similar ambition in 12 cases (6 in 2020) and an increase of ambition in 4 cases (1 in 2020). In other 20 cases, an estimate was provided only in the NIR and the changes could not be computed.

-(Coverage) In the NIRs, 23 MSs (out of 25) have provided at least some historical data (2016-2018) (92%), and 16 MSs have provided at least one estimate for the decade 2020-2030 (64%). In the NPF, only 12 MSs had provided at least one estimate for this decade.

Of all the alternative fuel and transport mode combinations, the pair CNG/road is the second best covered. Figure 4.1.1 ‑10 summarises the information for the CNG vehicle estimates and targeted publicly accessible refuelling points as provided in the NIRs for the next decade as well as the 2016 and 2018 situation.

Detailed Assessment of the Member States Implementation Reports on the National Policy Frameworks for the development of the market as regards alternative fuels in the transport sector and the deployment of the relevant infrastructure. Implementation of Art 10 (3) of Directive 2014/94/EU

EUROPEAN COMMISSION Brussels, 8.3.2021 SWD(2021) 49 final COMMISSION STAFF WORKING DOCUMENT Detailed Assessment of the Member States Implementation Reports on the National Policy Frameworks for the development of the market as regards alternative fuels in the transport sector and the deployment of the relevant infrastructure. Implementation of Art 10 (3) of Directive 2014/94/EU Accompanying the document REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT AND THE COUNCIL on the application of Directive 2014/94/EU on the deployment of alternative fuels infrastructure {COM(2021) 103 final}



Table of Contents 5 Full assessment of Member States' NIRs 5.0 Assessment of the Member States National Implementation Reports (NIR) 5.0.1 Structure of the assessment report 5.0.2 Main messages from the Commission assessment of the NPF 5.0.3 Overview of requirements’ fulfilment from Annex I of the Directive 5.0.4 Quantitative assessment: Vehicles and infrastructure 5.0.5 Measures assessment 5.0.6 Additional information on alternative fuels infrastructure developments 5.0.7 Summary of the assessment 5.0.8 Final remarks 5.0.9 ANNEX: Description of the Member State 5.1 Belgium (BE) 5.1.1 Main messages from the Commission assessment of the NPF 5.1.2 Overview of requirements’ fulfilment from Annex I of the Directive 5.1.3 Quantitative assessment: Vehicles and infrastructure 5.1.4 Measures assessment 5.1.5 Additional information on alternative fuels infrastructure developments 5.1.6 Summary of the assessment

5.1.7 Final remarks 5.1.8 ANNEX - Description of the Member State 5.2 Bulgaria (BG) 5.2.1 Main messages from the Commission assessment of the NPF 5.2.2 Overview of requirements’ fulfilment from Annex I of the Directive 5.2.3 Quantitative assessment: Vehicles and infrastructure 5.2.4 Measures assessment 5.2.5 Additional information on alternative fuels infrastructure developments 5.2.6 Summary of the assessment 5.2.7 Final remarks 5.2.8 ANNEX - Description of the Member State 5.3 Czechia (CZ) 5.3.1 Main messages from the Commission assessment of the NPF 5.3.2 Overview of requirements’ fulfilment from Annex I of the Directive 5.3.3 Quantitative assessment: Vehicles and infrastructure 5.3.4 Measures assessment 5.3.5 Additional information on alternative fuels infrastructure developments 5.3.6 Summary of the assessment 5.3.7 Final remarks 5.3.8 ANNEX - Description of the Member State

