IUE

Intelligent identifier for integration and logistics efficiency

It is a project created by EEA Grants with the purpose of reducing the amount of carbon dioxide emitted in the municipality of Sintra using an innovative logistics system.

The estimation of emissions through the bottom-upshould be based on the level Tier 2 From the EMEP/EEA methodological guide (EEA, 2019). This approach (Tier 2) considers, for the estimation of emissions of each pollutant (i), for each vehicle category (j) the following parameters (equation 1).

A digital solution that allows to orchestrate and manage in an optimized way all urban logistics processes

The development of a Unique Address Identifier, a code that unequivocally identifies a physical address, promoting increased effectiveness and efficiency of logistics activity

Implementation of a combination of complementary initiatives at the level of living labs that will amplify the impacts of the platform and the IUE

A digital solution that allows to orchestrate and manage in an optimized way all urban logistics processes

The development of a Unique Address Identifier, a code that unequivocally identifies a physical address, promoting increased effectiveness and efficiency of logistics activity

Implementation of a combination of complementary initiatives at the level of living labs that will amplify the impacts of the platform and the IUE

CO2 and logistics activity

The estimation of emissions will be based on a bottom-up approach, as this is considered to be the most reliable approach since it is based on a more detailed knowledge of the emitting sources (vehicle fleets related to logistics and waste collection) and implies the adjustment of the calculations to the specificity of the polluting sources.

União de Freguesias de Sintra e a União de Freguesias de Massamá e Monte Abraão are the first parishes to embrace the project that will apply solutions using technological features as a pillar of its construction, where an IUE will be generated for each property in the parish. This IUE in turn may have several functionalities, but initially the functionalities will be the reading of domestic meters, the use of the IUE for an environmental logistics/delivery system, and the IUE will be fundamental for the installation, maintenance and accounting of the bio-waste separated by the project participants.

Equation 1

This "integrated approach that contemplates intelligent logistics solutions, based on collaboration and sharing, in order to optimize the number of vehicles and resources needed in several operations" (1) with Living Laboratory in Massamá and Monte Abraão Parishes (Sintra Municipality) translates into concrete results, namely:

  • 1.

    Annual reduction of CO2 emissions produced by waste management and urban logistics activities;

  • 2.

    Reduction of kms traveled by vehicles involved in urban logistics operations in living labs;

  • 3.

    Increased vehicle occupancy rate for last-mile deliveries;

  • 4.

    Implementation of innovative mitigation/decarbonization measures.

Emission Factor (FEi,j,k) specific for pollutant (i), vehicle category (j) and technology (k) [given in g/km-vei];

Kilometers traveled (Qj,k,l) per vehicle, of category (j) and technology (k) [given in km];

Number of vehicles (Nj,k,l), of category (j) and technology (k) [given in vei].

I4EFFICIENCY

Considering the previous equation it will be necessary to gather disaggregated information regarding:

  • number of vehicles per category: light passenger, light goods, heavy passenger, heavy goods, motorcycles and mopeds;
  • fuel: diesel, gasoline, LPG, natural gas;
  • technology, represented by the Euro Standard: Pre-Euro and Euro 1 to Euro 6, obtained on the basis of the year/month of manufacture of the vehicle;
  • segment: mini, small, medium, large, obtained according to the cylinder capacity;
  • distance traveled.

The emission factors to be used in the estimation will be those in the EMEP/EEA guide (EEA, 2019), by vehicle category.

The estimation of emissions will be based on a bottom-up approach, as this is considered to be the most reliable approach since it is based on a more detailed knowledge of the emitting sources (vehicle fleets related to logistics and waste collection) and implies the adjustment of the calculations to the specificity of the polluting sources.

This "integrated approach that contemplates intelligent logistics solutions, based on collaboration and sharing, in order to optimize the number of vehicles and resources needed in several operations" (1) with Living Laboratory in Massamá and Monte Abraão Parishes (Sintra Municipality) translates into concrete results, namely:

  • 1.

    Ei - Emission of pollutant i (g);

  • 2.

    FCj,m - fuel consumption of vehicles of category j using fuel m (kg);

  • 3.

    EFi,j,m - Specific fuel consumption emission factor for pollutant i, for vehicle category j and fuel m (g/kg);

Note : the vehicle categories to be considered are: light vehicles (passenger and goods), heavy vehicles (passenger and goods) and all motorcycles and mopeds. The fuels to be considered include gasoline, diesel, liquefied propane gas (LPG) and natural gas.

Equation 2

Still regarding the estimation of emissions, if it is considered to estimate other pollutants, to determine the SO2 pollutant, the emissions are obtained through the variables indicated in Equation 3, where:

  • 1.

    SO2 Emission,m - SO2 emissions by fuel type m (g);

  • 2.

    KS,m - sulfur content of fuel m (g/g fuel);

  • 3.

    FCm - total fuel consumption m (g).

For pure electric (or battery electric) vehicles, the exhaust emissions will be zero, so they do not directly contribute to air pollutant emissions. All CO2 emissions that they produce implicitly will be due to the production of electricity, which is part of the power generation.

According to the factors published in Order No. 17313/2008, of June 26, for the purposes of accounting for carbon intensity by greenhouse gas emissions, it should be considered, in calculating CO2 emissions associated with electricity consumption, that the emission factor associated with electricity consumption is equal to 0.47 kgCO2e/kWh.

Equation 3

I4EFFICIENCY

Efficiency in waste management

The characterization should cover only the undifferentiated waste to determine the bio-waste content, which will allow the characterization of the baseline situation and the situation at the end of the project.

The methodology to be applied for the characterization of waste production in the living laboratory areas will be based on the sampling and characterization methodology recommended in Ordinance No. 851/2009, of August 7, which approves the technical standards for the characterization of urban waste.

Taking into account the number of accommodations in the implementation areas of the living labs it will be necessary to adjust the sample size since the current legislation refers to a minimum number of 21 samples of 350kg.

  • 1.

    Mixing the residues, making several revolving movements;

  • 2.

    Spreading the waste so that it forms a coarse disk;

  • 3.

    Divide this disk into four roughly equal parts, rejecting two opposite quarters;

  • 4.

    Mixture of the remaining rooms;

  • 5.

    Repeat the sequence of the previous steps until the desired sample weight is reached.

  • 1.

    Mixing the residues, making several revolving movements;

  • 2.

    Spreading the waste so that it forms a coarse disk;

  • 3.

    Divide this disk into four roughly equal parts, rejecting two opposite quarters;

  • 4.

    Mixture of the remaining rooms;

  • 5.

    Repeat the sequence of the previous steps until the desired sample weight is reached.

The processing of the data obtained should result in the average physical composition of the stream characterized, expressed in terms of the average values obtained for the percentage by weight of each category and subcategory, on a wet weight basis.
The following statistical parameters must also be determined at each category level:

Minimum;

Maximum;

Median;

Standard Deviation;

Coefficient of variation;

Erro percentual, com 95 % de probabilidade;

Confidence interval of the mean, with 95 % probability;

I4EFFICIENCY

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