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Background: The healthcare sector is responsible for 5.2% of CO2 emissions in Germany. The prescribing of medicines is a significant contributor. Among the prescribed medicines, metered dose inhalers (MDIs) play a decisive role due to the propellant gases they contain. A relevant reduction in emissions is possible by switching to dry powder inhalation systems (DPI). Aim: The guideline "Climate-conscious prescribing of inhalants" is intended to provide assistance in the prescribing of inhaled medicines by summarising the available evidence on the decision between DPI and MDI and explicitly addressing the aspect of climate damage caused by propellants. It addresses everyone involved in prescribing and counselling those affected. Materials and methods: The S2k guideline was drawn up under the joint leadership of DEGAM (German College of General Practitioners and Family Physicians) and the German Society for Pneumology and Respiratory Medicine (DGP) under moderation of the AWMF (Association of the Scientific Medical Societies in Germany). The main contents from a practical perspective are presented in this paper. Results: Overall, MDIs account for a high proportion of inhaler prescriptions in Germany. The aspects of climate damage should be considered when prescribing MDIs. In order to facilitate implementation in practice, several decision aids were developed. Conclusion: The guideline is the first to address the importance of prescribing a group of medicines in relation to climate change. The degree of implementation of the guideline recommendations can be determined by future analyses of prescription data. A planned practical test is required to check the acceptance of the decision aids. [ABSTRACT FROM AUTHOR]

In Germany, there are over 32,000 schools, representing great potential for climate protection. On the one hand, this applies to educational work, as understanding the effects of climate change and measures to reduce GHG emissions is an important step to empower students with knowledge and skills. On the other hand, school buildings are often in bad condition, energy is wasted, and the possibilities for using renewable energies are hardly used. In our "Schools4Future" project, we enabled students and teachers to draw up their own CO2 balances, identify weaknesses in the building, detect wasted electricity, and determine the potential for using renewable energies. Emissions from the school cafeteria, school trips, and paper consumption could also be identified. The fact that the data can be collected by the students themselves provides increased awareness of the contribution made to the climate balance by the various school areas. The most climate-friendly school emits 297 kg whilst the school with the highest emissions emits over one ton CO2 per student and year. Our approach is suitable to qualify students in the sense of citizen science, carry out a scientific investigation, experience self-efficacy through one's own actions, and engage politically regarding their concerns. [ABSTRACT FROM AUTHOR]

The quantification of emissions of greenhouse gases and air pollutants through the inversion of plumes in satellite images remains a complex problem that current methods can only assess with significant uncertainties. The anticipated launch of the CO2M (Copernicus Anthropogenic Carbon Dioxide Monitoring) satellite constellation in 2026 is expected to provide high-resolution images of CO2 (carbon dioxide) column-averaged mole fractions (XCO2), opening up new possibilities. However, the inversion of future CO2 plumes from CO2M will encounter various obstacles. A challenge is the low CO2 plume signal-to-noise ratio due to the variability in the background and instrumental errors in satellite measurements. Moreover, uncertainties in the transport and dispersion processes further complicate the inversion task. To address these challenges, deep learning techniques, such as neural networks, offer promising solutions for retrieving emissions from plumes in XCO2 images. Deep learning models can be trained to identify emissions from plume dynamics simulated using a transport model. It then becomes possible to extract relevant information from new plumes and predict their emissions. In this paper, we develop a strategy employing convolutional neural networks (CNNs) to estimate the emission fluxes from a plume in a pseudo- XCO2 image. Our dataset used to train and test such methods includes pseudo-images based on simulations of hourly XCO2 , NO2 (nitrogen dioxide), and wind fields near various power plants in eastern Germany, tracing plumes from anthropogenic and biogenic sources. CNN models are trained to predict emissions from three power plants that exhibit diverse characteristics. The power plants used to assess the deep learning model's performance are not used to train the model. We find that the CNN model outperforms state-of-the-art plume inversion approaches, achieving highly accurate results with an absolute error about half of that of the cross-sectional flux method and an absolute relative error of ∼ 20 % when only the XCO2 and wind fields are used as inputs. Furthermore, we show that our estimations are only slightly affected by the absence of NO2 fields or a detection mechanism as additional information. Finally, interpretability techniques applied to our models confirm that the CNN automatically learns to identify the XCO2 plume and to assess emissions from the plume concentrations. These promising results suggest a high potential of CNNs in estimating local CO2 emissions from satellite images. [ABSTRACT FROM AUTHOR]

