• Energy Research

A Life Cycle Assessment (LCA) with focus on carbon footprint, followed by Life Cycle Costing (LCC) of municipal solid waste (MSW) management were conducted in a residential area of a medium-sized European city of 80,000 inhabitants. The initial results showed high environmental impacts and lack of economic sustainability, due to the high amounts of waste landfilled, the low extent of separate collection, low performance of mechanical-biological treatment as well as absence from alternatives to landfilling of non-recyclable materials. Taking this result as a baseline scenario, three improvement.s were tested with the aim of turning the carbon footprint of the local MSW management system into a neutral value: (i) increased separate collection of recyclables, (ii) enhanced biogas production and (iii) refuse-derived fuel (RDF) production. Successively adding the improvements, three alternative improved scenarios were defined, until reaching a negative carbon footprint, meaning that an optimised system would avoid GHG emissions. The proposed changes were sufficient to achieve carbon neutrality, as well as reduce overall environmental impacts, but were not enough for achieving economic sustainability due to the great influence of collection costs, especially for separate collection. It was concluded that by using an adequate combination of several treatment options and increasing the separate collection of recyclable materials it is possible to turn MSW management into a carbon neutral activity as well as improve its economic balance.

A Life Cycle Assessment (LCA) with focus on carbon footprint, followed by Life Cycle Costing (LCC) of municipal solid waste (MSW) management were conducted in a residential area of a medium-sized European city of 80,000 inhabitants. The initial results showed high environmental impacts and lack of economic sustainability, due to the high amounts of waste landfilled, the low extent of separate collection, low performance of mechanical-biological treatment as well as absence from alternatives to landfilling of non-recyclable materials. Taking this result as a baseline scenario, three improvement.s were tested with the aim of turning the carbon footprint of the local MSW management system into a neutral value: (i) increased separate collection of recyclables, (ii) enhanced biogas production and (iii) refuse-derived fuel (RDF) production. Successively adding the improvements, three alternative improved scenarios were defined, until reaching a negative carbon footprint, meaning that an optimised system would avoid GHG emissions. The proposed changes were sufficient to achieve carbon neutrality, as well as reduce overall environmental impacts, but were not enough for achieving economic sustainability due to the great influence of collection costs, especially for separate collection. It was concluded that by using an adequate combination of several treatment options and increasing the separate collection of recyclable materials it is possible to turn MSW management into a carbon neutral activity as well as improve its economic balance.

CCS 2023. 5ª Conferência Campus Sustentável, realizada em Viana do Castelo, de 26-27 de outubro de 2023 One of the key components in practicing sustainability and developing a sustainable Higher Education Institution (HEI) is the role played by the institution community including students and its staff, both administrative and academics according to a whole-campus approach. However, there is still a lack of how to integrate sustainability-related approaches into the HEIs, by involving the whole community of the HEI, in particular in distance-learning institutions. Recognizing this lack, Universidade Aberta (UAb), the Portuguese Distance Learning University, has recently integrated sustainability approaches into the University actions by establishing the “UAb Sustainable Campus Group” in 2021. In line with Agenda 2030, the Group promotes the sustainability approaches at UAb in 5 core elements of sustainability actions at HEIs: i) planning and administration, ii) operation and innovation, iii) academia and research, iv) assessment and reporting, and v) involvement with the community. This paper aims to reveal the recent sustainability initiatives (2021-2023) carried out by the Group and enlighten how a distance-learning University's actions, programs, and approaches have been integrated into the core elements of sustainability actions at HEIs. Several steps were implemented to address sustainability initiatives at UAb based on the proposed framework, considering: 1) “planning and administration”, by the formal commitment to sustainability that arose in the UAb Strategic Plan (2019-2023) to enforce sustainability initiatives centered on people and social commitment; 2) “operation”, by UAb contribution to the resource efficiency programs; 3) “academia and research”, by designing a micro-credential module entitled “Sustainable Development Goals (SDGs)”, that were piloted in the academic year 2022-2023 and were evaluated the students’ knowledge improvement on SDGs and sustainability and their perceptions on the effectiveness of the training through pre- and postquestionnaires; 4) “assessment and reporting”, through self-assessment and reporting tools, among them, Time Higher Education Impact Ranking, which ranked the UAb sustainability initiatives towards SDGs 4, 5, 6, 8, 10, and 17. Also, with the collaboration of the administrative staff, the UAb Ecological Footprint (EF) was self-assessed through the University Ecological Footprint Calculator, which allows HEIs to calculate their consumption of natural resources and ecosystem services within the direct and indirect responsibilities of the administration, by bearing in mind the specific feature of distance learning. The results for “planning and administration” showed the UAb commitment to sustainable campus by the signatory of several associations, e.g., RCS-Portugal, AASHE - Association for the Advancement of Sustainability in Higher Education, and Lisbon Green Commitment to implement a set of measures to reduce the environmental impacts by 2030. In the “operation” element, the results revealed the UAb contribution to the Resource Efficiency Program in Public Administration - ECO.AP 2030 to reduce the consumption of resources and respective Greenhouse Gas emissions facilities, as well as the Energy Saving Plan (2022-2023). The results of the “academia and research” element based on the SDGs course showed the students’ knowledge improvement of both Sustainability and SDGs and a high level of satisfaction with the training, mainly appreciating the “diversified learning resources”. The results of the “assessment and reporting” based on THE displayed the UAb improvement on SDGs 4 and 5. The results of the EF calculator disclosed that “staff labour” was the main driver of the UAb Footprint in direct responsibility, whereas “energy consumption at home” was the main indirect contributor to the UAb Footprint, which showed the effect of distance learning on the results. Future work of the Group includes offering the SDGs micro-credential, targeting also public and private organizations, and later as a formal curricular unit in various degrees, thus enhancing the “involving the community” element. Also, based on the results of the assessment tools, the major drivers of unsustainability at UAb will be identified to assist the necessary improvement to reduce the environmental impact of UAb, thus reinforcing its commitment to building a more sustainable community. The framework for the implementation of sustainability in UAb can be adapted and tested in other HEI to produce robust knowledge and action in this context. info:eu-repo/semantics/publishedVersion

