Model-based Scenario Research Articles

Die österreichische Wettbewerbsfähigkeit im Check

Abstract Austria's economy is highly competitive internationally in many areas. Nevertheless, there are various economic policy challenges. In addition, the intensity of international competition is increasing. A comparison of Austria's economic environment with 21 other highly developed economies based on comparable indicators that are available across the board shows the most important areas where action is needed. This contribution identifies the respective challenges in different areas and provides solutions. Finally, the growth effects of selected measures are quantified based on a model-based scenario calculation.

Enhancing water management and urban flood resilience using Hazard Capacity Factor Design (HCFD) model: Case study of Eco-Delta city, Busan

This study evaluated future climate scenarios and the changes in urban flood resistance capacity in Busan Eco-Delta City using the hazard capacity factor design (HCFD) model. It analyzed the flood reduction effects of both gray and green infrastructure. Despite existing flood safety systems, there is a growing need to enhance urban flood resilience due to increasing heavy rainfall, unpredictable precipitation, and frequent typhoons driven by climate change. The HCFD model predicted urban flood volumes of Busan Eco-Delta City and analyzed the effectiveness of gray and green infrastructure in flood control. A stormwater management model (SWMM) simulated urban flood resistance capacity under the SSP1–2.6 climate change scenario. Results indicate that for an anticipated 500 mm rainfall over 3 h, green infrastructure can mitigate floods by 9 % to 17.6 %, while gray infrastructure can reduce flooding by 24 % to 32.1 %. The integration of gray and green infrastructure leads to an overall flood mitigation ranging from 47.1 % to 63.3 %. A notable contribution of this research is its predictive analysis of future flood scenarios using model-based scenario analysis and decision support algorithms, offering valuable insights into changes in urban flood resistance capacity and strategies for effective flood control decision-making.

Future perfect climates: A phenomenological rejoinder to the performativity of climate change mitigation pathways

From charting out climate change mitigation pathways to estimating price risks associated with the social cost of carbon, as environmentally concerned citizens of the twenty-first century, we live in a culture of foresight. Because of a growing integration of an ever-wider sample space of possible climate futures into the present, historical experience has become seemingly irrelevant for effectively predicting where our climate transitions are headed, in effect restricting our sense of futurity to its performativity in the present. What has been surprisingly absent as a theoretical and methodological approach among sociologists, however, are treatments of the performativity of the future as the expression of a historical praxis for prognosis, with its own mode of disclosure. By interrogating the temporal structure of anticipation that characterizes computer-based simulations of emissions scenarios, the paper illustrates how this praxis discloses the future in accordance with the grammatical tense of the future perfect. It then argues that this relationship between past and future is the cultural product of a historically particular set of prognostic techniques and technologies, namely, model-based scenario analysis. Against this background, the paper seeks to contribute to the rehabilitation of the relevance of historical experience by historicizing the social ontological status of the future that theories of performativity take as their starting point.

The variations of ENSO teleconnections during the Last Interglacial based on PMIP4 simulations

How the remote influences of El Niño-Southern Oscillation (ENSO) may vary in a warmer future remains a hot topic but still elusive. Based on the outputs from the Paleoclimate Modeling Intercomparison Project Phase 4, here we investigated the variations of ENSO teleconnections in boreal winter during the Last Interglacial (LIG), which shares large similarities with the projected climatic features in the 21st century. Our results suggested that the ENSO-temperature teleconnections on average weakened by ∼13.4% during the LIG relative to the preindustrial, whereas the response of precipitation amplified by ∼5.0%. However, there are large regional discrepancies in the variations of ENSO teleconnections in terms of the sign and amplitude. For example, we observed significant ENSO-temperature relationship changes at the high latitudes during the LIG, with a depressed linkage over northern North America but enhanced linkage over the high latitudes of Eurasia. The most profound change in ENSO-precipitation teleconnection occurred over the tropics during the LIG, characterized by a weaker response over the equatorial Pacific Ocean whereas a stronger response over the Maritime Continent and adjacent oceans. Regarding atmospheric circulations, we found that the ENSO-driven Walker Circulations over the Pacific and the Indian Ocean shifted westward during the LIG, whereas the descending branch of the ENSO-driven Atlantic Walker Circulation shifted eastward. Our results provide a model-based scenario for the variations of ENSO teleconnections during the LIG, but the underlying mechanisms deserve further investigation in the future.

