5 years of Vietnam membership at IIASA

Tran Thi Vo-Quyen, IIASA guest research scholar from the Vietnam Academy of Science and Technology (VAST), talks to Professor Dr. Ninh Khac Ban, Director General of the International Cooperation Department at VAST and IIASA council member for Vietnam, about achievements and challenges that Vietnam has faced in the last 5 years, and how IIASA research will help Vietnam and VAST in the future.

Professor Dr. Ninh Khac Ban, Director General of the International Cooperation Department at VAST and IIASA council member for Vietnam

What have been the highlights of Vietnam-IIASA membership until now?

In 2017, IIASA and VAST researchers started working on a joint project to support air pollution management in the Hanoi region which ultimately led to the successful development of the IIASA Greenhouse Gas – Air  Pollution Interactions and Synergies (GAINS) model for the Hanoi region. The success of the project will contribute to a system for forecasting the changing trend of air pollution and will help local policy makers develop cost effective policy and management plans for improving air quality, in particular, in Hanoi and more widely in Vietnam.

IIASA capacity building programs have also been successful for Vietnam, with a participant of the 2017 Young Scientists Summer Program (YSSP) becoming a key coordinator of the GAINS project. VAST has also benefited from two members of its International Cooperation Department visiting the IIASA External Relations Department for a period of 3 months in 2018 and 2019, to learn about how IIASA deals with its National Member Organizations (NMOs) and to assist IIASA in developing its activities with Vietnam.

What do you think will be the key scientific challenges to face Vietnam in the next few years? And how do you envision IIASA helping Vietnam to tackle these? 

In the global context Vietnam is facing many challenges relating to climate change, energy issues and environmental pollution, which will continue in the coming years. IIASA can help key members of Vietnam’s scientific community to build specific scenarios, access in-depth knowledge and obtain global data that will help them advise Vietnamese government officials on how best they can overcome the negative impact of these issues.

As Director General of the International Cooperation Department, can you explain your role in VAST and as representative to IIASA in a little more detail?

In leading the International Cooperation Department at VAST, I coordinate all collaborative science and technology activities between VAST and more than 50 international partner institutions that collaborate with VAST.

As the IIASA council representative for Vietnam, I participate in the biannual meeting for the IIASA council, I was also a member of the recent task force developed to implement the recommendations of a recent independent review of the institute. I was involved in consulting on the future strategies, organizational structure, NMO value proposition and need to improve the management system of IIASA.

In Vietnam, I advised on the establishment of a Vietnam network for joining IIASA and I implement IIASA-Vietnam activities, coordinating with other IIASA NMOs to ensure Vietnam is well represented in their countries.

You mentioned the development of the Vietnam-IIASA GAINS Model. Can you explain why this was so important to Vietnam and how it is helping to improve air quality and shape Vietnamese policy around air pollution? 

Air pollution levels in Vietnam in the last years has had an adverse effect on public health and has caused significant environmental degradation, including greenhouse gas (GHG) emissions, undermining the potential for sustainable socioeconomic development of the country and impacting the poor. It was important for Vietnam to use IIASA researchers’ expertise and models to help them improve the current situation, and to help Vietnam in developing the scientific infrastructure for a long-lasting science-policy interface for air quality management.

The project is helping Vietnamese researchers in a number of ways, including helping us to develop a multi-disciplinary research community in Vietnam on integrated air quality management, and in providing local decision makers with the capacity to develop cost-effective management plans for the Hanoi metropolitan area and surrounding regions and, in the longer-term, the whole of Vietnam.

About VAST and Ninh Khac Ban

VAST was established in 1975 by the Vietnamese government to carry out basic research in natural sciences and to provide objective grounds for science and technology management, for shaping policies, strategies and plans for socio-economic development in Vietnam. Ninh Khac Ban obtained his PhD in Biology from VAST’s Institute of Ecology and Biological Resources in 2001. He has managed several large research projects as a principal advisor, including several multinational joint research projects. His successful academic career has led to the publication of more than 34 international articles with a high ranking, and more than 60 national articles, and 2 registered patents. He has supervised 5 master’s and 9 PhD level students successfully to graduation and has contributed to pedagogical texts for postgraduate training in his field of expertise. 

Notes:
More information on IIASA and Vietnam collaborations. 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.

