Model-based Scenario Analysis Research Articles

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.

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 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.

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 estimation of climate change impacts on the drainage demand of low lying coastal areas in Northwest Germany along the North Sea

Study regionThe Northwest German part of the North Sea coast Study focusLow lying coastal areas are highly threatened by climate change. This is due to rising sea level and changing water balance caused by climate change. Adaptation of coastal protection and inland drainage to climate change requires precise predictions of future conditions. While information on sea level rise is globally available, coastal water balance projections need to be specific for regions where often no discharge data is available. To serve this demand, a model based scenario analysis was carried out for four water boards in Northwest Germany. These water boards are regional organisations responsible for drainage and surface water level regulation in marsh areas. A water balance model was calibrated and validated against data of pumping stations and tidal water gates. Subsequently, climate change impacts on runoff generation were estimated. New hydrological insights for the regionThe results indicate that runoff generation is expected to increase significantly in the wet season, same as the frequency of periods with large runoff volumes. The climate change impact signal is consistent over all investigated water board areas, indicating that the climate change signal dominates the spatial variability in soil properties and land use. The results emphasize the necessity to consider runoff generation projections for adjusting coastal drainage management. The scenario projections can be directly used for regional adaptation planning processes, taking into account the underlying uncertainties.

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.

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.

Modeling flexibility in energy systems — comparison of power sector models based on simplified test cases

Model-based scenario analyses of future energy systems often come to deviating results and conclusions when different models are used. This may be caused by heterogeneous input data and by inherent differences in model formulations. The representation of technologies for the conversion, storage, use, and transport of energy is usually stylized in comprehensive system models in order to limit the size of the mathematical problem, and may substantially differ between models. This paper presents a systematic comparison of nine power sector models with sector coupling. We analyze the impact of differences in the representation of technologies, optimization approaches, and further model features on model outcomes. The comparison uses fully harmonized input data and highly simplified system configurations to isolate and quantify model-specific effects. We identify structural differences in terms of the optimization approach between the models. Furthermore, we find substantial differences in technology modeling primarily for battery electric vehicles, reservoir hydro power, power transmission, and demand response. These depend largely on the specific focus of the models. In model analyses where these technologies are a relevant factor, it is therefore important to be aware of potential effects of the chosen modeling approach. For the detailed analysis of the effect of individual differences in technology modeling and model features, the chosen approach of highly simplified test cases is suitable, as it allows to isolate the effects of model-specific differences on results. However, it strongly limits the model’s degrees of freedom, which reduces its suitability for the evaluation of fundamentally different modeling approaches.

A Model-Based Scenario Analysis of the Impact of Forest Management and Environmental Change on the Understorey of Temperate Forests in Europe

A Model-Based Scenario Analysis of the Impact of Forest Management and Environmental Change on the Understorey of Temperate Forests in Europe

Systematic comparison of high-resolution electricity system modeling approaches focusing on investment, dispatch and generation adequacy

The transparency and open availability of energy system models and their input data are of particular importance due to their increasing complexity and policy relevance. In recent years, a large number of model-based scenario analyses have been carried out. These analyses are based on diverse model approaches and lead to a rather broad range of results, which due to different data structures and mathematical approaches are hardly directly comparable. In this paper, detailed and harmonized scenario input parameters are the basis of a systematic model experiment including four electricity system models. In the following, the different model approaches are classified and their respective results are discussed transparently. Consequently, differences in results can be interlinked directly with model properties. The results are compared focusing on a selection of output parameters, such as investment and dispatch decisions in flexible power plants, storage dispatch, wholesale electricity prices, CO2 emissions and generation adequacy in hours with critical supply situations in Germany until 2030. Differences in the results are traced back to conceptual differences as the models can be distinguished not only with regard to their mathematical approaches, but also to their level of detail. Results indicate that next to the differences of the mathematical approaches (i.e., linear optimization vs. agent-based simulation), the myopic foresight perspective (e.g., rolling planning algorithm with 24- and 36-hours loops vs. perfect foresight in a closed loop for one year) are decisive for the range of obtained results.

Post-COVID-19 Scenarios of Global Airline Traffic until 2040 That Reflect Airport Capacity Constraints and Mitigation Strategies

Although there has been an unprecedented decline in traffic volume due to the COVID-19 crisis, robust growth in global demand for air transport services in the past means that air traffic is expected to recover in the long term. While capacity constraints are currently not a major topic at airports due to the extremely low levels of traffic, there is growing evidence to suggest that important nodes of the worldwide airport network will struggle to deal with capacity constraints after the recovery. The objectives of this research were therefore as follows: to elaborate long-term global passenger and flight volume scenarios in a post-COVID-19 world; to conduct an empirical and model-based analysis of the impact of limited airport capacity on the future development of air traffic in these scenarios; and to derive general strategies for mitigating capacity constraints at certain international airports. Thus, the main aim of this paper is to present a model-based scenario analysis of the long-term impact of the COVID-19 crisis on the capacity situation for airports. Our results indicate that once the pandemic is over, the capacity crunch will remain on the airports’ agenda for some time.

