Source: EURLEX
Language: en
Format: md

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| 1.7.2019 | EN | Official Journal of the European Union | L 176/67 |

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COMMISSION IMPLEMENTING DECISION (EU) 2019/1119

of 28 June 2019

on the approval of efficient vehicle exterior lighting using light emitting diodes for use in internal combustion engine vehicles and non-externally chargeable hybrid electrified vehicles as an innovative technology for reducing CO2 emissions from passenger cars pursuant to Regulation (EC) No 443/2009 of the European Parliament and of the Council

(Text with EEA relevance)

THE EUROPEAN COMMISSION,

Having regard to the Treaty on the Functioning of the European Union,

Having regard to Regulation (EC) No 443/2009 of the European Parliament and of the Council of 23 April 2009 setting emission performance standards for new passenger cars as part of the Community's integrated approach to reduce CO2 emissions from light duty vehicles [(1)](#ntr1-L_2019176EN.01006701-E0001), and in particular Article 12(4) thereof,

Whereas:

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| (1) | On 6 September 2018, the manufacturers Toyota Motor Europe NV/SA, Opel Automobile GmbH – PSA, FCA Italy S.p.A., Automobiles Citroën, Automobiles Peugeot, PSA Automobiles SA, Audi AG, Ford Werke GmbH, Jaguar Land Rover, Hyundai Motor Europe Technical Center GmbH, Škoda Auto a.s., BMW AG, Renault SA, Honda Motor Europe Ltd, Volkswagen AG and Volkswagen AG Nutzfahrzeuge (the ‘applicants’), submitted a joint application for the approval of efficient vehicle exterior lighting using light emitting diodes (efficient LED lighting) for use in internal combustion engine vehicles and non-externally chargeable hybrid electrified vehicles as an innovative technology. The application has been assessed in accordance with Article 12 of Regulation (EC) No 443/2009 and Commission Implementing Regulation (EU) No 725/2011 [(2)](#ntr2-L_2019176EN.01006701-E0002). |

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| (2) | Efficient LED lighting is a lighting module equipped with light emitting diodes sources that has lower power consumption than conventional halogen lighting. |

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| (3) | The application has been assessed in accordance with Article 12 of Regulation (EC) No 443/2009, Implementing Regulation (EU) No 725/2011 and the Technical Guidelines for the preparation of applications for the approval of innovative technologies pursuant to Regulation (EC) No 443/2009 (Technical Guidelines, version July 2018). |

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| (4) | The application concerns CO2 savings from the use of efficient LED lighting as assessed by reference to the Worldwide Harmonised Light Vehicle Test Procedure (WLTP) set out in Commission Regulation (EU) 2017/1151 [(3)](#ntr3-L_2019176EN.01006701-E0003). |

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| (5) | Efficient LED lighting has already been approved by Commission Implementing Decisions 2014/128/EU [(4)](#ntr4-L_2019176EN.01006701-E0004), (EU) 2015/206 [(5)](#ntr5-L_2019176EN.01006701-E0005), (EU) 2016/160 [(6)](#ntr6-L_2019176EN.01006701-E0006), (EU) 2016/587 [(7)](#ntr7-L_2019176EN.01006701-E0007) and (EU) 2016/1721 [(8)](#ntr8-L_2019176EN.01006701-E0008) as an innovative technology capable of reducing CO2 emissions by reference to the New European Driving Cycle (NEDC) set out in Commission Regulation (EC) No 692/2008 [(9)](#ntr9-L_2019176EN.01006701-E0009). Based on the experience gained from those Decisions, as well as taking into account the current application, it has been satisfactorily and conclusively demonstrated that efficient LED lighting including one or more appropriate combinations of efficient LED lights, such as the low beam headlamp, high beam headlamp, front position, front fog, rear fog, front turn signal, rear turn signal, licence plate and reversing lamps, meet the eligibility criteria referred to in Article 12 of Regulation (EC) No 443/2009 and Implementing Regulation (EU) No 725/2011. |

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| (6) | The CO2 savings from the use of efficient LED lighting may be partially demonstrated on the WLTP test. However, the applicants have provided a testing methodology with which it can be demonstrated, in a way capable of producing repeatable, verifiable and comparable results, that the savings achieved, whilst taking the partial coverage into account, are at least 0,5 g CO2/km. |

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| (7) | In order to ensure continuity, in particular with regard to the transition from the application of the NEDC to the WLTP CO2 emissions test, it is appropriate to maintain halogen lighting as the baseline technology as provided for in Implementing Decisions 2014/128/EU, (EU) 2015/206, (EU) 2016/160, (EU) 2016/587, and (EU) 2016/1721. |

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| (8) | Manufacturers should have the possibility to apply with a type-approval authority for the certification of CO2 savings from the use of efficient LED lightings in internal combustion engine vehicles and non-externally chargeable hybrid electrified vehicles. The manufacturer should for that purpose ensure that the application for certification is accompanied by a verification report from an independent verification body confirming the level of CO2 savings to be certified and that all relevant conditions are met. |

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| (9) | If the type approval authority finds that the LED lighting does not satisfy the conditions for certification, the application for certification of the savings should be rejected. |

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| (10) | In order to facilitate a wider deployment of efficient LED lighting in new vehicles, a manufacturer should also have the possibility to apply for the certification of the CO2 savings from several efficient LED lightings by a single certification application. It is however appropriate to ensure that, where that possibility is used, a mechanism is applied that incentivises the deployment of only those LED lighting that offer the highest efficiency. |

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| (11) | The CO2 savings certified pursuant to this Decision are to be taken into account for the calculation of the average specific CO2 emissions of manufacturers starting from calendar year 2021. |

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| (12) | For the purposes of determining the general eco-innovation code to be used in the relevant type approval documents in accordance with Annexes I, VIII and IX to Directive 2007/46/EC of the European Parliament and of the Council [(10)](#ntr10-L_2019176EN.01006701-E0010), the individual code to be used for the innovative technology for efficient LED Lightings for internal combustion engine vehicles and non-externally chargeable hybrid electrified vehicles should be specified, |

HAS ADOPTED THIS DECISION:

Article 1

Approval

The technology used in efficient light emitting diodes (LED) lighting is approved as an innovative technology within the meaning of Article 12 of Regulation (EC) No 443/2009, where that innovative technology is used for the purpose of external lighting in internal combustion engine passenger cars and non-externally chargeable hybrid electrified passenger cars.

Article 2

Definition

For the purpose of this Decision, efficient LED lighting means a technology consisting of a lighting module that is equipped with light emitting diode (LED) sources that are used for the exterior lighting of a vehicle and that has a lower power consumption than conventional halogen lighting.

Article 3

Application for certification of CO2 savings

1.   Any manufacturer may apply for the certification of CO2 savings from one or several exterior efficient LED lightings where those are used for the external lighting of internal combustion engine M1 vehicles and non-externally chargeable hybrid electrified M1 vehicles. The efficient LED lighting shall include one or a combination of the following LED lights:

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| (a) | low beam headlamp (including adaptative front lighting system); |

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| (b) | high beam headlamp; |

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| (c) | front position lamp; |

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| (d) | front fog lamp; |

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| (e) | rear fog lamp; |

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| (f) | front turn signal lamp; |

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| (g) | rear turn signal lamp; |

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| (h) | licence plate lamp; |

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| (i) | reversing lamp; |

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| (j) | cornering lamp; |

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| (k) | static bending lamp. |

The LED light or the combination of LED lights forming the efficient LED lighting shall as a minimum provide the CO2 reduction specified in Article 9(1)(b) of Implementing Regulation (EU) No 725/2011 as demonstrated using the testing methodology set out in the Annex to this Decision.

2.   An application for the certification of the savings from one or a combination of efficient LED lighting shall be accompanied by an independent verification report confirming that the conditions set out in paragraph 1 are met.

3.   The type approval authority shall reject the application for certification if it finds that the conditions set out in paragraph 1 are not met.

Article 4

Certification of CO2 savings

1.   The reduction in CO2 emissions from the use of an efficient LED lighting referred to in Article 3(1) shall be determined using the methodology set out in the Annex.

2.   Where a manufacturer applies for the certification of the CO2 savings from more than one efficient LED lighting referred to in Article 3(1) in relation to one vehicle version, the type approval authority shall determine which of the efficient LED lighting tested delivers the lowest CO2 savings, and record the lowest value in the relevant type approval documentation. That value shall be indicated in the certificate of conformity in accordance with Article 11(2) of Implementing Regulation (EU) No 725/2011.

3.   The type approval authority shall record the verification report and the test results on the basis of which the savings were determined and shall make that information available to the Commission on request.

Article 5

Eco-innovation code

The eco-innovation code No 28 shall be entered into the type approval documentation where reference is made to this Decision in accordance with Article 11(1) of Implementing Regulation (EU) No 725/2011.

CO2 savings recorded by reference to that eco-innovation code may be taken into account for the calculation of the average specific emissions of a manufacturer starting from calendar year 2021.

Article 6

Entry into force

This Decision shall enter into force on the twentieth day following that of its publication in the Official Journal of the European Union.

Done at Brussels, 28 June 2019.

For the Commission

The President

Jean-Claude JUNCKER

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ANNEX

Methodology to determine the CO2 savings of efficient LED lighting by reference to the Worldwide Harmonised Light Vehicle Test Procedure

1.   INTRODUCTION

In order to determine the CO2 emission reductions that can be attributed to efficient LED lighting consisting of an appropriate combination of external vehicle LED lights for the use in internal combustion engine M1 vehicles and non-externally chargeable hybrid electrified M1 vehicles, it is necessary to establish the following:

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| (1) | the test conditions; |

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| (2) | the test equipment; |

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| (3) | the procedure to determine the power savings; |

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| (4) | the procedure to determine the CO2 savings; |

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| (5) | the procedure to determine the uncertainty of the CO2 savings. |

2.   SYMBOLS, PARAMETERS AND UNITS

Latin symbols

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| AFS | — | Adaptive Front lighting System |
| B | — | Baseline |
| CO2 | — | Carbon dioxide |
| Formula | — | CO2 savings [g CO2/km] |
| C | — | Number of classes of the adaptive front lighting system |
| CF | — | Conversion factor (l/100 km) - (g CO2/km) [gCO2/l] |
| EI | — | Eco-innovative |
| HEV | — | Hybrid Electrified Vehicle |
| Formula | — | CO2 correction factor,  Formula  as defined in Regulation (EU) 2017/1151 Sub-Annex 8 Appendix 2 |
| Formula | — | Average of the T values of  Formula  Formula |
| m | — | Number of efficient exterior LED lights composing the package |
| MT | — | Minimum threshold [g CO2/km] |
| n | — | Number of measurements of the sample |
| NOVC | — | Not Off-Vehicle Charging |
| P | — | Power consumption of the vehicle light [W] |
| Formula | — | Power consumption of the corresponding i light in a baseline vehicle [W] |
| Formula | — | Power consumption of the corresponding n sample for each class vehicle [W] |
| Formula | — | Power consumption for each class of vehicle (average of the n measurements) [W] |
| Formula | — | Power consumption of the Low beam AFS [W] |
| Formula | — | Average power consumption of the corresponding eco-innovative vehicle light [W] |
| ΔPi | — | Power savings of each efficient exterior LED light [W] |
| Formula | — | Standard deviation of the total CO2 savings [g CO2/km] |
| Formula | — | Standard deviation of the  Formula  Formula |
| Formula | — | Standard deviation of average of the T values of  Formula  Formula |
| Formula | — | Standard deviation of average of power consumption for each class of vehicle [W] |
| Formula | — | Standard deviation of the LED light power consumption in eco-innovative vehicle [W] |
| Formula | — | Standard deviation of the average LED light power consumption mean in eco-innovative vehicle [W] |
| Formula | — | Uncertainty or Standard deviation of average of power of the Low beam AFS [W] |
| T | — | Number of measurements performed by the manufacturer for the extrapolation of the  Formula |
| t | — | Driving duration of the Worldwide Light vehicles Test Cycle (WLTC) [s], which is 1 800 s |
| UF | — | Usage factor for the vehicle light [-] as defined in Table 6 |
| v | — | Mean driving speed of the Worldwide Light vehicles Test Cycle (WLTC) [km/h] |
| VPe | — | Consumption of effective power [l/kWh] |
| share c | — | Time percentage per speed band in each vehicle class |
| Image 1 | — | Sensitivity of calculated CO2 savings related to the LED light power consumption |
| Image 2 | — | Sensitivity of calculated CO2 savings related to the CO2 correction factor |
| ηA | — | Efficiency of the alternator [-] |
| ηDCDC | — | Efficiency of the DC-DC converter [-] |

Subscripts

Index (c) refers to number of class of the adaptive front lighting system measurement of the sample

Index (i) refers to each vehicle lights

Index (j) refers to measurement of the sample

Index (t) refers to each number of measurements of T

3.   TESTING CONDITIONS

The testing conditions shall fulfil the requirements of UN/ECE Regulations Nos 4 [(1)](#ntr1-L_2019176EN.01007101-E0001), 6 [(2)](#ntr2-L_2019176EN.01007101-E0002), 7 [(3)](#ntr3-L_2019176EN.01007101-E0003), 19 [(4)](#ntr4-L_2019176EN.01007101-E0004), 23 [(5)](#ntr5-L_2019176EN.01007101-E0005), 38 [(6)](#ntr6-L_2019176EN.01007101-E0006), 48 [(7)](#ntr7-L_2019176EN.01007101-E0007), 100 [(8)](#ntr8-L_2019176EN.01007101-E0008), 112 [(9)](#ntr9-L_2019176EN.01007101-E0009), 119 [(10)](#ntr10-L_2019176EN.01007101-E0010) and 123 [(11)](#ntr11-L_2019176EN.01007101-E0011). The power consumption shall be determined in accordance with point 6.1.4 of UN/ECE Regulation No 112, and points 3.2.1 and 3.2.2 of Annex 10 to that Regulation.

For the low beam adaptive front lighting system (AFS) falling within at least two of the Classes C, E, V or W as defined in Regulation UN/ECE No 123, unless it is agreed with the technical service that Class C is the representative/average LED intensity for the vehicle application, power measurements shall be done at the LED intensity of each class (Pc) as defined in Regulation UN/ECE 123. If Class C is the representative/average LED intensity for the vehicle application, power measurements shall be done in the same way as for any other exterior LED light included in the combination.

