Many of the SDGs are closely tied to food, land and water resources, leading to potential trade-offs, as well as synergies, between actions to achieve different SDGs.
Understanding these trade-offs is limited by disciplinary orientation of research efforts, a local focus that ignores global drivers, and the widespread use of modeling frameworks that are proprietary or too complex to be used by a broader community.
Researchers at Purdue University are developing simple, open-source tools for the analysis global-local linkages and linkages across resource-dependent SDGs.
Analyzing potential trade-offs and synergies between Sustainable Development Goals (SDGs) that are tied to land and water resources requires novel tools that can account for linkages across SDGs as well as between local analysis and global drivers. This article outlines the work of researchers at Purdue University to develop open-source applications accessible to a wide range of users.
The SDGs are closely linked to the world’s land and water resources
Eight of the 17 SDGs are closely tied to food, land and water. Yet these natural resources are already under intense pressure from growing population and rising per capita incomes. Can the future demands for food, fuel, clean water, biodiversity, climate change mitigation and poverty reduction be reconciled? Are we counting on the same hectare of land to satisfy conflicting SDGs? What are the trade-offs of favoring one goal over others? Are there win-win scenarios under which attainment of one SDG will also benefit others? The sustainable development challenge is a particularly ‘wicked’ problem since sustainability is fundamentally a local concept, requiring fine-scale analysis, yet sustainability stresses are often driven by global forces. Furthermore, aggressive pursuit of the SDGs will itself have consequences for global markets.
Limitations of current approaches
There is a great deal of important work underway in this area. Much of this research was featured at the recent Impacts World Conference 2017 in Potsdam, Germany in Potsdam, Germany. However, we believe that current approaches fall prey to one or more of the following key limitations.
- Disciplinary orientation: The SDG challenges are fundamentally interdisciplinary in nature. Approaches that are largely driven by economists, hydrologists or climate scientists, for example, will ultimately prove too narrow in their analyses.
- Local focus: By their very nature, land and water sustainability challenges often require local solutions, with the preferred approach depending on the institutions governing resource use, soil characteristics, groundwater aquifers, etc. Hence, most of the analyses of these issues are undertaken at the local level. However, when it comes to meeting the SDGs, local planning cannot take place in a vacuum. Future stresses will depend on national and global drivers of change, including population, income, domestic and trade policies and other broad scale factors. Local analysis, disconnected from these national and global drivers, cannot foresee future system stresses. It is also true that if the local sustainability solutions are sufficiently widespread (e.g., investment in biofuels), their effects can be felt at the regional, national and even global level. To capture these cross-scale effects, ‘global-to-local-to-global’ linkages must be considered.
- Proprietary and complex analysis frameworks: Tackling this kind of ‘wicked problem,’ requiring interdisciplinary collaboration and cross-scale modeling and analysis is a major undertaking and has typically been undertaken in large national and international institutions such as the Potsdam Institute for Climate Impact Research (PIK), the International Institute for Applied Systems Analysis (IIASA), the Netherlands Environmental Assessment Agency (PBL), and the Joint Global Change Research Institute (JCRI), among others. However, until recently, most of their modeling frameworks have been proprietary in nature. And when they are opened for others to use, the public response has generally been limited due to the complexity of the underlying modeling frameworks, which are continually enlarged to address new challenges. If one is to build a community of practice around analysis of the SDGs related to land and water resources, the models have to be open-source and must be developed bearing in mind that they will be used by a variety of individuals with differing backgrounds and skill levels. In short, the models should be as simple as possible.
GLASS: An innovative approach to analysis of the SDGs
In May of 2017, in response to these limitations of existing global sustainability analyses, Purdue University launched GLASS – Global to Local Analysis of Systems Sustainability – a project designed to tackle the challenge of linking local sustainability solutions to global drivers and consequences. The Project will establish an applied research consortium that facilitates and organizes a global community of practice around open-source analysis of the Global-to-Local-to-Global linkages underpinning the SDGs – and global sustainability more broadly. GLASS is built on the idea that fine-scale critical analysis of the SDGs, within a global framework, is required to promote responsible public and private investment, sustainable management of critical natural resources, and collective action.
Transformational Features of the Project
The deployment of open source tools on Purdue’s GeoHub aims to “transform the way in which analysis of the SDGs is undertaken.” By way of example, consider the recent publication of Liu et al. (2017) which examines the challenge of attaining sustainable levels of irrigation withdrawals by 2050 (Figure 1). The authors highlight the trade-offs between limiting irrigation on the one hand and ensuring food security and limiting cropland conversion and terrestrial carbon fluxes on the other. They utilize an existing analytical framework (SIMPLE: a Simplified International Model of agricultural Prices Land use and the Environment), which is flexible enough to accommodate a wide variety of disciplinary contributions, including climate impacts, water scarcity, biodiversity, terrestrial carbon stocks and food security. The globally gridded version (SIMPLE-G) allows for analysis of SDG trade-offs and synergies across 36,000+ grid cells globally. This model can be run, and results visualized, on the open-source, publicly available GeoHub, funded by the National Science Foundation (NSF) and hosted by Purdue University. This is but one of many tools, data sets and modeling frameworks available for members of this community of practice to use and build upon.
Figure 1: Irrigation vulnerability index at the sub-basin level, 2050 baseline relative to 2006 baseline. The 2050 baseline assumes the Representative Concentration Pathway 8.5 forcing scenario of the Intergovernmental Panel on Climate Change (IPCC). Given that fossil groundwater withdrawal is not included in total water supply, water available for irrigation can be negative in some sub-basins. That means irrigation water in these sub-basins comes from nonrenewable groundwater mining. These basins are defined as ‘deficit.’ ‘Sustainable’ and ‘unsustainable’ refer to vulnerability indices that are below and above 0.2, respectively. For more details, see Liu et al., (2017). “Achieving Sustainable Irrigation Water Withdrawals: Global Impacts on Food Security and Land Use” Environmental Research Letters 12(10). This model can be accessed and run on the GeoHub.
Who will benefit?
Beneficiaries of GLASS will include students and local communities seeking to understand the consequences of global change for local resource use and national stakeholders concerned with the interplay between alternative policies, as well as the national consequences of global agreements. It will also be of immediate usefulness to international policy makers focusing on attainment of the SDGs. Through the development of interactive tools deployed on the GeoHub, it will allow investors and decision makers to explore results and more clearly communicate trade-offs. These tools will include: interactive maps; annotated interactive dashboards; and data-heavy, multi-resolution tools allowing technical analysts at decision-making bodies to get “under the hood” of the models, design new scenarios and explore policy alternatives.
*This article was submitted by Thomas Hertel on behalf of his Purdue University Colleagues: Uris L.C. Baldos, Research Assistant Professor, Agricultural Economics; Laura Bowling, Professor of Agronomy; Keith Cherkauer, Associate Professor of Agricultural and Biological Engineering; Matthew Huber, Professor of Earth, Atmospheric and Planetary Sciences; David Johnson, Assistant Professor of Industrial Engineering and Political Science; Jing Liu, Big Idea Science Coordinator; Carol X. Song, Director of Scientific Solutions, ITaP Research Computing (RCAC); Dominique van der Mensbrugghe, Research Professor and Director, Center for Global Trade Analysis (GTAP), Department of Agricultural Economics.