Running global models in a castle in Europe

By Matt Cooper, PhD student at the Department of Geographical Sciences, University of Maryland, and 2018 winner of the IIASA Peccei Award

I never pictured myself working in Europe.  I have always been an eager traveler, and I spent many years living, working and doing fieldwork in Africa and Asia before starting my PhD.  I was interested in topics like international development, environmental conservation, public health, and smallholder agriculture. These interests led me to my MA research in Mali, working for an NGO in Nairobi, and to helping found a National Park in the Philippines.  But Europe seemed like a remote possibility.  That was at least until fall 2017, when I was looking for opportunities to get abroad and gain some research experience for the following summer.  I was worried that I wouldn’t find many opportunities, because my PhD research was different from what I had previously done.  Rather than interviewing farmers or measuring trees in the field myself, I was running global models using data from satellites and other projects.  Since most funding for PhD students is for fieldwork, I wasn’t sure what kind of opportunities I would find.  However, luckily, I heard about an interesting opportunity called the Young Scientists Summer Program (YSSP) at IIASA, and I decided to apply.

Participating in the YSSP turned out to be a great experience, both personally and professionally.  Vienna is a wonderful city to live in, and I quickly made friends with my fellow YSSPers.  Every weekend was filled with trips to the Alps or to nearby countries, and IIASA offers all sorts of activities during the week, from cultural festivals to triathlons.  I also received very helpful advice and research instruction from my supervisors at IIASA, who brought a wealth of experience to my research topic.  It felt very much as if I had found my kind of people among the international PhD students and academics at IIASA.  Freed from the distractions of teaching, I was also able to focus 100% on my research and I conducted the largest-ever analysis of drought and child malnutrition.

© Matt Cooper

Now, I am very grateful to have another summer at IIASA coming up, thanks to the Peccei Award. I will again focus on the impact climate shocks like drought have on child health.  however, I will build on last year’s research by looking at future scenarios of climate change and economic development.  Will greater prosperity offset the impacts of severe droughts and flooding on children in developing countries?  Or does climate change pose a hazard that will offset the global health gains of the past few decades?  These are the questions that I hope to answer during the coming summer, where my research will benefit from many of the future scenarios already developed at IIASA.

I can’t think of a better research institute to conduct this kind of systemic, global research than IIASA, and I can’t picture a more enjoyable place to live for a summer than Vienna.

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.

Shaping my scientific career

By Davit Stepanyan, PhD candidate and research associate at Humboldt University of Berlin, International Agricultural Trade and Development Group and 2019 IIASA Young Scientists Summer Program (YSSP) Award Finalist.

Participating in the YSSP at IIASA was the biggest boost to my scientific career and has shifted my research to a whole new level. IIASA provides a perfect research environment, especially for young researchers who are at the beginning of their career paths and helps to shape and integrate their scientific ideas and discoveries into the global research community. Being surrounded by leading scientists in the field of systems analysis who were open to discuss my ideas and who encouraged me to look at my own research from different angles was the most important push during my PhD studies. Having the work I did at IIASA recognized with an Honorable Mention in the 2019 YSSP Awards has motivated me to continue digging deeper into the world of systems analysis and to pursue new challenges.

© Davit Stepanyan

Although my background is in economics, mathematics has always been my passion. When I started my PhD studies, I decided to combine these two disciplines by taking on the challenge of developing an efficient method of quantifying uncertainties in large-scale economic simulation models, and so drastically reduce the need and cost of big data computers and data management.

The discourse on uncertainty has always been central to many fields of science from cosmology to economics. In our daily lives when making decisions we also consider uncertainty, even if subconsciously: We will often ask ourselves questions like “What if…?”, “What is the chance of…?” etc. These questions and their answers are also crucial to systems analysis since the final goal is to represent our objectives in models as close to reality as possible.

I applied for the YSSP during my third year of PhD research. I had reached the stage where I had developed the theoretical framework for my method, and it was the time to test it on well-established large-scale simulation models. The IIASA Global Biosphere Management Model (GLOBIOM), is a simulation model with global coverage: It is the perfect example of a large-scale simulation model that has faced difficulties applying burdensome uncertainty quantification techniques (e.g. Monte Carlo or quasi-Monte Carlo).

The results from GLOBIOM have been very successful; my proposed method was able to produce high-quality results using only about 4% of the computer and data storage capacities of the above-mentioned existing methods. Since my stay at IIASA, I have successfully applied my proposed method to two other large-scale simulation models. These results are in the process of becoming a scientific publication and hopefully will benefit many other users of large-scale simulation models.

Looking forward, despite computer capacities developing at high speed, in a time of ‘big data’ we can anticipate that simulation models will grow in size and scope to such an extent that more efficient methods will be required.