Test equipment

The following equipment shall be used, as shown in the Figure below:

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| — | a power supply unit (i.e. variable voltage supplier); |

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| — | two digital multimeters, one for measuring the DC-current, and the other for measuring the DC-voltage. In the Figure, a possible test set-up is shown, when the DC-voltage meter is integrated in the power supply unit. |

Test set-up

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)
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Current monitor

Variable voltage supplier

LED light

Measurements and determination of the power savings

For each efficient exterior LED light included in the combination the measurement of the current shall be performed as shown in the Figure at a voltage of 13,2 V. LED module(s) operated by an electronic light source control gear, shall be measured as specified by the applicant.

The manufacturer may request that other measurements of the current shall be done at other additional voltages. In that case, the manufacturer shall hand over verified documentation on the necessity to perform those other measurements to the type-approval authority. The measurements of the currents at each of those additional voltages shall be performed consecutively at least five times. The exact installed voltages and the measured current shall be recorded in four decimals.

The power consumption shall be determined by multiplying the installed voltage with the measured current. The average of the power consumption for each efficient exterior LED light (
![Formula](data:image/jpg;base64,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)
) shall be calculated. Each value shall be expressed in four decimals. When a stepper motor or electronic controller is used for the supply of the electricity to the LED lights, the electric load of that component part shall be excluded from the measurement.

Additional measurements for Low beam Adaptive Front Lighting System (AFS)

Table 1

Classes of Low beam AFS

|  |  |  |  |
| --- | --- | --- | --- |
| Class | See point 1.3 and footnote 2 of UN/ECE Regulation 123 | % LED Intensity | Activation Mode[(\*1)](#ntr*1-L_2019176EN.01007101-E0012) |
| C | Base Passing Beam (Country) | 100 % | 50 km/h < speed < 100 km/h  Or when no mode of another passing beam class is activated (V, W, E) |
| V | Town | 85 % | Speed < 50 km/h |
| E | Motorway | 110 % | Speed > 100 km/h |
| W | Adverse Conditions | 90 % | Windshield wiper active > 2 min |

Where the power measurements at the LED intensity of each class are needed, after conducting the measurements of each Pc, the power of the Low beam AFS (
![Formula](data:image/jpg;base64,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)
) shall be calculated as a weighted average of the LED Power during the WLTC speed bands, with the following Formula 1.

Formula 1

![Formula](data:image/jpg;base64,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)

Where:

|  |  |
| --- | --- |
|  | Formula  is the power consumption (mean of the n measurements) for each class; |

|  |  |
| --- | --- |
|  | Formula  is the WLTC time percentage per speed band in each class (WLTC last 1 800 s in total): |

Table 2

|  |  |  |
| --- | --- | --- |
| Speed band | Time | WLTC\_sharec (%) |
| < 50 km/h: | 1 058 s | 0,588 (58,8 %) |
| 50 – 100 km/h | 560 s | 0,311 (31,1 %) |
| > 100 km/h | 182 s | 0,101 (10,1 %) |

When the Low beam AFS only has 2 classes not covering all WLTC speeds (e.g. C & V), the weighting of Class C power shall also include the WLTC time not covered by the 2nd class (e.g. Class C time ‘t’ = 0,588 + 0,101)

The resulting power savings of each efficient exterior LED light (ΔPi) shall be calculated with the following Formula 2:

Formula 2

![Formula](data:image/jpg;base64,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)

where the power consumption of the corresponding baseline vehicle light is as specified in Table 3:

Table 3

Power consumptions for different baseline vehicle lights

|  |  |
| --- | --- |
| Vehicle light | Total electric power (PB)  [W] |
| Low beam headlamp | 137 |
| High beam headlamp | 150 |
| Front position | 12 |
| License plate | 12 |
| Front fog lamp | 124 |
| Rear fog lamp | 26 |
| Front turn signal lamp | 13 |
| Rear turn signal lamp | 13 |
| Reversing lamp | 52 |
| Cornering lamp | 44 |
| Static Bending lamp | 44 |

4.   CALCULATION OF THE CO2 SAVINGS AND STATISTICAL MARGIN

4.1.   Calculation of the CO2 savings

The total CO2 savings of the lighting package shall be calculated in accordance with the specific powertrain of the vehicle (i.e. Conventional, NOVC-HEV).

4.1.1.   Conventional Vehicles (Internal Combustion Engine only)

The CO2 savings shall be calculated in accordance with the following Formula 3:

Formula 3

![Formula](data:image/jpg;base64,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)

Where:

v: Mean driving speed of the WLTC [km/h], which is 46,60 km/h

ηA
: Efficiency of the alternator, which is 0,67

VPe
: Consumption of effective power as specified in Table 4

Table 4

Consumption of effective power

|  |  |
| --- | --- |
| Type of engine | Consumption of effective power (VPe)  [l/kWh] |
| Petrol | 0,264 |
| Petrol Turbo | 0,280 |
| Diesel | 0,220 |

CF: Conversion factor (l/100 km) - (g CO2/km) [gCO2/l] as specified in Table 5:

Table 5

Fuel conversion factor

|  |  |
| --- | --- |
| Type of fuel | Conversion factor (l/100 km) - (g CO2/km) (CF)  [gCO2/l] |
| Petrol | 2 330 |
| Diesel | 2 640 |

UFi
: Usage factor for the vehicle light [-] as defined in Table 6.

Table 6

Usage factor for different vehicle lights

|  |  |
| --- | --- |
| Vehicle light | Usage factor (UF)  [-] |
| Low beam headlamp | 0,33 |
| High beam headlamp | 0,03 |
| Front position | 0,36 |
| License plate | 0,36 |
| Front fog lamp | 0,01 |
| Rear fog lamp | 0,01 |
| Front turn signal lamp | 0,15 |
| Rear turn signal lamp | 0,15 |
| Reversing lamp | 0,01 |
| Cornering lamp | 0,076 |
| Static Bending lamp | 0,15 |

4.1.2.   Hybrid Vehicles (NOVC-HEV only)

The CO2 savings shall be calculated in accordance with the following Formula 4:

Formula 4

![Formula](data:image/jpg;base64,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)

Where:

ηDCDC
: Efficiency of the DC-DC converter

![Formula](data:image/jpg;base64,/9j/4AAQSkZJRgABAQABLAEsAAD/2wBDAAMCAgICAgMCAgIDAwMDBAYEBAQEBAgGBgUGCQgKCgkICQkKDA8MCgsOCwkJDRENDg8QEBEQCgwSExIQEw8QEBD/wAALCAAgAD4BAREA/8QAGgAAAgMBAQAAAAAAAAAAAAAAAAcFBgkBCP/EACsQAAEEAgEEAgIBBAMAAAAAAAECAwQFBhEHAAgSIRMiFDFBCRUWMjh2tP/aAAgBAQAAPwDRbmnlpjhTB5mf2GFZJkdbWockWCaNll12JHQhS1vrS663tCQn34+R9/rWyILt47iabuRxD/PcTwPLKageG4E66jx2UWGlrQv4Q28tRCVIIJUEj36J96i+Bu6ij7g7m1rsW4uz6qr6h2TGeureuZZrlyWHQ24w28h5fyLCt/6gj6nZHTu6Ojo6Old3Tf8AGnlX/ptx/wCNzrOHiTuGzvB+z3h6ntMUybH+FRPk0Wb5dXqDU10SJEgpRF0CtuOCtCVvp0pSvJCCCNnVTCKnEaPEKepwGJAjY5HhNJq2oAH44jFIKCjXogg73/O9nZPS5su6jjFjJ7XFMdhZVl0rH3zGu3saoJNjGrHQElSHnm0+HmArZQgqUNEEA+upnGe4nh/K8DveT67MorGK45OkV0+1mAx2UPMhHmB56J9rSkDWyr66366gE92fFcZ+A5kcLLcZp7eSzDrb2+x2VArZbzp00gPOIHx+f8F0IHr9/rpzAggEHYP6PXelZ3L0vIWW8O5LgvHGKQruxymrm0yzLtRBbhofjuID+y2v5PFSk/QaJ3++k7298Cch472fy+2TmriylsWa+mlwmUx8gS43brfefdCQr4QYqkFaPFz7aV9hrXUJ2wced5HDfCl9wpneO082ugVVk3id1CyH5Z8EFhwxoq2y2AvThQEKCkhIOtEAdXT+m9NpHu0XDquB8LdrUKmQr+P8fxyGLNMlz5kyEkBXy+0klXsgg++kl3XMYeOL67KcEqbBjCKnn6Pa5s8tSXYT6EOJTLkhLe0ri/MEg7B04le/Y69J96t5hTPaNyTYZK7DmVdhjb7ML7JcTJkvI1E+L3pai6ptSfE79bG+qDgHJXPvG/B3GGJt8cx59vA44RfZBcZLYPRIkT8ZpAMVx1tpwCQpJBAWQAEK3rR6fXBvKcbm3iPFeV4lHKp2snr0TkwZR24xskEeWh5J2CUqAAUkgj99Xro6Olbk3bHwjleTzcyssL/HuLQg2cissJVf/cvQGpSYziEyBpIGnAr1sfyerrXYJhdRh6OPq3FquPjTcRUFNSiKgRfx1AhTZb14lJBOwR72d/vpeUPaR2+47aVlrBwBL6qN0P1MafYy5sStcCgUqixn3VssFJA8fBCfEAAaHXOfOC8n5sdposPlV/H6KuK1zqNVOzOhWzhIKPykLUn5EI1sNnaSdEg6HTBwHF5mGYdV4xYZDJvJUBkoesJDaW1vrKiokIT9UJBOkoHpKQlI9Dr/2Q==)
: CO2 correction factor 
![Formula](data:image/jpg;base64,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)
, as defined in paragraph 2.2 of Appendix 2 to Sub-Annex 8 to Annex XXI to Regulation (EU) 2017/1151.

The efficiency of the DC-DC converter (ηDCDC
) shall be evaluated in accordance with the appropriate vehicle architecture, as specified in Table 7:

Table 7

Usage factor for different vehicle lights

|  |  |  |
| --- | --- | --- |
| # | Architecture | ηDCDC |
| 1 | Lights connected in parallel to the low voltage battery (lights fed directly from the high voltage battery via DCDC converter) | 0,xx |
| 2 | Lights connected in series after the low voltage battery, and the low voltage battery connected in series to the High voltage battery | 1 |
| 3 | High Voltage and low voltage batteries have exactly the same voltage (12 V, 48 V,…) as the lights | 1 |

For architecture #1, the efficiency of the DC-DC converter (ηDCDC
) shall be the highest value resulting from the efficiency tests performed in the operative electric current range. The measuring interval shall be equal or lower than 10 % of the operative electric current range.

4.2.   Calculation of the statistical margin

The statistical margin of the lighting package shall be calculated in accordance with the specific powertrain of the vehicle (i.e. Conventional, NOVC-HEV).

4.2.1.   Conventional Vehicles (Internal Combustion Engine only)

The statistical margin of the results of the testing methodology caused by the measurements shall be quantified. For each efficient exterior LED light included in the package the standard deviation shall be calculated in accordance with Formula 5:

Formula 5

![Formula](data:image/jpg;base64,/9j/4AAQSkZJRgABAQABLAEsAAD/2wBDAAMCAgICAgMCAgIDAwMDBAYEBAQEBAgGBgUGCQgKCgkICQkKDA8MCgsOCwkJDRENDg8QEBEQCgwSExIQEw8QEBD/wAALCABeAaIBAREA/8QAHQABAAIDAAMBAAAAAAAAAAAAAAcIBAUGAgMJAf/EAEsQAAEDAwIDAgwCBgcECwAAAAECAwQABQYHEQgSIRMxFBUYIkFRV1iVltPVCTIWNGF3l7MXI0JxgZHSMzhW5CQ2VWJzdXaTtdHU/9oACAEBAAA/APqnSlKUpSlKUpSlKUpSlK4DWXVSRpRZbLNt+HTcnuOQ3yNYIFuiSmY63JDyXFJJceUlCUgNq33Pqrh75rtrzYoYmucHWXzgXA32cDJLQ+4NwevKH99unf8AtFaHypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr13OkWs2o2ouQybPl/DjmOn8RiIqQ3cbzLhOsvOBaUhlIYdUrmIUVbkbbJPXuqXKUpSlKUpSlKUpSlKUpSoX4lP1vSH951o/kSqmilKUpSlKxLtdrXYbXLvd7uEeBb4DK5EqVJcDbTLSBupa1HolIAJJNeFkvVqySzwr/YpzU23XFhEmLJaO6HmljdK0n0gggg1nUpSlKUpSlKUrRWjOsNv+RXrELLk9tm3vHVNJu1uZkJVIhF1HO32rYPMnmSdwSOtbtZUlClIRzKAJCd9tz6q+f2quoXFppHpbYdfMs1Cutnzu65ymzN6bzGIMm2XCG9KU0xFjhhHahwspS72nalR87fY7V9AkklIJGxI6iv2lKUpSlKUpSlKUpSlKUqF+JT9b0h/edaP5EqpopSlRllHExoHhWWyMCyvVWwWzI4vZ9pan5G0r+sAKNmwOZW4IPmg12uIZji2f43BzDCr/CvVkubZdhz4TocZeSCUkpUPUQQfUQRW4pSqlasuo4p+JZnhmRMDmnenMWNkWoDLThAus11W8G1Ocqhu1sC84kjY7JB22q2TTTTDSGGG0tttpCEISNglIGwAHoFedKUpSlK0Wc5fAwHErnmNzt11nxbUwX3Y1rhLly3RuBs0yjdS1de4VA3l66Z+yfWv+Hlw/008vXTP2T61/w8uH+mnl66Z+yfWv8Ah5cP9NPL10z9k+tf8PLh/pp5eumfsn1r/h5cP9NcxxMMzIeMYvx56NY5c42T4dERLvFslQ1QpV6xpzbwqHLaUOYONJ3dRzAlspUdj0q0mH5XaM5xGzZrjj6ZFtvtvYuUNYUPPadbC0bkbjfZQ3qnWaaC8cWdaqXTUG5zNH21qIh4vJfmXCS/iMQq896E2WEtGWpPVTyxzEgJBSjpV3UghIBO5A6n11+0pSlKUpSlKUpSlKUpSlQvxKfrekP7zrR/IlVNFfOrju0YOkutNl4vzjkrMNPpT7MDUDHFvvKTHbUkNieylH5AEpRud/zpT6HFbWXxDhk4Rc8xa1ZpiWndouVmvcRqdBltSZPK8y4kKSobubjoe49QehqONR+AjH8Rvtr1m4Z7lkWPZni1waurNk8dvPW28IS6FPRFpkLUGe0aLjaSkhA3G6fSOlvWFZyOOA61OaO3a6Y7b9OTj1vubcmDzouKpKpCghtb6VpBbWWucj8xI/Keaug4GMMz3TXhzsGnWpGFzccvlifnB5l+THfQ6l6Y8+hTa2HFgjldSDvsdweh76sBSvB55mMy5IkOobaaSVrWs7JSkDckk9wAqov4bERWRabZ5rlcGXTc9UM7u12W+tYWlyK08WmEtqAHM2nZwAnvPN6K438SfINTNLrnptl2nWsmbY7+l2UxcduNug3FKIfg6k7lbaCglDnQ7q3IO/d0FbHi1Vq3wcYE3xCad685pkcKz3KDFuuK5a+zcIdxjuu8igh3s0usL3Unzkk9B3eu5GJX5OVYrZsoRGMZN4t8eeGSrmLYdbSvlJ9O3Ntv+yttSlKUr1S2PCor0Xt3We2bU32jSuVaNxtzJPoI7wfXXzz0cham5nxwawcPV/4jNVnMWwi1x5tqLV9Q3KC3RFUQ44GtlgdusDoO4b7mpC0x1X1W0c41GeD/ADXUG46j47kONfpBZrxeWmW7nbVIS+VsuuNISmQlRYX1KQRzI9R3ufSlYV7tEPILLPsNxb7SJcorsR9G5HM24gpUNxsR0J7qqz+GXeJS+G+RgU2a9Mc07yu84ql9aAlK22ZHaI5OpPKEvAed1GxHcBVs6UpSlKUpSlK4zVrVfFdGcOczTLTLXHMqNb4saGz2siXMkOBphhtO4HMtagN1EJHUkgDeuSxPiBn3DUAadahaVXzAZy7FKyFuVdbjAfiLisPssr/rY7ywlQU+ncK22HX0iu6Rqfpq4UhGoeMqK1BCQLtHPMonYAef1P7K6UEEbg9K/aUpSlKVC/Ep+t6Q/vOtH8iVU0Vq8otGPZBjd0seWQ4kqyT4b0e4szAksORlIIcDnN05eUnff0VT/wDDaxPJMNgakWfEsg8eaGIyJ4aez5TnNJfCVKTKU30G8fnASlZ2ClIWpI2USbqVxN21k09smq1k0UuV5cay/Ibc/dLfCER5SXYzJPOouhPZpI5VdCoHp+0b9tWFer3Z8btEy/5BdIttttvZVIly5TqWmWGkjdS1rUQEpA6knpXNaXav6d60Wa4ZDppkbd7tlsub1oflNNLQ34U0EFxKSsDmAC0+cNwfQTW3zj/qXf8A/wArl/ylVW78Lr/cg08/vun/AMjJqMfxb4vh2LaLQi++yJGokVousLKHEczahzIUPyqG+4PoNWCufBrphlM61v6j5Pn2dwrPMTcI1qybJpE23mQkHkcWwdkucu52Ctx1O4IJqG+LjUvOtLuJ3SDEmOIm64DgWft3NF5/qrW3GtiYcdvsiy7Ijq5OdxQCudShuoBO3QV2egdw1F1JnQs6xHXDMcw00yCLfbK7KuTdqakW+dEmBpi4Rlx46O1Q6GnglKkKACkqV37U/Dv1C1I1V0Yuue6pag3PJbmcjuFrbVKZjMtMMRlhKOVLLSPOO5Kid/RttVqaVAGYQ+PJ3KLo5gd60Mj46ZK/Fjd1iXZyYmPv5nbKbUEFe3U8o23PT11gW2F+Ie3cYq7tfeH56Cl9BktsQrwh1bXMOcIUVEBRTvsSCN9ulWOr5u6a6fNah/ig8QtuezDK8eEWywHw9j12XAec/qYA5VrQN1J677esA1cXAeF/TbTnIb/ndlkX2fm2QxDCkZVeriq5XRpnkCUoacfCkoSnYEJCdiQOYKAAqmOn/ENkycg1OxzVXjAzKNdcX1HTheOWe3tWJu4XGM5KRGQ8W3IZK+VSypakgJAQrpvsKkHiiyvia0J4aY97lavXpjKrVnabIxfERrcrx1aJMhRZedY7FSGnEoUEAJCTu2SRsrYXetMJdrt0W2v3SVcHWGghUmWpBefI71r5EpTufTskD9lZlVH4BPCV37iJet64xxlerl48VeDdn2XabI7cp5fR/sv2d+3pq3FKUpSlKUpSlV4465mAsaFpgan4ZKyXGLvkNqttxjwkuqmR2nZABkxg155eb/MkDffbbY77GEOGDFIWFcS0HGNGNar7q3pfPxecq8LvS03L9HpSVsdiwJZSOQulJ3jgA7I3Uk7JUOJ4RMbuea8E+X4RZdAY+dyrrkuQQ4rs6TAjw2XXCUIdUt1wPJ7MqB3QjmA/Kd6slptlV14StNtHNAdSIGTZlk17aNli3WA5HejOTkpcfMYuSHm1pS22ClClJAKGx6eg6+HxS4/Mj5+v+jjNG5GnUFq53KOpmEoyoi+33eiOIklp9KfBngoBYIKSNt+lSDpdqFa9WNPbBqTY7dPhWzJILVygtTktpfMd1IU2pSW1rCeZJB25txv12PSuqpSlKVC/Ep+t6Q/vOtH8iVU0VRfi/wCK7TG66qI4UMk1KfwfGWm0ys+yBoOdq/GUgKTaIxaQtQW8Fo7Vzpyt7pG5JAkyycffAvjdnhY/YNY7Hb7bbmERYkSNaprbTDSAAlCEhjYAAAACoqz78SfFdVMjxvQ7hRTd7lleaXRi2KyCTbFMRrPHU6A8+lt4AvOBoOKSCAkdFEkjlqXtVr5nuHcX2iFrtOod1ON5u1fYN0sTzEZcfeHA7Vt1DnZ9slSlqBUOfYlCdgOoMMzdXtZkTZCG+PK3spS8sJb/AKDpy+QBR2Tzbddu7f01NHC7meYZheb/AG/NeIiPqY23DbU1CRp4/jyIwKyFLUt1Oz2/QcoPTqdqxuBdxhVr1qSwtspTrLlGwQRsE9o1t3ej1VZh1pt5tbLzaXG3ElK0KG4UD3gj0iqj/hvOPYxp5qDojcnXfGGmefXe0lpwpHLFdc7ZhaGwTyNr5lkDu35tqmDVPhT0I1rvreR6n4W/fJzBbUyXLzOaaZWgbJWhpp5LaFgf2kpBPpNSJimLWbCseh4vjzUhq3QEFDCJEt6U4AVFR5nXlKcX1J6qUfV3VXTXzRHVjUHij0h1dx7GrBMxnTJF0VKamXUtSJ65jAb2Q32KkpCFJB3Urr16Dbrn2nAdcLdqVYZli05xnGdP8acvl5FohZS+7Iu93m8ykOPDsEobQFvSFFO60guAgeakCvsrTHVrhc4BdRsY1IsuCSHo13fvyGHpkifEurL76FmIUoMZxDgUkEKClAhP5TuQLYp1uah6xYBpQibjTUfK8aeuqGVrlJmuuNthaRFCWjHW0EJWVczoUkBOwO43mOlK9UuMzNivQ5AUWn21NLCVlJKVDY7EEEHY94O9QxjHBnw54dmaNQ8cwebDyRLrby7l+kNzW8+pspKA6VyD2qQUJ81fMk8o3FTRIW83Hdcjsh51KFFtsq5QtQHRO/o3PTeqbaP8OWrGDtauPZ1pNiGTOag5w5mduiDJVtNxVB5D7Dbj3g3OCh1tCiUpIO3cQSK0+tnDjxVazaL3nFr1AxJWVZFqEjKl8+RPmFbrbGS2mLDaJYJUrlRsohCBvzKO5VtUjzstuzXHJgdjyG0YsiVeMCuawlsuu3G1ONusqWwH+1DbiHCSoHsEq2Qeu29WPv8AeYWOWK5ZDcllES1xHpr6gCeVttBWo7DqeiTVXvwzrHNj8Nas8uVvchSdRcnu+WFhS90pRIfKG+Qd4SUNJI36nffuIq2FKVj3C4QLTCfud1nR4cOMguvyJDqW22kDvUpSiAkD1mtRjOf4JmpcGG5rYb92SQtzxZcmZXIncjc9mo7DcEb+sGt/SlKUpSvFSEr25kg7HcbjuPrrDuNsMu2SrfAnP2pyShSRKhobDrSiPzp50qRzD/vJI/ZUbcPHDpi3DVi83DcJyfJLlaJs1y49heH2HizIc2Lq0KbaQrziASCSN+7bc1xXFToZmuuGW6ZC3Y7Z7hjOHXxy93ZEq+yIEmUDHcZSy0WWipOxWFFfOnu22rUY3ofrJienmZ6b4xarNAxheLyrBi1kkZE5J5pEx6S7ImTJJihaSgvhCEpC90A8x5jvWix1ed6GXvhV0UzC3u7Jt8nHHn7NlDzcR2ZEtqt1vxewAktFDQUjmWkoWs7g7Am4FKUr8JAG5PSq5cO3F5adetbNVtLocFqNFwt+I5Y5HnBdzgqTyOydj0KO1AKFJ6FDiD/f03Ep+t6Q/vOtH8iVU0V6HIEF1ZcdhMLUrvUpsEn/ABrx8WW3/s+N/wC0n/6rybgwmVhxmGwhY7lJbAI/xqArXoPq9e9fcc1T1X1Fsd8suCO357GosK2qiykm4FLbYfIIQQzHSW/Tzk8x67bWErGuVug3eBItdziokxJbamX2VjdLiFDYpP7CK5LAtEtIdLZkq4ab6b49jMicnlkuWqA3GLw338/kA5uvrrtqqfqzGm8MvEpH4l4saWvTvPYbNh1D8HaC0WqU1smBdFoSObk6lpxZ35UkE+gVa1p1qQ0h9h1DjbiQtC0KBSpJG4II7wRXnSlet5hmQjs32UOJ332WkEb/AONV3yHBtWc94stP80uWCxbJiGmCb8GLqLo0/wCNW50NllkIZSAtpYWl0qCgQAlOxPN0sZSlKUpSvWY0cveEFhvtQNu05Rzf599Vc4x8uu+pvgfBzpPObeyzPeRGTyWStQx7Hd95El5SOiFugBptCvz856bEGrHYbiViwLE7NhOMQkxLTYYLFuhMJ7m2WkBCB/kB1rc0pVb+OTD8jzrBcNx/Ccpsduyf9MYc2y2u/MuO2zIJUdl94QJIQCOQpbU4OccpU0kdCQRB+mGW6n2rWfOLJnWnll0X1OYwyFOMzHrGL3ZrnZY850KkNxWVJd7ULkchUXFbJb35RtXXxeIfJ5mDwdTmuLW1jD7g6Wmb+rRq4pgApWW1KW8ZHK2lK0qSVLISCOpqYrZifEperdFu9o4ncOmwZrKJEaTHwEONPNKG6VoUJ+ykkEEEVk/oBxS+8div8Pf+fp+gHFL7x2K/w9/5+u90+suodktkiPqNnNuyict/nYkwrJ4sQ01ygchb7Z3mO4J5tx37bdN6rBxXytNk6opbyrLOJ+JNTbWP+j6couAtLaN1bblhooLp6lXnE/l327qhnwrRH/jfjx/yvH0quzwyDHf6ILWvF7tqHcYC35Kku574R45Cu1VzJe8ISlfKD+Xpty7bEiun1DnarQokNWlWOYxd5KnVCWi+3Z+ChtvbzShTLDxUd+8EDp6TXDeP+MH2Y6T/ADjcPt1PH/GD7MdJ/nG4fbqeP+MH2Y6T/ONw+3U8f8YPsx0n+cbh9urQ3mw8ReRX+1ZVftBdEbhebEortk+Tk81x+Eo77lpZtu6Cdz1Tt31Len0zU6bbJDmqWP43abgl/aO1Y7o9OaWzyjzlLdYZKVc3MNgkjYA7+iuL4pnsdZ0mknKLzqVbYKpsZJc09TIN4WrmJCE9ghSw303Ueg2ABPXY0v8ACtEf+N+PH/K8fSqb+Edem72o81WKZTxLzZyLU7vH1K8YeLFNlxvdTfbthHbA8u3XflKtt+u1trlb4t2t0q1zUqVHmMrjvJStSFFC0lKgFJIKTsT1BBHoqjesuMWnhm44tCNUsaiotGJZjbFaaXRtpJDSFIQBBSfOA6ksgb+hgkbnusLxKfrekP7zrR/IlVM6ublPIAVbdAe7eobcv3F6HFBrTLSgo3PKVZhcASPRuPF9ePj/AIwfZjpP843D7dTx/wAYPsx0n+cbh9up4/4wfZjpP843D7dTx/xg+zHSf5xuH26nj/jB9mOk/wA43D7dTx/xg+zHSf5xuH26nj/jB9mOk/zjcPt1eifcOLO6wZFsuekukEuHLaUxIjv5bPW262obKQpJt2ykkEgg9CDXrs7/ABV49aoljsWkGj0C3QGUx4sWNls9tphpI2ShCRbtkpAGwA6Csvx/xg+zHSf5xuH26nj/AIwfZjpP843D7dTx/wAYPsx0n+cbh9up4/4wfZjpP843D7dTx/xg+zHSf5xuH26nj/jB9mOk/wA43D7dTx/xg+zHSf5xuH26nj/jB9mOk/zjcPt1PH/GD7MdJ/nG4fbqeP8AjB9mOk/zjcPt1PH/ABg+zHSf5xuH26nj/jB9mOk/zjcPt1PH/GD7MdJ/nG4fbqeP+MH2Y6T/ADjcPt1am2QeJiy3m65FZ9DtFYV1vq23LnNj5PNbfmqbTyNl5YtvM4Up6DmJ2HQVtvH/ABg+zHSf5xuH26nj/jB9mOk/zjcPt1SZhsjM5WPRn8/tdot18UV+ER7TNclxUDmPJyOuNtqVunYndA2JI699buo91m0K0/15tdks2oUe4uxsfu7V8g+Az3YbjcxttxDa+0aIWOXtCobEdQPVtWhh6F4hp3a8szNFzv8AkeTzsfkW1y+5DcVTpqYSUrWmMhRAS21zkqKUpHMdirfYbVV4O9NdV9Yfw7MZ0xx7KsWx+wZLGu1tnzJFtemzUw3Z0hLwbT2iGkuEFWyjzADbpv1E1agahucGVl0a0mx9GLs4XcFtYmLzkNycjG3rYjKWH3VbcikLDZB3UnZSh6O7DkcVWrSdJtRtYrXg+IXfF8TtQvmP3qHcpXgWQRW1yUSm087QW062qONuikHnSQoghVWFwbLXL/huMX3IXbdCuWRW9iYiKy95pU40HChvn2UvlSrr09BNdPSlKUpVcuKVu56sZXhPC1YpsmLFy15d9zKVEfU09Fx6EtBU2FpIUhUl9TTKVD0Bz1VpXeATgzZ7btrPcm/B3kR3ubOboOzdXtyoVvK6KO42B6ncVKWjfDFo7oJcbldtMrHcoMm7MIjylS73NnBSEKKkgJkOrCTuT1SAalWlKUqtf4hulcvVLhaylVkQoX/EA3ldndbSkuNyIR7RXJv3Etdqkbekjv7q1M/VOHrZo9w3apQ1oJyHNrDJkJSoHs5IjSkvt9OnmupWn/DuFWqpSlKUpSsZ6522NITDkXCM0+ptbyWlupSstp/MsAnflG43PcN684kyJcIzc2BKZkx3khbbrKwtC0nuIUOhH7RUO8T2qWU4JYsWwzTpxhrNNSMhj4xZ5DzfapgJcSpcmcUf2+wYQtYB6FXLuCN6kjGrTbdP8TtOOS8ilS24DLUMXC7zO0ky3T053HFbczi1Enp6TsABsK3Ea4QJrj7UObHfXFc7J9LTiVFpewPKoA+adiDsevWsilK02Y3DJ7Vjc6fhmNx79eWkAxLdIniE2+oqAIU8ULCAASfynfbb01APCjrvq7qvqlrVhGq9vx6A7p7drdbokSylx1prtWXVOAvOBKndyhJ3KE7ddhtW0yzMch0T4l8Vt825zbhg2sjztrQxIcW6LJfmGi40WSSeVmS2FhSO5K2gobAqqwdKUpSlKUpWsyXGrHmFjmY1ktubn2y4NlmVGcJCHmz3pVsQSD6R6R0NabTnSnTrSOzuY/ppiMDHLY66XlQ4KChnnPeoI32STv12239NcXrToNctXszwPKhm0a2R8CuS7vEt71mTMblSlNLa3eUXUkoCHDskAdepJ6bcXkHB7PyDGs1xg6mxbbEy6xnGo8e2423Hi2i3OvOvykMMh4hTjzrxUXFlRTtsnbetLqFGvmnWu3DPiXhllv0aKmdZZTz+PNqkwkIgrS3LafKlKjF0obaIB2UQdj12q11KUpXHatZvftPMCumU4xgV1zG6RGVqi2i2qbS484EKI5lLUAhG485Q3I36JJ6Vy3D9xD4prloJaNckvMWuI7BcevLK3N022QwCJTayeuyClRBI6pKT6a4XhVx57VBOZ8T2b21RkasK8FskOSnzoeKMcyITO39kvArkLHpLyfVVeeErhx0ay7M+Ki35DhMSdCtuY3HH7bEeKnGbfE5VkGOhRKW3Ryo2dA50hCQCNqsvwAXW5Xrg40tn3ee/MkmzKaLz6ytZQ2+6hAJPU7ISlI/YBVg6UpSvVJjR5sZ2HKaS6w+hTTjahuFoUNiD+wg18y9BZUjTLLn+Ei7PntNNddrZPsiFk7qs85mW4zt0A2B2UdvS93D0/TmlKUpSlKoJqDoxppkf4pWMW664uw7Cm6fyMinxQtYZnT0zHEhyQjfZ0eaglCvNJQjcHapT4PLBbMH1m4kNPMYZMHG7Nl9ukW22NnaPCMq3NvPJZR3NoKz0SnYAAAd1ZmtrLEnjh4bmpTTbqWrdmL7aXEhQS6mHHCVgHuUATse8bmoz/FKwHFcismj90uFtKblP1JsthcnsOqakpgvF5S2kOJIKfO2UCOoUAR3V1jum2F6Rcd+lsTTSxs47EyHBL7EusWDu2zNTDVGLC3Uj/aODn6uK3Wdk7k7Va2/P3mNZLhIx2BHnXVqM6uFFkPllp58JJbQtwJUUJKtgVcp2B32NV5/TP8QT2H6QfOEz/wDNXXaW5LxZ3LLGomsGlun9mx1bLhcmWTJJEuS26Bugdk4wkKST0PnDbffrttUz1Tvg1/3qOLX/ANWWr+Q/XV8d3m4rpK6notGsGJ8qh3p3lKB2Po3B2qzNKUpSlKUpSlKUpSlKUr5oXXRXVTDeJLOeDnELe+1pbrlcmM1mXVtxSRa7UhZN1iNgeaFPK7NjpsQhbffv0+kCLLAj2NOO29tUCE3EEJhERRaMdoI5Ehsp6oKRtsR3bCoNxzgk0oxJvKG8dyvUmCrM3VyL46zmU5Lk19agVvLUF7lxWxSV95SpQ7jUg6KaI4boFh6MDwKXfFWRhZVFi3O6OzUxEnvbZ7Qns0bkq5R03JPpqQKUpSlUH4q9KZdh/ED4e9ZLSHm4OV3JFju4QpQbXJipW4wVjuJLazt/4Pd0Jq/FKUpSlKVCt64StNb7q9/TlKv+cNZclsxmpcfJ5TSGIpUVGM2hKtkMFRJ7Meb1NZuk/DFp/o1mF7zjE77mcm6ZIe0u5u2Rypzc57YBLzqHVEKcSkcqVHqEkgdK5bjAwi/PQMI12w61vXO+aPX4ZCu3x2Q4/PtSm1NXBhobElwsKKkpHepAHeQRtdQNEtGOL+w4jnt3yHIbjZWER7zYzaL8/EjJeG6mpPI0oDt0c6k8x85PUdNq81cI+nTupFn1Yl5bqJKyWxNJjwZUjLpjgbYHLzs8pXsW3CgFxPcvc829TdSla7IbK1kdjnWJ64XCC3PYUwqTb5S40loKG3M06ghSFD0KHUVDenHBxpbpTmc3PcOyPP2LrdpyLjdlP5ZMebur6OblVLQpRD/51dFb99c1qMw1xE8S2IaeWpvwnFdGbkjKspmjzmXLz2Shb7ek/lU4jnU+4OpRs13FVWapSlKUpSlKUpSlKUpSlfnKnm5+Uc222+3Xb1V+0pSlKUpWtvGN4/kD1ukXyyQp7tolpn29clhLioslKVJDrZI8xYSpQ5h12UfXWypSlKUpSlKVjwLfAtcVEG2Qo8SM3vyMsNJbQncknZKQANySf7yayKUpSlY8S3wIHbeAQo8bwl5Uh7sW0o7R1X5lq2HnKOw3J6nasilKUpSlKV//2Q==)

