**3. Results**

The di fferent results of the baseline scenario and the proposal of refurbishment strategies that affect both the city and the university will be broken down.

#### *3.1. Baseline Scenario*

The baseline is defined as the current scenario of the city of Donostia-San Sebastián and of the university campus related to the total global warming potential (GWP) emissions and emissions per habitant or user in 2015 year. The results of the city and the university are analysed separately for subsequent discussion.

#### 3.1.1. City of Donostia-San Sebastián

Data on the energy consumption of the city and its emissions are obtained from di fferent publications of the municipal, regional, and Basque Country authorities [41–47]. A comparative analysis of the information from the di fferent data sources is used to compose a sample of the most relevant outputs or results for this study (Table 1). The main districts that make up the city of Donostia-San Sebastián are modelled in NEST to determine the CO2 emissions of the sample (Table 1). The city of Donostia-San Sebastián is modelled in NEST (Figure 5), using information from di fferent origins. The building geometry in the model is defined using DXF files provided by the city planning department and from cadastral information.


**Table 1.** Data analysis for the city of Donostia-San Sebastián.

**Figure 5.** Two of the districts modelled in the neighbourhood evaluation for sustainable territories (NEST) for the city of Donostia-San Sebastián. (**a**) "Ensanche Cortazar" district; (**b**) "Parte Vieja" district.

Information on the energy performance of each building in the city is obtained from previous studies [29], and the Register of Energy Performance Certificates of the Basque Country [48]. The following statistics for the Donostia-San Sebastián inhabitants are determined from the mobility plan: 30% travel in private cars, 30% by bus, 3% by train, 25% by bicycle, and 17% by foot [46]. Based on Ecoinvent v3.0, NEST defines the environmental impact of the di fferent mobility systems. The conversion factor from car, bus, tram, train, bicycle, and walking to GWP will be 0.29, 0.10, 0.09, 0.08, 0.00, and 0.00 (kg CO2-eq/(user·km)), respectively.

A comparison of the results of the two calculation methodologies for the city of Donostia-San Sebastián shows that the monitoring global warming potential (GWP) data are 11% and 16% higher than the simulation results for the building and transportation sectors, respectively. Note that the main causes of this di fference are the uncertainty in many of the hypotheses used in the two calculation processes and di fferent quantification measures, such as the scope of the parameters. However, this di fference is acceptable because the simulation can be used to rapidly evaluate the most sustainable option among di fferent proposals. The identification of the most sustainable option remains valid after comparing the simulation and monitoring results. It is important to bear in mind the percentage deviation between the results of the two methodologies while acknowledging that simulation is indispensable.

In spite of the energy impact that it may suppose, the SEAP of Donostia-San Sebastián does not consider as input the energy consumption of the municipal sewage. Furthermore, the SEAP does not propose any strategy to reduce the environmental impact related to this process. Therefore, the municipal sewage will be out of the scope of this study.

#### 3.1.2. Campus of Donostia-San Sebastián

Several parameters are monitored and quantified for the university campus in Donostia-San Sebastián for each of the campus buildings from 2015–2017 (see Appendix A). Note that the monitoring is limited to inventorying the di fferent energy consumption points. Through the correct definition of "conversion factor" values, the energy consumption is transformed into environmental impact. For the natural gas source, the related impacts were deduced from the Ecoinvent database, applying the "Heat production, natural gas, at boiler modulating" process. The conversion factor from natural gas applied by this study to GWP will be 0.2 (kg CO2-eq/kWh). For the oil source, the related impacts were deduced from the Ecoinvent database, applying the "heat production, light fuel oil, at boiler 100 kW, non-modulating" process and its conversion factor applied by this study to GWP will be 0.34 (kg CO2-eq/kWh). Finally, the conversion factor from electricity (Spain 2016) applied during this case study to GWP will be 0.3 (kg CO2-eq/kWh).

Information for the buildings that compose the campus in Donostia-San Sebastián are obtained from di fferent UPV/EHU documents [49], to compile Table 2, which shows the environmental impacts associated with the mobility of users (workers, teachers, and students) of the Donostia-San Sebastián campus for 2015.


**Table 2.** Emissions from the university campus after the correction of the baseline scenario.

The first revision of the campus model is developed in parallel in NEST (Figure 6), based on a model developed by Leon et al. [38]. However, the monitoring data show that the initial simulation model needs to be calibrated in two regards. First, the number of campus users is adjusted, because 12,248 users were used in Leon et al.'s study [38], whereas a corresponding mean value of 11,066 is determined for 2015–2017 from the monitoring process. Second, regarding transportation, the information in the documents show a new hypothesis for the mode of displacement of the campus users [49]: 36% travel in a private car, 30% by bus, 12% by train, 15% by bicycle, and 7% by foot. Table 2 shows the GWP emissions of the Donostia-San Sebastián campus that are obtained after defining and modelling all of the hypotheses for each building and the transport scenario in NEST.