5.4 Denmark (DK) 5.4.1 Main messages from the Commission assessment of the NPF 5.4.2 Overview of requirements’ fulfilment from Annex I of the Directive 5.4.3 Quantitative assessment: Vehicles and infrastructure 5.4.4 Measures assessment 5.4.5 Additional information on alternative fuels infrastructure developments 5.4.6 Summary of the assessment 5.4.7 Final remarks 5.4.8 ANNEX - Description of the Member State 5.5 Germany (DE) 5.5.1 Main messages from the Commission assessment of the NPF 5.5.2 Overview of requirements’ fulfilment from Annex I of the Directive 5.5.3 Quantitative assessment: Vehicles and infrastructure 5.5.4 Measures assessment 181 5.5.5 Additional information on alternative fuels infrastructure developments 186 5.5.6 Summary of the assessment 187 5.5.7 Final remarks 190 5.5.8 ANNEX - Description of the Member State 192 5.6 Estonia (EE) 194 5.6.1 Main messages from the Commission assessment of the NPF 194

5.6.2 Overview of requirements’ fulfilment from Annex I of the Directive 195 5.6.3 Quantitative assessment: Vehicles and infrastructure 196 5.6.4 Measures assessment 202 5.6.5 Additional information on alternative fuels infrastructure developments 205 5.6.6 Summary of the assessment 206 5.6.7 Final remarks 208 5.6.8 ANNEX - Description of the Member State 210 5.7 Ireland (IE) 211 5.7.1 Main messages from the Commission assessment of the NPF 211 5.7.2 Overview of requirements’ fulfilment from Annex I of the Directive 213 5.7.3 Quantitative assessment: Vehicles and infrastructure 214 5.7.4 Measures assessment 221 5.7.5 Additional information on alternative fuels infrastructure developments 227 5.7.6 Summary of the assessment 228 5.7.7 Final remarks 231 5.7.8 ANNEX - Description of the Member State 232 5.8 Greece (EL) 233 5.8.1 Main messages from the Commission assessment of the NPF 233 5.8.2 Overview of requirements’ fulfilment from Annex I of the Directive 235 5.8.3 Quantitative assessment: Vehicles and infrastructure 237

5.8.4 Measures assessment 243 5.8.5 Additional information on alternative fuels infrastructure developments 246 5.8.6 Summary of the assessment 246 5.8.7 Final remarks 249 5.8.8 ANNEX - Description of the Member State 251 5.9 Spain (ES) 252 5.9.1 Main messages from the Commission assessment of the NPF 252 5.9.2 Overview of requirements’ fulfilment from Annex I of the Directive 253 5.9.3 Quantitative assessment: Vehicles and infrastructure 255 5.9.4 Measures assessment 264 5.9.5 Additional information on alternative fuels infrastructure developments 270 5.9.6 Summary of the assessment 271 5.9.7 Final remarks 274 5.9.8 ANNEX - Description of the Member State 276 5.10 France (FR) 277 5.10.1 Main messages from the Commission assessment of the NPF 277 5.10.2 Overview of requirements’ fulfilment from Annex I of the Directive 279 5.10.3 Quantitative assessment: Vehicles and infrastructure 280 5.10.4 Measures assessment 289 5.10.5 Additional information on alternative fuels infrastructure developments 296

5.10.6 Summary of the assessment 297 5.10.7 Final remarks 300 5.10.8 ANNEX - Description of the Member State 302 5.11 Croatia (HR) 303 5.11.1 Main messages from the Commission assessment of the NPF 303 5.11.2 Overview of requirements’ fulfilment from Annex I of the Directive 304 5.11.3 Quantitative assessment: Vehicles and infrastructure 306 5.11.4 Measures assessment 312 5.11.5 Additional information on alternative fuels infrastructure developments 316 5.11.6 Summary of the assessment 316 5.11.7 Final remarks 319 5.11.8 ANNEX - Description of the Member State 321 5.12 Italy (IT) 322 5.13 Cyprus (CY) 323 5.13.1 Main messages from the Commission assessment of the NPF 323 5.13.2 Overview of requirements’ fulfilment from Annex I of the Directive 325 5.13.3 Quantitative assessment: Vehicles and infrastructure 326 5.13.4 Measures assessment 331 5.13.5 Additional information on alternative fuels infrastructure developments 334 5.13.6 Summary of the assessment 335

