RUE Index as a Tool to Improve the Energy Intensity of Container Terminals—Case Study at Port of Valencia

: A container terminal is a high energy demanding organization. Giving priority to the investments that have a better impact on the minimization of energy consumption is a prime concern of the management board of the Port Authority and container terminals. This study presents a tool for the classiﬁcation of the processes that take place in the port. The Relevant Use of Energy ( RUE ) index is described and applied to a case study, the Port Authority of Valencia, which is the main port of the Mediterranean in container tra ﬃ c. Three di ﬀ erent functional units have been discussed for the assessment: Twenty-foot Equivalent Units (TEU), cargo handled, and several container ships that operate in the port. The implementation of speciﬁc projects following the recommendation of the RUE Index validated the tool and allowed a reduction of the energy intensity of 29.5%, and a 40% reduction of the cost of energy per TEU between 2008 and 2016. However, there is still room for improvement, and project lines are proposed for this matter. A partnership between the RUE Index and an energy management system ISO 50001 certiﬁed and ensures quality data giving conﬁdence to the decision-making process of the management board.


Introduction
Achieving a carbon-neutral economy is a priority [1]. The adequate management of the sources of energy is a key aspect of this mission. Ports, as nodes of the global economy, are expected to reduce emissions and energy consumption along the whole maritime supply chain [2]. Furthermore, port authorities are looking to protect local communities from any negative effect caused by their operations [3]. The minimization of energy consumption, together with air quality control is the main concern of European ports [4].
When assessing the energy consumption of an organization in absolute values, the evolution is irregular and unpredictable as external factors are involved. For example, the global economy influences the market and determines the business volume of a subsequent period even five years later. To make operational and investment decisions, relative values are needed. Traditionally, the energy intensity is calculated in economic terms (per units of gross domestic product, GDP) to assess the energy efficiency of a country or region [5]. This study proposes a tool with a more useful definition for decision-makers on an organization scale, defining a formal functional unit consistent with the activity of the company.
1. Plan: The definition of an index has been planned to provide quality information for decisionmaking to the authorities of a port where several container terminals can be operating.
2. Do: The index is defined within the framework of the energy management system.

Definition of Indicator
For the definition of the tool, as an index, the standard UNE 66175 [26] for the definition of indicators in 6 stages: Energies 2020, 13, 2556 4 of 19 1.
Selection of indicator 2.
Denomination of the indicator 3.
Calculation method: specification and information sources 4.
Specifications for representation 5.
Definition of responsibilities 6.
Definition of thresholds and objectives The index is validated through a case study. The processes of three container terminals in the Port of Valencia are identified, and the RUE Index for the set of processes is calculated for 2008 (base). Since then, the index is calculated every two years until 2012 and then every year.

Data Quality
The quality of the inputs of the tool when assessing the case study has been assessed by the DQR index. This methodology has been proposed by the European Commission [27] in the International Reference Life Cycle Data System, and considers six indicators for each parameter to be used in the assessment: These indicators are assessed by an expert panel with a ratio between 0 and 5 where 0 stands for "non-applicable", 1 is the best quality with a representation greater than 95%, 2 if it is between 85% and 95%, 3 if it is neutral with representation between 75% and 85%, 4 if it is bad (50% to 75%) and 5 is the worst punctuation under 50% of the representation. The DQR index is calculated as in Equation (1): where Xw is the worst level of all indicators, and i represents the number of indicators applied. If the result is equal or under 1.6 the data is considered "high quality"; between 1.6 and 3 (equal or less) is considered "basic quality"; over 3 is classified as "data estimated". Figure 2 shows the methodology applied to assess energy intensity. Each step is described below.