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.

What matters more in preventing adult deaths in India?

By Nandita Saikia, Postdoc Research Scholar at IIASA

What matters more when it comes to preventing unexpected and tragic adult deaths, between the ages of 15 and 60, in low- or middle-income countries?  Is it wealth? Or education?

With the advent of demographic and health surveys (DHS), empirical studies documented that the education level of mothers matters more than the wealth of the household when it comes to preventing deaths among children in these countries. However, the same question largely remained unanswered for adults, as such surveys rarely collect information on adult deaths and the socioeconomic status of the dead individuals. In these countries, in general, death registration systems are poor, which again hinders scientific studies addressing this issue.

© Donyanedomam | Dreamstime.com

One possible solution is the clever use of indirect methods or models on census and survey data, developed by demographers to derive rates from limited, deficient and defective data.  These methods use indirect information collected by surveys for a different purpose. For example, by using women’s siblings’ survival status, one can estimate maternal mortality, or by using women’s widowhood status, one can estimate male adult mortality.

In our recent study on India, we used one such method, called the Orphanhood method, to document life expectancy differences in adulthood by important socioeconomic characteristics. Because of the reasons mentioned above, there is hardly any scientific evidence on life expectancy differences by education or economic status in India, a country with exceptional cultural and socioeconomic diversity. The importance of studying adult mortality disparity in India also lies in the fact that India experiences relatively higher adult mortality than some of its neighboring countries with similar level of economic development. India’s official statistics shows that adult females belonging to the northeastern state of Assam have more than two times the mortality risk of adult females belonging to the southern state of Kerala. In addition, because of drastic reduction of under five deaths in India in recent years, more and more premature deaths in India will occur in adult age in near future.  We used adult parental survival data from a nationally representative large-scale survey, called the India Human Development Survey, 2015-2016, to estimate life expectancy at age 15 in 1998-1999.

We found that lower levels of education of the deceased adults or their offspring, leads to more disparity than any other socioeconomic characteristic, including income status of the offspring, caste, or religion. Literate adults of both sexes at age 15 lived about 3.5 years more than that of their illiterate counterparts. On average, parents of children educated to higher-secondary level (and above) gain an extra 3.8-4.6 years of adult life compared to parents of illiterate children. We found that disparity in adult life by caste and religion is much smaller than disparities arising from educational attainment. For example, female Hindu adults lived 1.3 years more than female non-Hindu adults and male Hindu adults lived 0.9 years more than male non-Hindu adults.

One inherent limitation of indirect demographic methods is that they cannot provide estimates in the most recent years. Despite our estimates referring to a time period about twenty years ago, they are still crucial, as this kind of disparity in adult deaths does not disappear in such a short time span. Our results suggest that investing in education can be more rewarding than anything else to prevent untimely deaths, and to prevent inequalities across population subgroups. Meanwhile, we suggest including appropriate indirect questions in surveys or censuses to track survival status by social group or small geographical area until vital registration systems in countries such as India become fully functional.

Reference:

Saikia N, Bora JK and Luy M (2019). Socioeconomic disparity in adult mortality in India: Estimations using the Orphanhood method. Genus DOI: 10.1186/s41118-019-0054-1 [pure.iiasa.ac.at/id/eprint/15730/]

 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.

This is not reality

By Sibel Eker, IIASA postdoctoral research scholar

© Jaka Vukotič | Dreamstime.com

Ceci n’est pas une pipe – This is not a pipe © Jaka Vukotič | Dreamstime.com

Quantitative models are an important part of environmental and economic research and policymaking. For instance, IIASA models such as GLOBIOM and GAINS have long assisted the European Commission in impact assessment and policy analysis2; and the energy policies in the US have long been guided by a national energy systems model (NEMS)3.

Despite such successful modelling applications, model criticisms often make the headlines. Either in scientific literature or in popular media, some critiques highlight that models are used as if they are precise predictors and that they don’t deal with uncertainties adequately4,5,6, whereas others accuse models of not accurately replicating reality7. Still more criticize models for extrapolating historical data as if it is a good estimate of the future8, and for their limited scopes that omit relevant and important processes9,10.

Validation is the modeling step employed to deal with such criticism and to ensure that a model is credible. However, validation means different things in different modelling fields, to different practitioners and to different decision makers. Some consider validity as an accurate representation of reality, based either on the processes included in the model scope or on the match between the model output and empirical data. According to others, an accurate representation is impossible; therefore, a model’s validity depends on how useful it is to understand the complexity and to test different assumptions.