**Figure 6.** Graphical evaluation of the simulation of impacts for the campus with NEST.

A comparison of the results of the two calculation methodologies for the Donostia-San Sebastián campus shows that the monitored GWP data are 14% lower for the building sector and 21% higher for the transport sector than the simulation results. However, the total difference between the two methodologies is only 4%.

Considering the differences in the results for the buildings, the simulation process of NEST is based on a series of default assumptions to assess the energy consumption and environmental behaviour of the buildings. Considering these assumptions, it is understood that there will be certain variation between the building simulation result and the real performance of the building [50–52]. The reasons for the performance gap in a particular building can be several but in general, the performance gap happens due to the accuracy of the default values in the building simulation, variation of the weather data, or the influence of user, understood as user behaviour. Regarding transportation, it is very difficult to completely match the hypotheses simulated in the initial model with the monitored data, because the latter are based on questionnaires given out to campus users. This difference in the hypotheses results in an estimated impact for the transportation sector that is 21% higher for the university survey data than that calculated by NEST simulation.

### *3.2. Refurbishment Scenarios*

#### 3.2.1. Joint Plan Scenarios

In previous studies by Oregi et al. [29] and Leon et al. [38], theoretical rehabilitation scenarios associated only with the university were proposed and were not related to the action plans of the city of Donostia-San Sebastián. By contrast, in this study, the values and strategies defined by the SEAP of Donostia-San Sebastián [42], are used as a starting point to align the strategies of the university at the general level with those of the municipality at the local level. The SEAP of Donostia-San Sebastián comprises four strategic lines of action: (1) Energy efficiency, (2) renewable energies, (3) mobility, and (4) waste. The guidelines proposed by the city plan for adequate waste managemen<sup>t</sup> (boosting second-hand markets (in particular), general awareness campaigns to promote reuse, promotion of reusable diapers, creating an infrastructure for territorial composting, taking advantage of surplus stores, etc.) are not aligned with university waste managemen<sup>t</sup> strategies. Therefore, waste-related improvement actions are outside the scope of this study.

The first part of the study results is based on SEAP data (Table 3) and indicates the GWP emissions resulting from the application of 100% of the strategies for each strategic line. The relevance of each strategy group for a 100% reduction of GWP emissions is presented. However, all of the strategies have not been and will not be applicable to the city of Donostia-San Sebastián. Thus, in collaboration with di fferent public stakeholders, the authors provide a critical review in the "revised data" section. This new section reflects three types of data: (1) The percentage of application or applicability of each strategy group; (2) the reduction in GWP emissions for this percentage of implementation; and lastly, (3) the relevance of each strategy group to 100% reduction in GWP emissions after reviewing the applicability of the strategies (more information about each SEAP strategy can be found in Appendix B). The authors use these revised values to determine the strategies for consideration in this study in terms of realisable actions implemented between 2011 and 2019 in the municipality of Donostia-San Sebastián.


**Table 3.** Summary table of improvement strategies according to the SEAP of Donostia-San Sebastián.

According to the SEAP data, the strategic line of mobility is the sector in which up to 60% of total GWP reduction can be obtained. Thus, the main action group is focused on the "Reduce transportation consumption", whose application contributes 55.2% to the total reduction in GWP emissions. The strategic line of energy e fficiency contributes 31.4% to the total reduction in GWP and includes action groups such as "Heating & cooling consumption reduction" and "reduce lighting consumption", whose application would contribute 14.0% and 9.9%, respectively, to the total reduction in GWP emissions. Within this line of e fficiency, there are 28 other strategies with an overall influence below one percent (see the data in Appendix B). Lastly, the strategic line of renewable systems contributes 8.6% to the total reduction in GWP. This line includes action groups such as "Photovoltaic" and "Biogas", whose application would contribute 2.8% and 2.5%, respectively, to the total reduction in GWP emissions.

The estimated reduction in GWP emissions changes completely under an objective and critical review of the application or applicability of these strategies. With the support of different public stakeholders, the authors have conducted an exhaustive study on the application of each of the strategies in the municipality of Donostia-San Sebastián, tracking all of the actions based on different public sources and data from the energy department of the city of Donostia-San Sebastián.

An immediate conclusion that can be drawn is that it is essential to maintain the implementation of mobility strategies because of their 97% applicability. An opposite conclusion is drawn for the implementation of renewable technologies in Donostia-San Sebastián, for which only three percent of the SEAP proposed objectives has been implemented over the last eight years. Lastly, the applicability of most of the strategies associated with the strategic line of efficiency is projected to exceed 50%, and these strategies should therefore be considered in the study.

