Dr. Janez Sušnik from the IHE Delft Institute for Water Education and NEXOGENESIS Coordinator discusses the use of machine learning to improve policy advice at the water-energy-food-ecosystem nexus.
Water, Energy and Food (WEF) form a coherent and interconnected system called the WEF nexus ( Hoff, 2011 ), which strongly interacts with ecosystems to form the broader WEFE nexus.
Ecosystems are the “foundation” of the WEFE nexus and help ensure the quantity, quality, timing and accessibility of WEF resources by providing services such as water purification, freshwater supply, pollution reduction and control, maintaining healthy landscapes, contributing to the growth of food and energy crops, providing pollinating insects for crop production through biodiversity, and providing biomass that acts as a global carbon sink and oxygen source through forest and floodplain ecosystems ( Bell et al., 2016 , Martinez-Hernandez et al., 2017 ).
The complex nexus of water, energy and food
The WEFE linkages are highly complex, with each sector interacting with the others and subject to “external factors” such as the effects of climate change and socio-economic development that regulate resource demand, consumption, exploitation, and even ecosystem degradation.
WEF interactions include the water required for irrigated agriculture, agricultural activities that affect water quality, water required for cooling thermal power plants and energy generation in hydroelectric plants, energy used to produce water (pumping, treatment), heat and cool water, treat, dispose and reuse wastewater, food and crop residues used to produce energy through biomass combustion and biofuel production, mechanization, food value chains and agricultural energy use in the production of synthetic fertilisers.
Therefore, in today's hyper-connected society, when considering the WEF sectors, it is not possible to consider each sector in isolation without considering its impact on the other sectors.As mentioned above, the interactions with ecosystems and their services add further complexity to the WEFE system.
The WEFE nexus does not exist in isolation, but is embedded within a broader system of climatic and socio-economic and political factors that mediate resource demand, use, extraction and pressures on ecosystems (which are already over-exploited; Richardson et al., 2023 ) and are influenced by the availability of sufficient, high-quality WEFE resources when needed. Managing such a complex system requires a holistic and integrated perspective that takes into account interactions between sectors.
A single-sector, silo approach to natural resource management is insufficient. But achieving intersectoral harmony in policymaking (i.e. achieving policy objectives without negatively impacting other sectors) is difficult enough even when considering the interrelated impacts of a single policy across a range of climate and socio-economic futures.
The situation becomes much more complex when multiple interacting policies are considered. For example, consider 10 hypothetical policies across a nexus sector. These policies all have their own goals and can be implemented one at a time or in combination with other policies as a set of policy suites. In this case, there are roughly 3.6 million unique ways to combine these 10 policies.
This raises the question: What is the most feasible combination? Which one will accomplish the most objectives while minimizing the negative impact on other sectors? (The caveat here is that in most cases, not everyone can “win” – there are always trade-offs. The question is how to minimize these trade-offs.) Obviously, we cannot explore all options manually. This is where machine learning comes in.
WEFE Nexus System Research
The European Commission's Horizon 2020 research project “Facilitating effective, intelligent, next-generation water-related policies using artificial intelligence and reinforcement learning to assess the Water-Energy-Food-Ecosystem (WEFE) nexus” (NEXOGENESIS) is studying the WEFE nexus system in five diverse studies, including a South African study, to explore how the nexus will evolve by 2050 under a range of climate and socio-economic futures.
NEXOGENESIS explores the potential impacts of multiple policies implemented across the WEFE sector to achieve multiple, sometimes conflicting, goals. The use of machine learning technologies allows us to assess a wide range of policy combinations and their impacts on WEFE resource pathways for different goals and different climate and socio-economic futures (not all policies will work the same under different conditions).
WEFE Nexus System Policy
The WEFE model and policy proposals are developed in close collaboration with local case study partners and a broad group of stakeholders. One of NEXOGENESIS' key goals is to provide a set of “policy packages” that have the potential to achieve multiple objectives across multiple sectors, wherever possible, while minimizing negative trade-offs. Under an uncertain future, policies can be more robust. Packages are recommended to local policy experts for further research and refinement.
In this way, millions of potential options are narrowed down to a few feasible packages. A more detailed local-level study can then be conducted, focusing on the feasibility, costs, (social) acceptability, etc. of the proposed policy packages that can help achieve WEFE resource security in the case studies. We hope that policies will be redesigned to take into account the complex nature of the interacting WEFE nexus and the fact that policy performance will vary in different futures.
Through the integration of powerful and novel stakeholder co-creation activities, governance assessment, WEFE system modelling and machine learning, NEXOGENESIS is well positioned to provide actionable policy recommendations for the five project case studies, as well as provide a framework and template that can be adopted by other regions, contributing to more integrated and streamlined policy development for integrated natural resource management.
References
- Bell A., Matthews N., Zhang W. 2016. Opportunities for promoting improved ecosystem services in agriculture under the water-energy-food nexus. Journal of Environmental Research and Science. 6: 183-191. DOI: https://doi.org/10.1007/s13412-016-0366-9
- Hoff H. 2011. Understanding Nexus: Background Paper for the Bonn 2011 Nexus Conference. 51 pp. Available at: www.sei-international.org/publications?pid=1977.
- Martinez-Hernandez E., Leach M., Yang A. 2017. Understanding water-energy-food ecosystem interactions using the nexus simulation tool NexSym. Applied Energy. 206: 1009-1021. DOI: https://doi.org/10.1016/j.apenergy.2017.09.022
- Richardson K., Steffen W., Lucht W., Bendtsen J., Cornell SE., Donges JF., Drüke M., Fetzer I., Bala G., von Bloh W., Feulner G., Fiedler S., Gerten D., Gleeson T., Hofmann M., Huiskamp W., Kummu M., Mohan C., Nogués-Bravo D., Petri S., Porkka M., Rahmstorf S., Schaphoff S., Thonicke K., Tobian A., Virkki V., Wang-Erlandsson L., Weber L., Rockström J. 2023. Earth crossing six of nine planetary boundaries. Science Advances. 9. eadh2458. DOI: https://doi.org/10.1126/sciadv.adh2458
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