This paper examines the current and prospective greenhouse gas (GHG) emissions of e-fuels produced via electrolysis and Fischer–Tropsch synthesis (FTS) for the years 2021, 2030, and 2050 for use in Germany. The GHG emissions are determined by a scenario approach as a combination of a literature-based top-down and bottom-up approach. Considered process steps are the provision of feedstocks, electrolysis (via solid oxide co-electrolysis; SOEC), synthesis (via Fischer–Tropsch synthesis; FTS), e-crude refining, eventual transport to, and use in Germany. The results indicate that the current GHG emissions for e-fuel production in the exemplary export countries Saudi Arabia and Chile are above those of conventional fuels. Scenarios for the production in Germany lead to current GHG emissions of 2.78–3.47 kgCO2-eq/L e-fuel in 2021 as the reference year and 0.064–0.082 kgCO2-eq/L e-fuel in 2050. With a share of 58–96%, according to the respective scenario, the electrolysis is the main determinant of the GHG emissions in the production process. The use of additional renewable energy during the production process in combination with direct air capture (DAC) are the main leverages to reduce GHG emissions. [ABSTRACT FROM AUTHOR]

High level of greenhouse gases emission in fossil fuels has induced a significant transition from conventional energy sources to renewable energy. However, using renewable energy in electricity grids has limitations, such as intermittency and lack of efficient energy storage. This paper focuses on constructing the quanto options contract to help renewable energy producers hedge the risk against low photovoltaic (PV) production and electricity prices. To achieve our objective, we first model the interday PV power production with a combination of a deterministic and stochastic model. We analyse empirical data for daily solar production in three operators in Germany ranging from 1 January 2016 to 31 December 2020. We discovered that all estimated parameters of the deterministic process are highly significant. Meanwhile,we use an autoregressive (AR) process to describe the random behavior in the production. The results demonstrated that AR(2) is suitable enough to explain its stochastic factor. For the error terms analysis, we observed a clear sign of seasonal heteroskedasticity supported by the left skewed density. In addition, we observed a clear seasonal pattern in the squared error terms, where we suggest using seasonal variance and skewed normal distribution to describe its dynamic. As for an application, we embed the PV model in the power price modeling. We found that AR(3) process is sufficient to explain the price behavior and that normal distribution best fits the error terms. Using the suggested PV and power price model, we construct the quanto options contract for renewable energy producers using Monte Carlo simulations. We discovered that the electricity price payoffs are consistent throughout the year, whereas PV and quanto options payoffs vary greatly depending on the season. The quanto options price result shows that the prices vary during four seasons, with the highest variation in July. [ABSTRACT FROM AUTHOR]