5.13.7 Final remarks 337 5.13.8 ANNEX - Description of the Member State 339 5.14 Latvia (LV) 340 5.14.1 Main messages from the Commission assessment of the NPF 340 5.14.2 Overview of requirements’ fulfilment from Annex I of the Directive 341 5.14.3 Quantitative assessment: Vehicles and infrastructure 342 5.14.4 Measures assessment 348 5.14.5 Additional information on alternative fuels infrastructure developments 351 5.14.6 Summary of the assessment 352 5.14.7 Final remarks 354 5.14.8 ANNEX - Description of the Member State 356 5.15 Lithuania (LT) 357 5.15.1 Main messages from the Commission assessment of the NPF 357 5.15.2 Overview of requirements’ fulfilment from Annex I of the Directive 358 5.15.3 Quantitative assessment: Vehicles and infrastructure 359 5.15.4 Measures assessment 368 5.15.5 Additional information on alternative fuels infrastructure developments 374 5.15.6 Summary of the assessment 374 5.15.7 Final remarks 378 5.15.8 ANNEX - Description of the Member State 380

5.16 Luxembourg (LU) 381 5.16.1 Main messages from the Commission assessment of the NPF 381 5.16.2 Overview of requirements’ fulfilment from Annex I of the Directive 383 5.16.3 Quantitative assessment: Vehicles and infrastructure 385 5.16.4 Measures assessment 391 5.16.5 Additional information on alternative fuels infrastructure developments 394 5.16.6 Summary of the assessment 395 5.16.7 Final remarks 397 5.16.8 ANNEX - Description of the Member State 399 5.17 Hungary (HU) 400 5.17.1 Main messages from the Commission assessment of the NPF 400 5.17.2 Overview of requirements’ fulfilment from Annex I of the Directive 401 5.17.3 Quantitative assessment: Vehicles and infrastructure 402 5.17.4 Measures assessment 410 5.17.5 Additional information on alternative fuels infrastructure developments 413 5.17.6 Summary of the assessment 415 5.17.7 Final remarks 418 5.17.8 ANNEX - Description of the Member State 420 5.18 Malta (MT) 421 5.18.1 Main messages from the Commission assessment of the NPF 421

5.18.2 Overview of requirements’ fulfilment from Annex I of the Directive 423 5.18.3 Quantitative assessment: Vehicles and infrastructure 424 5.18.4 Measures assessment 430 5.18.5 Additional information on alternative fuels infrastructure developments 435 5.18.6 Summary of the assessment 436 5.18.7 Final remarks 439 5.18.8 ANNEX - Description of the Member State 440



5Full assessment of Member States' NIRs 5.0Assessment of the Member States National Implementation Reports (NIRs) The following sections present the assessments of each individual NIR. The official date for the submission of the NIRs to the Commission was 18 November 2019. By that date only nine of the expected 28 NIRs had been delivered (by Belgium, Czechia, Germany, Ireland, Greece, Spain, Austria, Poland and Slovakia). Thus, an extension was granted to the remaining MSs to submit their NIRs. By the 1st of May 2020, which was the ultimate date for submitting the missing NIRs, as agreed between the Commission and the Member States, 3 NIRs were still outstanding. Later, the United Kingdom delivered its NIR in the beginning of September, followed by Portugal. Italy, despite announcing the submission of its NIR by 30 September, had not yet delivered it by the time this document was finalised. Similarly to the NPF reports, the 25 NIRs received and assessed vary in terms of length (from a minimum of 3 pages to a maximum of 205 pages) and level of detail provided for each of the subsections. However, there is an overall improvement in the consistency of the reports among MSs compared to the NPFs. Several MSs followed more closely the structure of the requirements listed in Annex I of the Directive and the guidance documents provided by the Commission for the reporting. The remaining countries structured their reports differently, with some substantial deviations from both Annex 1 and the guidance documents. Although the completion and delivery of the Excel® reporting template provided by the Commission was not a mandatory requirement of the Directive, 19 MSs have submitted the template as an annex separate to the report. From the remaining 6 MSs that have not submitted a separate Excel® template, 4 MSs included some tables into the main body of the report. In conclusion, although there has been an overall improvement in the quality and consistency of the NIRs when compared to the NPFs, there are still several important gaps and flaws in the information reported by the MSs. When there was the suspicion that a NIR presented mistakes, the MS was asked to provide clarification. Unfortunately this was not always feasible and the assessment of some NIRs would not have been possible without filling some gaps, or retrieving additional information. In such circumstances, the assessor would collect the necessary data (for example from the NPF, when such value was not reported in the NIR, or using a value from EAFO, when both the NIR and NPF were lacking information), and highlight it in the assessment report. However, this has shifted the burden of the reporting (and the assumptions that underpin such process) onto the Commission. 5.0.1Structure of the assessment report The assessment of the individual NIR is based on the assessment methodology described in Chapter 2 and follows a fixed structure, which consists of: 1.Main messages from the Commission assessment of the NPF

