By Valeria Javalera Rincón, IIASA CONACYT Postdoctoral Fellow in the Ecosystems Services and Management and Advanced Systems Analysis programs.
What is more important: water, energy, or food?
If you work in the water, energy or agriculture sector we can guess what your answer might be! But if you are a policy or decision maker trying to balance all three, then you know that it is getting more and more difficult to meet the growing demand for water, energy, and food with the natural resources available. The need for this balance was confirmed by the 17 Sustainable Development Goals, agreed by 193 countries, and the Paris climate agreement. But how to achieve it? Intelligent cooperation is the key.
The thing is that water, energy, and food are all related in such a way that are reliant on each other for production or distribution. This is the so-called Water-Energy-Food nexus. In many cases, you need water to produce energy, you need energy to pump water, and you need water and energy to produce, distribute, and conserve food.
Many scientists have tried to relate or to link models for water, agriculture, land, and energy to study these synergic relationships. In general, so far, there are two ways that this has been solved: One is integrating models with “hard linkages” like this:
In the picture there are six models (let’s say water, land use, hydro energy, gas, coal, food production models) that are then integrated into just one. The resulting integrated model then preserves the relationships but is complex, and in order to make it work with our current computer power you often have to sacrifice details.
Another way is to link them is using so-called “soft linkages” where the output of one model is the input of the next one, like this:
In the picture, each person is a model and the input is the amount of water left. These models all refer to a common resource (the water) and are connected using “soft linkages.” These linkages are based on sequential interaction, so there is no feedback, and no real synergy.
The intelligent linker agent
But what if we could have the relations and synergies between the models? It would mean much more accurate findings and helpful policy advice. Well, now we can. The secret is to link through an intelligent linker agent.
I developed a methodology in which an intelligent linker agent is used as a “negotiator” between models that can communicate with each other. This negotiator applies a machine-learning algorithm that gives it the capability to learn from the interactions with the models. Through these interactions, the intelligent linker can advise on globally optimal actions.
When I came to IIASA, I was asked to apply this approach to optimize trading between cities in the Shanxi region of China. I used a set of previously development models which aimed to distribute water and land available for each city in order to produce food (eight types of crops) and coal for energy. The intelligent linker agent optimizes trading between cities in order to satisfy demand at the lowest cost for each city.
The purpose of this exercise was to compare the solutions with those from “hard linkages” – like those in the first picture. We found that the intelligent linker is flexible enough to find the optimal solution to questions such as: How much of each of these products should each city export/import to satisfy global demand at a global lower economic and ecological cost? What actions are optimal when the total production is insufficient to meet the total demand? Under what conditions is it preferable to stop imports/exports when production is insufficient to supply the demand of each city?
The answers to these questions can be calculated by the interaction with the models of each city just by the interfacing with the intelligent linker agent, this means that no major changes in the models of each city were needed. We also found that, under the same conditions, the solutions using the intelligent linker agent were in agreement with those found when hard linking was used.
My next challenge is to build a prototype of a “distributed computer platform,” which will allow us to link models on different computers in different parts of the world—so that we in Austria could link to a model built by colleagues in Brazil, for example. I also want to link models of different sectors and regions of the globe, in order to prove that intelligent cooperation is the key to improving global welfare.
Javalera V, Morcego B, & Puig V, Negotiation and Learning in distributed MPC of Large Scale Systems, Proceedings of the 2010 American Control Conference, Baltimore, MD, 2010, pp. 3168-3173. doi: 10.1109/ACC.2010.5530986
Valeria J, Morcego B, & Puig V, Distributed MPC for Large Scale Systems using Agent-based Reinforcement Learning, In IFAC Proceedings Volumes, Volume 43, Issue 8, 2010, Pages 597-602, ISSN 1474-6670, ISBN 9783902661913, https://doi.org/10.3182/20100712-3-FR-2020.00097.