The quantification of emissions of greenhouse gases and air pollutants through the inversion of plumes in satellite images remains a complex problem that current methods can only assess with significant uncertainties. The anticipated launch of the CO2M satellite constellation in 2026 is expected to provide high-resolution images of CO2 column-averaged mole fractions (XCO2), opening up new possibilities. However, the inversion of future CO2 plumes from CO2M will encounter various obstacles. A challenge is the CO2 plume low signal-to-noise ratio, due to the variability of the background and instrumental errors in satellite measurements. Moreover, uncertainties in the transport and dispersion processes further complicate the inversion task. To address these challenges, deep learning techniques, such as neural networks, offer promising solutions for retrieving emissions from plumes in XCO2 images. Deep learning models can be trained to identify emissions from plume dynamics simulated using a transport model. It then becomes possible to extract relevant information from new plumes and predict their emissions. In this paper, we employ convolutional neural networks (CNN) to estimate the emission fluxes from a plume in a pseudo XCO2 image. Our dataset used to train and test such methods includes pseudo images based on simulations of hourly XCO2, NO2 and wind fields near various power plants in Eastern Germany, tracing plumes from anthropogenic and biogenic sources. CNN models are trained to predict emissions from three power plants that exhibit diverse characteristics. The power plants used to assess the deep learning model's performance are not used to train the model. We find that the CNN model outperforms state of the art plume inversion approaches, achieving highly accurate results with an absolute error about half of that of the cross-sectional flux method. Furthermore, we show that our estimations are only slightly affected by the absence of NO2 fields or a detection mechanism as additional information. Finally, interpretability techniques applied to our models confirm that the CNN automatically learns to identify the XCO2 plume and to assess emissions from the plume concentrations. These promising results suggest a high potential of CNNs in estimating local CO2 emissions from satellite images. [ABSTRACT FROM AUTHOR]

The German Federal Climate Change Act includes binding annual greenhouse gas (GHG) emission targets for the economic sectors energy, industry, buildings, transport, agriculture and waste. For sectors that fail to meet their targets, climate policy measures have to be implemented immediately. However, some sectors may only have achieved their targets thanks to the COVID-19 pandemic, while others may have missed their targets because of COVID-19. For policy making, it is therefore important to disentangle the effects of the COVID-19 pandemic from the effects of policy and other drivers on emissions. In this paper, the effects of the pandemic on GHG emissions in Germany in 2020 are estimated at the national level and at the levels of the economic sectors. Counterfactual emissions are estimated here based on autoregressive econometric models, thereby distinguishing between different factors of emissions based on decomposition analysis. The findings at the national level suggest that the COVID-19 pandemic lowered GHG emissions in 2020 in Germany by about 41 Mt CO2-eq (5.15%) compared to counterfactual emissions. Accordingly, about 58% of the reduction in emissions between 2019 and 2020 in Germany may be attributed to the pandemic. The findings at the sectoral level imply that without the COVID-19 pandemic, all sectors with the exception of the transport sector, would have met their emissions target in 2020 as set in the Federal Climate Change Act. The transport sector would have missed its emission target in 2020 without COVID-19, suggesting that existing policies were not strong enough to bend the curve of emissions trends sufficiently down. In contrast, the buildings sector failed to meet its target in 2020 because of COVID-19, prompting immediate climate policy measures in this sector. The COVID-19 pandemic lowered greenhouse gas (GHG) emissions in Germany in 2020 by 5.5% compared to counterfactual emissions and accounted for about 58% of total estimated reductions between 2019 and 2020. Without COVID-19, all sectors but the transport sector would have met their emissions targets. Climate policy response should take into account the effects of extraordinary events like COVID-19 suppressing GHG emissions based on counterfactual emissions because failure to implement measures early on may turn out to be costly in the medium term once the transitory emissions reductions no longer occur. Counterfactual emissions should be considered prior to crediting emissions surplus or deficit to subsequent years. [ABSTRACT FROM AUTHOR]

To mitigate climate change, individual greenhouse gas emissions need to decline substantially. This paper empirically explores the relationship between individual carbon footprints and carbon literacy as well as socio-economic and attitudinal factors. To operationalize carbon literacy, we distinguish between carbon knowledge and carbon engagement. Our econometric analysis uses widely representative survey data for 1000 individuals in Germany and distinguishes between components of an aggregate carbon footprint and of carbon footprints related with electricity consumption, heating, motorized individual transport, aviation, and dietary choices. We find a negative and sizeable correlation between carbon engagement and the aggregate footprint, as well as the footprints related to electricity consumption and diet. For example, a one-unit increase in our index reflecting carbon engagement corresponds to a decrease in the aggregate carbon footprint of about 4%. Furthermore, for carbon knowledge we find a negative correlation with the carbon footprint from diet. We also find significant correlations between the carbon footprints and gender, age, income, education, environmental preferences, and policy orientation, which generally exhibit the intuitively expected signs, but differ somewhat across activities. Overall, our findings support the notion that fostering carbon engagement represents a more effective strategy for reducing individuals' carbon footprints than enhancing carbon knowledge. • We study the relationship between carbon literacy and carbon footprints. • We break down carbon literacy into carbon knowledge and carbon engagement. • Individual-level carbon footprints calculated from representative data in Germany. • Carbon footprints calculated for electricity use, heating, transport, and diet. • Econometric findings stress role of carbon engagement for carbon footprints. • Carbon footprints are correlated with socio-economic factors and policy orientation. [ABSTRACT FROM AUTHOR]