Energy Intensity Methodology
Energies 2020, 13, x FOR PEER REVIEW 5 of 20 The goal and scope of the study should be well defined from the first stage to ensure relevant findings. What is going to be analyzed and how results are going to be used is essential information

Goal and Scope Definition
The goal and scope of the study should be well defined from the first stage to ensure relevant findings. What is going to be analyzed and how results are going to be used is essential information to address a proper study. The quality of data required, the allocation procedure, and any assumption taken must be clearly established here. A functional unit is required to give the right dimension to the results (ISO 14040, [28]). The functional unit does not necessarily need to correspond to the operational unit; however, if the results of the study are going to be part of the management of the organization, the understanding between the management board and researchers' language should be ensured.

Inventory Analysis
The inventory analysis will draw the full picture of the organization within the system boundaries previously defined. Detailed information about each process included in the system boundaries of the study and its information source must be well described. These processes have been classified into operational processes and infrastructure. The energy consumption should be disclosed by type of energy (the type of fuel or electricity source). The data quality analysis is carried out in this stage, given sufficient information to dimension quantify the results obtained in the following stage.

Energy Intensity
The energy intensity for organizations is calculated as the relationship between energy consumption for a certain year and the functional unit in Equation (2).
where I stands for energy intensity, E represents the energy consumption in joules (J), and FU is the functional unit defined in the first stage of the study.

Interpretation of Results
The interpretation of results should be carried out considering the goal, scope, system boundaries, and assumptions made. The quality of data gives information regarding the uncertainty of results and is significant when addressing the following stage. Results and its interpretation must be exhaustively reported when the goal of the study is to serve as a tool for managers, as is the case with the study presented in this article.

Results
Following the PDCA cycle described in the previous section, once planned, the index has been defined (Section 3.1) and applied to the case study (Section 3.2). The DQR has been assessed, and the results of the case study have been analyzed. In this section, the results of applying the methodology described in the previous section are presented. The interpretation and discussion of results, along with the proposal of new actions and predictions are described in Section 4.

Proposal of RUE INDEX
The concept of RUE is proposed, defined as substantial energy consumption that classifies the potential for improving energy performance according to ISO 50001. To obtain the RUE for each process considered, a set of four parameters was defined and evaluated by an expert panel: The assessment of the relative magnitude (RM) relativizes the consumption value with respect to the total energy consumption by means of the percentage value obtained after 80/20 analysis of the consumption of the energy aspects evaluated in a category or process. A five-level scale is provided to allocate in an aggregate manner those aspects whose relative consumption represents from 1% to 5% of consumption, from 5% to 10% in the second, from 10% to 15% in the third, from 15% to 20% in the fourth and finally all the energy aspects that exceed 20% of consumption. Those aspects that represent 80% of the global consumption evaluated are scored applying the Pareto rule. The weight of this criterion is 50% according to expert evaluation through the application of Saaty's Analytical Hierarchy Process (AHP). Energy efficiency (EE) is assessed based on the characteristics of the machinery. For better classifications, a degradation in the evaluation is proposed through 5% ranges. The weight of this criterion is 20% according to expert evaluation through the application of the AHP. Recoverability is assessed by thermography or an analysis of the minimum thermal jump necessary to be exploited. Five levels of evaluation are proposed. In those cases where such studies are not available, the maximum is assigned. The weight of this criterion is 15% according to expert evaluation through the application of the AHP. Finally, the potential use of renewable energy index (PRE) is assessed by considering the possibility of introducing this type of energy. The lowest score is for those processes where it is not possible to implement renewable energy. The weight of this criterion is 15% according to expert evaluation through the application of the AHP. The scale for each parameter is shown in Table 1. The final score is assessed using Equation (3): If RUE is less than 40%, it is classified as "less important", if it is equal or between 40% and 70% is considered "outstanding", if it is higher than 70% is "significant".
The use of results obtained for a certain year is an input to the general management system in this case study as it is part of the decision-making process to define new projects to improve the performance of the organization. In this stage, opportunities for improvement are highlighted, and predictions can be made over different scenarios as information for the management board. All analyses related to possible projects must be properly documented as it is valuable information even if projects are not developed for some reason. The final stage, once projects are chosen, is the planning and implementation, followed by a new assessment to see the benefits. The return of investment is a key indicator of any management board. It is the decision of the organization if this information is public or private.

