By Fanni Daniella Szakal, 2021 IIASA Science Communication Fellow
In an attempt to foster economic development for Brazil, the government is planning to open up indigenous and protected areas for mining. But will this truly lead to economic development for the country? 2021 Young Scientists Summer Program (YSSP) participant, Sebastian Luckeneder is using spatial modeling to find out.
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As the largest rainforest on the planet, the Amazon harbors the highest biodiversity of all ecosystems and is home to many indigenous tribes. It is also literally sitting on a goldmine of natural resources. There are plans in the works to open up protected and indigenous areas of the rainforest to mining activities, which is expected to bring more wealth and development for the country, but at the same time, it will also pose a threat to the environment and indigenous communities.
At first glance, the issue looks like the classic trade-off between economic growth versus environmental and social disruption. In reality however, mining affects social, environmental, and economic spheres both directly and indirectly, creating a complex network of interactions that potentially defy the current dogma.
Mining relies heavily on machines while creating relatively few jobs in comparison to the investment of capital it requires. In addition, mining companies are often large international corporations, which means that most of the profits gained from mining operations in a particular country end up outside that country’s borders.
“One could say that just the very few benefit from extractive activities, whereas many have to pay the cost,” says Sebastian Luckeneder, a 2021 YSSP participant at IIASA, when referring to the environmental destruction, disruption of livelihoods, and displacement of indigenous communities that mining would bring about.
As a second-year PhD candidate at the Institute for Ecological Economics of Vienna University of Economics and Business (WU), Luckeneder is studying the environmental and socioeconomic impacts of mining activities. At IIASA, he used spatial modeling to understand how mining and land use affect regional economic growth in Brazil in the hopes of finding the best economic solution for the country.
Using GDP growth as a proxy for economic development, he looked at the impacts of mining and other types of land use between 2000 and 2020. The model incorporates data on mining, agriculture, and land-use change, as well as other socioeconomic factors, such as employment and infrastructure for about 5,500 municipalities in Brazil.
The study is as complex as it sounds: Luckeneder’s main challenge is to set up a theoretical framework that depicts how the environmental and socioeconomic factors influence each other. Once his comprehensive model is complete, he hopes to get a clear picture of how mining affects the Brazilian economy.
He suspects that while mining activities would bring some economic gains, these might not be sustainable, as the environmental and social upheaval that follow the opening of a mine could negatively impact development in the long-run.
While economic development is important, in the current climate crisis, decisions to enable activities that lead to deforestation cannot be taken lightly. Luckeneder hopes that his results will be used to inform the political debate in Brazil and support policy decisions by the way of science.
By Charlotte Janssens, guest researcher in the IIASA Ecosystems Services and Management Program and researcher at the University of Leuven and Petr Havlík, Acting Ecosystems Services and Management Program Director.
Charlotte Janssens and Petr Havlik write about their recent study in which they found that world trade can relieve regional impacts of climate change on food production and provide a way to reduce the risk of hunger.
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In a warmer world, decreasing crop yields and rising food prices are expected to strongly jeopardize the achievement of Sustainable Development Goal (SDG) 2 – ending global hunger. Climate change has consequences for food production worldwide, but there are clear differences between regions. Sufficient food is expected to remain available in the Northern hemisphere, while in regions such as sub-Saharan Africa or South Asia, falling crop yields may lead to higher food prices and a sharp rise in hunger.
In our recent publication in Nature Climate Change, we find that world trade can relieve these regional differences and provide a way to reduce hunger risks under climate change. For example, if regions like Europe and Latin America where wheat and corn thrive increase their production and export food to regions under heavy pressure from the warming of the Earth, food shortages can be reduced.
Global Hunger by 2050
The State of Food and Nutrition Security in the World 2020 reports that globally almost 690 million people were at risk of hunger in 2019. Many factors determine how global hunger will develop in the future, including population growth and economic development, as demonstrated in a study in Environmental Research Letters. Our article uses the “middle-of-the-road” socioeconomic pathway where population reaches 9.2 billion, income grows according to historical trends, and the number of undernourished people decreases to 122 million by 2050. Within this socioeconomic setting, we investigate the impact of different climate change scenarios and trade policies on global hunger by 2050.
The worst-case climate scenario of a 4°C warming leads to an extra 55 million people enduring hunger – a 45% increase compared to the situation without climate change. In a protectionist trade environment where vulnerable regions cannot increase their food imports as a response to climate impacts, this effect rises to 73 million. The largest hunger risks are located in South Asia and sub-Saharan Africa, with respectively a 33 million and 15 million increase in people at risk of hunger in the worst-case climate scenario.
Where barriers to trade are eliminated, “only” 20 million people endure food shortages due to climate change. While this number is high, it is a vast improvement on the 73 million people that would potentially be exposed to hunger without the suggested measures. In the milder climate change scenarios, an intensive liberalization of trade may prevent even more people from enduring hunger owing to global warming. Yet a liberalization of international trade may also involve potential dangers. If Asian countries increase rice exports without making more imports of other products possible, they could well end up with a food shortage within their own borders.