Morcego B, Javalera V, Puig V, & Vito R (2014). Distributed MPC Using Reinforcement Learning Based Negotiation: Application to Large Scale Systems. In: Maestre J., Negenborn R. (eds) Distributed Model Predictive Control Made Easy. Intelligent Systems, Control and automation: Science and Engineering, vol 69. Springer, Dordrecht
Javalera Rincón V, Distributed large scale systems: a multi-agent RL-MPC architecture, Universitat Politècnica de Catalunya. Institut d’Organització i Control de Sistemes Industrials,Doctoral thesis. 2016. http://upcommons.upc.edu/handle/2117/96332
Note: This article gives the views of the author and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
Two things are distinctly noticeable when you meet Cornelius Hirsch—a cheerful smile that rarely leaves his face and the spark in his eyes as he talks about issues close to his heart. The range is quite broad though—from politics and economics to electronic music.
After finishing high school, Hirsch decided to travel and explore the world. This paid off quite well. It was during his travels, encompassing Hong Kong, New Zealand, and California, that Hirsch started taking a keen interest in economic and political systems. This sparked his curiosity and helped him decide that he wanted to take up economics for higher studies. Therefore, after completing his masters in agricultural economics, Hirsch applied for a position as a research associate at the Austrian Institute of Economic Research and enrolled in the PhD-program of the Vienna University of Economics and Business to study trade, globalization, and its impact on rural areas. Currently, he is looking at subsidies and tariffs for farmers and the agricultural sector at a global scale.
As part of the 2017 Young Scientists Summer Program at IIASA, Hirsch is digging a little deeper to analyze how foreign direct investments (FDI) in agricultural land operate. “Since 2000, the number of foreign land acquisitions have been growing—governmental or private players buy a lot of land in different countries to produce crops. I was interested in knowing why there are so many of these hotspots in the world— sub-Saharan Africa, Papua New Guinea, Indonesia—why are people investing in these areas?,” says Hirsch.
Increased food demand from a growing world population is leading to an increased rate of investment in agriculture in regions with large stretches of fertile land. That these regions are largely rain-fed make them even more attractive for investors as they save the cost of expensive irrigation services. In fact, Hirsch argues that “the term land-grabbing is misleading. It should actually be water-grabbing as water is the foremost deciding factor—even more important than simply land abundance.”
Some researchers have found an interesting contrast between FDI in traditional sectors, such as manufacturing, and the ones in agricultural land. While investors in the former look for stable institutions and good governmental efficiency, FDI in land deals seems to target regions with less stable institutions. This positive relationship between corruption and FDI is completely counterintuitive. Hirsch says that one reason could be that “sometimes weaker institutions are easier to get through when it comes to such vast amount of lands. A lot of times these deals and contracts are oral and have no written proof—the contracts are not transparent anyway.”
For example in South Sudan, the land and soil conditions seem to be so good that investors aren’t deterred despite conflicts due to corrupt practices or inefficient government agencies.
One area that often goes unnoticed is the violation of land rights of indigenous communities. If a government body decides to sell land or give out production licenses to investors for leasing the land without consulting the actual community, it is only much later that the affected community finds out that their land has been given away. Left with no land and hence no source of livelihood, these communities are forced to migrate to urban areas.
A strain of concern enters his voice as Hirsch talks about the impact. “Land as big as two times the area of Ecuador has been sold off in the past—but it accounts for a tiny percentage of the global production area.” With rising incomes and greater consumption of meat, a lot of land is used to produce animal feed crops. “This is a very inefficient way of using land,” he says.
During the summer program at IIASA, Hirsch is generating data that will help him look at these deals in detail and analyze the main factors that are taken into consideration before finalizing a land deal. At the moment he is only able to give an overview of land-grabbing at the global level. With more data on the location of the deals he can look at the factors that influence these decisions in the first place such as the proximity between the two countries involved in agricultural investments and the size of their economies.
While there is always huge media coverage when a scandal about these land acquisitions comes out in the open, Hirsch seems determined to dig deeper and uncover the dynamics involved.
About the researcher Cornelius Hirsch is a research associate at the Austrian Institute of Economics and Research (WIFO). At IIASA he is working under the supervision of Tamas Krisztin and Linda See in the Ecosystems Services and Management Program (ESM).
