Addressing social impacts of emerging technologies in the mining sector: opportunities and challenges
Politecnico di Milano, Italy
Social sustainability of products or services is increasingly discussed in research, companies and among policy makers. However, few practical applications in literature focus on assessing the social sustainability of emerging technologies. Currently a new froth flotation technology is being developed under FineFuture project, funded by the European Union Horizon 2020 research and innovation programme (grant agreement No 821265). If implemented, this technology can valorise fine mineral particles instead of discarding them as waste. As part of the project, a Social Life Cycle Assessment (S-LCA) is ongoing. The study started with an internal survey, to identify the social subcategories possibly influenced by the implementation of the technology. The decision to submit the survey to the project partners was based on the consideration that the developers of the technology are the most capable of predicting its potential impacts. Then, after the definition of Goal and Scope, the first questionnaires for data collection were administered to the mining companies involved in the project. Unfortunately, problems arose regarding data confidentiality. Moreover, the use of other possible tools for collecting primary data was made impracticable due to COVID-19 pandemic. Consequently, a revision of the Goal and Scope was performed. It was decided to proceed with a Hot Spot Analysis of the industrial sector (other mining and quarrying, NACE classification), selected country by country (based on the different companies), to ensure the identification of the critical social issues. The study relies on the Product Social Impact Life Cycle Assessment (PSILCA) database. The results provide a clear indication of the social hot spots of the industrial sector related to each country involved in the project and will help to identify social risks both in the current and future scenarios, where the FineFuture technology will be implemented.
Identifying social impacts of bioeconomy upstream activities through literature review and participatory approaches
1UCD School of Biosystems and Food Engineering, University College Dublin, Ireland; 2BiOrbic Bioeconomy Research Centre, Dublin, Ireland; 3Department of Civil and Environmental Engineering, Universidade Federal da Paraiba, João Pessoa, Brazil
The bioeconomy has been recognized as a strategy to improve major environmental and socioeconomic challenges. Most of the bioeconomy technologies are still in the early development stage, thus, universities and research institutes play a crucial role in it. SLCA can be used to support a sustainable transition to a bioeconomy. This research aimed to identify social impact subcategories and indicators to properly assess the upstream activities of the bioeconomy value chain and define its relative importance.
Existing SLCA were analysed in terms of how applicable they are to bioeconomy stakeholders. A participatory approach with scientists and research students was used to identify key social issues. Thus, an online questionnaire will be applied to rank the impact categories based on their relevance.
The literature indicates that, due to the inherently different nature of institutional operations compared to industry, its stakeholders face different societal challenges. The available SLCA frameworks and guidelines lack categories and indicators to address major actors of the bioeconomy. The preliminary results show that the categories “Hours of Work” should provide indicators reflecting the self-regulation profile of research activities, and “Fair salary” must consider the presence of research students aiming to acquire learning experience rather than financial payments. Also, research integrity and the protection of research participants should be considered. A set of impact categories and indicators aligned with the bioeconomy principles will be developed including positive impacts. A stakeholder category for R&D was created to provide a comprehensive basis for categorizing the impact categories related to bioeconomy actors and facilitate a straightforward comparison with other studies that follow the Guidelines.
This study can contribute to the development of a bioeconomy-specific framework and database.
Automatic identification of social hotspots in product lifecycles building on Life Cylce Inventory databases using the STAR method
1Fraunhofer IBP, Germany; 2University of Stuttgart; 3Ramboll
Social aspects of value chains have gained increasing attention not only in the LCA research community but also in industries. Questions arise on how social impacts of different products can be measured and assessed to meet social compliance requirements and reporting standards. For these purposes information is required on social impacts of the whole supply chain of products analogue to the life cycle thinking in LCA. Latest methods and databases have contributed to advance the feasibility and validity of social LCA, but still issues like integration and automation remain.
To make use of the available solutions in LCA a process-based approach to assess social impacts is needed. It should allow a consideration of indicators and hotspots along the value chain and distribution along geolocation and different sectors. We have developed the STAR method, which enables the quantification and mapping of social indicators across the value chain of a product system. It builds on the life cycle working environment (LCWE) approach, one of the first SLCA methods and the first to allow for a fully embedded quantitative assessment of social impacts along the (material) life cycle. STAR makes use of the structure of LCA inventory databases. It uses process-related information, such as the use of materials and energy sources and the geographic location, and supplements these with socio-economic data. Building on this clustering, the inventory models are iterated to generate harmonized socio-economic sub-systems. These are used to calculate a social profile for each process step. We will present a major database and method update meeting the requirements of the UNEP SLCA framework and allowing to automatically identify social hotspots building on LCA data in early design stages without additional primary data demand.
