By Husam Ibrahim, International Science Council (ISC)
The IIASA-ISC Enhancing Governance for Sustainability Report identifies the lessons learnt from the ongoing COVID-19 pandemic in relation to upgrading risk governance.
Credit: Adam Islaam – IIASA
As some governments and their administrations, individuals, and science systems begin to adapt to COVID-19, the struggle still continues in many countries. With that, the world is slowly leveraging the insights this pandemic has offered, standing at the cusp of a new world, which faces multiple stressors and is in need of more resilient governance.
Globally, national governments were put under the microscope. Some, such as Singapore and South Korea, succeeded with evidence-based, swift national leadership coupled with clear crisis communication. This proved useful for containing the spread of the COVID-19 virus and with it brought necessary recovery initiatives. In other countries, such as the United States, tackling the crisis has been characterized by governance challenges, including crisis plans with layers of shared responsibility being ignored in favor of “management by panic” approaches.
The pandemic has highlighted the flaws of neoliberal governance that prioritizes economic growth, deregulation and a separation between people and nature ahead of policies centered around human and ecosystem health and wellbeing.
To this effect, the IIASA-ISC Enhancing Governance for Sustainability Report goes beyond just considering the roles and responsibilities of governments, and adopts a broader definition of governance as, “the totality of actors, rules, conventions, processes, and mechanisms concerned with how relevant…information is collected, analyzed and communicated, and how management decisions are taken”.
In a world confronted with future risks such as spiraling climate change, ecosystem collapse and dwindling resources, global governance needs to be reformed.
The report states that the global community needs to engage in multi-directional and more integrated learning, problem identification and decision making. This should enable the shift towards more sustainable and equitable development in an ever-riskier world.
A disease with no respect for borders requires a collective response, said Volkan Bozkir, President of the United Nations General Assembly, adding that, “COVID-19 is a practice test that exhibits our weaknesses; we must build resilience now for whatever comes tomorrow.”
The pandemic highlighted widespread global fragmentation, which was initially observed through uncoordinated and sometimes competing actions. The report explains that organizations and agencies with similar objectives fought over resources, when instead they should have been bridging their divides and working cooperatively to eliminate competition. In the meantime, as the divide is bridged, special crisis provisions should be established for activation in case urgent action is needed again.
The report also recommends strengthening science–policy interactions to enable evidence-based decision-making, in which science systems collaborate with governments at all governance levels. Global and regional collaboration is especially important given the uneven scientific capabilities across countries and the need to tackle the pandemic everywhere to achieve health outcomes for all.
Working effectively at the interface of science and policy has been a challenge for many countries, which warrants further investigation. However, scientists have tried to step up to the challenges in some unprecedented ways.
For example, online repositories started publishing COVID-19 studies as pre-prints so that their findings could be used by all scientists quickly. As a result, researchers have identified and shared hundreds of viral genome sequences, and several hundreds of clinical trials have been launched, bringing together hospitals and laboratories around the globe.
Mukhisa Kituyi, the Secretary-General of the United Nations Conference on Trade and Development, referred to international scientific collaboration in reference to COVID-19, as the “engine of global science” and said, “It is thus crucial that scientific responses are based on international collaboration that brings together the best minds and available data from different countries for the benefit of all”
Therefore, to reform global governance, evidence-sharing arrangements need to be centered on a global level with reliable evidence, which must be made available swiftly in times of crises. In order for this to happen, the report recommends the creation of specialized advisory bodies that offer consultations on a regular and on-demand basis. The report also suggests involving diverse stakeholder perspectives in these consultations.
Another key point to enhancing sustainable governance is risk reduction management, which should be a fundamental component of decision-making and a part of the investment in sustainable development. The report states that a global socio-ecological resilience and risk dialogue should be launched, engaging policymakers, civil society, the private sector, and the scientific community in mapping risks and their drivers at different scales and discussing their implications for risk governance, prevention and preparedness. Such an engagement process would increase the understanding and communication of the compound, systemic nature of risks driven by infectious diseases, climate change, and other socio-ecological stressors.
“A more holistic approach to risk that better takes into account the many intricate links between nature and people is sorely needed if we are to achieve the Sustainable Development Goals.”
– Anne-Sophie Stevance, ISC
Unifying fragmented global organizations and governance, forming scientific evidence-based policies with the help of science systems, and enhancing levers pertaining to risk management are only some of the recommendations in the report. For more information on upgrading risk governance read the IIASA-ISC Enhancing Governance for Sustainability Report.
