Comparison of macroeconomic developments in ten scenarios of energy system transformation in Germany: National and regional results

t Background: Different strategies have been proposed for transforming the energy system in Germany. To evaluate their sustainability, it is necessary to analyze their macroeconomic and distributional effects. An approach to do this analysis in an integrated consistent framework is presented here. Methods: Comparing ten energy transition scenarios with emission reduction targets by 2050 of 80% or 95%, respectively, allows evaluating a broad range of energy system transformation strategies with respect to the future technology and energy carrier mix. For this purpose, an energy system model and a macroeconometric model are combined, thus re-modeling the unified scenarios. An important extension of the model was concerned with the integration of synthetic fuels into the energy-economy model. One focus besides the overall macroeconomic assessment is the regional analysis. For this purpose, own assumptions on the regional distribution of the expansion of renewable energies were developed. Results: The effects on gross domestic product (GDP) and employment are similar on average from 2030 to 2050 across the scenarios, with most of the more ambitious scenarios showing slightly higher values for the socioeconomic variables. Employment in the construction sector shows the largest effects in most scenarios, while in the energy sector employment is lower in scenarios with high energy imports. At the regional level, the differences between scenarios are larger than at the national level. There is no clear or stable regional pattern of relative loss and profit from the very ambitious transformation, as not only renewable energy expansion varies, and hydrogen strategies enter the scene approaching 2050. Conclusions: From the relatively small differences between the scenarios, it can be concluded that, from a macroeconomic perspective, it is not decisive for the overall economy which (supply side) strategy is chosen for the transformation of the energy system. More effort needs to be put into improving assumptions and modeling approaches related to strategies for achieving the final 20% CO2 reduction, for example the increasing use of hydroge

Sustainability assessments of energy scenarios: citizens’ preferences for and assessments of sustainability indicators

Background: Given the multitude of scenarios on the future of our energy systems, multi-criteria assessments are increasingly called for to analyze and assess desired and undesired effects of possible pathways with regard to their environmental, economic and social sustainability. Existing studies apply elaborate lists of sustainability indicators, yet these indicators are defined and selected by experts and the relative importance of each indicator for the overall sustainability assessments is either determined by experts or is computed using mathematical functions. Target group-specific empirical data regarding citizens’ preferences for sustainability indicators as well as their reasoning behind their choices are not included in existing assessments. Approach and results: We argue that citizens’ preferences and values need to be more systematically analyzed. Next to valid and reliable data regarding diverse sets of indicators, reflections and deliberations are needed regarding what different societal actors, including citizens, consider as justified and legitimate interventions in nature and society, and what considerations they include in their own assessments. For this purpose, we present results from a discrete choice experiment. The method originated in marketing and is currently becoming a popular means to systematically analyze individuals’ preference structures for energy technology assessments. As we show in our paper, it can be fruitfully applied to study citizens’ values and weightings with regard to sustainability issues. Additionally, we present findings from six focus groups that unveil the reasons behind citizens’ preferences and choices. Conclusions: Our combined empirical methods provide main insights with strong implications for the future development and assessment of energy pathways: while environmental and climate-related effects significantly influenced citizens’ preferences for or against certain energy pathways, total systems and production costs were of far less importance to citizens than the public discourse suggests. Many scenario studies seek to optimize pathways according to total systems costs. In contrast, our findings show that the role of fairness and distributional justice in transition processes featured as a dominant theme for citizens. This adds central dimensions for future multi-criteria assessments that, so far, have been neglected by current energy systems models

Integrated Multidimensional Sustainability Assessment of Energy System Transformation Pathways

Sustainable development embraces a broad spectrum of social, economic and ecological aspects. Thus, a sustainable transformation process of energy systems is inevitably multidimensional and needs to go beyond climate impact and cost considerations. An approach for an integrated and interdisciplinary sustainability assessment of energy system transformation pathways is presented here. It first integrates energy system modeling with a multidimensional impact assessment that focuses on life cycle‐based environmental and macroeconomic impacts. Then, stakeholders’ preferences with respect to defined sustainability indicators are inquired, which are finally integrated into a comparative scenario evaluation through a multi‐criteria decision analysis (MCDA), all in one consistent assessment framework. As an illustrative example, this holistic approach is applied to the sustainability assessment of ten different transformation strategies for Germany. Applying multi‐criteria decision analysis reveals that both ambitious (80%) and highly ambitious (95%) carbon reduction scenarios can achieve top sustainability ranks, depending on the underlying energy transformation pathways and respective scores in other sustainability dimensions. Furthermore, this research highlights an increasingly dominant contribution of energy systems’ upstream chains on total environmental impacts, reveals rather small differences in macroeconomic effects between different scenarios and identifies the transition among societal segments and climate impact minimization as the most important stakeholder preferences