Where:

n: Number of measurements of the sample, which is at least 5

Where the standard deviation of the power consumption of each efficient exterior LED light (
![Formula](data:image/jpg;base64,/9j/4AAQSkZJRgABAQABLAEsAAD/2wBDAAMCAgICAgMCAgIDAwMDBAYEBAQEBAgGBgUGCQgKCgkICQkKDA8MCgsOCwkJDRENDg8QEBEQCgwSExIQEw8QEBD/wAALCAAiADgBAREA/8QAGwABAAMBAAMAAAAAAAAAAAAAAAYHCAUCBAn/xAAsEAABBAEDAwMCBwEAAAAAAAABAgMEBQYABxEIEjETFCFUkhUWGEFXldFh/9oACAEBAAA/APotu7vFj2z9ZTv2tfPtLTJbVqjoqmAlHr2E91KlIaSpxSW2x2oUpS1qSkBJ/fgHkbbb32GaZzebdZZtjd4TeUVXCt3W7KZDkNPR5Tr7bZbcjOrSfmOvnnjjxqzveRPqmfvGnvIn1TP3jT3kT6pn7xp7yJ9Uz9415tutujuacSsD45SedZe6+EbVWmJYFiG8mL2NhjeQ5WmG7Z1Ye9/ROphyXG57HpAkdikALJSoBtSyQeNUVtTt5BTk25uJY5mrW+m0NViLF/X22YxxasQ7dmQ8oV4mJKC9w0XV8JPaguA9oJPf6dHXYfcdIcLq6Y6Xdj3q5qFJtbOhcgvRnhFYkONLSxJUpaC6Q2SApHBJAHJ4B0/tn069J+5u3mNbi1/Tpi9fGyWqjWrMWZUNJfYQ82lYQsD47gFcakv6POlv+BcL/q2/80/R50t/wLhf9W3/AJqd4FtpgG11U9R7d4hV47XyHzKdjV8dLLa3SkJKyB+/CUjn/g1JSlKiCpIJHjkeNQ7dp4V21eVCHUT5rjtRLYYh1kJcl951xpSEJQ02CokqUP24HkkD51l/os6a8NyLpWwvCd9drchTcY67J9/S5IZ7MX1TLdcbUIriww6goUg8BJTz5HPOu51fyckxfdnYaNgDV/KVd3U+pl43V5M9URrOK1AW4htQS4lpHpqAUFcA/Hbz8gaqvLbLJ6Pp53mv7bOtwcbzr8osZWMbcyGe5+UZLciWy2hqWp4q4fCUq9BR4ISFAFJT27AxLNE0UPbnCfwO/tXshpw+7Zh1L7UP046XCuU6676vLiiQngLJPPgA6srTTTUTyfafbHNbmJkWX4BQXNrAQW4s2dXtPPx0nnkNrUCpHPJ8EedcxzYDY56vsqp7aLEHIdw81IsWF07Cm5jrYIbW8kp4cUkKUAVc8cnjVf5/08TLXf8A2X3MwjH8Yq6fbVNlHnKCizKdiSYy2Wo7KENEem2pZcCVLA5UrgDyb/00000001//2Q==)
) leads to an error in the CO2 savings (
![Formula](data:image/jpg;base64,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)
) that error shall be calculated by means of Formula 6:

