Supporting the Implementation of SDGs

Supporting the Implementation of SDGs

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observations-and-science
Figure 1: Relevance of earth observations and science and technology support for the implementation and monitoring of progress towards the SDGs and associated targets

Geospatial data, including observations of the natural and build environment and data on the socio-economic system, will be crucial for both the monitoring of progress towards the targets and the planning of actions to make progress

The Agenda 2030 agreed upon by the United Nations in 2015 aims to achieve 17 Sustainable Development Goals (SDGs) addressing almost all societal, economic and environmental issues impacting the sustainability of our modern global society. Each of the goals comes with a set of specific targets. In order to monitor progress, a monitoring framework consisting of currently 240 indicators has been developed. The governments of the world are challenged with the development of policies and the implementation of actions that would lead to progress towards these ambitious goals and their associated targets.

Science and technology support is needed to inform policy development and to monitor progress (Figure 1). Geospatial data, including observations of the natural and build environment and data on the socio-economic system, will be crucial for both the monitoring of progress towards the targets and the planning of actions to make progress. The formal monitoring of progress towards the targets is based on the monitoring framework defined by the Interagency and Expert Group on SDG monitoring (IAEG-SDGs) and accepted by the United Nations Statistical Commission. The monitoring framework has defined indicators for each of the targets. For many of the currently 240 indicators traditional earth observations (EO) are required for quantification, and even more of the indicators require information on the built environment. The Group on Earth Observations (GEO) in its GEO Initiative 18 (GI-18) is developing methods for the quantification of those indicators that depend on EOs for quantification (A in Figure 1). The Horizon 2020 Project “ConnectinGEO” developed a goal-based approach to the identification of essential variables and applied this approach to the SDG indicators.

This research showed that only some of the SDG Indicators require traditional EOs of the natural environment for quantification, while many of the essential variables of the SDG monitoring framework are related to the built environment. This brings up the question to what extent some of these essential variables could be extracted from available earth observations or gathered by using Big Data analysis, citizen scientists, the Internet of Things and other forms of crowdsourcing. However, the integration of the new data, including data from “human sensors”, into an integrated database available for analysis and models remains a challenge.

For many of the indicators, the integration of socio-economic statistical data with environmental data is of importance (B in Figure 1). It is anticipated that GEO could use it convening power to facilitate this integration and develop the Global Earth Observation System of System (GEOSS) towards the platform for this integration.

Applying the goal-based approach to the SDG Targets (C in Figure 1) shows that many of the targets would benefit from indicators that are directly related to the natural environment. A revision of the monitoring framework could take this finding into account and account for the linkage of the socio-economic and environmental aspect reflected in the SDGs.

Besides supporting the monitoring, there is an urgent need to support the planning of actions and the development of policies that would facilitate progress towards the SDG targets (D in Figure 1). In some cases, research is needed to better understand the causes of the current problems and to develop the avenues to more sustainability. The EOs are important for most of this research. For many of the goals, tools to assess the impact of actions and policies are lacking and models need to be developed. In order to capture the complexity these models need to embrace a hybrid approach. To capture the complexity, agent-based models will be crucial. The development and validation of these models depend on a number of factors. The former depends on how well the real world has been modeled and applied to the agent-based model. It has to be ensured that the best available modeling parameters and framework is applied by coupling the agent-based model with existing models. In applications associated with the built environment it would be prudent to couple a Geo-design model with the agent-based model. Data and knowledge integration can be achieved using the already developed concepts of geo design. A geo design hub can be a starting point for the integration of built and natural environment with socio-economic data and support policy development (Figure 2). A combination of such a hub with a model Web to answer ‘What-if’ question can help to facilitate cross-domain data integration. To promote such interdisciplinary and cross-domain tools, relevant stakeholders need to see benefits that they cannot achieve on their own.

A-Geo-Design-Hub
Figure 2: A Geo-Design Hub combines components of simulation tools for the assessment of policy impacts

Similarly, environmental or sociological models can be coupled with the agent-based model. Validation of the agent-based models to assess policy impacts will to a large extent depend on integrated geospatial environmental and socio-economic data (Figure 3).

While data availability is important and necessary, it is not a sufficient condition for the support of SDG implementation and monitoring. The decision makers on SDG implementation are normally far away from data on the value chain from EOs to end users and more interested in information or knowledge. Thus, there is a need for a transition from data to information to knowledge and the development of tools that provide access to knowledge. In this process, it is important that these tools are co-design with the end users. Importantly, research on how decisions are made and what type of knowledge might be needed for that would provide a basis to generate the tools for the decision makers to create the knowledge as needed instead of delivering a range of predefined knowledge products. Currently, most of the data delivery, information and knowledge systems have a push component to push out what providers created while a pull component to create the knowledge when needed does not exists. In order to provide usable support for both the implementation and monitoring of SDGs, a pull component needs to be developed in a co-design and co-creation process.

Progress towards open data has been a success over the recent years and GEO played an important role in this. For the integration of socio-economic and environmental data, more progress is needed. Open data and the democratizing of access to data and information are very important for the success of the Agenda 2030 particularly for the buy-in of a broad range of stakeholders. But equally important is open knowledge and a democratizing of access to knowledge derived from EOs and other data. Policies that would support the integration of socio-economic and environmental data, support cross-domain collaborations, open data and open knowledge, and facilitate the development of cyber infrastructure for data integration and tool development would be beneficial for the Agenda 2030 and the implementation and monitoring of the SDGs.

Agent-based-models
Figure 3: Agent-based models coupled with geospatial information for the assessment of policy impacts. From Rebaudo et al., 2011, JASSS, 14, 7

GEO can use its convening power to bring together the groups that can achieve the identification of all relevant EVs for both the current and a future updated monitoring framework and the integration of socio-economic and environmental data. The Socio-Economic and Environmental Information Needs Knowledge Base (SEEIN-KB), which is currently under development in the GEO Foundational Task GD-09 “Knowledge Base Development” will be an important tool for this important work. GEO also has sufficient involvement of Science and Technology communities to facilitate support for the implementation of the SDGs. An important task for GEO is to establish a working relationship with the IAEG-SDG to participate in the review of the current monitoring framework.

The GI-18 is strongly focusing on the development of training and capacity building programs directly related to the use of EOs for the monitoring of the SDGs. The implementation plan of GI-18 includes a number of tangible goals to be achieved by 2020. The development of toolboxes supporting the implementation of SDGs is an important contribution to this effort. The global nature of GEO allows for actions that lead to capacity building on a global scale.

Shelley-Ann Jules-Plag
Engineering Management and System Engineering,
Old Dominion University,
Norfolk, VA, US

Dr. Hans-Peter Plag
Director, Mitigation and Adaptation Research Institute (MARI) and Professor, Ocean, Earth and Atmospheric Sciences, Old Dominion University, Norfolk, VA, US

Part of this work was supported through funding from the European Union Framework Program for Research and Innovation

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