COVID-19 and impacts on global food systems and household welfare: Introduction to a special issue.

The food system, and those who depend on it, have been strongly but unevenly affected by COVID‐19. Overall, the impacts on food systems, poverty, and nutrition have been caused by a combination of a generalized economic recession and disruptions in agri‐food supply chains. This paper provides an overview of the contributions to this Special Issue of Agricultural Economics. The papers in this volume confirm that both income shocks and supply disruptions have affected food security and livelihoods the most where supply chains were more poorly integrated, and poverty and market informality had greater presence before COVID‐19. Yet, as the pandemic still has societies worldwide in a stranglehold, outcomes remain uncertain and reliable data are still sparsely available. This Special Issue of Agricultural Economics provides new insights of the pandemics impact on food systems, household welfare, and food security, building on both model‐based scenario analysis and new survey data. These methods have proven helpful in providing these insights amidst the unprecedented shock that the pandemic has caused to production systems and livelihoods worldwide. However, they also suffer from obvious limitations identified in this editorial overview paper and require substantial improvement in order to understand the changes in economic behavior and functioning of food supply chains induced by the pandemic.

COVID-19 and Financial Sustainability of Academic Institutions

The COVID-19 pandemic has had a significant impact on higher education. Steering academic institutions through the pandemic is a complex and multifaceted task that can be supported with model-based scenario analysis. This article studies the short-term and long-term effects of the pandemic on the financial health of a college using scenario analysis and stress testing with a system dynamics model of a representative tuition-dependent college. We find that different combinations of the pandemic mitigation protocols have varying effects on the financial sustainability of an academic institution. By simulating six individual components of the COVID-19 shock, we learn that due to the causal complexity, nonlinear responses and delays in the system, the negative shocks can propagate widely through the college, sometimes with considerable delays and disproportionate effects. Scenario analysis shows that some pandemic mitigation choices may destabilize even financially healthy institutions. The article concludes that higher education needs new sustainable business models.

Integrative Scenario Assessment as a Tool to Support Decisions in Energy Transition

Energy scenarios represent a prominent tool to support energy system transitions towards sustainability. In order to better fulfil this role, two elements are widely missing in previous work on designing, analyzing, and using scenarios: First, a more systematic integration of social and socio-technical characteristics of energy systems in scenario design, and, second, a method to apply an accordingly enhanced set of indicators in scenario assessment. In this article, an integrative scenario assessment methodology is introduced that combines these two requirements. It consists of: (i) A model-based scenario analysis using techno-economic and ecological indicators; (ii) a non-model-based analysis using socio-technical indicators; (iii) an assessment of scenario performances with respect to pre-determined indicator targets; (iv) a normalization method to make the two types of results (model-based and non-model-based) comparable; (v) an approach to classify results to facilitate structured interpretation. The combination of these elements represents the added-value of this methodology. It is illustrated for selected indicators, and exemplary results are presented. Methodological challenges and remaining questions, e.g., regarding the analysis of non-model-based indicators, resource requirements, or the robustness of the methodology are pointed out and discussed. We consider this integrative methodology being a substantial improvement of previous scenario assessment methodologies.

COVID-19 and Financial Sustainability of Academic Institutions

The COVID-19 pandemic has had a significant impact on higher education. Steering academic institutions through the pandemic is a complex and multifaceted task that can be supported with model-based scenario analysis. This article studies the short-term and long-term effects of the pandemic on the financial health of a college using scenario analysis and stress testing with a system dynamics model of a representative tui-tion-dependent college. We find that different combinations of the pandemic mitigation protocols have vary-ing effects on the financial sustainability of an academic institution. By simulating six individual components of the COVID-19 shock, we learn that due to the causal complexity, nonlinear responses and delays in the sys-tem, the negative shocks can propagate widely through the college, sometimes with considerable delays and disproportionate effects. Scenario analysis shows that some pandemic mitigation choices may destabilize even financially healthy institutions. The article concludes that higher education needs new sustainable business models.