Case Study: Valencia Port
The energy consumption of all the processes and machinery included in the organization's activity is needed to assess the energy intensity of an organization. A rigorous internal system that registers data and statistics for the annual report and Carbon Footprint Assessment of the PAV has allowed the compilation of this information with sufficient detail on an annual basis. The analysis of the results of a certain period has allowed the organization to define new projects to improve the EE of their activities.
Hereafter, the tool described in the previous section is developed and applied to the case study of a container port in the Mediterranean Sea, the second main port in Spain. It can be applied for the period 2008-2016 on an annual basis because starting in 2008; the organization has rigorously recorded its energy consumption with the aim of assessing its carbon footprint under the ISO 14064 standard [29].

Goal and Scope Definition
The goal of the energy intensity assessment for PAV is to improve the environmental performance of the organization along with the financial results, by minimizing the energy costs to enhance competitiveness. This goal is linked to the previous assessment of the carbon footprint. The scope of the study includes all the processes developed by the organization and managed under their internal management system. System boundaries are defined within the process where the organization has operational control to apply improvement measures.
The definition of the functional unit for a container port such as the Port of Valencia has been discussed with several stakeholders and managers of the port authorities and the port terminals as it is the aim of this system to serve as a tool for decision-making. The operations in this type of terminal are quantified in tons or Twenty-foot Equivalent Units (TEU). Therefore, the functional unit for this study has been defined as the "number of TEU" although the cargo handled and the number of container ships, are also considered.
When possible, direct measures are preferred to ensure high-quality data. Any assumption should be clearly described and justified. The DQR index is assessed for all variables considered.

Inventory Analysis
The inventory analysis of this case study describes all the processes involved within the system boundaries and identified in the energy review carried out by the energy management system. As mentioned before, the period between 2008 and 2016 was considered. However, between 2008 and 2012, the system gathered information every 2 years, whereas between 2013 and 2016, the system gathered data annually due to the need for monitoring the implemented measures. Figure 3 shows the energy consumption by energy type. Energy consumption associated with diesel comprised 83.6% of the total energy consumption in 2008. As this study will show, the electrification of machinery and consumption optimization reduced diesel consumption to 74.1% in 2016.

Operation Processes
Under this category, all operational processes developed in the stockyard are considered. The energy consumption of storage depends mainly on 4 variables: Type of machinery/vehicle used (fuel/electric, age, etc.) 2.
Distance (from the mooring point to the stockpile, from the vehicles' parking to the mooring point or stockpile, etc.) 3.
Stock requirements of the container (if it is refrigerated it will require plugged electricity) 4.
Storage time  Except for gantry cranes, powered by electricity, all types of machinery are mainly powered by diesel. RTG, trucks, and Forklifts are mainly used to move loaded containers. Another significant process is the electrical supply of refrigerated containers, commonly called reefers. The inventory of the fleet of each type of machinery and the number of plugs for reefers (reefer points) during the period under analysis is shown in Table 2.  It is in this area where the movements of containers take place. To carry these movements, the organization uses 5 different vehicles/machines for different maneuvers: Except for gantry cranes, powered by electricity, all types of machinery are mainly powered by diesel. RTG, trucks, and Forklifts are mainly used to move loaded containers. Another significant process is the electrical supply of refrigerated containers, commonly called reefers. The inventory of the fleet of each type of machinery and the number of plugs for reefers (reefer points) during the period under analysis is shown in Table 2.  Figure 4 shows the energy consumption of each type of vehicle/machine for the period 2008-2016. Forklifts and ECH are presented together due to a limitation of the energy management system. However, it should be considered that it has a low contribution concerning the other machines (between 4% and 7%). Machines are highlighted as the main energy consumers: RTGs and Trucks. Together, they consume over 70% of the energy.
Energies 2020, 13, 2556 9 of 19 Figure 4 shows the energy consumption of each type of vehicle/machine for the period 2008-2016. Forklifts and ECH are presented together due to a limitation of the energy management system. However, it should be considered that it has a low contribution concerning the other machines (between 4% and 7%). Machines are highlighted as the main energy consumers: RTGs and Trucks. Together, they consume over 70% of the energy.