Machine learning-enhanced model-based scenario optimization for DIII-D

Scenario development in tokamaks is an open area of investigation that can be approached in a variety of different ways. Experimental trial and error has been the traditional method, but this required a massive amount of experimental time and resources. As high fidelity predictive models have become available, offline development and testing of proposed scenarios has become an option to reduce the required experimental resources. The use of predictive models also offers the possibility of using a numerical optimization process to find the controllable inputs that most closely achieve the desired plasma state. However, this type of optimization can require as many as hundreds or thousands of predictive simulation cases to converge to a solution; many of the commonly used high fidelity models have high computational burdens, so it is only reasonable to run a handful of predictive simulations. In order to make use of numerical optimization approaches, a compromise needs to be found between model fidelity and computational burden. This compromise can be achieved using neural networks surrogates of high fidelity models that retain nearly the same level of accuracy as the models they are trained to replicate while reducing the computation time by orders of magnitude. In this work, a model-based numerical optimization tool for scenario development is described. The predictive model used by the optimizer includes neural network surrogate models integrated into the fast Control-Oriented Transport simulation framework. This optimization scheme is able to converge to the optimal values of the controllable inputs that produce the target plasma scenario by running thousands of predictive simulations in under an hour without sacrificing too much prediction accuracy.

China's pathway towards a net zero and circular economy: A model-based scenario analysis

The remarkable economic growth of China over recent decades has been accompanied by significant achievements in poverty reduction. However, this growth has also led to increased resource consumption and greenhouse gas (GHG) emissions. It's now evident that addressing the triple challenges of climate change, biodiversity loss, and pollution hinges in part on China's capacity to curtail its resource use and emissions. Our study utilises an integrated economic modelling framework to assess China's role in the global context. We evaluate ambitious policies in resource efficiency, GHG reduction, and land-use transformation within China. Our findings indicate that, under such ambitious policies, China could likely achieve peak material use and emissions by 2030 and attain net zero emissions by 2050. Moreover, our analysis underscores that China can play a pivotal role in helping achieve global climate goals in collaboration with the rest of the world. Implementing well-designed policies, including the adoption of a circular economy approach, might enable China to meet these environmental targets with minimised economic costs.

The Russia-Ukraine war decreases food affordability but could reduce global greenhouse gas emissions

The Russian invasion of Ukraine has the potential to exacerbate food insecurity around the world, as both countries are major exporters of grains and other agricultural products. In this model-based scenario study, medium-term effects of the war are quantified on agricultural production, trade flows, market prices, food security, land use, and greenhouse gas emissions. The scenarios assess the possible consequences of macro-economic and agricultural production impacts in Ukraine, trade sanctions against Russia, and conflict-related energy price developments for global trade, food security, and greenhouse gas emissions. From a food security perspective, we conclude that there is enough food on the global level, but higher food and energy prices cause problems for low-income populations, spending a large part of their income on staple foods. Agricultural production and area expansion in parts of the world other than Ukraine and Russia could pose a risk to biodiversity and lead to higher greenhouse gas emissions related to land. However, total greenhouse gas emissions might decrease as lower emissions from less use of fossil energy due to higher energy and fertilizer prices in the whole economy dominate additional emissions resulting from land use change.