You can also watch the discussion on Learning from COVID-19 and upgrading sustainable governance as part of the launch event for the Bouncing Forward Sustainably: Pathways to a post-COVID World initiative, which explores the key themes of Sustainable Energy, Governance for Sustainability, Strengthening Science Systems, and Resilient Food Systems.
This blog post was first published on the website of the International Science Council. Read the original article here.
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 Marie Franquin, External Relations Officer in the IIASA Communications and External Relations Department
Marie Franquin writes about her first six months as part of the IIASA Communications and External Relations team.
This year has certainly been a great challenge for all of us, migrating our lives online and our offices to the living-room. Last summer, I finished my PhD and was ecstatic to have found a job at IIASA that encompassed day-to-day work on my favorite skills: international stakeholder engagement, policy interface, and interacting with researchers, including early career ones!
All of these aspects were covered in the newly launched 2021-2030 IIASA Strategy that was published in the winter. My challenge remained to know how I could best apply my science to policy and research skills to contribute to these goals. How do I help a systems analysis research community move towards more impact and increasing stakeholder engagement?
It quickly became obvious that my position in the external relations team required multitasking and honing a series of skills. The first and top skill that I have kept developing for the past six months was interacting with international stakeholders from all over the world, which included not only our National Member Organization (NMO) representatives and researchers from these countries, but also IIASA researchers and alumni. Working at IIASA I have already gained experience in developing relationships with stakeholders of the research community all over the world.
The IIASA stakeholder community also sheds new light on the value of the institute’s expertise in systems analysis for building international scientific partnerships, whether it be formal ones with my colleague Sergey Sizov and his science diplomacy expertise, or by facilitating research partnerships between our NMO countries and IIASA researchers.
With my colleague Monika Bauer, I am also learning about the future of stakeholder engagement and how to build virtual communities, like she’s doing with IIASA Connect:
“We are building the global systems analysis network on IIASA Connect. This tool allows colleagues, alumni, the institute’s regional communities, and collaborators to directly engage with each other and take advantage of the institute’s international and interdisciplinary network. It is something completely new for the organization,” she explains.
Our recent partnership with the Strategic Initiatives (SI) Program was aimed at better understanding the IIASA NMO countries and their individual research priorities for the next decades. I learned about local challenges and strengths and how countries have managed to move forward as a nation or by working hand in hand with their neighbors.
Coming from a research background, I am fascinated by the insights I am gaining working with IIASA communications colleagues on how to promote research and its impacts. I particularly enjoyed working with Ansa Heyl, helping IIASA experts build their policy brief submissions for the recent T20 Italy call for abstracts. As part of my skillset and center of interest, I aim to apply my science to policy skills here at IIASA to support the researchers and impacts of the amazing work done across the institute.
Having mostly worked with and for early career researchers for several years, I remain sensitive to their needs for career development opportunities. I am therefore excited to work with colleagues in the institute’s Capacity Development and Academic Training (CDAT) program to further define and support research excellence at IIASA, especially in the very promising next generation of systems scientists.
Few workplaces are so well connected and offer so many opportunities to develop such a broad range of skills as the IIASA Communications and External Relations team. As we are working towards fulfilling the IIASA Strategy’s aim of strengthening partnerships, I look forward to continuing to interact with IIASA researchers and supporting the institute’s goals of making sure the work done at IIASA positively impacts society. So come and chat with me!
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 Shorouk Elkobros, IIASA Science Communication Fellow
Assessing energy-related choices and the behaviors of households can help us transition to a low-carbon economy. How can research provide more effective decision-making tools to policymakers for better climate change mitigation policies?
We live at a defining moment for climate change, where today’s actions affect tomorrow’s reality. Every little climate-friendly decision counts. Whether we decide to insulate our houses, put solar panels on our rooftops, or invest in energy-efficient appliances. However, our personal and energy-related decisions vary based on our awareness, age, education, income, energy provider services, social norms, culture, and many other factors. Researchers are starting to pay attention to how this diversity is not well represented in the economic models that politicians use to plan climate change policies.
@ VectorMine | Dreamstime.com
Designing policies inspired by people
Households contribute an average of 70% of global greenhouse gas emissions. Limiting global emissions requires holistic policy approaches that take households’ behaviors and lifestyle decisions into account. Adding such a dimension can potentially upscale low carbon behavioral and social changes to national and global levels, which is fundamental to tackling climate change.
Worried about the future of the planet and motivated to support policymakers in designing better climate change mitigation policies, the authors of a recent study published in the journal Environmental Modeling & Software aspired to build bridges through interdisciplinary research. The study presented a novel interdisciplinary method that aims to integrate households’ energy behavior and social dynamics in climate-energy-economy models and thus help politicians design policies inspired by people.