Our study shows not only that the challenge of ending global hunger is strongly determined by the extent of progress on SDG 13 (climate action), but also that achievement of SDG 2 (zero hunger) is affected by developments articulated in SDG 9 (resilient infrastructure). We find that international trade can relieve regional food shortages and reduce hunger, particularly where trade barriers are eliminated. Such trade integration requires phasing out import tariffs as well as the facilitation of trade through investment in transport infrastructure and technology. Especially in low-income regions such as sub-Saharan Africa infrastructure is weak. In its 2018 African Economic Outlook, the African Development Bank (AfDB) estimates that between USD 130 billion and 170 billion a year is needed to bridge the infrastructure gap in the region by 2025. Given that infrastructure finance averaged only USD 75 billion in recent years, and the largest contribution is coming from budget-constrained national governments, alternative financing through institutional and private investments could be crucial in the face of climate change.
Crisis and Protectionism
In times of crisis, countries are inclined to adopt a protectionist stance. For example, in the face of the current COVID-19 pandemic, several countries have temporarily closed their borders for the export of important food crops (see IFPRI Food Trade Policy Tracker for updated information). Some commentators warn that such measures can have large detrimental effects on food security. Our study finds that also in the context of climate change, a well-thought-out liberalization of trade is needed in order to be able to relieve food shortages properly.
Janssens C, Havlík P, Krisztin T, Baker J, Frank S, Hasegawa T, Leclère D, Ohrel S, et al. (2020). Global hunger and climate change adaptation through international trade. Nature Climate Change
This blog post first appeared on the SDG Knowledge Hub website. Read the original post here.
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 Serguei Kaniovski, Economist with the Austrian Institute of Economic Research (WIFO)
Serguei Kaniovski and colleagues from IIASA and the Steklov Mathematical Institute of the Russian Academy of Sciences revisited a classic growth model in resource economics using recent advances in optimal control theory.
The late 1960s and early 1970s gave rise to Doomsday Models that predicted a collapse of Western Civilization under the pressure of over-population and environmental pollution. The very influential 1972 Club of Rome’s report on the “Limits to Growth” painted a gloomy picture, sparking an ongoing debate. One question was whether the scarcity of natural resources like fossil fuels would limit growth and cause a substantial decline in people’s standard of living.
The Doomsday reasoning was met with doubt by the economists of that time, leading the future Nobel Prize laureate and growth theorist, Robert Solow, to state that “the various Doomsday Models are worthless as science and as guides to public policy“. In a combined effort, economists developed a class of growth models with resource constraints. The conclusions they reached using the Dasgupta-Heal-Solow-Stiglitz (DHSS) modeling framework offered a more optimistic outlook.
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Economic applications have been well ahead of the mathematical theory used for identifying optimal economic policies, leaving some model solutions unexposed and some technical issues unsettled. The theory that allows us to identify optimal policies and describe the model dynamics was originally developed in the 1950s for engineering applications but has since become the main tool for analyzing economic growth models. These models however contain many features that are not standard to optimal control theory – a subfield of mathematics that deals with the control of continuously operating dynamic systems – which makes a fully rigorous analysis difficult. The key theoretical challenges are infinite planning horizons and nonstandard control constraints.
In our latest paper we offer a complete and rigorous analysis of the welfare-maximizing investment and depletion policies in the DHSS model with capital depreciation and arbitrary (decreasing, constant, and increasing) returns to scale. The investment policy specifies the portion of the final output to be invested in capital. A depletion policy says how fast a finite stock of exhaustible resources should be used. We prove the existence of a solution and characterize the behavior of solutions for all combinations of the model parameters using necessary rather than sufficient (Arrow’s theorem) optimality conditions.
In the main case of decreasing, constant, or weakly increasing returns to scale, the optimal investment and depletion policies converge to a constant share of output invested in capital and a constant rate of depletion of the natural resource. The optimal investment ratio decreases with the longevity of capital and impatience. The relationship between the optimal investment ratio and the output elasticity of produced capital is ambiguous. The performed analytical analysis identifies those relationships among model parameters that are critical to the optimal dynamics. In this, it differs from more conventional scenario-based approaches. From a practical point of view, application of the model to real data could be helpful for evaluating actual depletion and investment policies.
Strongly increasing returns to scale make it optimal to deplete the resource without investing in produced capital. Whether a zero-investment strategy is followed from the outset, from an instant of time, or asymptotically will depend on the sizes of the capital and resource stocks. In some special cases of increasing returns, welfare-maximizing investment and extraction policies may not exist under strong scale effects in resource use. This occurs when an initial stock of capital is small relative to the initial resource stock. It implies that it would have been impossible to formulate a welfare-maximizing policy in the early history of humanity, when produced capital was scarce and resources were abundant.