Infrastructure
The Port of Valencia had 5 docks during the studied period. However, the docking line and the storage area have changed during the analyzed period. Table 3 shows the evolution of the port infrastructures. It should be noticed that some dredging and expansion work has been carried out between 2012 and 2014, reducing both the docking line and the stockyard surface with a significant increase of the stockyard surface in 2015. Two different areas can be clearly identified in a container terminal regarding its infrastructure: the stockyard, offices, and workshops including, changing rooms for personnel. The energy consumption of the stockyard is caused by two processes: lighting and electricity for reefers. However, the last one is considered an operational process and has been mentioned in the previous section.
The type of lights and the time that those lights are on are the two factors that determine the consumption of electricity associated with this process. The time is variable as it depends on the operations that take place in the stockyard. If no night shifts are needed, the lighting is minimized to security services; if the terminal is working during the night, different lighting systems will be required to be on. Therefore, lighting at the stockyard depends on the volume of operations and how/when those take place.
Almost all organizations have offices and some personnel facilities, such as changing rooms, canteen or dining rooms, etc. Electricity for lighting, computers, and air conditioning and sometimes fuel or gas for heating systems is the energy consumers of these facilities. For the case study, no fuel or gas consumption is needed, because the offices, as well as all the other systems, are powered by electricity. Figure 5 shows the energy consumption of infrastructures for the period under analysis.

Infrastructure
The Port of Valencia had 5 docks during the studied period. However, the docking line and the storage area have changed during the analyzed period. Table 3 shows the evolution of the port infrastructures. It should be noticed that some dredging and expansion work has been carried out between 2012 and 2014, reducing both the docking line and the stockyard surface with a significant increase of the stockyard surface in 2015. Two different areas can be clearly identified in a container terminal regarding its infrastructure: the stockyard, offices, and workshops including, changing rooms for personnel. The energy consumption of the stockyard is caused by two processes: lighting and electricity for reefers. However, the last one is considered an operational process and has been mentioned in the previous section.
The type of lights and the time that those lights are on are the two factors that determine the consumption of electricity associated with this process. The time is variable as it depends on the operations that take place in the stockyard. If no night shifts are needed, the lighting is minimized to security services; if the terminal is working during the night, different lighting systems will be required to be on. Therefore, lighting at the stockyard depends on the volume of operations and how/when those take place.
Almost all organizations have offices and some personnel facilities, such as changing rooms, canteen or dining rooms, etc. Electricity for lighting, computers, and air conditioning and sometimes fuel or gas for heating systems is the energy consumers of these facilities. For the case study, no fuel or gas consumption is needed, because the offices, as well as all the other systems, are powered by electricity. Figure 5 shows the energy consumption of infrastructures for the period under analysis. TEU, Cargo, and Container Ships (Functional Unit) As previously described, the functional unit defined for this case study is the number of TEU managed in the port. The number of containers varies regarding the business volumes of the companies that require these services. Although it is not the objective of this study, it is important to highlight that the global economy and its crisis directly influence the number of containers moved between ports. Table 4 shows the number of TEU managed in the Port of Valencia for the period 2008-2016, where the business growth in recent years can be observed. Cargo and container ships that operated in this period are also included. It is important to notice that container ships do not have a linear relationship with TEU as cargo ships do. The definition of TEU is strictly related to cargo ships. However, the number of ships that operate in the port is not directly linked. It is expected that a higher number of ships implies a higher amount of TEU. However, ships also move empty containers, although it is not desirable. This is the reason the number of ships that have an influence on the operation processes of the port is also considered in this study for analysis purposes. In the period under study, there has also been an evolution of the fleet. Now, bigger ships carrying more containers are often seen. Thus, a direct relationship cannot be established between the TEU, or cargo handled and the number of ships.