Sustainability assessments of energy scenarios: citizens' preferences for and assessments of sustainability indicators

Given the multitude of scenarios on the future of our energy systems, multi-criteria assessments are increasingly called for to analyze and assess desired and undesired effects of possible pathways with regard to their environmental, economic and social sustainability. Existing studies apply elaborate lists of sustainability indicators, yet these indicators are defined and selected by experts and the relative importance of each indicator for the overall sustainability assessments is either determined by experts or is computed using mathematical functions. Target group‑specific empirical data regarding citizens' preferences for sustainability indicators as well as their reasoning behind their choices are not included in existing assessments. We argue that citizens' preferences and values need to be more systematically analyzed. Next to valid and reliable data regarding diverse sets of indicators, reflections and deliberations are needed regarding what different societal actors, including citizens, consider as justified and legitimate interventions in nature and society, and what considerations they include in their own assessments. For this purpose, we present results from a discrete choice experiment. The method originated in marketing and is currently becoming a popular means to systematically analyze individuals' preference structures for energy technology assessments. As we show in our paper, it can be fruitfully applied to study citizens' values and weightings with regard to sustainability issues. Additionally, we present findings from six focus groups that unveil the reasons behind citizens preferences and choices. Our combined empirical methods provide main insights with strong implications for the future development and assessment of energy pathways: while environmental and climate-related effects significantly influenced citizens preferences for or against certain energy pathways, total systems and production costs were of far less importance to citizens than the public discourse suggests. Many scenario studies seek to optimize pathways according to total systems costs. In contrast, our findings show that the role of fairness and distributional justice in transition processes featured as a dominant theme for citizens. This adds central dimensions for future multi-criteria assessments that, so far, have been neglected by current energy systems models

Automation of tree‐ring detection and measurements using deep learning

Abstract Core samples from trees are a critical reservoir of ecological information, informing our understanding of past climates, as well as contemporary ecosystem responses to global change. Manual measurements of annual growth rings in trees are slow, labour‐intensive and subject to human bias, hindering the generation of big datasets. We present an alternative, neural network‐based implementation that automates detection and measurement of tree‐ring boundaries from coniferous species. We trained our Mask R‐CNN extensively on over 8000 manually annotated ring boundaries from microscope‐imaged Norway Spruce Picea abies increment cores. We assessed the performance of the trained model after post‐processing on real‐world data generated from our core processing pipeline. The CNN after post‐processing performed well, with recognition of over 98% of ring boundaries (recall) with a precision in detection of 96% when tested on real‐world data. Additionally, we have implemented automatic measurements based on minimum distance between rings. With minimal editing for missed ring detections, these measurements were 98% correlated with human measurements of the same samples. Tests on other three conifer species demonstrate that the CNN generalizes well to other species with similar structure. We demonstrate the efficacy of automating the measurement of growth increment in tree core samples. Our CNN‐based system provides high predictive performance in terms of both tree‐ring detection and growth rate determination. Our application is readily deployable as a Docker container and requires only basic command line skills. Additionally, an easy re‐training option allows users to expand capabilities to other wood types. Application outputs include both editable annotations of predictions as well as ring‐width measurements in a commonly used .pos format, facilitating the efficient generation of large ring‐width measurement datasets from increment core samples, an important source of environmental data

Beyond Climate Change. Application of multi-attribute decision making methods for a sustainability assessment of German energy system transformation pathways

A multitude of pathways for decarbonizing energy systems have been formulated. In the development of these scenarios, the focus is often only on system costs at a given CO2 emission reduction. However, when assessing the sustainability of energy systems in a broader sense, many more aspects need to be considered: In addition to greenhouse gas emissions, energy systems induce further environmental and socio-economic impacts and must meet requirements for security of supply and cost efficiency. For assessing the compatibility of a future energy system with sustainability concepts, alternative pathways must be compared holistically using dedicated assessment methods, which is facilitated by multi-attribute decision making methods (MADM). With the target of identifying sustainable transformation pathways, we assess ten transformation scenarios for the example of Germany, using the three MADM methods weighted sum method, PROMETHEE II and TOPSIS. We find that top ranks are not completely stable across methods, but there are scenario clusters which rank high, medium and low for all methods. In the top ranks, there are both less ambitious scenarios that aim at reducing direct CO2 emissions by 80 %, and more ambitious scenarios with a reduction by at least 95 %. We conclude that scenarios with more ambitious climate protection goals are not necessarily more (or less) sustainable than scenarios that aim for a reduction of 80 % only