2.Overview of the fulfilment of the requirements in Annex I of the Directive 3.Quantitative assessment: Vehicles and infrastructure 4.Measures assessment 5.Additional information on alternative fuels infrastructure developments 6.Summary of the assessment 7.Final remarks 8.ANNEX: Description of the Member State Before explaining each point in more detail, it is important to stress that the assessment of the MS NIRs is carried out principally with reference to the Directive and to the respective NPF. This is particularly important when looking at the comparison of the ambition level between NIR and NPF concerning the AFV estimates and AFI targets, and concerning the measures. These assessments are “internal”, in the sense that they refer to what has been presented in the NIR compared to what had been presented in the NPF by the same MS. It would be wrong to make a comparison among Member States based on such considerations. Furthermore, point seven (Final remarks) contains the concluding remarks from the Commission, based on the assessment of the national implementation reports of each Member State. In this section, the Commission might provide some additional comments on the latest development of the European policies on alternative fuels transport systems and infrastructure (for example the Green Deal). 5.0.2Main messages from the Commission assessment of the NPF The NIR assessment reports start with an excerpt from the Commission assessment of the NPFs (European Commission, 2019). This excerpt is the summary of the NPF assessment for each MS and provides the background for a better understanding of how the NIRs relate to the initial plans of the MS, as reported in the NPF. 5.0.3Overview of requirements’ fulfilment from Annex I of the Directive The Directive requested the MSs to prepare and submit their NIRs in accordance with the requirements listed in Annex I. In this second section of the assessment report, a table (the Check List) that looks like Table 5.0.3‑1 shows how each MS complied with this request.



The first two columns report the content of Annex I to the Directive. The third column shows for which transport mode and alternative fuels the MS provided the required information in its NIR. When an alternative fuel or a transport mode is explicitly mentioned in the NIR, then it is mentioned also in column three; when all alternative fuels and/or transport modes are mentioned, this is indicated by the word “All”. If instead in the NIR there was just a generic mention of the AF/transport mode, this is indicated by using the word “Combination” or “AF (in general)”. Because there was no agreed methodology to assign a score to the level of quality and detail of the information provided, no score is provided here, but only a flag. When at least some information is reported in the third column, in the fourth column there is a “Y or Yes” (on a green background), otherwise there is an “N or No” (on an orange background). 5.0.4Quantitative assessment: Vehicles and infrastructure