DQR Index
The DQR index is calculated to assess data quality. The energy management system and the accounting system have allowed the collection of all the data required for this assessment. Table 5 shows the result of the data quality analysis, where it can be seen that a high-quality index is the most frequent. It can be noticed that except for two types of machinery (Forklifts and ECH), the DQR is high as the energy management system allows a detailed and complete data acquisition. The energy consumption of Forklifts and ECH is treated as one unique indicator. This is the reason the parameters C and M are assessed with the representativeness of 50%. The overall DQR index for these inputs is basic quality.

TEU, Cargo, and Container Ships (Functional Unit)
As previously described, the functional unit defined for this case study is the number of TEU managed in the port. The number of containers varies regarding the business volumes of the companies that require these services. Although it is not the objective of this study, it is important to highlight that the global economy and its crisis directly influence the number of containers moved between ports. Table 4 shows the number of TEU managed in the Port of Valencia for the period 2008-2016, where the business growth in recent years can be observed. Cargo and container ships that operated in this period are also included. It is important to notice that container ships do not have a linear relationship with TEU as cargo ships do. The definition of TEU is strictly related to cargo ships. However, the number of ships that operate in the port is not directly linked. It is expected that a higher number of ships implies a higher amount of TEU. However, ships also move empty containers, although it is not desirable. This is the reason the number of ships that have an influence on the operation processes of the port is also considered in this study for analysis purposes. In the period under study, there has also been an evolution of the fleet. Now, bigger ships carrying more containers are often seen. Thus, a direct relationship cannot be established between the TEU, or cargo handled and the number of ships.

DQR Index
The DQR index is calculated to assess data quality. The energy management system and the accounting system have allowed the collection of all the data required for this assessment. Table 5 shows the result of the data quality analysis, where it can be seen that a high-quality index is the most frequent. It can be noticed that except for two types of machinery (Forklifts and ECH), the DQR is high as the energy management system allows a detailed and complete data acquisition. The energy consumption of Forklifts and ECH is treated as one unique indicator. This is the reason the parameters C and M are assessed with the representativeness of 50%. The overall DQR index for these inputs is basic quality.

Energy Intensity
The results of this case study include both the assessment of the energy intensity and the application of the RUE Index. Results are discussed considering the projects related to EE and environment implemented in the Port of Valencia during the period under analysis.
Once the inventory is complete, and an adequate quality of data is ensured, the energy intensity assessment can be developed. Figures 6 and 7 show the results per year obtained where total results in megajoules (MJ) per functional unit are shown as a line with data labels.

Energy Intensity
The results of this case study include both the assessment of the energy intensity and the application of the RUE Index. Results are discussed considering the projects related to EE and environment implemented in the Port of Valencia during the period under analysis.
Once the inventory is complete, and an adequate quality of data is ensured, the energy intensity assessment can be developed. Figures 6 and 7 show the results per year obtained where total results in megajoules (MJ) per functional unit are shown as a line with data labels. Figure 6. Energy intensity results for the case study using TEU as the functional unit. Figure 6. Energy intensity results for the case study using TEU as the functional unit. In both cases, the main contributors are RTG and trucks, as expected due to their consumption. However, the total energy intensity behaves differently depending on the functional unit applied. The port activity is not only linked to the amount of TEUs but also to the traffic of container ships as there are some fixed costs in terms of energy required for the maintenance of the stockyard and for cargo movements, both loaded and empty. Table 6 shows the result of the RUE Index applying the methodology described in Section 2 for the period 2008-2016. Results highlight the processes with a relevant environmental impact due to their energy consumption. The tool has been applied yearly, with data supplied by the energy management system and disclosing significant information to the management board in order to boost those projects, which guarantee that the investment return will include environmental as well as economic benefits.  In both cases, the main contributors are RTG and trucks, as expected due to their consumption. However, the total energy intensity behaves differently depending on the functional unit applied. The port activity is not only linked to the amount of TEUs but also to the traffic of container ships as there are some fixed costs in terms of energy required for the maintenance of the stockyard and for cargo movements, both loaded and empty. Table 6 shows the result of the RUE Index applying the methodology described in Section 2 for the period 2008-2016. Results highlight the processes with a relevant environmental impact due to their energy consumption. The tool has been applied yearly, with data supplied by the energy management system and disclosing significant information to the management board in order to boost those projects, which guarantee that the investment return will include environmental as well as economic benefits.