Strict regulations imposed by the European Union (EU) with the target of reducing greenhouse gas (GHG) emissions oblige the biogas plant operators to manage digestate properly. Digestate is a by-product of biogas plants and is used extensively as an organic fertilizer. Digestate drying is a commercially available technology. According to the Fachverband Biogas (German Biogas Assiciation) (2018), 500–700 dryers are in use in Germany's biogas industry. Thermal drying can meet the requirements for hygienization and produce a transportable and storable fertilizer as well. Moreover, the GHG emissions from dried digestate are considerably lower than emissions from moist digestate. In addition to the high energy demand of the drying process, emissions occur during drying, resulting in both a decrease in the digestate's fertilizing value and some negative effects on the environment. Consequently, accurate investigation of the drying kinetics is needed in order to optimize the process with regard to energy consumption, drying time, and the amount of emissions. Drying of digestate faces many difficulties because of material specific properties as well as environmental issues. Digestate tends to stick to the dryer in high moisture content. In spite of additional energy requirements, back-mixing or additives is usually utilized to cope with this problem. These methods also have a positive effect on drying rate and considerably reduce drying time. However, they should be investigated exactly because they can sometimes have negative effect in case of excessive usage. Drying rate and also ammonia emission can be controlled by operational conditions, as well. On account of wide variety of digestate, availability of a generalized reliable model will be of benefit as it allows the engineers and designers to develop and optimize the drying processes. However, determination of primary data should be regarded as the first step. It should be noted that even though obtaining an efficient drying process with regards to both energy consumption and emission is not possible, a relative commitment can be achieved. Thanks to the advances of computational resources, numerical modeling could be considered a key instrument for future progress in designing and optimization of drying process provided that reliable input data are obtainable. This paper aims to review the literature on drying techniques, drying kinetics, and emission behavior of digestate. [ABSTRACT FROM AUTHOR]

This paper examines individual preferences for a reduction of life-cycle CO 2 emissions in vehicle purchase decisions. The empirical analysis is based on data from a stated choice experiment among citizens in Germany that refers to decisions between three types of electric vehicles and one conventional vehicle that are characterized by several attributes. With respect to CO 2 emissions, we specifically examine emissions in vehicle production besides the commonly considered emissions in vehicle use. Our econometric analysis reveals a strong stated preference for the reduction of both components of life-cycle CO 2 emissions. Furthermore, we find that conventional vehicles are significantly preferred over electric vehicles. Surprisingly, environmental attitudes, i.e. environmental awareness and ecological policy identification, have no significant effects on the reduction of CO 2 emissions. Instead, citizens with a strong environmental identity rather tend to avoid the purchase of conventional vehicles. We discuss several practical policy implications. [ABSTRACT FROM AUTHOR]