A review of existing model-based scenarios achieving SDGs: progress and challenges

Abstract Non-Technical Summary In 2015, the United Nations articulated the ambition to move toward a prosperous, socially inclusive, and environmentally sustainable future for all by adopting the Sustainable Development Goals (SDGs). However, little is known about the pathways that could lead to their concurrent achievement. We provide an overview of the current literature on quantitative pathways toward the SDGs, indicate the commonly used methods and indicators, and identify the most comprehensive pathways that have been published to date. Our results indicate that there is a need for more scenarios toward the full set of SDGs, using a wider range of underlying narratives. Technical Summary Quantitative goal-seeking scenario studies could help to explore the needed systems' transformations to implement the 2030 Agenda for Sustainable Development by identifying enabling conditions and accounting for the synergies and trade-offs between the SDGs. Given that the SDGs were adopted some time ago, here, we review the existing global scenario literature to determine what it can offer in this context. We found only a few scenarios that address a large set of SDGs, while many more deal with specific clusters of 2–6 SDGs. We identified the most frequent clusters and compared the results of the most comprehensive sustainable development scenarios. The latter is complicated because of the diversity of methods, indicators, and assumptions used. Therefore, we suggest that an effort is needed to develop a wider set of scenarios that would achieve multiple SDGs, using a more standardized framework of targets and indicators. Social Media Summary This study reviews the current global pathways toward the SDGs and shows the need for a broader set of SDG scenarios.

The race between hydrogen and heat pumps for space and water heating: A model-based scenario analysis

This paper analyses different levels and means of the electrification of space and hot water heating using an explorative modelling approach. The analysis provides guidance to the ongoing discussion on favourable pathways for heating buildings and the role of secondary energy carriers such as hydrogen or synthetic fuels. In total, 12 different scenarios were modelled with decarbonisation pathways until 2050, which cover all 27 member states of the European Union. Two highly detailed optimisation models were combined to cover the building stock and the upstream energy supply sector. The analysis shows that decarbonisation pathways for space and water heating based on large shares of heat pumps have at least 11% lower system costs in 2050 than pathways with large shares of hydrogen or synthetic fuels. This translates into system cost savings of around €70 bn. Heat pumps are cost-efficient in decentralised systems and in centralised district heating systems. Hence, heat pumps should be the favoured option to achieve a cost-optimal solution for heating buildings. Accordingly, the paper makes a novel and significant contribution to understanding suitable and cost-efficient decarbonisation pathways for space and hot water heating via electrification. The results of the paper can provide robust guidance for policymakers.

Exploring potential futures: Evaluating the influence of deep uncertainties in urban planning through scenario planning: A case study in Rome, Italy

Cities play a critical role in developing adaptable strategies to address the challenges posed by climate change. However, the inherent complexity of urban environments and their uncertain future conditions necessitate exploring innovative approaches and tools to assist the current planning practices. This paper presents a workflow rooted in model-based scenario planning for long-term adaptation planning given uncertain futures. To demonstrate the workflow’s effectiveness, a pertinent case study was conducted in a flood-prone area of Rome. The study employed a land-use change model to examine potential urban growth patterns, considering the uncertain implementation of new poles of attraction. This interdisciplinary study constitutes an initial stride toward implementing uncertainty within urban planning frameworks. Future prospects encompass the integration of multiple models for cross-scale analysis, embracing further critical environmental and social aspects. This research contributes to advancing urban resilience strategies. It enhances the understanding of adapting to an uncertain future in the face of climate change, as urban areas must embrace comprehensive planning to ensure flexible adaptation when faced with climate-driven uncertainties in long-term planning. In conclusion, the study underscores that embracing uncertainty is a challenge and a pivotal opportunity to shape resilient and adaptable urban futures.