CCS 2023. 5ª Conferência Campus Sustentável, realizada em Viana do Castelo, de 26-27 de outubro de 2023 One of the key components in practicing sustainability and developing a sustainable Higher Education Institution (HEI) is the role played by the institution community including students and its staff, both administrative and academics according to a whole-campus approach. However, there is still a lack of how to integrate sustainability-related approaches into the HEIs, by involving the whole community of the HEI, in particular in distance-learning institutions. Recognizing this lack, Universidade Aberta (UAb), the Portuguese Distance Learning University, has recently integrated sustainability approaches into the University actions by establishing the “UAb Sustainable Campus Group” in 2021. In line with Agenda 2030, the Group promotes the sustainability approaches at UAb in 5 core elements of sustainability actions at HEIs: i) planning and administration, ii) operation and innovation, iii) academia and research, iv) assessment and reporting, and v) involvement with the community. This paper aims to reveal the recent sustainability initiatives (2021-2023) carried out by the Group and enlighten how a distance-learning University's actions, programs, and approaches have been integrated into the core elements of sustainability actions at HEIs. Several steps were implemented to address sustainability initiatives at UAb based on the proposed framework, considering: 1) “planning and administration”, by the formal commitment to sustainability that arose in the UAb Strategic Plan (2019-2023) to enforce sustainability initiatives centered on people and social commitment; 2) “operation”, by UAb contribution to the resource efficiency programs; 3) “academia and research”, by designing a micro-credential module entitled “Sustainable Development Goals (SDGs)”, that were piloted in the academic year 2022-2023 and were evaluated the students’ knowledge improvement on SDGs and sustainability and their perceptions on the effectiveness of the training through pre- and postquestionnaires; 4) “assessment and reporting”, through self-assessment and reporting tools, among them, Time Higher Education Impact Ranking, which ranked the UAb sustainability initiatives towards SDGs 4, 5, 6, 8, 10, and 17. Also, with the collaboration of the administrative staff, the UAb Ecological Footprint (EF) was self-assessed through the University Ecological Footprint Calculator, which allows HEIs to calculate their consumption of natural resources and ecosystem services within the direct and indirect responsibilities of the administration, by bearing in mind the specific feature of distance learning. The results for “planning and administration” showed the UAb commitment to sustainable campus by the signatory of several associations, e.g., RCS-Portugal, AASHE - Association for the Advancement of Sustainability in Higher Education, and Lisbon Green Commitment to implement a set of measures to reduce the environmental impacts by 2030. In the “operation” element, the results revealed the UAb contribution to the Resource Efficiency Program in Public Administration - ECO.AP 2030 to reduce the consumption of resources and respective Greenhouse Gas emissions facilities, as well as the Energy Saving Plan (2022-2023). The results of the “academia and research” element based on the SDGs course showed the students’ knowledge improvement of both Sustainability and SDGs and a high level of satisfaction with the training, mainly appreciating the “diversified learning resources”. The results of the “assessment and reporting” based on THE displayed the UAb improvement on SDGs 4 and 5. The results of the EF calculator disclosed that “staff labour” was the main driver of the UAb Footprint in direct responsibility, whereas “energy consumption at home” was the main indirect contributor to the UAb Footprint, which showed the effect of distance learning on the results. Future work of the Group includes offering the SDGs micro-credential, targeting also public and private organizations, and later as a formal curricular unit in various degrees, thus enhancing the “involving the community” element. Also, based on the results of the assessment tools, the major drivers of unsustainability at UAb will be identified to assist the necessary improvement to reduce the environmental impact of UAb, thus reinforcing its commitment to building a more sustainable community. The framework for the implementation of sustainability in UAb can be adapted and tested in other HEI to produce robust knowledge and action in this context. info:eu-repo/semantics/publishedVersion