This section starts with a table that looks like the following: Table 5.0.4‑1 National AFV estimates and AFI targets established in one NIR at the horizon 2020, 2025 and 2030 and their comparison with the NPF situation For each AF/transport mode pair, the table shows three rows. The first row displays the state of play reported by the MS in the NIR in 2018 for AF vehicles/vessels/locomotives/airplanes (AFV) and for their respective infrastructure (AFI). If the NIR did not report any data but the NPF did, then the latter is shown. Then, the AFV estimates and AFI targets for the years 2020, 2025 and 2030 are also reported as given in the NIR. The second and third rows present the results of the calculations regarding changes between NIR and NPF, and attainment. These calculations follow the procedure explained in subsection 2.1.2 and subsection 2.1.4 of the assessment methodology. As a general rule, the table shows the following AF/transport mode pairs for which the AFI Directive included specific infrastructure deployment requirements in its articles: ·Electricity/road (Article 4(1)) ·CNG/road (Article 6(7)) ·LNG/road (Article 6(4)) ·LNG/waterborne maritime (if applicable to the MS) (Article 6(1)) ·LNG/waterborne inland (if applicable to the MS) (Article 6(2)) If the MS has provided additional information in its NIR regarding other AF/transport mode pairs, these are also shown in the table. For example, the Table 5.0.4 ‑1 also includes data on hydrogen/road because they were in the NIR of that MS. The assessment report continues with the presentation and analysis of the most important points from Table 5.0.4 ‑1 . The order of such presentation is the following: 1.Road 1.1.Electricity ·Vehicles ·Infrastructure ·Ratio ·Information on charging efficiency 1.2.CNG ·Vehicles

·Infrastructure ·Ratio 1.3.LNG ·Vehicles ·Infrastructure ·Ratio 1.4.Hydrogen ·Vehicles ·Infrastructure ·Ratio 1.5.Biofuels ·Vehicles ·Infrastructure ·Ratio 1.6.LPG ·Vehicles ·Infrastructure ·Ratio 1.7.Other AF (if applicable) ·Vehicles

·Infrastructure ·Ratio 2.Rail 2.1.Electricity (if applicable) ·Vehicles ·Infrastructure 2.2.LNG (if applicable) ·Vehicles ·Infrastructure 2.3.Hydrogen (if applicable) ·Vehicles ·Infrastructure 2.4.Biofuels (if applicable) ·Vehicles ·Infrastructure 3.Waterborne maritime (if applicable to the MS) 3.1.Electricity (if applicable) ·Vessels ·Infrastructure (shore-side electricity supply) 3.2.LNG (if applicable)

·Vessels ·Infrastructure 3.3.Hydrogen (if applicable) ·Vessels ·Infrastructure 3.4.Biofuels (if applicable) ·Vessels ·Infrastructure 4.Waterborne inland (if applicable to the MS) Same scheme as for waterborne maritime 5.Air 5.1.Electricity (if applicable) ·Airplanes ·Infrastructure (electricity supply for stationary airplanes) 5.2.Biofuels (if applicable) ·Airplanes ·Infrastructure (renewable jet fuel refuelling points in airports within the TEN-T Core Network) It shall be noted that only for the pairs of alternative fuels and road, in addition to the analysis of vehicles and infrastructure, there is a point called “Ratio”, which is also indicated as “Sufficiency index”, representing the proportional relation between alternative fuels vehicles and infrastructure (see subsection 2.1.5 of the assessment methodology and (European Commission, 2019)). In particular, for the electricity/road pair, an assessment is provided concerning the adequacy of the ratio between the number of vehicles and the number of recharging points, either for the period 2016 - 2018, or estimated for 2020, 2025 and 2030. The adequacy assessment is based mainly on the indicative threshold value of 10, as mentioned by the Directive. However, the share of high power (>22kW) recharging points in the total recharging points is also taken in consideration. For the CNG/road pair, the adequacy assessment is based on the indicative value of 600 as considered in (European Commission, 2019). For all the other AFs/road pairs, only the sufficiency index is shown, without any comment. The electricity/road pair has also an additional point on “Information on charging efficiency”, which is a specific requirement of Annex I to the Directive. When available in the NIR, this information is summarised in the assessment report.

5.0.5Measures assessment This part of the assessment report is carried out as described in the assessment methodology (Section 2.2). It contains five headings. Headings 1, 2, 3 and 5 are dedicated to the description and analysis of the main points related to the four different classes of measures required by Annex I of the Directive; heading 4 shows the result of a more detailed assessment of the Policy and Deployment & Manufacturing measures, as described in Section 2.2. 1.Legal measures ·Legislative & Regulatory ·Administrative 2.Policy measures ·Measures to ensure national targets