“I have always been interested in the science-policy-society aspect of mitigating climate change. Climate change is a collective challenge that we need to address together to come up with better solutions for future generations,” notes study lead author Leila Niamir, a researcher jointly associated with the Mercator Research Institute on Global Commons and Climate Change, Berlin and the IIASA Transitions to New Technologies Program.
Better models for a better future
Climate change mitigation policies play a pivotal role in achieving ambitious environmental targets like the Paris Agreement or the Sustainable Development Goals (SDGs). To be able to formulate appropriate mitigation policies, decision makers need assessment tools to measure complex systems quantitatively. In the past decade, a variety of assessment tools have emerged, which have since been predominantly used to support climate change policy debates. In the study, Niamir argues that current assessment models are missing bottom-up and grassroots dynamics, they cannot project realistic variables of what households’ lifestyles and social movement are, and they therefore may not be sufficient to provide reliable information for policymakers.
There is a gap between what policymakers’ current assessment tools can offer and what social scientists and behavioral economists highlight as pro-environmental behavior and climate change mitigation movements. By adding this complex behavior and social perspective to the models, the researchers make it easier for policymakers to design future policies to accommodate different societal behaviors and lifestyles.
Niamir and her team presented a novel method for systematically upscaling grassroots dynamics by linking the best of both “top-down” macroeconomic computable general equilibrium (CGE) models and “bottom-up” empirical agent-based models (ABM). Their approach demonstrates that with computational ABM directly linked to survey data and macroeconomic CGE models, individual behavioral diversity and social influences can be considered when designing implementable and politically feasible policy options.
“We need better assessment tools to quantitatively explore the complex climate-energy-economy system, and reveal the potential of demand-side mitigation strategies. To see substantial changes, we need a mix of external interventions, from soft information policies aimed at raising awareness bottom-up, to financial incentives altering the macro landscape of energy markets and technological transitions. Only modular and integrated models can help policymakers quantitatively explore this complex system and plan for changes in the coming decades,” says Niamir.
Towards a low-carbon economy
We cannot tackle what we do not know. Pathways to a low-carbon economy future entail diminishing the growing discrepancy between mitigation policies and individual and collective behaviors. When redesigning our socio-environmental systems to mitigate climate change, we need to start looking at people as case studies rather than numbers. To transition to a low-carbon economy and accelerate decarbonization, policymakers must adopt novel models that integrate energy consumption, individual behavior, heterogeneity, and social influence into current assessment tools.
“Mitigating climate change indeed requires a massive effort from individual and social movements to advance national and international collaboration. Each individual small step towards shrinking our carbon footprint creates cascading changes in social behavior and consequently mitigates climate change,” Niamir concludes.
Reference:
Niamir L, Ivanova O, & Filatova T (2020). Economy-wide impacts of behavioral climate change mitigation: linking agent-based and computable general equilibrium models. Environmental Modelling & Software 134: e104839. [pure.iiasa.ac.at/16671]
Note: This article gives the views of the authors, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
Over the past decade, the open-source movement (e.g., the Free Software Foundation (FSF) and the Open Source Initiative (OSI)) has had a tremendous impact on the modeling of energy systems and climate change mitigation policies. It is now widely expected – in particular by and of early-career researchers – that data, software code, and tools supporting scientific analysis are published for transparency and reproducibility. Many journals actually require that authors make the underlying data available in line with the FAIR principles – this acronym stands for findable, accessible, interoperable, and reusable. The principles postulate best-practice guidance for scientific data stewardship. Initiatives such as Plan S, requiring all manuscripts from projects funded by the signatories to be released as open-access publications, lend further support to the push for open science.
Alas, the energy and climate modeling community has so far failed to realize and implement the full potential of the broader movement towards collaborative work and best practice of scientific software development. To live up to the expectation of truly open science, the research community needs to move beyond “only” open-source.
Until now, the main focus of the call for open and transparent research has been on releasing the final status of scientific work under an open-source license – giving others the right to inspect, reuse, modify, and share the original work. In practice, this often means simply uploading the data and source code for generating results or analysis to a service like Zenodo. This is obviously an improvement compared to the previously common “available upon reasonable request” approach. Unfortunately, the data and source code are still all too often poorly documented and do not follow best practice of scientific software development or data curation. While the research is therefore formally “open”, it is often not easily intelligible or reusable with reasonable effort by other researchers.
What do I mean by “best practice”? Imagine I implement a particular feature in a model or write a script to answer a specific research question. I then add a second feature – which inadvertently changes the behavior of the first feature. You might think that this could be easily identified and corrected. Unfortunately, given the complexity and size to which scientific software projects tend to quickly evolve, one often fails to spot the altered behavior immediately.