Abstract This work develops an artificial neural network (ANN) model using genetic algorithms to estimate the annual amount (kg/inhabitant/year) of separately-collected household packaging waste. The ANN model comprises one input layer, one hidden layer with seven neurons and one output layer. Ten variables affecting the amount of separately-collected packaging waste were identified and used in the ANN model. These variables are related to the level of education of the population, the size and level of urbanisation of the municipality, social aspects related to poverty and economic power and factors intrinsic to the waste collection service. A comparison between ANN and regression models for the estimation of packaging waste is also carried out. The performance of the proposed ANN model for a data set of 42 municipalities located in the centre of Portugal, measured by the R2, is 0.98. This value is 34% higher than the best regression model applied to the same data set (R2 = 0.73), indicating that ANN has a significantly higher explanatory power than traditional regression techniques. Another advantage is that ANN is not as sensitive to outliers as regression. However, ANN is more complex, has a higher number of variables, and the model development and interpretation of the results are more difficult. Nevertheless, the higher performance of ANN makes it a valuable tool in the definition of strategies to increase recycling and achieve circular economy goals.

Abstract This work develops an artificial neural network (ANN) model using genetic algorithms to estimate the annual amount (kg/inhabitant/year) of separately-collected household packaging waste. The ANN model comprises one input layer, one hidden layer with seven neurons and one output layer. Ten variables affecting the amount of separately-collected packaging waste were identified and used in the ANN model. These variables are related to the level of education of the population, the size and level of urbanisation of the municipality, social aspects related to poverty and economic power and factors intrinsic to the waste collection service. A comparison between ANN and regression models for the estimation of packaging waste is also carried out. The performance of the proposed ANN model for a data set of 42 municipalities located in the centre of Portugal, measured by the R2, is 0.98. This value is 34% higher than the best regression model applied to the same data set (R2 = 0.73), indicating that ANN has a significantly higher explanatory power than traditional regression techniques. Another advantage is that ANN is not as sensitive to outliers as regression. However, ANN is more complex, has a higher number of variables, and the model development and interpretation of the results are more difficult. Nevertheless, the higher performance of ANN makes it a valuable tool in the definition of strategies to increase recycling and achieve circular economy goals.

This work has been funded by Portuguese National Funds through FCT – Portuguese Foundation for Science and Technology under CERNAS (UID/AMB/00681/2013). Celia Dias-Ferreira and Veronica Oliveira have been funded through FCT “Fundacao para a Ciencia e para a Tecnologia” by POCH – Programa Operacional Capital Humano within ESF – European Social Fund and by national funds from MCTES (SFRH/BPD/100717/2014; SFRH/BD/115312/2016). The authors would like to thank Sabrina Hvid for carrying out ICP analyses.

This work has been funded by Portuguese National Funds through FCT – Portuguese Foundation for Science and Technology under CERNAS (UID/AMB/00681/2013). Celia Dias-Ferreira and Veronica Oliveira have been funded through FCT “Fundacao para a Ciencia e para a Tecnologia” by POCH – Programa Operacional Capital Humano within ESF – European Social Fund and by national funds from MCTES (SFRH/BPD/100717/2014; SFRH/BD/115312/2016). The authors would like to thank Sabrina Hvid for carrying out ICP analyses.