Exploring Synergies between the Sustainable Development Goals and Citizen Science
SUPERVISOR: Marianne PENKER
PROJECT ASSIGNED TO: Dilek FRAISL
Adopted by the United Nations (UN) in September 2015, the 2030 Agenda for Sustainable Development intertwines social, economic and environmental targets to address the most pressing, global issues of our time in the form of 17 Sustainable Development Goals (SDGs) and 169 targets. Building upon the 8 Millennium Development Goals (MDGs) that were adopted in 2002, the SDG framework allows nations to monitor a broader and more complex set of issues relevant to sustainable development around the world over a period of 15 years, from 2015 to 2030. Tracking and monitoring the implementation of the SDGs is critical for establishing progress, which requires a systematic review of the social, economic and environmental dimensions of the SDGs. As inputs, more, accurate, accessible, timely and spatially disaggregated data are needed. Even though data availability and quality have improved over the last decade, it is recognized that more data are needed (IAEG, 2014) to ensure that “no one is left behind”, which is a key component of the agenda (UN HLP, 2013) while addressing all aspects of the SDGs from eradicating poverty and inequality to combatting climate change (SDSN, Open Data Watch, 2015). Traditional data collection methods, e.g. administrative records, statistical surveys, censuses, etc. need to be strengthened, and a much wider set of data are needed to address the SDGs, which move beyond poverty (in the MDGs) to broader social, economic and environmental issues. To achieve this, new, innovative ways of data production and analysis using Earth Observation, mobile data, social media, sensors, etc. need to be developed and adopted (UN, 2017).
In addition to Earth Observation and other new geospatial data sets such as mobile phone data, another key source of data to support the SDGs is that arising from citizen generated data or citizen science. Citizen science is defined as the involvement of citizens in scientific research (Bonney et al., 2009; Silvertown et al., 2009) and has a long history going back to the previous century. One of the most successful projects called eBird has led to the collection of 260 million bird observations (Sullivan et al., 2014). There are hundreds of citizen science projects currently ongoing (see www.scistarter.org for a list), many of which have relevance to the SDGs. This indicates that citizen science can help leverage the SDG efforts as an active operator and monitor of change with the application of new methodologies to enhance the reliability of such data (UN, 2017). To date, however, only a few studies have appeared on CS and SDGs (Hsu et al. 2014; West and Patemen, 2016).
CS can also support SDG implementation through transformative practices by engaging citizens in scientific activities including co-design of research questions and research methodology, conducting the research, and interpreting the findings. This may result in broadened scientific knowledge generation, and science being adapted to the needs of society, which could enhance social interactions between science, citizens and policy makers. However, only a few published studies (Land-Zandstra et al., 2016; Bela et al., 2016; Jordan et al., 2012; Brossard et al., 2012; Evans et al., 2005) have looked at the learning outcomes of CS initiatives and their effects on attitude and behavior change, which provide evidence that better understanding of scientific content, improved scientific skills, and raised awareness of environmental issues can assist the implementation of the SDGs. In most cases, the learning processes in CS projects are not evaluated or reported. Due to the current gap in knowledge concerning the transformative effects of CS, it is difficult to increase the transformative potential of such initiatives (Bela et al., 2016).
The overall aim of this research is to investigate how CS can enhance SDG monitoring and implementation, and the potential opportunities and challenges this presents such as scalability and data quality, among others.
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