The liability of multinational entities that have caused greenhouse gas emissions through their extraction activities in the fossil fuel industry has pushed climate change on to the international agenda. Those corporations now face challenges from litigants who have initiated negligence or nuisance actions for breach of duty of care. Suits against individual corporations are now possible due to the development of attribution science that can register the extent of each liability. Litigants from third-world countries have to satisfy locus standi in bringing claims which is difficult to establish in some jurisdictions. For example, in Lliuya v. RWE, (Az. 2 O 285/15, OLG Hamm) 24/1/2017, a South American farmer brought a claim in Germany against RWE-AG, Germany's largest electricity producer, to challenge the effects of greenhouse gas emissions from its plant in Peru. This paper asks: Is it possible for foreign litigants to bring a private tort action against the defendant corporation that has allowed greenhouse gas emissions, in that corporation's domestic courts, and to surmount the obstacle of locus standi? It concludes that it is possible for litigants from developing countries to litigate effectively because the foreign courts' statutory protections, such as the doctrine of separation of powers or political rights doctrine can be overcome if climate litigation is viewed as a human rights matter. The prospects of success of litigants has increased by the impact of event attribution science that can quantify the extent of private actor liability in climate change. [ABSTRACT FROM AUTHOR]

• Multi-modal energy system planning approach to support political decision makers. • Energy sector coupling enables efficient transition pathway to achieve climate goals. • Main drivers: electrification of heat and mobility sectors and use of synthetic fuels. • High impact of the electricity exchange on the operation and costs of the system. In order to reach the goals of the United Nations Framework Convention on Climate Change, a stepwise reduction of energy related greenhouse gas emissions as well as an increase in the share of renewable energies is necessary. For a successful realization of these changes in energy supply, an integrated view of multiple energy sectors is necessary. The coupling of different energy sectors is seen as an option to achieve the climate goals in a cost-effective way. In this paper, a methodical approach for multi-modal energy system planning and technology impact evaluation is presented. A key feature of the model is a coupled consideration of the sectors electricity, heat, fuel and mobility. The modeling framework enables system planners to optimally plan future investments in a detailed transition pathway of the energy system of a country, considering politically defined climate goals. Based on these calculations, in-depth analyses of energy markets as well as electrical transmission and distribution grids can be performed using the presented optimization models. Energy demands, conversion and storage technologies in households, the Commerce, Trade and Services (CTS) area and the industry are modeled employing a bottom-up modeling approach. The results for the optimal planning of the German energy system until 2050 show that the combination of an increased share of renewable energies and the direct electrification of heat and mobility sectors together with the use of synthetic fuels are the main drivers to achieve the climate goals in a cost-efficient way. [ABSTRACT FROM AUTHOR]

This paper quantifies changes in the trend of greenhouse gas footprints associated with household consumption covering a period of 20 years. We combine greenhouse gas emissions related to the supply chain (embedded) and consumption (direct) with households' consumption expenditures from the sample survey of income and expenditure (EVS), and present the first comprehensive study on trends in greenhouse gas footprints of German households. We find a decay in changes in GHG footprints as a response to increases in household expenditure over time. In addition, our estimated own-price elasticities for electricity and heating indicate an increasing demand response to higher energy prices. However, this does not translate to a clear decreasing trend in the total GHG footprint because of the role of changes in the demand of other commodities and changes in expenditure levels across years. Further, comparing results of the more advanced EASI demand system with those of the more widely used QUAIDS approach, we find sizable differences. • Mean GHG footprints of German households have fallen by 21% from 1998 to 2018. • Heating, transport and food contribute most to the mean GHG footprint in all years. • The expenditure elasticity of GHG footprints is quite stable at 0.92 (+/− 0.01). • Own-price elasticities for electricity and heating show increasing demand responses. • QUAIDS is too restrictive compared to the more flexible EASI demand system. [ABSTRACT FROM AUTHOR]

Schools play an important role in achieving climate protection goals, because they lay the foundation of knowledge for a responsible next generation. Therefore, schools as institutions have a special role model function. Enabling schools to become aware of their own carbon footprint (CF) is an important prerequisite for being able to tap the substantial CO2 reduction potential. Aiming at the direct involvement of students in the assessment process, a new assessment tool was developed within the Schools4Future project that gives students the opportunity to determine their own school's CF. With this instrument the CO2 emissions caused by mobility, heating and electricity consumption as well as for food in the school canteen and for consumables (paper) can be recorded. It also takes into account existing renewable energy sources. Through the development of the tool, not only a monitoring instrument was established but also a concrete starting point from which students could take actions to reduce Greenhouse Gas (GHG) emissions. This paper presents the tool and its methods used to calculate the CF and compares it with existing approaches. A comparative case study of four pilot schools in Germany demonstrates the practicability of the tool and reveals fundamental differences between the GHG emissions. [ABSTRACT FROM AUTHOR]