The strategies considered in this study based on a critical interpretation of the SEAP data are shown in Table 4. Following the existing SEAP guidelines, these strategies will be applied over a 10-year period (2020–2030). A cut-off is defined for action groups with applicability that is greater than 20% and a contribution above two percent to reducing final GWP emissions. Contrary to some European guidelines [53–55], this study will not consider any strategy associated with the strategic line of renewable technologies because of the low applicability of these strategies in the municipality of Donostia and the reduced impact on the final results proposed by SEAP. Regarding the final selected strategies, the applicability selection criterion has been maintained, but the selection criterion for the contribution to the reduced final GWP emissions has been changed to 0.4%. The strategies in this study are selected based on the percentage contribution to the total GWP reduction from the data reviewed (see Appendix B).

As shown in Table 4, for the 10 strategies to be evaluated by NEST in this study, SEAP has limited application to a particular building typology. For example, four of the ten strategies defined in Table 4 are limited to residential or commercial buildings. In addition, strategy 8 ("Acquisition of clean vehicles by the city") is limited to city vehicles. Therefore, although 10 strategies are proposed for evaluation at the municipal level in the study, only five of these strategies can be applied at the university campus level. Thus, universities should analyse different municipal policies for mobility on their campuses to achieve an optimal and coherent global mobility policy.

#### 3.2.2. Results of Joint Refurbishment Scenario

A separate NEST model is developed for each strategy, and the reduction in the GWP emissions by the application of each strategy is shown in Table 5. In addition, three new scenarios are identified: In the first scenario (strategy 11), all of the energy efficiency strategies are applied together; in the second scenario (strategy 12), all of the mobility strategies are applied together; and in the third scenario (strategy 13), all of the strategies in Table 5 are applied together. The emissions reduction is calculated using the following values from Table 1; Table 2: GWP emissions from Donostia-San Sebastián of 6.96 × 10<sup>8</sup> and 9.68 × 10<sup>6</sup> kg CO2-eq from the university campus.


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**Table 5.** Number of GWP emissions avoided in each strategy.

The results of the analysis for the city of Donostia-San Sebastián show that, as shown by the SEAP plan for the city of Donostia-San Sebastián, strategy 10 for the city's mobility model results in the highest GWP emissions reduction of 20.6% (1.43 × 10<sup>8</sup> kg CO2-eq) relative to the 2015 scenario. The implementation of efficiency strategies reduces GWP emissions by up to 11.2% (7.75 × 10<sup>7</sup> kg CO2-eq). Within this strategic line, the following strategies stand out: "Renewable shop lighting" (6 strategy), "Improving the efficiency of residential buildings by replacing windows and energy-rehabilitating housing" (strategy 7) and "Developing guidelines with savings measures for the tertiary sector" (strategy 3), which reduce 2015 GWP emissions by 3.4% (2.37 × 10<sup>7</sup> kg CO2-eq), 2.5% (1.73 × 10<sup>7</sup> kg CO2-eq), and 2.2% (1.55 × 10<sup>7</sup> kg CO2-eq), respectively. Finally, the application of all of the strategies considered in this study (strategy 13), would reduce GWP emissions by 33.3% (2.32 × 10<sup>8</sup> kg CO2-eq) annually compared to the current scenario. The signatory cities to the 2017 Global Covenant of Mayors for Climate and Energy committed to reducing emissions in 2030 by 40% of those for the base year 2007. Considering that GWP emissions of the city of Donostia-San Sebastián were 9.92 × 10<sup>8</sup> kg CO2-eq in 2007 [42], an evaluation of the scenarios proposed by this study shows that the city of Donostia-San Sebastián could meet its commitment by implementing this joint plan with the university. In turn, the city would meet the objective set by the European Commission [56], which defined an objective of reducing GWP emissions from reference year by a minimum of 40% by 2030.

As shown in Table 4, only five strategies for reducing GWP emissions from the university campus have been applied. In comparison to the results for the city, given that the impact of the buildings is 57% of the total GWP impact of the campus, the strategic line with the greatest amount of reduced GWP emissions is that of energy efficiency, at a reduction of up to 21.3% (2.06 × 10<sup>6</sup> kg CO2-eq) of total campus emissions. Within this line, strategy 3 ("Preparation of guidelines with savings measures for the tertiary sector") stands out by reducing emissions by 11.5% (1.11 × 10<sup>6</sup> kg CO2-eq). An improved mobility scenario can also significantly reduce total GWP campus emissions by up to 13.2% (1.28 × 10<sup>6</sup> kg CO2-eq).

Table 5 shows a second measure that can be used to analyse the impact of GWP on each user in the city and the university campus. The effect of applying each strategy is similar to the total results. However, the impacts of the university campus are approximately 10 times below those of the city. There are two contributions to this difference. First, the level of energy efficiency of the different buildings of the campus is quite high, resulting in lower consumption than for older buildings in different city districts. Second, more users consume the same number of resources in the university than in the city. Therefore, the impact per person is lower for the university than for the city, where there is a lower density.