This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
By David Leclère, IIASA Ecosystems Services and Management Program
August was the warmest ever recorded globally, as was every single month since October 2015. It will not take long for these records to become the norm, and this will tremendously challenge food provision for everyone on the planet. Each additional Celsius degree in global mean temperature will reduce wheat yield by about 5%. While we struggle to take action for limiting global warming by the end of the century to 2°C above preindustrial levels, business as usual scenarios come closer to +5 °C.
However, we lack good and actionable knowledge on this perfect storm in the making. Despite the heat, world wheat production should hit a new record high in 2016, but EU production is expected to be 10% lower than last year. In France, this drop should be around 25-30% and one has to go back to 1983 to find yields equally low. Explanations indeed now point to weather as a large contributor. But underlying mechanisms were poorly anticipated by forecasts and are poorly addressed in climate change impacts research.
Second, many blind spots remain. For example, livestock has a tremendous share in the carbon footprint of agriculture, but also a high nutritional and cultural value. Yet, livestock were not even mentioned once in the summary for policymakers of the last IPCC report dedicated to impacts and adaptation. Heat stress reduces animal production, and increases greenhouse gas emissions per unit of product. In addition, a lower share of animal products in our diet could dramatically reduce pollution and food insecurity. However, we don’t understand well consumers’ preferences in that respect, and how they can be translated in actionable policies.
How can we generate adequate knowledge in time while climate is changing? To be able to forecast yields and prevent dramatic price swings like the 2008 food crisis? To avoid bad surprises due to large missing knowledge, like the livestock question?
In short: it will take far more research to answer these questions—and that means a major increase in funding.
I recently presented two studies by our team at a scientific conference in Germany, which was organized by a European network of agricultural research scientists (MACSUR). One was a literature review on how to estimate the consequences of heat stress on livestock at a global scale. The other one presented scenarios on future food security in Europe, generated in a way that delivers useful knowledge for stakeholders. The MACSUR network was funded as a knowledge hub to foster interactions between research institutes of European countries. In many countries, the funding covered travels and workshops, not new research. Of course, nowadays researchers have to compete for funding to do actual research.
So let’s play the game. The MACSUR network is now aiming at a ‘Future and Emerging Technologies Flagship’, the biggest type of EU funding: 1 billion Euros over 10 years for hundreds of researchers. Recent examples include the Human Brain Project, the Graphene Flagship, and the Quantum Technology Flagship. We are trying to get one on modeling food security under climate change.
Such a project could leapfrog our ability to deal with climate change, a major societal challenge Europe is confronted with (one of the two requirements for FET Flagship funding). The other requirement gave us a hard time at first sight: generating technological innovation, growth and jobs in Europe -but one just needs the right lens. First, agriculture already sustains about 44 million jobs in the EU and this will increase if we are serious about reducing the carbon content of our economy. Second, data now flows at an unprecedented speed (aka, big data). Think about the amount of data acquired with Pokemon Go, and imagine we would harness such concept for science through crowdsourcing and citizen-based science. With such data, agricultural forecasts would perform much better. Similarly, light drones and connected devices will likely open a new era for farm management. Third, we need models that translate big data into knowledge, and not only for the agricultural sector. Similarly, models can also be powerful tools to confront views and could trigger large social innovation.
To get this funding, we need support from a lot of people. The Graphene project claimed support from than 3500 actors, from citizens to industrial players in Europe. We have until end of November to reach 3500 votes, at least. If you think EU should give food security under climate change the same importance as improving the understanding of the human brain, or developing quantum computers, we need you. This will simply never happen without you! Please help us out with two simple actions:
Go the proposal, and vote for/comment it (see instructions, please highlight the potential for concrete innovations)!
Spread the word – share this post with your friends, your family, and your colleagues!
Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
By IIASA Deputy Director General Nebojsa Nakicenovic and Caroline Zimm, IIASA Transitions to New Technologies Program and The World in 2050 (TWI2050) initiative. (Originally published on The Guardian)
2015 marked a historic turning point. The sustainable development goals (SDGs) unanimously adopted by the United Nations last September provide an aspirational narrative and specific targets for human development: a world free from hunger, injustice and absolute poverty; a world with universal education, health and employment; a world with inclusive economic growth, based on transparency, dignity and equity.
The 17 SDGs’ call for “global citizenship and shared responsibility” and provide legitimacy for a new global social contract for a grand transformation toward a sustainable future. They fully acknowledge the scientific advances achieved during the last three decades that have established compelling evidence that otherwise, as the UN general assembly warned, “the survival of many societies, and of the biological support systems of the planet, is at risk.” Humanity has pushed the Earth system and its global commons to their limits and the SDGs provide us with the long-needed paradigm shift towards realizing the opportunity of a sustainable future for all.
The climate agreement adopted in Paris last December has further strengthened understanding that our society depends on sustainable stewardship of the global commons, shared by us all – and particularly on the stability of the climate system. The Earth system can no longer be viewed as an economic or social externality. Last year we moved beyond the traditional view of global commons as merely the common heritage of humankind outside national jurisdiction. Now we must move beyond national sovereignty to deal with the Earth system and human systems holistically, as the SDGs require. The Paris agreement is a huge step in the right direction.
Time is running out, so we must take urgent action to implement the UN 2030 agenda. Just 14 years are left – less than the wink of an eye in the history of human development, or of the Holocene’s stable Earth systems. But where to start? Which of the 17 goals, which of the 169 targets should be tackled first? Policy makers, the media, civil society and scientists all ask these questions.
However, the 2030 agenda stresses that the SDGs are indivisible and integrated – and cumulative, since efforts to achieve them must be sustained well into the second half of the century, especially in preserving the regulating function of the global commons, Some of the goals, such as SDG13 on climate, must operate on a time scale longer than century.
Sustainable Development Goal 6: Clean water and sanitation. Photo by Albert Gonzalez Farran, UNAMID
Moreover, there are interactions between and among the SDGs. For example, achieving SDG7, the energy goal, could jeopardize SDGs related to water, health and climate. Tackled in harmony, however, these goals can support one another: there would, for example, be clear health benefits from reducing indoor and outdoor air pollution through global decarbonization. Jointly implementing all the SDGs would contribute both to further human development and to safeguarding the commons and the stability of the Earth systems. Importantly, joint implementation that avoids silo-type thinking would be cheaper and faster than tackling them separately.
All these goals should be achieved in such a way as to maximize synergies and minimize investment costs and trade-offs. The SDG credo “leave no one behind” also applies to the SDGs themselves. They are indivisible. We have to deliver on all of them if we want to succeed.
The SDGs are very ambitious but it appears that tackling them together will help humanity make rapid progress and enter a new era for human societies and the Earth system. Yet, many interactions – and their scope – are unknown, and this hampers holistic policy making. We lack clear understanding of the benefits of achieving SDGs and of costs of inaction, especially when it comes to regional and national differences. We urgently need this fact-based information.
We have a plethora of knowledge, but need new ways to synthesize, integrate and share it so as to use its full potential in support of the SDGs and the global commons. Science – one of the strongest voices of the environment in governance – must become more active and leave its ivory tower to engage more intensely with other stakeholders.
This is why we at IIASA, together with the Stockholm Resilience Center, and the Sustainable Development Solutions Network have launched the scientific initiative The World in 2050 (TWI2050), designed to provide the scientific knowledge to support the policy process and implementation of the 2030 agenda.
TWI2050 aims to address the full spectrum of transformational challenges in fulfilling the SDGs in an integrated way so as to avoid potential conflicts among them and reap the benefits of potential synergies through achieving them in unison. This requires a systemic approach.
The time for “climate-only” or “economic development-only” approaches is over. We urgently need an integrated understanding of the processes that account for the inter-linkages between the economy, demography, technology, environment, climate, human development, all global commons and planetary boundaries. TWI2050 brings together leading policymakers, analysts, and modelling and analytical teams to collaborate in developing pathways towards the sustainable futures and policy frameworks necessary for achieving the needed transformational change.
