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To reach its climate targets and secure its energy supply, Switzerland needs to multiply the annual deployment of renewable energy technologies, especially solar PV (as also shown in the SWEET EDGE inter-model comparison). However, household solar PV deployment is lagging and needs to be accelerated significantly through a supportive policy environment. Yet, the fragmentation of Switzerland’s energy policy landscape is large, resulting in higher search/transaction costs, risks and inefficient allocation of capital, which may represent a significant barrier to accelerated solar PV deployment. In this Policy White paper, we analyze the extent to which policy in Switzerland relevant for household PV is fragmented geographically along cantons and municipalities, and functionally along a set of policy instruments. Our results show that cantons and municipalities have vastly different policies, including taxation rules, building codes, subsidies and many more. At the same time, more than 500 (largely) state-owned electricity utilities have implemented widely diverging solar feed-in-tariffs (5 to 22 Rp/kWh) and charge very different electricity prices (10 to 32 Rp/kWh). Overall, there is little evidence that cantons and municipalities push for highly ambitious policies, with only a few cantons using their leeway to provide stronger financial incentives to households/investors. Instead, the institutional structure seems to generate a tendency towards low-ambition compromise in the policy environment. Using a techno-economic model, we show that this policy fragmentation indeed creates a massive variance in the profitability – one of the key determinants of technology deployment – of rooftop solar PV installations. While rooftop household solar PV is highly profitable in some municipalities due to local subsidies, a favorable tax environment, or high tariffs, other municipalities have relatively unfavorable policy environments. Based on our analyses, we propose a strategy to reduce this policy fragmentation while increasing ambition. We argue in favor of a targeted harmonization of selected policy instruments to create stronger incentives for accelerated solar PV deployment and to reduce unnecessary barriers, such as harmonizing the taxation of profits from solar PV installations on the cantonal level. Through their ownership of electricity utilities, municipalities and cantons should also work toward reducing the stark geographical differences in feed-in tariffs and electricity prices, while allowing for local differences where appropriate. In sum, the White Paper argues that a targeted harmonization of the highly fragmented energy policy landscape, aiming at more ambitious minimal standards, is key for an accelerated deployment of solar PV, and thus for reaching the targets of Switzerland’s Energy Strategy 2050.

We introduce a deep learning-based deconvolution approach for 1H NMR spectra, developed by leveraging concepts from the field of physics informed-learning, intelligent labeling, and tailored high dynamic range (HDR) spectral preprocessing. Since automation and faster workflows are major concerns in NMR spectroscopy, the algorithm handles uncorrected spectra without strict assumptions on phase and baseline correction as well as line shape. Due to the lack of high quality and consistently labeled experimental spectra in quantities needed to train modern deep learning models, we relied on synthetic spectra creation. Moreover, instead of training with synthetic spectra consisting of single lines, we created synthetic multiples that further supported a realistic deconvolution. We achieved super-human performance on corrected and uncorrected synthetic spectra. Finally, and most importantly, the results on synthetic data translate well to experimental spectra despite the covariate shift. Thus, this tool is a promising candidate for automated expert-level deconvolution of experimental HDR 1H NMR spectra.

The identification and characterization of signal peaks in NMR spectra is a crucial yet time-consuming and error-prone stage in the determination of complex chemical compounds. The introduction of automation in the NMR analysis can ease the workflow while increasing the robustness and reproducibility of the results. Here, we present a novel supervised deep learning method to perform automatic detection and classification of multiplets in one dimensional proton NMR spectra. The method consists of a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks. The training set is composed of a large number of synthetic spectra containing classes of basic nonoverlapping multiplets only. All networks in the ensemble produce the same prediction for basic multiplets, while resonances not represented in the training set cause arbitrary errors that differ across the networks. Therefore, high output variance in the ensemble is an indicator of the presence of overlapping multiplets. Being able to distinguish between basic and overlapping multiplets is a decisive stage. Together with classification within different resonance categories, it helps to perform automated peak picking and coupling constants extraction. We show that our model can discriminate signal regions effectively and minimize classification errors between different categories of resonances. Most importantly, we demonstrate that the network generalizes remarkably well on real experimental proton NMR spectra. The evaluation is carried out through the implementation of a specific statistical procedure for quantitatively testing the ensemble prediction against experts’ annotations.