One solution to this risk is “continuous integration” and automated testing. This is a practice common in software development: for each new feature, we write specific tests in an as-simple-as-possible example at the same time as implementing the function or feature itself. These tests are then executed every time that a new feature is added to the model, toolbox, or software package, ensuring that existing features continue to work as expected when adding a new functionality.
Other practices that modelers and all researchers using numerical methods should follow include using version control and writing documentation throughout the development of scientific software rather than leaving this until the end. In addition, not just the manuscript and results of scientific work should be scrutinized (aka “peer review”), but such appraisal should also apply to the scientific software code written to process data and analyze model results. In addition, like the mentoring of early-career researchers, such a review should not just come at the end of a project but should be a continuous process throughout the development of the manuscript and the related analysis scripts.
In the course that I teach at TU Wien, as well as in my work on the MESSAGEix model, the Intergovernmental Panel on Climate Change Special Report on Global Warming of 1.5°C scenario ensemble, and other projects at the IIASA Energy Program, I try to explain to students and junior researchers that following such best-practice steps is in their own best interest. This is true even when it is just a master’s thesis or some coursework assignment. However, I always struggle to find the best way to convince them that following best practice is not just a noble ideal in itself, but actually helps in doing research more effectively. Only when one has experienced the panic and stress caused by a model not solving or a script not running shortly before a submission deadline can a researcher fully appreciate the benefits of well-structured code, explicit dependencies, continuous integration, tests, and good documentation.
A common trope says that your worst collaborator is yourself from six months ago, because you didn’t write enough explanatory comments in your code and you don’t respond to emails. So even though it sounds paradoxical at first, spending a bit more time following best practice of scientific software development can actually give you more time for interesting research. Moreover, when you then release your code and data under an open-source license, it is more likely that other researchers can efficiently build on your work – bringing us one step closer to a community of open science!
Note: This article gives the views of the authors, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
By Sibel Eker, researcher in the IIASA Energy Program
IIASA researcher Sibel Eker explores the usefulness and reliability of COVID-19 models for informing decision making about the extent of the epidemic and the healthcare problem.
In the early days of the COVID-19 pandemic, when facts were uncertain, decisions were urgent, and stakes were very high, both the public and policymakers turned not to oracles, but to mathematical modelers to ask how many people could be infected and how the pandemic would evolve. The response was a plethora of hypothetical models shared on online platforms and numerous better calibrated scientific models published in online repositories. A few such models were announced to support governments’ decision-making processes in countries like Austria, the UK, and the US.
With this announcement, a heated debate began about the accuracy of model projections and their reliability. In the UK, for instance, the model developed by the MRC Centre for Global Infectious Disease Analysis at Imperial College London projected around 500,000 and 20,000 deaths without and with strict measures, respectively. These different policy scenarios were misinterpreted by the media as a drastic variation in the model assumptions, and hence a lack of reliability. In the US, projections of the model developed by the University of Washington’s Institute for Health Metrics and Evaluation (IHME) changed as new data were fed into the model, sparking further debate about the accuracy thereof.
This discussion about the accuracy and reliability of COVID-19 models led me to rethink model validity and validation. In a previous study, my colleagues and I showed that, based on a vast scientific literature on model validation and practitioners’ views, validity often equates with how good a model represents the reality, which is often measured by how accurately the model replicates the observed data. However, representativeness does not always imply the usefulness of a model. A commentary following that study emphasized the tradeoff between representativeness and the propagation error caused by it, thereby cautioning against an exaggerated focus on extending model boundaries and creating a modeling hubris.
Following these previous studies, in my latest commentary in Humanities and Social Sciences Communications, I briefly reviewed the COVID-19 models used in public policymaking in Austria, the UK, and the US in terms of how they capture the complexity of reality, how they report their validation, and how they communicate their assumptions and uncertainties. I concluded that the three models are undeniably useful for informing the public and policy debate about the extent of the epidemic and the healthcare problem. They serve the purpose of synthesizing the best available knowledge and data, and they provide a testbed for altering our assumptions and creating a variety of “what-if” scenarios. However, they cannot be seen as accurate prediction tools, not only because no model is able to do this, but also because these models lacked thorough formal validation according to their reports in late March. While it may be true that media misinterpretation triggered the debate about accuracy, there are expressions of overconfidence in the reporting of these models, even though the communication of uncertainties and assumptions are not fully clear.