Discussion
Once the energy intensity and the RUE Index have been assessed, results are discussed. The results of the projects implemented by PAV are considered for the interpretation of results. Furthermore, applying the index presented in the study, a proposal for new actions, and the prediction of its impact are presented.

Interpretation of Results
Three relevant projects were implemented before the period under analysis. They are included in the table just for information purposes, although the benefits could not be assessed due to lack of data. Table 7 shows projects implemented since 2008, although the first projects were registered in 2010. The table includes the benefits in terms of reduction of energy consumption over the consumption of the process/infrastructure before the implementation of the project. Table 7. PAV Projects.

Year Project Benefit
Before 2008 Improvement of the refueling process management for vehicles and machinery Improvement of the start-stop protocol for different machines Installation of solar panels for sanitary facilities in the workshops Electricity consumption reduction (gantry cranes): Installation of black box pilot tests on cranes 1.00% Table 7. Cont. EFICONT project was financed by the Spanish National R+D+I Plan with the goal of improving EE on container terminals facilities. CLIMEPORT Project [30] focused on the assessment of different methodologies to fight global climate change, financed by the MED program (Europe in the Mediterranean). The GREEN CRANES project [31] main goal was the improvement of the eco-efficiency of port cranes; it was financed by the European Commission. SEATERMINALS [32] is focused on integrating smart and energy-efficient technologies in ports. State law allowed project funding since 2011 [33]. The law allowed bonus port taxes if part of that bonus is invested in projects aimed at improving EE or the port's environmental performance.

Year
Regarding Figure 6, gantry crane, RTG, trucks, and reefer points are classified as significant for all the years under analysis. Although improvements were made, the RUE does not change during this period. Forklifts and ECH are classified as outstanding. Office and exterior lighting have changed their classification along the period under analysis, due to the implementation of certain projects directly related to these areas.
For an in-depth analysis, the relationship between energy intensity and the functional unit is assessed. Figure 8 shows the evolution of the energy intensity in both functional units and the evolution of the functional units during the period under study. The increase of only 23% in TEU was not proportional to the increase in container ships. The optimization of cargo load and operation during the period under study is evident. Results show that between 2008 and 2016, the organization has decreased its energy consumption by 29%, with 31% of TEU increase. Figure 9 shows the evolution of the energy intensity variation where the influence of projects described in Table 7 overall energy consumption is assessed. The benefits of the implementation of energy-related projects are shown. It should be noticed that although the number of TEU between 2012 and 2013 was reduced by 3.2% due to the economic crisis, the energy intensity was reduced by 8.3% over that period. Even when the stockyard surface increased by 11.4% in 2014, the increase of energy consumption was only 7.6%. Therefore, an increase in 3.9% in TEU generated an increase of 3.6% in energy intensity.   Figure 9 shows the evolution of the energy intensity variation where the influence of projects described in Table 7 overall energy consumption is assessed. The benefits of the implementation of energy-related projects are shown. It should be noticed that although the number of TEU between 2012 and 2013 was reduced by 3.2% due to the economic crisis, the energy intensity was reduced by 8.3% over that period. Even when the stockyard surface increased by 11.4% in 2014, the increase of energy consumption was only 7.6%. Therefore, an increase in 3.9% in TEU generated an increase of 3.6% in energy intensity.  The energy intensity was reduced by 29.5% during the full period. The general trend is the reduction of energy intensity regardless of the cargo handled by the port. The implemented measures increased machinery efficiency; thus, the same cargo can be managed with less energy consumption. The installed capacity needed for the same activity was significantly reduced.  The energy intensity was reduced by 29.5% during the full period. The general trend is the reduction of energy intensity regardless of the cargo handled by the port. The implemented measures increased machinery efficiency; thus, the same cargo can be managed with less energy consumption. The installed capacity needed for the same activity was significantly reduced.
The savings in monetary terms are also important to consider the feasibility of projects. Figure 10 shows the cost of energy per functional unit on the period under analysis. Energy cost has been normalized by applying the Consumer Price Index (CPI) of Spain for 2008 (year base). The total number of TEU handled are also represented. It can be seen that the investments made to improve the EE based on the results of the RUE Index have reduced the cost of the energy per functional unit. In monetary terms, PAV reduced the cost of energy per TEU a 40% and 38% considering the cost per cargo handled (tons).