Best management practices scenario analysis to reduce agricultural nitrogen loads and sediment yield to the semiarid Mar Menor coastal lagoon (Spain)

Agriculture is a major source of diffuse pollution, where nitrogen and sediment pollution of water bodies are its main associated environmental impacts. Best management practices are effective tools for preventing and minimizing water pollution. Water quality models and model-based scenario analyses are useful tools for assessing impacts of best management practices and to identify appropriate strategies on the watershed scale. This study was conducted in the southernmost Mar Menor basin, one of the largest saltwater coastal lagoons in Europe and threatened by diffuse nutrient and sediment export from the agricultural landscape. This study evaluates the impact of several management practices on nitrogen and sediment loads, and horticultural crop yield, to identify an appropriate management strategy on the watershed scale. Both structural and nonstructural management practices scenarios were evaluated: three scenarios representing field operations, two coastal line buffers, a new fertilizer management strategy and a change in the productive cultivation system from three-crop rotation to two-crop rotation. Each management practice reduced a certain type of diffuse pollution and, therefore, a combined set of changed management practices is necessary to cope with all agricultural pollution types. Contour farming, combined with hedgerow field borders, was effective in sediment yield and surface organic nitrogen export reduction terms, while improved fertilizer management reduced surface nitrate export and leaching with minimal impacts on crop yields.

Impact of sector coupling on the market value of renewable energies – A model-based scenario analysis

Decarbonizing the energy supply by substituting fossil fuels with renewable energy sources (RES) is a key task for the coming decades in order to achieve the EU's ambitious climate protection targets. Information about the possible development and marketability of RES in the electricity sector is essential for assessing future funding needs. However, rising shares of fluctuating RES generation in the energy system reduce the average market prices and increase price volatility. Balancing price variations requires a considerable degree of flexibility. Additional flexibility in the electricity market through closer interconnection between the electricity sector and other demand sectors makes it possible to keep the market values of RES closer to the general market price level, irrespective of their shares. Such sector coupling can thus contribute to a cost-efficient transition to a low-carbon energy system. This paper examines the impact of efficient sector coupling on the market values of RES in a European energy system with ambitious decarbonization. We analyze different scenarios by applying the Enertile model, which uses an integrated cost optimization approach with flexibility options due to sector coupling and provides a detailed future development of RES. In our analysis, we examine three flexibility options: smart charging of electric vehicles, decentralized heat pumps in buildings, and multivalent district heating grids. We show that the flexible use of electricity in district heating has a significant impact on market values, while the impact of both flexible electric vehicle charging and flexible heating with heat pumps is rather small. Short-term flexibility due to load shifting of the charging or heating process shows only a limited effect on market values. Fuel switching in district heating, however, offers the possibility to change the absolute demand for electricity in direct response to RES feed-in and drastically reduces the curtailment of RES.

Co-benefits of black carbon mitigation for climate and air quality

Mitigation of black carbon (BC) aerosol emissions can potentially contribute to both reducing air pollution and climate change, although mixed results have been reported regarding the latter. A detailed quantification of the synergy between global air quality and climate policy is still lacking. This study contributes with an integrated assessment model-based scenario analysis of BC-focused mitigation strategies aimed at maximizing air quality and climate benefits. The impacts of these policy strategies have been examined under different socio-economic conditions, climate ambitions, and BC mitigation strategies. The study finds that measures targeting BC emissions (including reduction of co-emitted organic carbon, sulfur dioxide, and nitrogen dioxides) result in significant decline in premature mortality due to ambient air pollution, in the order of 4 to 12 million avoided deaths between 2015 and 2030. Under certain circumstances, BC mitigation can also reduce climate change, i.e., mainly by lowering BC emissions in the residential sector and in high BC emission scenarios. Still, the effect of BC mitigation on global mean temperature is found to be modest at best (with a maximum short-term GMT decrease of 0.02 °C in 2030) and could even lead to warming (with a maximum increase of 0.05 °C in case of a health-focused strategy, where all aerosols are strongly reduced). At the same time, strong climate policy would improve air quality (the opposite relation) through reduced fossil fuel use, leading to an estimated 2 to 5 million avoided deaths in the period up to2030. By combining both air quality and climate goals, net health benefits can be maximized.

Modelling alternative futures of global food security: Insights from FOODSECURE

Global economic models have been increasingly used to project food and agricultural developments for long term-time horizons, but food security aspects have often been limited to food availability projections. In this paper, we propose a broader framework to explore the future of food and nutrition security with a focus on food availability, food access, and a reasonable proxy for food utilisation. This framework is applied to a new set of stakeholder-designed scenarios of alternative future worlds that were developed for the FOODSECURE project and are structured around the two dimensions of inequality and sustainability. The framework is tested with two global models, MAGNET-IMAGE and GLOBIOM, and illustrated through an assessment of the possible trade-offs between food and nutrition security and sustainability in each of the worlds. Our results indicate that more equal worlds improve food security over a wider range of food security indicators and neglecting the sustainability dimension might revert food security gains over time. This paper concludes that there is a need for model-based scenario analysis to assess the complex and multi-dimensional characteristics of global food security.