In the presentation the calculation approach as well as its methodological framework will be shown and the potential results of the social profile will be exemplified.
Social LCA to evaluate the valorisation of sulphidic mine residues for metals and minerals recovery
1KU Leuven, Department of Materials Engineering; Leuven, Belgium; 2Catapa; Ghent, Belgium; 3Boliden Mineral AB; Boliden, Sweden
Sulphidic mine residues (SMRs) are leftover produced during the metals extraction process. Although SMRs can contain valuable metals at low concentrations, they are primarily stored in tailings ponds. Such storage facilities require careful maintenance. On the other hand, the development of new technologies to make the valorisation of SMRs economically and technically feasible can (i) allow the recovery of valuable secondary metals and minerals, (ii) avoid further storage facilities, (iii) reduce the adverse effects related to the oxidation and acidic mine drainage.
In this context, the Horizon 2020 project NEMO (Near-zero-waste recycling of low-grade sulphidic mining waste for critical-metal, mineral and construction raw-material production in a circular economy) investigates new technologies to recover metals and minerals from SMRs, with pilot-scale applications at two real mining sites in Finland. At the same time, NEMO aims at analysing the potential social implications of the proposed technologies through a Social Life Cycle Assessment (S-LCA). This study presents the results of the social analysis, carried out by integrating data collected directly on-site with data from the literature (Ecoinvent and PSILCA databases).
The results of the S-LCA have emphasized the main social aspects that can hinder or boost the sustainability of the whole SMRs valorisation process. In particular, the S-LCA highlighted the potential hotspots for the three stakeholder categories that are mainly involved in the SMRs valorisation process (workers, local communities and society).
The study provides useful insights on potential risks and opportunities when undertaking a project for mine tailings valorisation, supporting the further development of a comprehensive methodology for sustainability assessment.
APPLYING INTUITIONISTIC FUZZY LOGIC IN SOCIAL LIFE CYCLE IMPACT ASSESSMENT
Deakin University, Australia
Despite advances in Social Life Cycle Assessment (S-LCA) over the last two decades, the definition and selection of assessment criteria for designing an impact category in S-LCA remain a methodological challenge. The key challenges relate to the difficulty in objectively distributing social impacts across the product and the complexities in precisely representing indicators using the established subcategories and stakeholders. Intuitionistic fuzzy sets (IFS) provide a useful approach to handle cases where available information is not sufficient for a definition of an imprecise concept. There has been an upsurge in the use of participatory techniques in eliciting opinions about social impacts. However, many of these approaches are unable to handle the imprecision in the opinion data leading to misrepresentation of the impacts. Using structured interviews and questionnaires, data is collected on the social impacts of a social infrastructure project in Australia. IFS is used to analyse the data obtained across the social life cycle of the project. It was found that majority of the subcategory indicators (76%) are interpreted differently by stakeholder groups and that the dominant social impact in social infrastructure projects relate to workers and community. It is therefore suggested that initiatives to facilitate common understanding of social issues be adopted to yield better modelling of the social impacts in infrastructure projects.
An approach to evaluate reliability, validity, and objectivity of data collection in S-LCA
1PONZIO SRL, Italy; 2Università degli Studi 'G. d'Annunzio' Chieti - Pescara
The Guidelines for Social Life Cycle Assessment of Products and Organisations 2020 suggest guidance methods to address data quality in S-LCA. There are several techniques to collect appropriate information and this study evaluates one, checking whether it meets the criteria of reliability, validity and objectivity as set out in the S-LCA guidelines. The technique used is the interview, conducted by dispensing a structured questionnaire, in the company of the case under study. Although semi-structured interviews are easy for information to be acquired quickly, they require a longer time in the design phase of the questionnaire as they need revisions and tests to be approved and implemented.
Specifically, Cronbach's Alpha method, a statistical index to check the fidelity of a test over time, was used for the reliability degree. In addition, for the validity criterion, a triangulation of the data was carried out, involving the three "internal" components of the company: employees, trade union representatives, company management. In order to assure objectivity, the entire questionnaire was created and administered by two researchers, one internal to the company organisation and one external, ensuring an unbiased process. The tests on the questionnaire demonstrated an acceptable level of consistency.
Given the importance of the topic, this work leads to several future developments. Indeed, testing other methods of data collection such as test-retest, administering the same questionnaire after a certain period or performing a focus group that would allow the respondents to speak in depth, choosing their own words.