Proposal of Future Actions and Prediction of Results
There is still room for improvement, so considering the priorities, the following measures are proposed: • Implementation of start-stop systems in trucks with the goal of reducing fuel consumption by 10%.
• Hybridization of RTG aiming at reducing 70% of their energy consumption.
• LED lighting installation on trucks and RTG, reducing their consumption an additional 2%.
• Implementation of energy criteria in operational protocols, reducing the fuel consumption an extra 1%.
• Implementing systems designed to recover heat from combustion engines.
Regarding electricity consumption, the installation of LED lighting in all offices may reduce energy consumption by 44%. Furthermore, the installation of photovoltaic panels in reefers platforms, over roofs, and cranes to reduce electricity consumption may add a decrease of nearly 16%.
The implementation of these measures would account for a relevant improvement in the RUE Index results for the processes, as shown in Table 8.

Proposal of Future Actions and Prediction of Results
There is still room for improvement, so considering the priorities, the following measures are proposed: • Implementation of start-stop systems in trucks with the goal of reducing fuel consumption by 10%.

•
Hybridization of RTG aiming at reducing 70% of their energy consumption. • LED lighting installation on trucks and RTG, reducing their consumption an additional 2%. • Implementation of energy criteria in operational protocols, reducing the fuel consumption an extra 1%. • Implementing systems designed to recover heat from combustion engines.
Regarding electricity consumption, the installation of LED lighting in all offices may reduce energy consumption by 44%. Furthermore, the installation of photovoltaic panels in reefers platforms, over roofs, and cranes to reduce electricity consumption may add a decrease of nearly 16%.
The implementation of these measures would account for a relevant improvement in the RUE Index results for the processes, as shown in Table 8.

Conclusions
The energy management system provides direct data. Having an ISO 50001 verified energy management system ensured not only good data quality but also the availability of all data required.
This new methodology aimed at assessing the relevance of the use of energy by each process has allowed project prioritization and has proven to be a useful tool in daily management. The minimization of energy costs has improved the competitiveness of container terminals. Terminals reduced their energy cost per TEU by 40%. The identification of the machinery and installations which generated a significant impact on energy consumption was crucial. The quality of the data used gave confidence to the port management board during the definition of projects and investments required for the implementation of the proposed measures. The project portfolio has proven to be efficient as the implementation of these projects allowed significant changes in energy consumption, both energy intensity and cost.
Based on the RUE analysis, there is still room for improvement. A proposal has been made along with a prediction of the improvement that can be achieved.
Regarding related previous studies, although there are some energy consumption assessments that have been previously carried out, these studies present results with a different approach focusing on the carbon footprint of the terminal (t CO 2 ). Also, their focus is only on one terminal and not in the port itself. The functional unit also changes; therefore, results are not comparable. However, it is important to highlight that these studies where energy consumption data is required to produce estimations applying simulations or mathematical models, pointed out the relevance of having quality data and the significance of being able to have an environmental impact assessment.
Further research within the same case study might address the carbon footprint of this Spanish terminal, where some comparisons can be made.