Aseev S, Besov K, & Kaniovski S (2019). Optimal Policies in the Dasgupta—Heal—Solow—Stiglitz Model under Nonconstant Returns to Scale. Proceedings of the Steklov Institute of Mathematics 304 (1): 74-109. [pure.iiasa.ac.at/15946]
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 Tobias Sieg, IIASA Young Scientists Summer Program alumnus
IIASA Young Scientists Summer Program alumnus Tobias Sieg explains how risk assessments considering uncertainties can substantially contribute to better risk management and consequently to the prevention of economic impacts.
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According to the World Economic Forum’s Global Risk Landscape 2018, extreme weather events and natural disasters are ranked among the top three global risks. For many regions, hydro-meteorological risks – in other words, weather or water related events like cyclones or floods that pose a threat to populations or the environment – constitute the biggest threat. This calls for a comprehensive scientific risk assessment with a particular focus on large associated uncertainties.
Assessing the risk of hydro-meteorological hazards without considering these uncertainties, is like entering a pitch-dark labyrinth. You have no idea where you are and where you will end up. If you enter with a flashlight, you might still not immediately know exactly where you will end up, but at least you can assess your possibilities for finding a way out.
We should all care to see those possibilities and to identify uncertainties, since the consequences of hydro-meteorological hazards can have severe impacts on socioeconomic systems, and global- and climate change could favor the occurrence of floods. An increase in extreme weather events, such as heavy precipitation can be expected along with an increasingly warmer climate. In combination with uncontrolled socioeconomic development, these extreme weather events could potentially trigger more intense hazardous flood events in the future. Appropriate management of their consequences is therefore required, starting from today, while pro-actively thinking about the future. To that end, risk management policy and practice need reliable estimates of direct and indirect economic impacts.
The reliability of existing estimates is usually quite low and, what is maybe even worse, they are not communicated properly. This may signal a false sense of certainty regarding the prediction of future climate-related risks.
In two recent studies, my co-authors and I developed and applied a novel method, which specifically focuses on the communication of the reliability of economic impact estimates and the associated uncertainties. The proposed representation of uncertainties enables us to shed some light on the possibilities of how a specific event can affect economic systems. As a Young Scientists Summer Program (YSSP) participant with the IIASA Risk and Resilience Program, I applied the method together with my supervisors Thomas Schinko and Reinhard Mechler, to estimate the overall economic impacts of a major flood event in Germany in 2013.
The estimated overall economic impacts comprise both direct and indirect impacts. Direct impacts are usually caused by physical contact of the floodwater with buildings, while indirect impacts can also occur in regions that are not directly affected by a flood. For example, obstructions of the infrastructure can lead to delayed deliveries, in turn leading to negative impacts for the production of goods outside the flooded areas. The crucial novelty of this method is the integrated assessment of direct and indirect economic impacts. In particular, by considering how the uncertainties associated with the estimation of direct economic impacts propagate further into the estimates of indirect economic impacts.
Being able to reproduce what has happened in the past is essential to making credible predictions about what could potentially happen in the future. A comparison of reported direct economic impacts and model-based estimates reveals that the estimation technique already works quite reliably. The good news is that anyone can help to increase the predictive reliability even further. The method uses the crowdsourced OpenStreetMap dataset to identify affected buildings. The more detailed the given information about a building is, the more reliable the impact estimations can get.
Our study reveals that the potential of short-term indirect economic impacts (without considering recovery) are quite high. In fact, our results show that the indirect impacts can be as high as the direct economic impacts. Yet, this varies a lot for different economic sectors. The manufacturing sector, for instance, is much more affected by indirect economic impacts, since it is heavily dependent on well-functioning supply chains. This information can be used in emergency risk management where decisions have to be made about giving immediate help to companies of a specific sector to reduce high long-term indirect economic impacts.
We are now looking at different possibilities of how flood events could affect the economic system. Having a range of possibilities of the relation between these impacts makes them transferable between different regions with similar economic systems. Our results are therefore also relevant more broadly beyond the German case. This representation of uncertainties can help to get to a more credible and consistent risk assessment across all spatial scales. Thus, the method is able to potentially facilitate the fulfillment of some of the calls of the UN Sendai Framework for Disaster Risk Reduction.
Detailed risk assessments considering uncertainties can substantially contribute to better risk management and consequently to the prevention of economic impacts – direct and indirect, both now and in the future.
 Sieg T, Schinko T, Vogel K, Mechler R, Merz B & Kreibich H (2019). Integrated assessment of short-term direct and indirect economic flood impacts including uncertainty quantification. PLoS ONE 14(4): e0212932. [pure.iiasa.ac.at/15833]
 Sieg T, Vogel K, Merz B & Kreibich H (2019). Seamless estimation of hydro-meteorological risk across spatial scales. Earth’s Future. https://doi.org/10.1029/2018EF001122
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.