Elevated markers of thrombo-inflammatory activation predict outcome in patients with cardiovascular comorbidities and COVID-19 disease: insights from the LEOSS registry

Aims: SARS-CoV-2 infection is associated with adverse outcomes in patients with cardiovascular disease. Here, we analyzed whether specific biomarkers predict the clinical course of COVID-19 in patients with cardiovascular comorbidities. Methods and results: We enrolled 2147 patients with SARS-CoV-2 infection which were included in the Lean European Open Survey on SARS-CoV‑2 (LEOSS)-registry from March to June 2020. Clinical data and laboratory values were collected and compared between patients with and without cardiovascular comorbidities in different clinical stages of the disease. Predictors for mortality were calculated using multivariate regression analysis. We show that patients with cardiovascular comorbidities display significantly higher markers of myocardial injury and thrombo-inflammatory activation already in the uncomplicated phase of COVID-19. In multivariate analysis, elevated levels of troponin [OR 1.54; (95% CI 1.22–1.96), p < 0.001)], IL-6 [OR 1.69 (95% CI 1.26–2.27), p < 0.013)], and CRP [OR 1.32; (95% CI 1.1–1.58), p < 0.003)] were predictors of mortality in patients with COVID-19. Conclusion: Patients with cardiovascular comorbidities show elevated markers of thrombo-inflammatory activation and myocardial injury, which predict mortality, already in the uncomplicated phase of COVID-19. Starting targeted anti-inflammatory therapy and aggressive anticoagulation already in the uncomplicated phase of the disease might improve outcomes after SARS-CoV-2 infection in patients with cardiovascular comorbidities

Beyond Climate Change. Application of multi-attribute decision making methods for a sustainability assessment of German energy system transformation pathways

A multitude of pathways for decarbonizing energy systems have been formulated. In the development of these scenarios, the focus is often only on system costs at a given CO2 emission reduction. However, when assessing the sustainability of energy systems in a broader sense, many more aspects need to be considered: In addition to greenhouse gas emissions, energy systems induce further environmental and socio-economic impacts and must meet requirements for security of supply and cost efficiency. For assessing the compatibility of a future energy system with sustainability concepts, alternative pathways must be compared holistically using dedicated assessment methods, which is facilitated by multi-attribute decision making methods (MADM). With the target of identifying sustainable transformation pathways, we assess ten transformation scenarios for the example of Germany, using the three MADM methods weighted sum method, PROMETHEE II and TOPSIS. We find that top ranks are not completely stable across methods, but there are scenario clusters which rank high, medium and low for all methods. In the top ranks, there are both less ambitious scenarios that aim at reducing direct CO2 emissions by 80 %, and more ambitious scenarios with a reduction by at least 95 %. We conclude that scenarios with more ambitious climate protection goals are not necessarily more (or less) sustainable than scenarios that aim for a reduction of 80 % only

Elevated markers of thrombo-inflammatory activation predict outcome in patients with cardiovascular comorbidities and COVID-19 disease: insights from the LEOSS registry

Aims: SARS-CoV-2 infection is associated with adverse outcomes in patients with cardiovascular disease. Here, we analyzed whether specific biomarkers predict the clinical course of COVID-19 in patients with cardiovascular comorbidities. Methods and results: We enrolled 2147 patients with SARS-CoV-2 infection which were included in the Lean European Open Survey on SARS-CoV-2 (LEOSS)-registry from March to June 2020. Clinical data and laboratory values were collected and compared between patients with and without cardiovascular comorbidities in different clinical stages of the disease. Predictors for mortality were calculated using multivariate regression analysis. We show that patients with cardiovascular comorbidities display significantly higher markers of myocardial injury and thrombo-inflammatory activation already in the uncomplicated phase of COVID-19. In multivariate analysis, elevated levels of troponin [OR 1.54; (95% CI 1.22-1.96), p < 0.001)], IL-6 [OR 1.69 (95% CI 1.26-2.27), p < 0.013)], and CRP [OR 1.32; (95% CI 1.1-1.58), p < 0.003)] were predictors of mortality in patients with COVID-19. Conclusion: Patients with cardiovascular comorbidities show elevated markers of thrombo-inflammatory activation and myocardial injury, which predict mortality, already in the uncomplicated phase of COVID-19. Starting targeted anti-inflammatory therapy and aggressive anticoagulation already in the uncomplicated phase of the disease might improve outcomes after SARS-CoV-2 infection in patients with cardiovascular comorbidities. Graphic abstract: Elevated markers of thrombo-inflammatory activation predict outcome in patients with cardiovascular comorbidities and COVID-19 disease: insights from the LEOSS registry [GRAPHICS]