Ecologies of integrated modeling: configuring policy-relevance in Swedish climate governance

Due to the long timescales and deep uncertainties involved, comprehensive model-building has played a pivotal role in creating shared expectations about future trajectories for addressing climate change processes, mobilizing a network of knowledge-based experts who assist in defining common problems, identifying policy solutions, and assessing the policy outcomes. At the intersection between climate change science and climate governance, where wholly empirical methods are infeasible, numerical simulations have become the central practice for evaluating truth claims, and the key medium for the transport and translation of data, methods, and guiding principles among the actors involved. What makes integrated assessment unique as a comprehensive modeling-effort is that it is explicitly policy-oriented, justified by its policy-relevance. Although recognized by the Intergovernmental Panel on Climate Change as invaluable to their review assessments, the role of integrated modeling in implementations of the Paris Agreement, such as in impact assessments of climate legislation on the national level, is far less known. Taking as its starting-point the boundary-work carried out in public administration, this paper examines how foresight knowledge produced with the help of model-based scenario analysis has been made relevant in Swedish climate policymaking, focusing on the processes by which key indicators for political action become institutionalized through the choice and use of model parameters. It concludes by arguing for an expanded understanding of policy-relevance, beyond institutional approaches and toward a process-based point of view, treating relevance as something in-the-making.

Model-based scenarios for achieving net negative emissions in the food system

Most climate mitigation scenarios point to a combination of GHG emission reductions and CO2removal for avoiding the most dangerous climate change impacts this century. The global food system is responsible for ~1/3 of GHG emissions and thus plays an important role in reaching emission targets. Consumers, technology innovation, industry, and agricultural practices offer various degrees of opportunity to reduce emissions and remove CO2. However, a question remains as to whether food system transformation can achieve net negative emissions (i.e., where GHG sinks exceed sources sector wide) and what the capacity of the different levers may be. We use a global food system model to explore the influence of consumer choice, climate-smart agro-industrial technologies, and food waste reductions for achieving net negative emissions for the year 2050. We analyze an array of scenarios under the conditions of full yield gap closures and caloric demands in a world with 10 billion people. Our results reveal a high-end capacity of 33 gigatonnes of net negative emissions per annum via complete food system transformation, which assumes full global deployment of behavioral-, management- and technology-based interventions. The most promising technologies for achieving net negative emissions include hydrogen-powered fertilizer production, livestock feeds, organic and inorganic soil amendments, agroforestry, and sustainable seafood harvesting practices. On the consumer side, adopting flexitarian diets cannot achieve full decarbonization of the food system but has the potential to increase the magnitude of net negative emissions when combined with technology scale-up. GHG reductions ascribed to a mixture of technology deployment and dietary shifts emerge for many different countries, with areas of high ruminant production and non-intensive agricultural systems showing the greatest per capita benefits. This analysis highlights potential for future food systems to achieve net negative emissions using multifaceted “cradle-to-grave” and “land-to-sea” emission reduction strategies that embrace emerging climate-smart agro-industrial technologies.

Stakeholder-guided, model-based scenarios for a climate- and water-smart electricity transition in Ghana and Burkina Faso

In support of West Africa's potential energy transition under climate change, an international team of scientists and a wide range of local stakeholders in Ghana and Burkina Faso jointly assessed different mitigation and adaptation pathways for energy and water supply and demand, including their implications for achieving SDGs, in a transdisciplinary approach. They iteratively co-developed a range of future scenarios based on i) stakeholder knowledge and priorities, ii) countries' national plans, and iii) simulations with a set of complementary energy and water models. Unlike current national plans, the more ambitious scenarios indicate that the projected rapid increase in electricity demand could be met almost entirely from renewables, but that a diversification beyond hydropower will be necessary. Phasing out fossil fuels would bring West African countries's energy policies in line with Paris Agreement targets and generate additional socio-economic and environmental co-benefits.