Given this variety of views, we conducted a text-mining analysis on a large body of academic literature to understand the prevalent views and approaches in the model validation practice. We then complemented this analysis with an online survey among modeling practitioners. The purpose of the survey was to investigate the practitioners’ perspectives, and how it depends on background factors.

According to our results, published recently in Eker et al. (2018)1, data and prediction are the most prevalent themes in the model validation literature in all main areas of sustainability science such as energy, hydrology and ecosystems. As Figure 1 below shows, the largest fraction of practitioners (41%) think that a match between the past data and model output is a strong indicator of a model’s predictive power (Question 3). Around one third of the respondents disagree that a model is valid if it replicates the past since multiple models can achieve this, while another one third agree (Question 4). A large majority (69%) disagrees with Question 5, that models cannot provide accurate projects, implying that they support using models for prediction purposes. Overall, there is no strong consensus among the practitioners about the role of historical data in model validation. Still, objections to relying on data-oriented validation have not been widely reflected in practice.

Figure 1

Figure 1: Survey responses to the key issues in model validation. Source: Eker et al. (2018)

According to most practitioners who participated in the survey, decision-makers find a model credible if it replicates the historical data (Question 6), and if the assumptions and uncertainties are communicated clearly (Question 8). Therefore, practitioners think that decision makers demand that models match historical data. They also acknowledge the calls for a clear communication of uncertainties and assumptions, which is increasingly considered as best-practice in modeling.

One intriguing finding is that the acknowledgement of uncertainties and assumptions depends on experience level. The practitioners with a very low experience level (0-2 years) or with very long experience (more than 10 years) tend to agree more with the importance of clarifying uncertainties and assumptions. Could it be because a longer engagement in modeling and a longer interaction with decision makers help to acknowledge the necessity of communicating uncertainties and assumptions? Would inexperienced modelers favor uncertainty communication due to their fresh training on the best-practice and their understanding of the methods to deal with uncertainty? Would the employment conditions of modelers play a role in this finding?

As a modeler by myself, I am surprised by the variety of views on validation and their differences from my prior view. With such findings and questions raised, I think this paper can provide model developers and users with reflections on and insights into their practice. It can also facilitate communication in the interface between modelling and decision-making, so that the two parties can elaborate on what makes their models valid and how it can contribute to decision-making.

Model validation is a heated topic that would inevitably stay discordant. Still, one consensus to reach is that a model is a representation of reality, not the reality itself, just like the disclaimer of René Magritte that his perfectly curved and brightly polished pipe is not a pipe.

References

  1. Eker S, Rovenskaya E, Obersteiner M, Langan S. Practice and perspectives in the validation of resource management models. Nature Communications 2018, 9(1): 5359. DOI: 10.1038/s41467-018-07811-9 [pure.iiasa.ac.at/id/eprint/15646/]
  2. EC. Modelling tools for EU analysis. 2019  [cited  16-01-2019]Available from: https://ec.europa.eu/clima/policies/strategies/analysis/models_en
  3. EIA. ANNUAL ENERGY OUTLOOK 2018: US Energy Information Administration; 2018. https://www.eia.gov/outlooks/aeo/info_nems_archive.php
  4. The Economist. In Plato’s cave. The Economist 2009  [cited]Available from: http://www.economist.com/node/12957753#print
  5. The Economist. Number-crunchers crunched: The uses and abuses of mathematical models. The Economist. 2010. http://www.economist.com/node/15474075
  6. Stirling A. Keep it complex. Nature 2010, 468(7327): 1029-1031. https://doi.org/10.1038/4681029a
  7. Nuccitelli D. Climate scientists just debunked deniers’ favorite argument. The Guardian. 2017. https://www.theguardian.com/environment/climate-consensus-97-per-cent/2017/jun/28/climate-scientists-just-debunked-deniers-favorite-argument
  8. Anscombe N. Models guiding climate policy are ‘dangerously optimistic’. The Guardian 2011  [cited]Available from: https://www.theguardian.com/environment/2011/feb/24/models-climate-policy-optimistic
  9. Jogalekar A. Climate change models fail to accurately simulate droughts. Scientific American 2013  [cited]Available from: https://blogs.scientificamerican.com/the-curious-wavefunction/climate-change-models-fail-to-accurately-simulate-droughts/
  10. Kruger T, Geden O, Rayner S. Abandon hype in climate models. The Guardian. 2016. https://www.theguardian.com/science/political-science/2016/apr/26/abandon-hype-in-climate-models