The uncertainty and urgency associated with pandemic decision-making is familiar to many policymaking situations from climate change mitigation to sustainable resource management. Therefore, the lessons learned from the use of COVID models can resonate in other disciplines. Post-crisis research can analyze the usefulness of these models in the discourse and decision making so that we can better prepare for the next outbreak and we can better utilize policy models in any situation. Until then, we should take the prediction claims of any model with caution, focus on the scenario analysis capability of models, and remind ourselves one more time that a model is a representation of reality, not the reality itself, like René Magritte notes that his perfectly curved and brightly polished pipe is not a pipe.
References
Eker S (2020). Validity and usefulness of COVID-19 models. Humanities and Social Sciences Communications 7 (1) [pure.iiasa.ac.at/16614]
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 Nicole Arbour, external relations manager in the IIASA Communications and External Relations department
As Canadian expats in Austria, one of the things that has particularly struck my family and I is the orderliness with which the country is dealing with the pandemic. As quarantine policies were put into place, we saw panic toilet paper hoarding in other countries, but here in Austria people were (amazingly) compliant and seemed to obey instructions and timelines provided by the authorities. We never worried about our basic needs. Grocery stores were always well stocked, public transit was always there and on time – and masks were readily available when required as physical barrier to protect others.
Expert opinions, governments, and publics are making it clear that there is no one-size-fits-all solution to this pandemic. What works in Austria might not be what worked for South Korea; and likely not the same as what works in other parts of Europe. Consider the Canadian landscape. There is huge variation in sociopolitical and cultural dynamics between and within provinces and territories. What works for some parts of Canada (virtual home schooling, grocery shopping) is impossible for others (Canada’s North). Cultural norms (multigenerational living, child/elder care) vary across the vast landscape. The “At Home on the Land” initiative – aimed at the particular needs of Indigenous communities is an example of a culturally-grounded way to address the pandemic. Finding solutions isn’t always as intuitive as we might like.
Humans tend to look for the easiest way out – we want simple solutions to complex problems. We don’t seem to want to think about the problems, we want them magically disappear. And thinking “outside of the box” isn’t always appreciated. Hand washing, clean water and the advent of antibiotics have made enormous leaps in our ability to tackle public health outbreaks – significant results. Where the bubonic plague is estimated to have killed 30%-60% of Europe’s population in the Middle Ages, modern outbreaks are now quickly identified and contained (were you even aware of the 2017 outbreak in Madagascar?). Understanding transmission routes has significantly impacted public health outcomes. The identification of tainted water as a vector for cholera transmission by John Snow led to the advent of modern epidemiology. But, as we find solutions to larger challenges, those that remain are more complex with increasing numbers of variables making solutions harder to come by.
There is some global agreement: lots of testing, quick results/containment, use of masks/physical barriers for community protection, social distancing, data collection. However, certain measures work better in some jurisdictions than others. What policies and practices are working and why are they working in these contexts? What is applicable in different contexts?
Our current global situation, has reminded me of a presentation I saw on the 2014 Ebola outbreak (Professor Melissa Leach, IDS), and how important it is to remember the human factor in crises. She discussed how the key elements that made the Ebola pandemic so persistent – despite the best efforts of global public health engagement – was a/the failure to understand how historic context, trust, cultural dynamics played into the spread of the virus. Those providing interventions did not appreciate how historic context (i.e. post-colonialism, slavery, medical testing scandals) and mistrust in the intentions of Western interventions factored into the willingness of the local population to accept the solutions provided. Awareness of social structures, influencers and leaders, and co-creation were also important to developing solutions that would be adopted by affected communities.
Evidence is more than the numbers of tests, infections and deaths. It is understanding the social context of communities, society writ large, and how they interact within and between. It’s about understanding historical context and how it feeds into local culture, social interactions and trust relationships. It’s about community dynamics, power struggles and the struggle for some to meet basic survival needs. It’s about timing of decision-making, political landscapes and different ways of leading. As with many of our global challenges, it’s a complex and multifaceted systems problem – in which the human factor is a huge driver.
As we strive for solutions to this global crisis – bring on innovation, research and science funding. We will need these – but please, also bring along those who study the complexity that is humanity: epidemiologists, anthropologists, economists, ethicists, political scientists, sociologists, futurists, etc. In an era where evidence is being questioned, fake news is rampant and anti-science sentiments are strong, it is crucial that we remember that one piece to engaging with this and the world’s other wicked problems is our relationships with our communities – the ones we are trying to protect. Public trust, built on understanding of the importance of human dynamics is key to broad acceptance and uptake. Solutions need to be palatable to society, or they won’t be adopted.
As we focus on the virus, let’s not forget the humans.
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