Swiss electricity supply scenarios: Perspectives from the young generation

While numerous studies have argued that public preferences need to be considered in energy transitions, the public has rarely informed the choice of normative scenarios from energy systems modelling. Using the case of Swiss electricity supply in 2035, we focus on a specific citizen group—school pupils of 10 to 18 years old—and use educational workshops to form and elicit their preferences in a way that helps choose scenarios from modelling. Initially, the 164 involved pupils had a simplistic understanding that future electricity supply mainly needs solar PV and electricity savings, but after our workshops they developed a more complete view that involved other technologies. The mean preferred scenario of the pupils for modelling relied on 88% renewable electricity, complemented by small shares of nuclear power, natural gas-based generation, and almost no net electricity imports. When compared to preferred scenarios for modelling elicited from 79 adult citizens and 60 energy experts in Switzerland, the pupils were less ambitious, since adults and experts preferred scenarios with 99% and 97% renewable sources on average. Most pupils opted for much more renewable electricity and less fossil fuel-based generation and imports than existing 82 model-based scenarios published in 2011–2018, indicating that the pupils’ preferred scenarios should complement existing scenarios.

Fast model-based scenario optimization in NSTX-U enabled by analytic gradient computation

Model-based optimization offers a systematic approach to advanced scenario planning. In this case, the feedforward-control inputs (actuator trajectories) that are needed to attain and sustain a desired scenario are obtained by solving a nonlinear constrained optimization problem. This class of problems generally minimize a cost function that measures the difference between desired and actual plasma states. Several numerical optimization algorithms, such as sequential quadratic programming, require repeated calculation of the cost function gradients with respect to the input trajectories. Calculating these gradients numerically can be computationally intensive, increasing the time needed to solve the feedforward-control optimization problem. This work introduces a method to analytically calculate these cost function gradients from the current profile evolution model. This can significantly reduce the computational time and allow for fast feedforward-control optimization, which would eventually enable optimal scenario planning between discharges. The performance of the feedforward optimizer with analytical gradients is compared to a traditional optimization algorithm based on numerical gradients for different NSTX-U scenarios. The plasma dynamics in the optimization algorithm are simulated using the Control Oriented Transport SIMulator (COTSIM). Results of the work show that analytical gradients consistently reduce the computation time while achieving trajectories that are comparable to those obtained by traditional optimization algorithms based on numerical gradients.

A collective blueprint, not a crystal ball: How expectations and participation shape long-term energy scenarios

The development of energy systems is not a technocratic process but equally shaped by societal and cultural forces. Key instruments in this process are model-based scenarios describing a future energy system. Applying the concept of fictional expectations from social economics, we show how energy scenarios are tools to channel political, economic, and academic efforts into a common direction. To impact decision-making, scenarios do not have to be accurate — but credible and evoke coherent expectations in diverse stakeholders. To gain credibility, authors of scenarios engage with stakeholders and appeal to the authority of institutions or quantitative methods.From these insights on energy scenarios, we draw consequences for developing and applying planning models, the quantitative tool energy scenarios build on. Planning models should be open and accessible to facilitate stakeholder participation, avoid needlessly complex methods to minimize expert bias and aim for a large scope to be policy relevant. Rather than trying to simulate social preferences and convictions within engineering models, scenario development should pursue broad and active participation of all stakeholders, including citizens.

Model-based scenario analysis for effective site-specific weed control on grassland sites

The site-specific management of weeds in grassland is often challenging because different weed control strategies have different trade-offs regarding the required resources and treatment efficiency. So, the question arises whether a wide tractor-based system with section control or a small agricultural robot has a higher weed control performance for a given infestation scenario. For example, a small autonomous robot moving from one weed to the next might have much shorter travel distances (and thus lower energy and time costs) than a tractor-mounted system if the locations of the weeds are relatively isolated across the field. However, if the plants are highly concentrated in small areas so-called clusters, the increased width of the tractor-mounted implement could be beneficial because of shorter travel distances and greater working width.An additional challenge is the fact that there is no complete knowledge of the weed locations. Weeds may not have been detected, for example, due to their growth stage, occlusion by other objects, or misclassification. Weed control strategies must therefore also be evaluated with regard to this issue. Thus, in addition to the driving distance, other metrics are also of interest, such as the number of plants that were actually controlled or the size of the total treatment area.We performed this investigation for the treatment of the toxic Colchicum autumnale, which had been detected in drone images of extensive grassland sites. In addition to real data, we generated and analyzed simulated weed locations using mathematical models of stochastic geometry. These offer the possibility to simulate very different spatial distributions of toxic plant locations. Different treatment strategies were then virtually tested on this data using Monte Carlo simulations and their performance was statistically evaluated.