Formula 6

![Image 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FdVrX26gCo+J1qpD5TLTTXADNy22hHPbXFL5U65jsdT7TXhYOHeR4RfL29gV4tTVmu8hUxVsucVxaYslQ9ZTS21pIQo9Sgg/Yawrjwwy+dwfybEZuWwp94yKU69JnvRFpaZQ4QVIbbCyQAAAnrqgwMXb4pnGeGxgvWhdj7SGbimG24h8RuyP1ipRSU/V2AN/+VXcO6o9w4tN2sOFWyy3qTClS4MdEcvRG1IQtKBoHlUSQdDr1qQ0ClKUEc4pPGPwzyiQlfZqas8tYV+qQyvRqtOKeLZrevJ3sowG/3Sy36226PJbahSFMqlgMjmZJBHU7JH+ICrA4zk/mmypA/wCktUhvfs5kFO/61vcXaDON21kHYREZTv26QBQcm+TRCl8Vsekt3DjLxQtuTW1ZRcICLwEgDegtAUkq5fAg9QR18Kt38xdz/bdxW/75T/yVW3lK4TeuF3ECPx44dtFKUOj6egtj1FpV0U4QP7Kh0V7DpXtrovhnmllz/C4GUWJ8ORZbe1IJ9ZlwfWbV7FA9P6+NBUec8FcvhYjc5uOca+JLl1jR1vR25d152nCkE8hCUpPUDQ699WH5O9xn3bgjiNxuk2ROnSLY0t+RIcK3HF9dlSj1J++p7XxtCG0BCEhKR3ADQFAWpKEKWtQSlI2ok6AHtrQYTlkDLo8udaGJKrY072Uec4jlama2FKa8SgEa5iAD11sdahPlE3KVLi47w7tklbEzMLkIb7jZ0tuEgc8lQ+3kHL/2qmOTZDinDjE2Jd4fRZ7JECIzam4zi22RrSE6bSeUdNbPTuHjQSWlR5WaY6nC28w87fNkcbDqJCYT5UUE6CuzCOfXjvl7uvd1rVDivgRwU5wL4r8nQ72JneZSOTm3reuTm5d9ObXL9tBNqVXznGbh61HYkv3adHjPrQhp96zTW2lFZAR66mgnqSNdfGrBB2NigUpSgUpSgUpSgVqMuvgx6yO3Zy3zJ0dghUhMVIUttr+05ykgqCR1IGzruBrb18IBBB60GLZ7lAvFrjXS2SmpcKU0l1h5tW0rQRsEVXfFPsLlJuOB4xb4zmQZJF5btLLQUmDEKS327vtVoFKEd5PXuBrX8KnjiHF3KuGg9S1Otpvlkb1pLTbqiH2U/Ylz1gPDmqa3Th1hVzu0q6z8dhvzpYSJD6ged0JGkhRB66HQUG5xiBDtWN2y2W93tYcSI0ww5zc3MhCAlJ347AHWlZNrgw7ZbY1tt8duNDitJZYZbGktoSNJSPsAAFKCoofEbOcsm5h+QkDHz+TF0etpt9x7XzmatoDmUlSVpS2FHYTsHeuuqtmyPzJNpjP3GJ5pLW2C6xzb5FezYrnPiNZ3slZuF5s/D7LMX4oNvqRDnWxl1DEjlc0hTr4AaUhSNEhfUd3XxlmRSb/e+LGH8OsiuDrcNVjfut1TBeUwZjyFhCEFSCFBsb2QkjZ7+lBdg60qIY3jFnwR2/Xdu7SmLVKCJDjMyWtxiH2aSFqQpxR5QrvI7ulLJxJxK73+JY4c97z2c0p6Gl2K42mS2kbK21KAChrrsGgl9Kh83iXhkOa7HkXhKGmJHmr0vslmM09vl7NT2uQK3oaJ76mAIIBBBB7jQK1WTy75Dt4dsNoi3WUVgFmRO81SE+J5+RfX7NVta1eTWh+8wBFj3y6WZQWFdvb1tpcIH9n9IhY1+FBGfp/iZ+z+yf8Aic/6at5idwyecZP5RY/BtARy9h5tdPO+03vm3+iRy60PbvfhrrpPyAuf7TM4/jw/9NW8xTH5VjMkycnvt87bl5RcnGVdlrf1ezbR376733DuoN7Q/VNKHqCKCA8KlJfyXPZCASBfyyVHv2hlvY+71qn1V7wVPNIzl497mWzfu9VLSR/QVYVBzr5XnC6dMYY4tYQtULLcdAkOKa6GSyjrv7VJG/vTseyrN4A8QUcTOF9syrzYxZDoUzKa16oeQeVfL7Uk9R99R/jpkt2ut0h8JMKkdlkF8bK7jMR1+i7f3OOnXctQ9VI9p+6rFwnGrTiGK2/G7HHEeBBZDTSfE671KPionZJ8SaDcVRPlau80vhrBUkKZkZbH7QHxCUkj+tXtVE+Vs3yyuG05auVqNlsftDruCkkD+tBew7qxHLlb23FNuToqFpOlJU8kEH99ZY7qrK+cAeEV6vU283TCocmfOfXIkvF94FxxaipSiAsAbJJ6UFg/S1s/vCJ/HT/nT6Wtn94RP46f86onI+E/k92aXJifm6fukmIkKlNWxmXKVHBGxz8izykjqAepHWvyeGXkvowmNmb1iszNgk67Oa5KkJQCTrlV6+0nYIIPcaC9l3O1LQULnwlJUNEF5BB/rXjFkWCKSYz1sZJ7y2ttO/3VRdu4deStPuESBFtuPqkzBuK2qc+gv/8AU5ljm7x3VLvRt4J+4UL+ZkfMoLHXKsS5KZK5FuU+n6rhW2VD8e+oxxHtKcrZgxo2XRrdCYdLkmNtC0TPYhz1htHftPcd9e4VH/Rt4J+4UL+ZkfMp6NvBP3ChfzMj5lBPsS5osEQ5l+jXWWVqUFNhDYSnwSlCT0AArBzrPbJh1zx633ZExT2QTxAhlhsKAcI6FeyNJ+0b+6tRhfBbhjh2QM3/ABrE4tuuTKFobfQ86opChyq6KWR1B13VNbharZcXor1wt0OW5Ed7WMt9hKyyv9ZBIPKr7R1oPadITEhPylpUpLLanFBI6kAE9P3VT2GcQuIuY43acyxu1Y3Ns02ehl+2hbgmRWC92a3C4VhClJG1FPKOncT421f5E2JYbhLtsIT5rMZxyPFK+Xt3EpJSjfXXMQBvw3XOhx6TJynHMh4aYXk+FZI9dGHMijORnI9uVGO+35+Ydk6ruCS31PfQXA5n1yS4pI4a5uoJUQFCPE0dHvH/ALR3V8/OBcv2Z5x/LxP9RUFuT8VPH3J7RfcyulttKLNGmx2jcQyhp1a3Aso6d2kg661suDWemFwzfvWf5Iy3b03mTEtV1uRSwqbFSvTK1dACTpWjocwG9UEo/OBcv2Z5x/LxP9RUhxW9yL3GeekY9eLIW18obuKGkqcGt8yezcWNeHUitXA4kYDOt9wuETLbK9FtyEuTXUSklMdJ6BSz/ZB9pra2fJ8du1kZvdsvUCVbJDnZMymn0lpxfNy8oV3E83T76Db1z/5ZUdSWcFu7fZ9pAyBko5k7O1uNJ6HwroCufvLLkKU3gdnbLfaT8gZCAo9TyONK6UHQIpQUoNNlU3IYUdlePWOHdnVLIcRIuPmgQn2g9mvf3aFR76f4mfs/sn/ic/6apDlNkkXuOy1HyG8WQtrKi5bltJUv7Fdo2sa+4Co/+QFz/aZnH8eH/pqCR4vMvkyAp2/2eLapIWQlmPO86SU+3m5EaP2arPkzIsYpEiSyyVd3aOBO/wB9YOMWd+zQFRpF8ut5UpZX29wW2pwD9UdmhA1+FaTiHwxwbP3ob2YY+xdnISVpjlx1xHZhZBV9RQ3vlHf7KCRm62sjRuEPR/8Avp/zqP2nHuH1pySTkdttthiXiUFB+a0G0vOhRBVzKB2dkD91RL0beCfuFC/mZHzKejbwT9woX8zI+ZQTvIo2JZFbzb76i0XKKVBXZSVNrSCPEbPQ17WY43Zrc1b7Uu2QojQ0hllxCUp/AGq+9G3gn7hQv5mR8yno28E/cKF/MyPmUFnfS1s/vCJ/HT/nT6Wtn94RP46f86rH0beCfuFC/mZHzKejbwT9woX8zI+ZQWd9LWz+8In8dP8AnT6Wtn94RP46f86rH0beCfuFC/mZHzKejbwT9woX8zI+ZQWd9K2z+8In8dP+dexlM+aKlIcS4ylJVzIIUCBvetfdVV+jbwT9woX8zI+ZViYnjdkxXG4uO2C3twbVFSpLMZJUpKQpRUrqoknZUT1PjQa3hZnVl4i4dHymwJlogPrW2gSWwhwFB0dgEjv+2pRWJaLXbbRBTBtNviW+IgkpYjMpabST36SkACsugg3Ht5DHCW+qXvSm2m+niVPISB/WppDbLURpogAoQlOh4aFQLyiPW4UT2vB6ZAaP2BUxkGrCHdQeFwhxbhAfgzWG5EaQ2pp1pwbStJGiCPYRXHkR+5+S5xuNpSiRcOH+VOc0VoHao7nME9P8SCoA/rJI7yK7GkvsxozsmQ6hplpBW4tZ0lKQNkk+yqPwaEeMvEA8Rr1HK8Osy1x8XhPJ9WU5vTk1ST39RpH3b6eIXm2oLQFp7iNivtKUFCyZv0z5bFvtzhStOP4+/IbHX1FPpbST7OoPhUi8r3/5dsr6/wDQtf8ArIqMMwzbPLiVOc+recbU01zKHUtBsnQ+4VZ3GzFpWacKcixmCWhMnQ1JjdqdI7UesjZ8BzAdfCgy0ADhYkDoBZOmv/0VQi//AKd6u/8A+AH/ANcVYcbM8gc4aox8YDk5yhNtENyMqCpEYPdnyFQkn9EUb9bYVsjw3Wj4gYbd8b8jxeAxIE69XcWpuClq3xlvKW8pYUo6SOiAQep6d1Bl2DOHLicBwO54PcG7deoXK+9eGW+ydDDKFp7IIcVs8wB9YDQAPfV0plRjMMIPN+cBsOFrfrBBOt69m6jPDIMXDBMekS7PKiS4URtoN3GEWX2HEoCF6Cxsb13jvFaCxXZxflF3uyzrDam5bePsyI90jurL7kUyFJSy4CABpQWrpvvFBMc6ya3YdiNyye7JfVBtzJeeDCApZSPYCRs/jWTjF5iZDjVsyCAHBDuUNqYwHE8q+zcQFp2NnR0R03WVcIUO4QnYU+KxLivJ5XWX2wtCx7Ck7BH31+4kdiJFaixWW2I7KA2002gJQhIGgkAdAABrQoKqkZ/luQZ1leLYSzj7EnGQ2lxu7B1S5q1o5hycik8ifDmPN18K3zGbZFFt8FN14d5K9cHIrbsoW5Md1hpxQ9ZsKW8kkj7vxNVnxVtUPKJ99emcO8qtWaQ3HWLFe7Kw7uSgD9CtT6AEJBPRSXOgHca9szeya2XHg/FyzJplvmXEuQ7/AObSg0y6tEcr2SBrfNrqD1oLI/OBcv2Z5x/LxP8AUU/OBcv2Z5x/LxP9RUT4W5G8zxDzCGxk797we2QWH03Ca4FpiyTzFxpLoA50hICjvfLvVTax8TeHt7uce2WjMLHOmSiQwyzLSpTpAJPKPHoCensoMnGsqmXi4GI/huS2hAQVdvPaYS2eoHLtDyzvr7PA1Jq0mN5bjGRomuWG+265Igr7OWqM+lYZV19Veu49D0NbaJIYlxm5MV5t9lxPMhxtQUlQ9oI76CmeJKjbfKl4a3JJSBOgz7cv2q2AsD+lXWO6qT4qDz/ym+FtvRvnisz5yyPBIRy9fs61dg7qBSlKBoVEM3wWPkF/tOSwblIs9/tIcRFmsoSsKbX9dpxB6LQdb1sHfjUvpQU7xM4eZHJ4dZcGrzNyW73VcV8xXAGWiiOtKiy03shPMkHez6x76kNszOJfcpstvh4bfwpTTjjk6ZanY7duPZqHIVLSBzn6uh7asGlBzramLtE8nm4cJZmJ3h7J1RpNsSkQnFRX3HFr5ZXnGuTk9YOFRPQgjvqeZ6cvx3F4qLLkcZuc1bWottt6YPbuz56Uq6FRUP0ZATvQHKOZRNWdUXyLB7Ve8gRfJEy6x5zcYxULiy1NhLZOyAB3bOtkdToewUG9sy5y7XHXc0MomFsF5LR2gK8dH2Vl1iWe3xrVbGLdECwwwjkRzrKla+0nqay6BSlKBQ91KHu/GggfBAc2O3aSBpMi+TnE+3Xa66/7tZvF3OoOAYa/eZDSpc1xQj26C31cmSV9G2kjvOz3+wbrQ8F7tDtfBc5BdZKWYzb9wlyX1dAEJku7V+5Irl2D5SOK3HjPKznNLffbjCtpUjGrdFS0WYgPqqeWFrG3SAPu2fYKDqngXgs/GbTNv+UPImZjkLol3iSB0Qr+wwj2IbHqge3Zqxq5f9Njh17s5V/Dj/Np6bHDr3Zyr+HH+bQdPFxsOBsrSFkbCd9SPuqn/LDtzkrgrLubKT2llmxblsd4S06Co/7u6r7gXxQi8YvKbl3+BAehW+1Y6qPEaklJd9Z1POo8pIGzroD4V0jlVmiZDjVysU5CXI0+K5HdSobHKtJH/GgyLNNauNpiXBhQU1KYQ8gjuKVpCh/51lEgAkkADvJqr/Jou0l7hy3i92cJveMPOWmclX1j2StNr17FI5SKsLIYTtxx+425h7sXZUV1lDn6ilIKQr8Cd0Fd45nl8yyPdL9g+P2deOx5LraZs6UppdyU0eVxxoIQrSfVKQpZ2SO4Cqax1x6V5Due3JbCo7Nwl3KXHZUd9m2t4EJ9nQ77ulT3GY+fWjgdbOG2PYpLhZKxG8xkTpQSmEwNnnfDgP6TmG9JTs7V11o1suKOEu495LMzh5jFsnXaSLYmDHbjNFa3HCdqWfYCeYkn20EQ4mzYWecAsbwnFWXrzkD7VsDPm7KimEpAbK3XHNcrYSAepPXuroy3foIEdiRKS+820lC3CobWoAAq/E9ajeDx/POE1rt9zt8uKpNpbiyYsptTTiVJaCFAjv7wdEVQ3Cm12a/8K7VYbjjWRuyLvLfgG9svLHm3rvFLvaFe/V5Eju69xoOp+dPNy8w37N19qncitMRHlQYYo9upRsUxxf6dYStbRbShSkg8pICleHj91WBa7xKl5zdbYmfZnoESM0Qyy8VS2niTzdqnuCda1499BIqUpQKapSgriNgF2PGS6ZtPl2aZb5sJqEiG5DKnGkNqKkqCySOY8x309nsrWeVPKbhcNoKhPYty/p62lt91HOhvlkoUVFOxtKQCojY6DvFW1SgoJGZQZ+D8SWrnltoyaSxbXXBdLfDDLDbDjZS3H6FRKgsKPLzK+sOvXVetzvTT3k9cP7lZ5UOXHamWSK+24wHUKUHWmlp6/VWhYPXwKavio/nmH2jNLO1ar0Zfm7UluUjzeQppQcbO0HY69Do/eBQSAd1c9cc0flH5UXCfF29q+ji9eHgB0CUnY39/ZV0IhPIgJ2Toa2Tsmqe4Nx28s4oZpxNXp2IuUmzWZW9p7GMClxxP/WcUsfhQXEO6lKUClKUClKUGNdXZbNvecgMNvyUpPZIcc5EE/wCI6JA+4E1UWJ8U80yazZYq24raE3HGJr8WQXZ7gjyFNp5gG9IK9kfrAAbHU1cjwJaWANkpNUvwRx692KLxScvFtkQkXC/ypUQup12zRZSAtPtGwf3UFjcL8sZzjALPlTEZUVNxjh1TClcxaV3KTvx0QetSQEHuIOqorg3lDOHeSPbcieCVGJCcLKFHQceU6pLaPxWUj7t1reAuQSsa4v3ThvdL6u9Ju0JN7iS3JIdPblKe3aGu4BQWQPYKDoR91DLK3nCQhCSpWh4AbrQcOszsee4qxkuOPPPW59SktrdaLaiUnR9U9e+pEoBQKVAEHoQfGse3QYVuipi2+HHiR0naWmGg2gfcAAKCvr3nWR2vjjZsGftdrRaLxFdkRbgp9YcUpv67XLy65+4jr3Vm43leS3birkONi3Wr6DsoaDk5t9ZeU44jnS1ycvLzAaKuviPHetX5ReO3q5Y3aslxWAZ2SYxcmrjAjp+s+nfK6yD4cyD/AEqTcK8ekY/iaE3LkVeJ7q59zcT3LkunmX+A2Ej7BQSqlKUClKUED43/AKTGLbF1tUi9wUJ33b7YK6/7tTwd1V9xpWreGRwfVkZVCbWP1k8rqv8A+Qa1flOcWI3Crh47PZKHL3P5o9rZUNjtNdXFD9VI6n2nQ8aDT8V58zibnA4P49IW1aY4RIy+e0f9myTtMNJ8FuePsT+NXLa4MS2W2NbrfHbjRIrSWmWkDSUISNAD7gK404PeU7wxwDEUWwWHLZ1zkuKl3We4iOVy5S+q1k9r3b6AeAAqZ+mxw692cq/hx/m0HUFflpxDqeZtaVp7tpOxXKl88tbDF2eWi0YzkQuCmVJjl8MIbSsghJUQ4ToHR6CrY8kloo8n3F3lchXJYckLKRraluKUSft2TQYnlBRPoPI8I4ntoHZ45cyxclgH1IUpPZOrP2JPKevcN1b6FJWkLQoKSRsEHYIrHukCHc7bJt1wjtyYklpTTzTg2laFDRBH3Vo+HuPXHFrWuxP3Q3K1xeVFrW8D5w0z1/ROK7l8vQJV0JHeNjZCS6FKUoFQu2YPJi8WJ+ev3xUhcu3ptyYZihKWmErLiQFBWyQpR6kdd1NKUClKUDQqu+J2B3jK81xG9xZ9rah49KXJXGlRVOmQVp5FDe9Acp6dO+rEpQanIfM7bidyVpmLGaiOqOgEJQOU9endXP3ADLkNo4eWe4ZpZb5HctQiMWSLbwmTb5QbSpLrqipR9RtDrZV6nVwDlOzrpelBS3CW6QbniXE0WWeyXk364uhxLfMAFIBQrlP1knlP36NTjglKXM4TY1KcaZaW5b21KQy3yIB/wp8B9lSi6QmbjbZNvkKcSzJaU04W1cquVQ0dHw6GoyziEmxcP2MSwu5LtjbQDDUmUpUhyOySeYo2eqwD6uzoH7tUENwxk5b5RmS5cNOWzHISLDBc0dLkKPaSCk9xA2lPTx3Vw1p8Mxu14njkWxWhooixkn1lna3Fk7U4s/2lKJJJ9prcUClKUClKUClKUClKUClKUClKUCvxIcDTDjpBIQkqIH2CtZlWSWTF7Wq5X2e3DjJ7ioFSlH2JSkFSj9wrVWnMbHlvDqTk+PTxIgLjPlDvKUlJQFAgg9QQR3Gg1fAOGlHByyRnwh1C2nlKBTsEKecVog9/fWkwiTitz4t5ra+aeXrWzH7e2z7cy3EiJ0rTjCgNkLAJJPs/dLODTYb4VY3yp5Qu3tOffzDm3+O9/jUTwnEMqiccM7yi5QIkW1XyJHjQ3USkvLHZBQ5lN8vjzb1s6113ugkf07wq7vpjDP5mL/nXlm8UN4m5c8Os+LSFGK5JRLlMJcYCEo5klIbG18w3og6H21pjwpvBJP5Xwev/AOK2/wD5KkWXRslgcP0WWwWqNfpq4ZhuKckNwUJ22U9pyhJSBv8AspA14UH64OvM3Th5YMicgwY865W5l99UeOhoErSFEeqO7rUxqC8DYOTWbh/bMdyezRrdItMRmIhxicJCZAQnRWNJBT3dx3U6oKzyXHpGI5+5xGx6M9IYuCUR8igsp2XGk9ESW0jvW315gOqkk+IFWWkhSQodxGxX2lApSlBjXRmS/bZLER9MeQ42pLbqkc4QojQJT469lVXYuF2X2Th0vC7ZnTUdhTbiEzE21IfQXFKUpSTvQPrHR8N9Kt2lBA5+DXOTxWsuaDIEpYtUFyEmEYoJcQ5y85K+/ZKUn7NfbUexFyIPKjzNUdKkByyQW3FBpaUreStzmGyOUqCSnej4irdr5yp3vQ37dUH2lKUClKUClKUClKUES4mzb99FNWLF2XBd7uVMNzOU9nAb0O0kKPtSD6qe8qI8Aa2uFY5bsTxaBj1qbKIkJkNoJ+ss+K1HxUo7JPtNbilApSlApSlApSlArwuDHnUCRGCuUvNKRza7tgjf9a96UFOt8Gp6eH2JYaclZVb7FKEqS0uEFonqStSkJWCeiRzb14kA+FZuUcIkzeIdhy/HrjDsEizq2AxASpT6TzBaFKJ+qoKI14VatKBSlKBSlKBSlKBSlRLJeI+HY9kdux663llm5XCQmMy0ElWlqB5QtQ9VG9dxOzsdKDW8WEF/IsAYB0U5Gl/Z7tIjvdPv9YVKcpRBTY5U2bbTcBEYW6lpuMl506STptKuhUddB4mozn6g5xJwKISFbly3gj28jH1vw5v61Lr6ZabHNMCKmXLEdfYsKdDYcXynSSsg8uz46OqCv+G0zBPzU2C+zrnDkwpjWmblfGY8d+SeZX19gJ5tJPQeArfQblw0nSkRIU/EpMhw6Q0y9GWtX3AHZqJ8PuHWQxuBuM4bc5kO03K0jldWmKxcG1gFetB1OhvmB2Bsa141tsd4cXS13mPPeyaJJQ0dlpOOwmSr/toSFD8KDWcQ3p9lzrD4MOxY0zZbreUwZBXES5IdBZcXtPQJQNpA8T0q1YrLMeOhlhptppA0lCEhKQPsA6Cqs4v2/PLrl2Mv4/i0GdAsNzRcVPPXZLCpH6JaC2lHIeXXP3k9dVaMBb7kJlySwI76kAuNBYXyK11HMO/XtoPalKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUHm8wy8pC3WW3FNklBUkEp2NHXsrm6U9+ajiHmeFK3+T+XWubd7KnfqsS0tK7dnv6BXePwrpWqd8rrDWMm4Rzbk095rdLEDOhSUkhSddFo2OulJJFBPeFLYZ4YYs0CSEWaIN+39CipLWqw1DbeI2htpIQ2mCwlKQNAANp0K2tApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlApSlAqC8a8BjZ3w8ulkYCItxWUyoMlACVNy2+ra99Ou+m/YanVKDnzhfnKs/yThpPmJ7K7W+Pd4t0b/VktIZbV+B3zCug6oizYVFxnyun7nAWEw73ZX5xjDfK1J50IcWkdw5wATV70ClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUH//2Q==)