Such a grand transformation goes beyond a purely technology-centered view of the world or the substitution of one technology by another. It encompasses social and behavioral changes at all levels, as well as technological ones. Incremental changes, now being experienced in some areas, are useful but will not suffice: we have waited too long and the window for action is closing rapidly in some domains including such global commons as climate. We will need radical changes in human behavior and technological paradigms. TWI2050 will look beyond 2030 to 2050 – and, in some cases, even to 2100 – to draw a vision of the world where the SDGs are eventually fulfilled.
The SDGs and the Paris agreement show what institutional international governance can achieve with joined forces. We have entered a new era of global governance, acknowledging the complexity and the connectivity of human development with the global commons and the Earth system. TWI2050 hopes to serve the global community with the best science available in tackling these key global challenges for humankind.
By Daniel Mason-D’Croz, Senior Research Analyst at International Food Policy Research Institute (IFPRI) (This post was originally published on the IFPRI Research Blog)
There are many challenges confronting decision makers in building robust and effective policies. They must balance pressing short-term needs with long-run challenges. They must confront these varying demands while facing imperfect knowledge of the complex systems (i.e. the economy, the environment, etc.) in which their policies will have impact. Above all, they also face the same uncertainty about the future as the rest of us, making perfect prediction about future outcomes impossible.
Nevertheless, decision makers must make choices in response to future challenges; inaction itself is an implicit choice, as change is inevitable. The challenge is to find a way to improve decision making, and in Multi-factor, multi-state, multi-model scenarios: exploring food and climate futures for Southeast Asia, recently published in Environmental Modelling Software, we believe we have presented a unique methodology to improve the decision-making process, by leveraging a participatory stakeholder-driven scenario development process with a multi-model ensemble to interactively explore future uncertainty with regional stakeholders.
This methodology was first applied in a workshop in Vietnam, where a diverse set of stakeholders from a wide range of sectors in Cambodia, Laos, and Vietnam collaborated to develop four multidimensional scenarios focusing on future agricultural development, food security, and climate change. Through building these multidimensional scenarios, stakeholders were challenged to consider potential interactions between varied parts of complex systems, like society and the environment. By doing this with a diverse set of stakeholders from public and private sectors, participants considered the future in a holistic and multidisciplinary manner. They were asked not only how different the future might look from the present, but also how they might respond to and shape future change. In so doing, regional stakeholders gained a better understanding of future uncertainty, while introspectively reviewing their own assumptions on the drivers of change, while creating four diverse scenarios that presented challenging plausible futures.
These scenarios were then quantified and simulated using a series of climate models, crop simulation models, and economic models including IFPRI’s IMPACT model and IIASA’s GLOBIOM model. Quantifying the scenarios in models can assist decision makers by pairing the qualitative aspects of the scenarios with quantitative analysis that systematically considers complex interactions and potential unintended consequences. Doing this quantification across a multi-model ensemble maintains the scenario diversity and richness, which in turn ensures that a broad possibility space is maintained throughout the process. This offers decision makers a larger test bed in which to evaluate potential policies. This multidimensionality and diversity of scenario outputs has been well received in the region, allowing them to be adapted and reused in a variety of policy engagements in Cambodia, Laos, and Vietnam.
In Cambodia, scenario results were used to inform their Climate Change Priorities Action Plan (CCPAP) to better target and prioritize the spending of its 164 million U.S. dollar projected budget, a policy engagement that was done over 6 to 8 months as scenario analysis and use were embedded in the CCPAP
In Laos, scenario results were presented in a regional workshop led by CCAFS and UNEP WCMC to evaluate regional policies for economic development, agricultural development, and climate change and consider potential environmental tradeoffs
In Vietnam, scenario results were shared in a workshop led by CCAFS and FAO to review and revise climate-smart agriculture investments proposals by considering the potential effectiveness of different investments under various climatic and socioeconomic conditions