A model-based scenario analysis of the impact of forest management and environmental change on the understorey of temperate forests in Europe

The temperate forest understorey is rich in terms of vascular plant diversity and plays a vital functional role. Given the sensitivity of this forest layer to forest management and global environmental change and the limited knowledge on its long-term dynamics, there is a need for decision support systems that can guide temperate forest managers to optimize their management in terms of understorey outcomes. In this study, using understorey resurvey data collected from across temperate Europe, we developed Generalized Additive Models (GAM) to predict four understorey properties based on forest management and environmental change data, and implemented this model in a web-based tool as a prototype understorey Decision Support System (DSS). Using seventy-two combined climate change, nitrogen(N) deposition and forest management scenarios, applied to two case study regions in Europe, we predicted temperate forest understorey biodiversity dynamics between 2020 and 2050. A sensitivity analysis subsequently allowed to quantify the relative importance of canopy opening, N deposition and climate change on understorey dynamics. Our study showed that, regardless of regions, understorey richness and the proportion of forest specialists generally decreased among most scenarios, but the proportion of woody species and the understorey vegetation total cover increased. Climate warming, N deposition, and increases in canopy openness all influenced understorey dynamics. Climate warming will shift composition towards a selection of forest generalists and woody species, but a less open canopy could mitigate this shift by increasing the proportion of forest specialists. The case studies also showed that these responses can be context-dependent, especially in terms of responses to N deposition.

Model-based scenarios of EU27 electricity supply are not aligned with the perspectives of French, German, and Polish citizens

With the European Green Deal, the European Union (EU27) aims to achieve an ambitious decarbonization of its electricity supply, while actively involving its citizens. Scenarios from energy and electricity sector models seek to inform this transition, although it is unclear to what extent these scenarios are aligned with the views of the citizens. In this study, four multi-organization, multi-model ensembles of existing electricity supply scenarios have been compiled for France, Germany, Poland, and the whole EU27 in 2035, leading to 612 scenarios in total. These scenarios were then compared with 601 preferred scenarios elicited from French, German, and Polish citizens in a survey with an interactive scenario tool. Results show that model-based and citizens’ preferred scenarios converged only on having moderate shares of onshore and offshore wind power and low shares of biomass and waste incineration. In contrast to the majority of model-based scenarios, most Polish and German citizens preferred a deeper decarbonization for their national electricity supply, while French citizens preferred a deeper denuclearization. Additionally, most citizens of all three countries used significant shares of solar photovoltaics and low shares of fossil fuels with carbon capture and storage, hence diverging from model-based scenarios. Similar patterns were found for the EU27 scenarios: many model-based scenarios included large shares of nuclear power, natural gas, and coal, while surveyed citizens preferred scenarios dominated by diverse renewable sources. European modelers should now quantify these missing scenarios so that the EU27 energy transition can be informed by modeling that is in line with citizens’ perspectives.

COVID-19 and Global Poverty and Food Security

The impacts of coronavirus disease 2019 (COVID-19) on food systems, poverty, and nutrition have been caused by generalized economic recession and disruptions in agrifood supply chains. This article reviews a growing empirical literature assessing those impacts. The review confirms that income shocks and supply disruptions have affected food security and livelihoods more where supply chains were poorly integrated and poverty where market informality had a greater presence before COVID-19. Yet, as the pandemic persists, outcomes remain uncertain and reliable data are still sparsely available. This review also reveals how methodological approaches have evolved during the pandemic, ranging from model-based scenario analyses, telephone survey evidence, case-study analyses, and data collection on policy responses and their effects. This review concludes that while we have good insight into the COVID-19 pandemic's impacts on food security, there is still much that we do not know, requiring much more rigorous hypothesis testing based on reliable and observed data.