4.2.2.   Hybrid Vehicles (NOVC-HEV only)

The statistical margin of the results of the testing methodology caused by the measurements shall be quantified. For each efficient exterior LED light included in the package the standard deviation shall be calculated in accordance with Formula 7:

Formula 7

![Formula](data:image/jpg;base64,/9j/4AAQSkZJRgABAQABLAEsAAD/2wBDAAMCAgICAgMCAgIDAwMDBAYEBAQEBAgGBgUGCQgKCgkICQkKDA8MCgsOCwkJDRENDg8QEBEQCgwSExIQEw8QEBD/wAALCABeAaIBAREA/8QAHQABAAIDAAMBAAAAAAAAAAAAAAcIBAUGAgMJAf/EAEsQAAEDAwIDAgwCBgcECwAAAAECAwQABQYHEQgSIRMxFBUYIkFRV1iVltPVCTIWNGF3l7MXI0JxgZHSMzhW5CQ2VWJzdXaTtdHU/9oACAEBAAA/APqnSlKUpSlKUpSlKUpSlK4DWXVSRpRZbLNt+HTcnuOQ3yNYIFuiSmY63JDyXFJJceUlCUgNq33Pqrh75rtrzYoYmucHWXzgXA32cDJLQ+4NwevKH99unf8AtFaHypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr13OkWs2o2ouQybPl/DjmOn8RiIqQ3cbzLhOsvOBaUhlIYdUrmIUVbkbbJPXuqXKUpSlKUpSlKUpSlKUpSoX4lP1vSH951o/kSqmilKUpSlKxLtdrXYbXLvd7uEeBb4DK5EqVJcDbTLSBupa1HolIAJJNeFkvVqySzwr/YpzU23XFhEmLJaO6HmljdK0n0gggg1nUpSlKUpSlKUrRWjOsNv+RXrELLk9tm3vHVNJu1uZkJVIhF1HO32rYPMnmSdwSOtbtZUlClIRzKAJCd9tz6q+f2quoXFppHpbYdfMs1Cutnzu65ymzN6bzGIMm2XCG9KU0xFjhhHahwspS72nalR87fY7V9AkklIJGxI6iv2lKUpSlKUpSlKUpSlKUqF+JT9b0h/edaP5EqpopSlRllHExoHhWWyMCyvVWwWzI4vZ9pan5G0r+sAKNmwOZW4IPmg12uIZji2f43BzDCr/CvVkubZdhz4TocZeSCUkpUPUQQfUQRW4pSqlasuo4p+JZnhmRMDmnenMWNkWoDLThAus11W8G1Ocqhu1sC84kjY7JB22q2TTTTDSGGG0tttpCEISNglIGwAHoFedKUpSlK0Wc5fAwHErnmNzt11nxbUwX3Y1rhLly3RuBs0yjdS1de4VA3l66Z+yfWv+Hlw/008vXTP2T61/w8uH+mnl66Z+yfWv8Ah5cP9NPL10z9k+tf8PLh/pp5eumfsn1r/h5cP9NcxxMMzIeMYvx56NY5c42T4dERLvFslQ1QpV6xpzbwqHLaUOYONJ3dRzAlspUdj0q0mH5XaM5xGzZrjj6ZFtvtvYuUNYUPPadbC0bkbjfZQ3qnWaaC8cWdaqXTUG5zNH21qIh4vJfmXCS/iMQq896E2WEtGWpPVTyxzEgJBSjpV3UghIBO5A6n11+0pSlKUpSlKUpSlKUpSlQvxKfrekP7zrR/IlVNFfOrju0YOkutNl4vzjkrMNPpT7MDUDHFvvKTHbUkNieylH5AEpRud/zpT6HFbWXxDhk4Rc8xa1ZpiWndouVmvcRqdBltSZPK8y4kKSobubjoe49QehqONR+AjH8Rvtr1m4Z7lkWPZni1waurNk8dvPW28IS6FPRFpkLUGe0aLjaSkhA3G6fSOlvWFZyOOA61OaO3a6Y7b9OTj1vubcmDzouKpKpCghtb6VpBbWWucj8xI/Keaug4GMMz3TXhzsGnWpGFzccvlifnB5l+THfQ6l6Y8+hTa2HFgjldSDvsdweh76sBSvB55mMy5IkOobaaSVrWs7JSkDckk9wAqov4bERWRabZ5rlcGXTc9UM7u12W+tYWlyK08WmEtqAHM2nZwAnvPN6K438SfINTNLrnptl2nWsmbY7+l2UxcduNug3FKIfg6k7lbaCglDnQ7q3IO/d0FbHi1Vq3wcYE3xCad685pkcKz3KDFuuK5a+zcIdxjuu8igh3s0usL3Unzkk9B3eu5GJX5OVYrZsoRGMZN4t8eeGSrmLYdbSvlJ9O3Ntv+yttSlKUr1S2PCor0Xt3We2bU32jSuVaNxtzJPoI7wfXXzz0cham5nxwawcPV/4jNVnMWwi1x5tqLV9Q3KC3RFUQ44GtlgdusDoO4b7mpC0x1X1W0c41GeD/ADXUG46j47kONfpBZrxeWmW7nbVIS+VsuuNISmQlRYX1KQRzI9R3ufSlYV7tEPILLPsNxb7SJcorsR9G5HM24gpUNxsR0J7qqz+GXeJS+G+RgU2a9Mc07yu84ql9aAlK22ZHaI5OpPKEvAed1GxHcBVs6UpSlKUpSlK4zVrVfFdGcOczTLTLXHMqNb4saGz2siXMkOBphhtO4HMtagN1EJHUkgDeuSxPiBn3DUAadahaVXzAZy7FKyFuVdbjAfiLisPssr/rY7ywlQU+ncK22HX0iu6Rqfpq4UhGoeMqK1BCQLtHPMonYAef1P7K6UEEbg9K/aUpSlKVC/Ep+t6Q/vOtH8iVU0Vq8otGPZBjd0seWQ4kqyT4b0e4szAksORlIIcDnN05eUnff0VT/wDDaxPJMNgakWfEsg8eaGIyJ4aez5TnNJfCVKTKU30G8fnASlZ2ClIWpI2USbqVxN21k09smq1k0UuV5cay/Ibc/dLfCER5SXYzJPOouhPZpI5VdCoHp+0b9tWFer3Z8btEy/5BdIttttvZVIly5TqWmWGkjdS1rUQEpA6knpXNaXav6d60Wa4ZDppkbd7tlsub1oflNNLQ34U0EFxKSsDmAC0+cNwfQTW3zj/qXf8A/wArl/ylVW78Lr/cg08/vun/AMjJqMfxb4vh2LaLQi++yJGokVousLKHEczahzIUPyqG+4PoNWCufBrphlM61v6j5Pn2dwrPMTcI1qybJpE23mQkHkcWwdkucu52Ctx1O4IJqG+LjUvOtLuJ3SDEmOIm64DgWft3NF5/qrW3GtiYcdvsiy7Ijq5OdxQCudShuoBO3QV2egdw1F1JnQs6xHXDMcw00yCLfbK7KuTdqakW+dEmBpi4Rlx46O1Q6GnglKkKACkqV37U/Dv1C1I1V0Yuue6pag3PJbmcjuFrbVKZjMtMMRlhKOVLLSPOO5Kid/RttVqaVAGYQ+PJ3KLo5gd60Mj46ZK/Fjd1iXZyYmPv5nbKbUEFe3U8o23PT11gW2F+Ie3cYq7tfeH56Cl9BktsQrwh1bXMOcIUVEBRTvsSCN9ulWOr5u6a6fNah/ig8QtuezDK8eEWywHw9j12XAec/qYA5VrQN1J677esA1cXAeF/TbTnIb/ndlkX2fm2QxDCkZVeriq5XRpnkCUoacfCkoSnYEJCdiQOYKAAqmOn/ENkycg1OxzVXjAzKNdcX1HTheOWe3tWJu4XGM5KRGQ8W3IZK+VSypakgJAQrpvsKkHiiyvia0J4aY97lavXpjKrVnabIxfERrcrx1aJMhRZedY7FSGnEoUEAJCTu2SRsrYXetMJdrt0W2v3SVcHWGghUmWpBefI71r5EpTufTskD9lZlVH4BPCV37iJet64xxlerl48VeDdn2XabI7cp5fR/sv2d+3pq3FKUpSlKUpSlV4465mAsaFpgan4ZKyXGLvkNqttxjwkuqmR2nZABkxg155eb/MkDffbbY77GEOGDFIWFcS0HGNGNar7q3pfPxecq8LvS03L9HpSVsdiwJZSOQulJ3jgA7I3Uk7JUOJ4RMbuea8E+X4RZdAY+dyrrkuQQ4rs6TAjw2XXCUIdUt1wPJ7MqB3QjmA/Kd6slptlV14StNtHNAdSIGTZlk17aNli3WA5HejOTkpcfMYuSHm1pS22ClClJAKGx6eg6+HxS4/Mj5+v+jjNG5GnUFq53KOpmEoyoi+33eiOIklp9KfBngoBYIKSNt+lSDpdqFa9WNPbBqTY7dPhWzJILVygtTktpfMd1IU2pSW1rCeZJB25txv12PSuqpSlKVC/Ep+t6Q/vOtH8iVU0VRfi/wCK7TG66qI4UMk1KfwfGWm0ys+yBoOdq/GUgKTaIxaQtQW8Fo7Vzpyt7pG5JAkyycffAvjdnhY/YNY7Hb7bbmERYkSNaprbTDSAAlCEhjYAAAACoqz78SfFdVMjxvQ7hRTd7lleaXRi2KyCTbFMRrPHU6A8+lt4AvOBoOKSCAkdFEkjlqXtVr5nuHcX2iFrtOod1ON5u1fYN0sTzEZcfeHA7Vt1DnZ9slSlqBUOfYlCdgOoMMzdXtZkTZCG+PK3spS8sJb/AKDpy+QBR2Tzbddu7f01NHC7meYZheb/AG/NeIiPqY23DbU1CRp4/jyIwKyFLUt1Oz2/QcoPTqdqxuBdxhVr1qSwtspTrLlGwQRsE9o1t3ej1VZh1pt5tbLzaXG3ElK0KG4UD3gj0iqj/hvOPYxp5qDojcnXfGGmefXe0lpwpHLFdc7ZhaGwTyNr5lkDu35tqmDVPhT0I1rvreR6n4W/fJzBbUyXLzOaaZWgbJWhpp5LaFgf2kpBPpNSJimLWbCseh4vjzUhq3QEFDCJEt6U4AVFR5nXlKcX1J6qUfV3VXTXzRHVjUHij0h1dx7GrBMxnTJF0VKamXUtSJ65jAb2Q32KkpCFJB3Urr16Dbrn2nAdcLdqVYZli05xnGdP8acvl5FohZS+7Iu93m8ykOPDsEobQFvSFFO60guAgeakCvsrTHVrhc4BdRsY1IsuCSHo13fvyGHpkifEurL76FmIUoMZxDgUkEKClAhP5TuQLYp1uah6xYBpQibjTUfK8aeuqGVrlJmuuNthaRFCWjHW0EJWVczoUkBOwO43mOlK9UuMzNivQ5AUWn21NLCVlJKVDY7EEEHY94O9QxjHBnw54dmaNQ8cwebDyRLrby7l+kNzW8+pspKA6VyD2qQUJ81fMk8o3FTRIW83Hdcjsh51KFFtsq5QtQHRO/o3PTeqbaP8OWrGDtauPZ1pNiGTOag5w5mduiDJVtNxVB5D7Dbj3g3OCh1tCiUpIO3cQSK0+tnDjxVazaL3nFr1AxJWVZFqEjKl8+RPmFbrbGS2mLDaJYJUrlRsohCBvzKO5VtUjzstuzXHJgdjyG0YsiVeMCuawlsuu3G1ONusqWwH+1DbiHCSoHsEq2Qeu29WPv8AeYWOWK5ZDcllES1xHpr6gCeVttBWo7DqeiTVXvwzrHNj8Nas8uVvchSdRcnu+WFhS90pRIfKG+Qd4SUNJI36nffuIq2FKVj3C4QLTCfud1nR4cOMguvyJDqW22kDvUpSiAkD1mtRjOf4JmpcGG5rYb92SQtzxZcmZXIncjc9mo7DcEb+sGt/SlKUpSvFSEr25kg7HcbjuPrrDuNsMu2SrfAnP2pyShSRKhobDrSiPzp50qRzD/vJI/ZUbcPHDpi3DVi83DcJyfJLlaJs1y49heH2HizIc2Lq0KbaQrziASCSN+7bc1xXFToZmuuGW6ZC3Y7Z7hjOHXxy93ZEq+yIEmUDHcZSy0WWipOxWFFfOnu22rUY3ofrJienmZ6b4xarNAxheLyrBi1kkZE5J5pEx6S7ImTJJihaSgvhCEpC90A8x5jvWix1ed6GXvhV0UzC3u7Jt8nHHn7NlDzcR2ZEtqt1vxewAktFDQUjmWkoWs7g7Am4FKUr8JAG5PSq5cO3F5adetbNVtLocFqNFwt+I5Y5HnBdzgqTyOydj0KO1AKFJ6FDiD/f03Ep+t6Q/vOtH8iVU0V6HIEF1ZcdhMLUrvUpsEn/ABrx8WW3/s+N/wC0n/6rybgwmVhxmGwhY7lJbAI/xqArXoPq9e9fcc1T1X1Fsd8suCO357GosK2qiykm4FLbYfIIQQzHSW/Tzk8x67bWErGuVug3eBItdziokxJbamX2VjdLiFDYpP7CK5LAtEtIdLZkq4ab6b49jMicnlkuWqA3GLw338/kA5uvrrtqqfqzGm8MvEpH4l4saWvTvPYbNh1D8HaC0WqU1smBdFoSObk6lpxZ35UkE+gVa1p1qQ0h9h1DjbiQtC0KBSpJG4II7wRXnSlet5hmQjs32UOJ332WkEb/AONV3yHBtWc94stP80uWCxbJiGmCb8GLqLo0/wCNW50NllkIZSAtpYWl0qCgQAlOxPN0sZSlKUpSvWY0cveEFhvtQNu05Rzf599Vc4x8uu+pvgfBzpPObeyzPeRGTyWStQx7Hd95El5SOiFugBptCvz856bEGrHYbiViwLE7NhOMQkxLTYYLFuhMJ7m2WkBCB/kB1rc0pVb+OTD8jzrBcNx/Ccpsduyf9MYc2y2u/MuO2zIJUdl94QJIQCOQpbU4OccpU0kdCQRB+mGW6n2rWfOLJnWnll0X1OYwyFOMzHrGL3ZrnZY850KkNxWVJd7ULkchUXFbJb35RtXXxeIfJ5mDwdTmuLW1jD7g6Wmb+rRq4pgApWW1KW8ZHK2lK0qSVLISCOpqYrZifEperdFu9o4ncOmwZrKJEaTHwEONPNKG6VoUJ+ykkEEEVk/oBxS+8div8Pf+fp+gHFL7x2K/w9/5+u90+suodktkiPqNnNuyict/nYkwrJ4sQ01ygchb7Z3mO4J5tx37bdN6rBxXytNk6opbyrLOJ+JNTbWP+j6couAtLaN1bblhooLp6lXnE/l327qhnwrRH/jfjx/yvH0quzwyDHf6ILWvF7tqHcYC35Kku574R45Cu1VzJe8ISlfKD+Xpty7bEiun1DnarQokNWlWOYxd5KnVCWi+3Z+ChtvbzShTLDxUd+8EDp6TXDeP+MH2Y6T/ADjcPt1PH/GD7MdJ/nG4fbqeP+MH2Y6T/ONw+3U8f8YPsx0n+cbh9urQ3mw8ReRX+1ZVftBdEbhebEortk+Tk81x+Eo77lpZtu6Cdz1Tt31Len0zU6bbJDmqWP43abgl/aO1Y7o9OaWzyjzlLdYZKVc3MNgkjYA7+iuL4pnsdZ0mknKLzqVbYKpsZJc09TIN4WrmJCE9ghSw303Ueg2ABPXY0v8ACtEf+N+PH/K8fSqb+Edem72o81WKZTxLzZyLU7vH1K8YeLFNlxvdTfbthHbA8u3XflKtt+u1trlb4t2t0q1zUqVHmMrjvJStSFFC0lKgFJIKTsT1BBHoqjesuMWnhm44tCNUsaiotGJZjbFaaXRtpJDSFIQBBSfOA6ksgb+hgkbnusLxKfrekP7zrR/IlVM6ublPIAVbdAe7eobcv3F6HFBrTLSgo3PKVZhcASPRuPF9ePj/AIwfZjpP843D7dTx/wAYPsx0n+cbh9up4/4wfZjpP843D7dTx/xg+zHSf5xuH26nj/jB9mOk/wA43D7dTx/xg+zHSf5xuH26nj/jB9mOk/zjcPt1eifcOLO6wZFsuekukEuHLaUxIjv5bPW262obKQpJt2ykkEgg9CDXrs7/ABV49aoljsWkGj0C3QGUx4sWNls9tphpI2ShCRbtkpAGwA6Csvx/xg+zHSf5xuH26nj/AIwfZjpP843D7dTx/wAYPsx0n+cbh9up4/4wfZjpP843D7dTx/xg+zHSf5xuH26nj/jB9mOk/wA43D7dTx/xg+zHSf5xuH26nj/jB9mOk/zjcPt1PH/GD7MdJ/nG4fbqeP8AjB9mOk/zjcPt1PH/ABg+zHSf5xuH26nj/jB9mOk/zjcPt1PH/GD7MdJ/nG4fbqeP+MH2Y6T/ADjcPt1am2QeJiy3m65FZ9DtFYV1vq23LnNj5PNbfmqbTyNl5YtvM4Up6DmJ2HQVtvH/ABg+zHSf5xuH26nj/jB9mOk/zjcPt1SZhsjM5WPRn8/tdot18UV+ER7TNclxUDmPJyOuNtqVunYndA2JI699buo91m0K0/15tdks2oUe4uxsfu7V8g+Az3YbjcxttxDa+0aIWOXtCobEdQPVtWhh6F4hp3a8szNFzv8AkeTzsfkW1y+5DcVTpqYSUrWmMhRAS21zkqKUpHMdirfYbVV4O9NdV9Yfw7MZ0xx7KsWx+wZLGu1tnzJFtemzUw3Z0hLwbT2iGkuEFWyjzADbpv1E1agahucGVl0a0mx9GLs4XcFtYmLzkNycjG3rYjKWH3VbcikLDZB3UnZSh6O7DkcVWrSdJtRtYrXg+IXfF8TtQvmP3qHcpXgWQRW1yUSm087QW062qONuikHnSQoghVWFwbLXL/huMX3IXbdCuWRW9iYiKy95pU40HChvn2UvlSrr09BNdPSlKUpVcuKVu56sZXhPC1YpsmLFy15d9zKVEfU09Fx6EtBU2FpIUhUl9TTKVD0Bz1VpXeATgzZ7btrPcm/B3kR3ubOboOzdXtyoVvK6KO42B6ncVKWjfDFo7oJcbldtMrHcoMm7MIjylS73NnBSEKKkgJkOrCTuT1SAalWlKUqtf4hulcvVLhaylVkQoX/EA3ldndbSkuNyIR7RXJv3Etdqkbekjv7q1M/VOHrZo9w3apQ1oJyHNrDJkJSoHs5IjSkvt9OnmupWn/DuFWqpSlKUpSsZ6522NITDkXCM0+ptbyWlupSstp/MsAnflG43PcN684kyJcIzc2BKZkx3khbbrKwtC0nuIUOhH7RUO8T2qWU4JYsWwzTpxhrNNSMhj4xZ5DzfapgJcSpcmcUf2+wYQtYB6FXLuCN6kjGrTbdP8TtOOS8ilS24DLUMXC7zO0ky3T053HFbczi1Enp6TsABsK3Ea4QJrj7UObHfXFc7J9LTiVFpewPKoA+adiDsevWsilK02Y3DJ7Vjc6fhmNx79eWkAxLdIniE2+oqAIU8ULCAASfynfbb01APCjrvq7qvqlrVhGq9vx6A7p7drdbokSylx1prtWXVOAvOBKndyhJ3KE7ddhtW0yzMch0T4l8Vt825zbhg2sjztrQxIcW6LJfmGi40WSSeVmS2FhSO5K2gobAqqwdKUpSlKUpWsyXGrHmFjmY1ktubn2y4NlmVGcJCHmz3pVsQSD6R6R0NabTnSnTrSOzuY/ppiMDHLY66XlQ4KChnnPeoI32STv12239NcXrToNctXszwPKhm0a2R8CuS7vEt71mTMblSlNLa3eUXUkoCHDskAdepJ6bcXkHB7PyDGs1xg6mxbbEy6xnGo8e2423Hi2i3OvOvykMMh4hTjzrxUXFlRTtsnbetLqFGvmnWu3DPiXhllv0aKmdZZTz+PNqkwkIgrS3LafKlKjF0obaIB2UQdj12q11KUpXHatZvftPMCumU4xgV1zG6RGVqi2i2qbS484EKI5lLUAhG485Q3I36JJ6Vy3D9xD4prloJaNckvMWuI7BcevLK3N022QwCJTayeuyClRBI6pKT6a4XhVx57VBOZ8T2b21RkasK8FskOSnzoeKMcyITO39kvArkLHpLyfVVeeErhx0ay7M+Ki35DhMSdCtuY3HH7bEeKnGbfE5VkGOhRKW3Ryo2dA50hCQCNqsvwAXW5Xrg40tn3ee/MkmzKaLz6ytZQ2+6hAJPU7ISlI/YBVg6UpSvVJjR5sZ2HKaS6w+hTTjahuFoUNiD+wg18y9BZUjTLLn+Ei7PntNNddrZPsiFk7qs85mW4zt0A2B2UdvS93D0/TmlKUpSlKoJqDoxppkf4pWMW664uw7Cm6fyMinxQtYZnT0zHEhyQjfZ0eaglCvNJQjcHapT4PLBbMH1m4kNPMYZMHG7Nl9ukW22NnaPCMq3NvPJZR3NoKz0SnYAAAd1ZmtrLEnjh4bmpTTbqWrdmL7aXEhQS6mHHCVgHuUATse8bmoz/FKwHFcismj90uFtKblP1JsthcnsOqakpgvF5S2kOJIKfO2UCOoUAR3V1jum2F6Rcd+lsTTSxs47EyHBL7EusWDu2zNTDVGLC3Uj/aODn6uK3Wdk7k7Va2/P3mNZLhIx2BHnXVqM6uFFkPllp58JJbQtwJUUJKtgVcp2B32NV5/TP8QT2H6QfOEz/wDNXXaW5LxZ3LLGomsGlun9mx1bLhcmWTJJEuS26Bugdk4wkKST0PnDbffrttUz1Tvg1/3qOLX/ANWWr+Q/XV8d3m4rpK6notGsGJ8qh3p3lKB2Po3B2qzNKUpSlKUpSlKUpSlKUr5oXXRXVTDeJLOeDnELe+1pbrlcmM1mXVtxSRa7UhZN1iNgeaFPK7NjpsQhbffv0+kCLLAj2NOO29tUCE3EEJhERRaMdoI5Ehsp6oKRtsR3bCoNxzgk0oxJvKG8dyvUmCrM3VyL46zmU5Lk19agVvLUF7lxWxSV95SpQ7jUg6KaI4boFh6MDwKXfFWRhZVFi3O6OzUxEnvbZ7Qns0bkq5R03JPpqQKUpSlUH4q9KZdh/ED4e9ZLSHm4OV3JFju4QpQbXJipW4wVjuJLazt/4Pd0Jq/FKUpSlKVCt64StNb7q9/TlKv+cNZclsxmpcfJ5TSGIpUVGM2hKtkMFRJ7Meb1NZuk/DFp/o1mF7zjE77mcm6ZIe0u5u2Rypzc57YBLzqHVEKcSkcqVHqEkgdK5bjAwi/PQMI12w61vXO+aPX4ZCu3x2Q4/PtSm1NXBhobElwsKKkpHepAHeQRtdQNEtGOL+w4jnt3yHIbjZWER7zYzaL8/EjJeG6mpPI0oDt0c6k8x85PUdNq81cI+nTupFn1Yl5bqJKyWxNJjwZUjLpjgbYHLzs8pXsW3CgFxPcvc829TdSla7IbK1kdjnWJ64XCC3PYUwqTb5S40loKG3M06ghSFD0KHUVDenHBxpbpTmc3PcOyPP2LrdpyLjdlP5ZMebur6OblVLQpRD/51dFb99c1qMw1xE8S2IaeWpvwnFdGbkjKspmjzmXLz2Shb7ek/lU4jnU+4OpRs13FVWapSlKUpSlKUpSlKUpSlfnKnm5+Uc222+3Xb1V+0pSlKUpWtvGN4/kD1ukXyyQp7tolpn29clhLioslKVJDrZI8xYSpQ5h12UfXWypSlKUpSlKVjwLfAtcVEG2Qo8SM3vyMsNJbQncknZKQANySf7yayKUpSlY8S3wIHbeAQo8bwl5Uh7sW0o7R1X5lq2HnKOw3J6nasilKUpSlKV//2Q==)