Technological change, i.e. the invention, innovation and diffusion of new technologies, is a key driver of economic development and societal progress. There is widespread agreement that, historically, energy technologies have been at the core of most technological revolutions. Yet, the transition to and diffusion of fossil fuel-based energy technologies has come at high societal and ecological costs, most notably climate change. A fast and deep transition to low-carbon technologies – particularly renewable energy and efficiency technologies – is the main lever to address climate change. While deployment of these technologies has grown significantly over the last decades – largely policy-induced – this transition needs to be further accelerated and deepened through public policies. In light of various trade-offs and competing policy goals, implementing and designing these policies is an intrinsically political endeavor. A growing body of literature at the intersection of public policy, political science, and innovation studies covers these aspects of energy politics. Yet, energy politics not only influence technological change through public policy – technological change can also, in turn, influence politics. A better understanding of this inverse effect of technological change on politics is necessary to formulate politically feasible and effective energy policy. While a nascent body of literature deals with these aspects in the context of the transition to renewable energy and efficiency technologies, how exactly such low-carbon technological change affects what aspects of politics still remains a black box. In an exploratory approach, this dissertation attempts to address this research gap with the following overarching question: How does low-carbon technological change affect energy politics? To answer this question, this cumulative dissertation is built on a heuristic framework: On an abstract level, it argues that technological change can affect politics through both its expanding and (re)distributional capacity. It further proposes that politics can be disaggregated into the categories of interests, ideas, and institutions, on the level of both elite and mass politics. The individual papers in this dissertation cover various elements of this heuristic framework and leverage a plurality of qualitative and quantitative methods, and individual case studies. Focusing on how technological change affects the interests and ideas of elite politics, Paper 1 examines how the transition to renewable energy technologies influenced the composition and strength of advocacy coalitions in the German energy sector. The main contribution of this paper is to substantiate the mechanisms through which policy-induced technological change affects coalitions, and to link these mechanisms to patterns of actor movements underlying coalition change. Paper 2 also focuses on aspects of ideas in elite politics and touches upon institutions as moderating factor. It examines how technological change drives regulators’ perceived feasibility of more stringent public and private regulation of energy efficiency technologies in the Swiss building sector. The contribution of this paper is to highlight that the interaction among public and private regulation can run through the mechanism of technological change. Also focusing on ideas and institutions in elite politics, paper 3 examines how technological change affects the positions of political parties on energy technologies in Germany, France, and the United Kingdom. The paper shows that technological change is a driver of party positions and their salience, and that this effect is mediated by party and party system characteristics. Finally, paper 4 examines interests in mass politics by focusing on how the decline in coal mining affects voting behavior in presidential elections in the United States. The paper shows that also decline in technologies can result in political effects, in this case resistance in form of voting in favor of pro-coal candidates. Based on a mixed methods approach and systematic data collection, these four papers give novel empirical insights into how technological change affects interests, ideas, and institutions in elite and mass energy politics. Based on these insights, the papers engage in theory-building. Notably, the dissertation provides a framework in which energy politics is described as a dynamic feedback loop of public policy, technological change, and politics. Further, the dissertation substantiates various mechanisms that link technological change to politics, and analyzes the effects of technological change on a variety of relevant political actors. Doing so, it contributes to current academic debates in public policy, political science, and innovation studies on energy politics. Further, this dissertation also has policy implications: Policymakers’ focus should be on the expanding and (re)distributional effects of technological change on energy and climate politics. More sensibility to the locus and nature of these political struggles could enable effective forward-looking policy strategies that sow the seeds today for broader political support tomorrow. Finally, future research should aim at testing the theory built in this dissertation with more quantitative research methods. Future research should also build on this exploratory dissertation by expanding the empirical scope to other low-carbon technologies, and expand the policy feedback logic to other policy outcomes such as nature-based solutions and behavioral change.

ISSN:1752-7457 ISSN:1469-3062

This report summarized the results of the work in Task 1.2 of the SmartResilience project. Within the Work Package "Establishing the project baseline and the common framework", Task 1.2 "Analysis of existing assessment approaches, indicators and data sources" addresses the first 3 of the five main project objectives namely: identifying existing indicators suitable for assessing resilience of SCIs identifying new "smart" resilience indicators (RIs) - including thos from Big Data developing a new advanced resilience assessment methodology based on smart RIs ("resilience indicators cube", including the resilience matrix)

This report is targeting a framing of what SmartResilience actually wants to measure - "resilience"-, taking relevant research results and existing guidelines and standards into account.This is especially challenging due to the vast variety of understandings, definitions, concepts and applications of the term, including usages in different research areas or fields of application. In addition, for the reason of this variety, a huge number of articles and reports discussing the term, its understangings and usages on a theoretical basis have been developed. Even several coprehensive reviews on the term, including qualitative and quantitative literature analysis as well as expert reviews, have already been conducted.