Where:

n: Number of measurements of the sample, which is at least 5

The CO2-emission correction factor 
![Formula](data:image/jpg;base64,/9j/4AAQSkZJRgABAQABLAEsAAD/2wBDAAMCAgICAgMCAgIDAwMDBAYEBAQEBAgGBgUGCQgKCgkICQkKDA8MCgsOCwkJDRENDg8QEBEQCgwSExIQEw8QEBD/wAALCAAgAD4BAREA/8QAGgAAAgMBAQAAAAAAAAAAAAAAAAcFBgkBCP/EACsQAAEEAgEEAgIBBAMAAAAAAAECAwQFBhEHAAgSIRMiFDFBCRUWMjh2tP/aAAgBAQAAPwDRbmnlpjhTB5mf2GFZJkdbWockWCaNll12JHQhS1vrS663tCQn34+R9/rWyILt47iabuRxD/PcTwPLKageG4E66jx2UWGlrQv4Q28tRCVIIJUEj36J96i+Bu6ij7g7m1rsW4uz6qr6h2TGeureuZZrlyWHQ24w28h5fyLCt/6gj6nZHTu6Ojo6Old3Tf8AGnlX/ptx/wCNzrOHiTuGzvB+z3h6ntMUybH+FRPk0Wb5dXqDU10SJEgpRF0CtuOCtCVvp0pSvJCCCNnVTCKnEaPEKepwGJAjY5HhNJq2oAH44jFIKCjXogg73/O9nZPS5su6jjFjJ7XFMdhZVl0rH3zGu3saoJNjGrHQElSHnm0+HmArZQgqUNEEA+upnGe4nh/K8DveT67MorGK45OkV0+1mAx2UPMhHmB56J9rSkDWyr66366gE92fFcZ+A5kcLLcZp7eSzDrb2+x2VArZbzp00gPOIHx+f8F0IHr9/rpzAggEHYP6PXelZ3L0vIWW8O5LgvHGKQruxymrm0yzLtRBbhofjuID+y2v5PFSk/QaJ3++k7298Cch472fy+2TmriylsWa+mlwmUx8gS43brfefdCQr4QYqkFaPFz7aV9hrXUJ2wced5HDfCl9wpneO082ugVVk3id1CyH5Z8EFhwxoq2y2AvThQEKCkhIOtEAdXT+m9NpHu0XDquB8LdrUKmQr+P8fxyGLNMlz5kyEkBXy+0klXsgg++kl3XMYeOL67KcEqbBjCKnn6Pa5s8tSXYT6EOJTLkhLe0ri/MEg7B04le/Y69J96t5hTPaNyTYZK7DmVdhjb7ML7JcTJkvI1E+L3pai6ptSfE79bG+qDgHJXPvG/B3GGJt8cx59vA44RfZBcZLYPRIkT8ZpAMVx1tpwCQpJBAWQAEK3rR6fXBvKcbm3iPFeV4lHKp2snr0TkwZR24xskEeWh5J2CUqAAUkgj99Xro6Olbk3bHwjleTzcyssL/HuLQg2cissJVf/cvQGpSYziEyBpIGnAr1sfyerrXYJhdRh6OPq3FquPjTcRUFNSiKgRfx1AhTZb14lJBOwR72d/vpeUPaR2+47aVlrBwBL6qN0P1MafYy5sStcCgUqixn3VssFJA8fBCfEAAaHXOfOC8n5sdposPlV/H6KuK1zqNVOzOhWzhIKPykLUn5EI1sNnaSdEg6HTBwHF5mGYdV4xYZDJvJUBkoesJDaW1vrKiokIT9UJBOkoHpKQlI9Dr/2Q==)
 shall be determined from a set of T measurements performed by the manufacturer, in accordance with paragraph 2.2 of Appendix 2 to Sub-Annex 8 to Annex XXI to Regulation (EU) 2017/1151. For each measurement, electric balance during the test and the measured CO2-emissions shall be recorded.

In order to evaluate the statistical error of 
![Formula](data:image/jpg;base64,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)
, all T combinations without repetitions of T-1 measurements shall be used to extrapolate T different values of 
![Formula](data:image/jpg;base64,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)
 (i.e. 
![Formula](data:image/jpg;base64,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)
). The extrapolation shall be performed in accordance with the method defined in paragraph 2.2 of Appendix 2 to Sub-Annex 8 to Annex XXI to Regulation (EU) 2017/1151.

The standard deviation of 
![Formula](data:image/jpg;base64,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)

![Formula](data:image/jpg;base64,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)
 shall be calculated in accordance with Formula 8.

Formula 8

![Formula](data:image/jpg;base64,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)

Where:

T: Number of measurements performed by the manufacturer for the extrapolation of the 
![Formula](data:image/jpg;base64,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)
 as defined in paragraph 2.2 of Appendix 2 to Sub-Annex 8 to Annex XXI to Regulation (EU) 2017/1151.

![Formula](data:image/jpg;base64,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)
: mean of the T values of 
![Formula](data:image/jpg;base64,/9j/4AAQSkZJRgABAQABLAEsAAD/2wBDAAMCAgICAgMCAgIDAwMDBAYEBAQEBAgGBgUGCQgKCgkICQkKDA8MCgsOCwkJDRENDg8QEBEQCgwSExIQEw8QEBD/wAALCAAkAEQBAREA/8QAGgABAQADAQEAAAAAAAAAAAAAAAcFBggJBP/EACwQAAEEAQQCAgIABgMAAAAAAAECAwQFBgAHERIIIRMxFCIJFRYXOEFRdrL/2gAIAQEAAD8A9Ct+d7oGwOEv7g3mEZNf0sBC3rJ2jZYdVAZSBy66l11s9Pf2nsRwSQB7182xe/ddv3gP9ycdwHK6alkMh+tcuWY7K7Jv9v2ZSh5frlHHK+vPII5HvWH8d/KKj8km5lhim2ed0tPFQot295XNR4UtxLnxqaYcQ6v5FAhXPA4HU+/oG1aaaaaahnnL/iBu5/1Wb/41wxgPkFllD45bC4rnuKZJjGwdlG/pvJ8miK+GVPkEKKUAgFbMEqPRTiSlboDoSQE/t6k47WUNLQ19Ti0OHEp4kZtqAxDSlLDbASOgbCfXXrxxx641K3vLDax67tKfGYeV5WzQyVw7izx7HpVhAr3kH90OPtoKVqT77BvuRweffrWbpfIzZy92znbxR8zisYfAmSoK7SSC2246w8ppQbB/ZwqWkhASCpZIABJ41h4vlTtim2q6zJYOV4mxfS0QKeyyPH5NfCsX1glttt5xICVKA5SHOhPIH361YtNNRby6xHcvcjZDJdr9s8Ur7eZl1bJq3pE22EJEALRwl3gtr+X3z+o6/X3qbbc7CZ5K8LH/ABp3h2qqJsiooU1MNqPkKFosnQVKDyXfhH4qkK6lJIV7Gta2ewnzQ258V8u2V3FoKqdMqcTtImK31XffPOB/HWIsVbSkDspJPVCwocJSkcHjnVT8BLHG5viHts1jy4qTX06INm00gNqYsGiUyUOp4BS78gUVdhyeeffPOuet/wAYRXUOzeaYfVzoO2FTvpLnZG9IUlyB2VYOgyyEcoMUyA4W1EcJ5H0Tq8fxC7PFmvDvcBu9SxLNpXtRKdoAOLfsXHUCL8I55UsOdVDryeEk++NarY+QW+Oyu0keXc7WxpkTbnAqO2ye0vrR2G5YyXGAHo0RaWVtrkIU2oKCljlS0f8AOunMXzWlyiupZTTwhzruoZumqyUoImNx1pQSpbR/YdVOJSo8cBR41n9NNNSi88WNiMgyGwyiZgqY865cLtqK+wlQWLNZ+zKZYcQ3I5+j8iVcgnn7Ot5XgWFLww7dLxSqVi5hfy005io/E/F69fi+Ljr14/1xrQsY8U9hsSu6vIazBzIm0SgqnNnZS7BusIHAMVuS6tEfgeh8YTwAAPoaw29fjrmO7uaQMjZ3mfqairjpEPH36GPPhNTAokTerih3fTyOilAhHHKQCedSW62wzbAPN+gzisrtyMmrLHDqymTNbs3fwlSm5iUSRKXwUIaTGT+SW1dQ46P1JWrjXZ2mmmmmmmmmv//Z)

Where the standard deviation of the power consumption of each efficient exterior LED light (
![Formula](data:image/jpg;base64,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)
) and the standard deviation of the 
![Formula](data:image/jpg;base64,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)

![Formula](data:image/jpg;base64,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)
 lead to an error in the CO2 savings (
![Formula](data:image/jpg;base64,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)
), that error shall be calculated by means of Formula 9.

Formula 9

![Image 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)

4.3.   Statistical margin for Low beam AFS

Where the Low beam AFS is present, formulae 9 shall be adapted to take into account the additional measurements required.

The value of the uncertainty (
![Formula](data:image/jpg;base64,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)
) that is to be used for the Low beam AFS shall be calculated with the following formulae 10 and 11:

Formula 10

![Formula](data:image/jpg;base64,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)

Formula 11

![Formula](data:image/jpg;base64,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)

Where:

n: Number of measurements of the sample, which is at least 5

![Formula](data:image/jpg;base64,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)
: mean of the n values of Pc

5.   ROUNDING

The calculated CO2 savings value (
![Formula](data:image/jpg;base64,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)
) and the statistical margin of the CO2 saving (
![Formula](data:image/jpg;base64,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)
) shall be rounded to a maximum of two decimal places.

Each value used in the calculation of the CO2 savings may be applied unrounded or rounded to the minimum number of decimal places which allows the combined impact of all rounded values on the savings to be lower than 0,25 gCO2/km.

6.   STATISTICAL SIGNIFICANCE

It shall be demonstrated for each type, variant and version of a vehicle fitted with the efficient LED lightings that the uncertainty of the CO2 savings calculated in accordance with Formula 6 or Formula 9 is not greater than the difference between the total CO2 savings and the minimum savings threshold specified in Article 9(1) of Implementing Regulation (EU) No 725/2011 (see Formula 12).

Formula 12

![Formula](data:image/jpg;base64,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)

Where:

|  |  |  |
| --- | --- | --- |
| MT | : | minimum threshold [g CO2/km] |
| Formula | : | total CO2 saving [g CO2/km] |
| Formula | : | standard deviation of the total CO2 saving [gCO2/km] |

Where the total CO2 emission savings of the efficient LED lighting as determined in accordance with the testing methodology set out in this Annex are below the threshold specified in Article 9(1)(b) of Implementing Regulation (EU) No 725/2011 the second subparagraph of Article 11(2) of that Regulation shall apply.

---

[(1)](#ntc1-L_2019176EN.01007101-E0001)  
[OJ L 4, 7.1.2012, p. 17](./../../../legal-content/EN/AUTO/?uri=OJ:L:2012:004:TOC).

[(2)](#ntc2-L_2019176EN.01007101-E0002)  
[OJ L 213, 18.7.2014, p. 1](./../../../legal-content/EN/AUTO/?uri=OJ:L:2014:213:TOC).

[(3)](#ntc3-L_2019176EN.01007101-E0003)  
[OJ L 285, 30.9.2014, p. 1](./../../../legal-content/EN/AUTO/?uri=OJ:L:2014:285:TOC).

[(4)](#ntc4-L_2019176EN.01007101-E0004)  
[OJ L 250, 22.8.2014, p. 1](./../../../legal-content/EN/AUTO/?uri=OJ:L:2014:250:TOC).

[(5)](#ntc5-L_2019176EN.01007101-E0005)  
[OJ L 237, 8.8.2014, p. 1](./../../../legal-content/EN/AUTO/?uri=OJ:L:2014:237:TOC).

[(6)](#ntc6-L_2019176EN.01007101-E0006)  
[OJ L 148, 12.6.2010, p. 55](./../../../legal-content/EN/AUTO/?uri=OJ:L:2010:148:TOC).

[(7)](#ntc7-L_2019176EN.01007101-E0007)  
[OJ L 323, 6.12.2011, p. 46](./../../../legal-content/EN/AUTO/?uri=OJ:L:2011:323:TOC).

[(8)](#ntc8-L_2019176EN.01007101-E0008)  
[OJ L 302, 28.11.2018, p. 114](./../../../legal-content/EN/AUTO/?uri=OJ:L:2018:302:TOC).

[(9)](#ntc9-L_2019176EN.01007101-E0009)  
[OJ L 250, 22.8.2014, p. 67](./../../../legal-content/EN/AUTO/?uri=OJ:L:2014:250:TOC).

[(10)](#ntc10-L_2019176EN.01007101-E0010)  
[OJ L 89, 25.3.2014, p. 101](./../../../legal-content/EN/AUTO/?uri=OJ:L:2014:089:TOC).

[(11)](#ntc11-L_2019176EN.01007101-E0011)  
[OJ L 222, 24.8.2010, p. 1](./../../../legal-content/EN/AUTO/?uri=OJ:L:2010:222:TOC).

[(\*1)](#ntc*1-L_2019176EN.01007101-E0012)  Activation speeds to be checked for each vehicle application in accordance with UN/ECE Regulation No 48 section 6, chapter 6.22, paragraphs 6.22.7.4.1 (class C), 6.22.7.4.2 (class V), 6.22.7.4.3 (class E), 6.22.7.4.4 (class W).

---

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