Germany is the leader among countries belonging to the Organisation for Economic Co-operation and Development (OECD) in reducing its emissions of the Kyoto basket of greenhouse gases (GHGs), having accomplished an emissions reduction of 18.9 percent from 1990 to 2000. This paper aims to explain the reasons for this success and to examine the impacts of the adoption of the Kyoto Protocol on the development of Germany's climate policy. In order to identify the factors that determined this outcome, we divide our examination of the policy process in Germany since 1987 into five periods and will evaluate it with a focus on policy measures implemented and actors as well as the institutions involved. It concludes that domestic factors, including the participation of the Greens in the coalition government, mainly determined Germany's success in developing advanced climate policies and measures to reduce its GHG emissions. The paper also highlights the importance of the linkage of climate policy with other policies, which provided incentives to those who would otherwise have opposed or remained neutral on the development of climate policy. As such, the adoption of the Kyoto Protocol did not have a direct impact on climate policy development in Germany, but it did have an indirect impact in that it triggered the development of common and coordinated policies and measures at the European Union (EU) level in order to achieve its quantified GHG reduction target of 8 percent committed to under the Kyoto Protocol, as seen in the introduction of an EU-wide emissions trading scheme. [ABSTRACT FROM AUTHOR]

With the introduction of its energy concept in 2010, the German government set ambitious targets for the country's energy and climate policy. According to this concept, greenhouse gas (GHG) emissions will have to be reduced by 80% by 2050, as compared to 1990 levels, and renewables will have to supply 80% of all electricity needs by the same year. Additionally, Germany has decided to phase out its nuclear energy by 2022. This study investigates the possible components to achieve these targets. The analysis is based on an hourly simulation model EnergyPlan. Three scenarios are developed to investigate the potential development of the German energy supply system until 2050. The results indicate renewable shares of 92% and 81% for scenarios B and A, respectively, by 2050 compared to 69% in the reference scenario. The proposed renewable energy system is even found to involve lower costs than today's energy system (i.e. total annual cost for scenario B is € 260 bn compared to € 293 bn in the reference scenario). The results show that a massive decarbonization of the German energy system until 2050 seems technically and economically feasible, if smart grid costs are disregarded and if this sustainable energy transformation is accompanied by political and genuine public willingness to actually achieve the set goals and take the necessary steps. • In this paper low carbon technologies are used to estimate the CO 2 mitigation potential in Germany. • The EnergyPlan model has been used in this work. • Three scenarios are developed by considering different policies for Germany. • The result shows 90% CO 2 emission reduction and 92% renewable generation possible in B scenario. [ABSTRACT FROM AUTHOR]

The European power system will have to change significantly in next decades to meet the ambitious targets set by the upcoming European Climate Law. In particular, intermittent renewable energy will be increasingly injected in the system creating potential stability problems. Already in the last five years it has become evident that if the grid expansion does not keep pace with increasing demand for transmission capacity, congestion will happen more and more often in the European power network. To handle congestion, Transmission System Operators extensively use redispatching, requiring individual plants to alter their market-based functioning pattern in order to cope with technical limitations of the network. While the cost of redispatching actions is well known, the change in emission patterns of both greenhouse gases and air pollutants caused by these actions is not, mostly because of a lack of detailed publicly available data. This paper covers such a gap taking Germany as a case study, thanks to the quality of the available data. Following the standard methodology for estimating the direct emissions from fossil fuelled and renewable fed power plants, the study estimates the changes in the emissions of four regulated air pollutants (namely NO x , SO 2 , Particulate Matter and CO) together with those of CO 2 related to redispatching, for the years 2015–2019. The case study of Germany shows how the management of congestions through redispatching does not necessarily imply an increase in the emissions associated with power production in a country, although it has not been possible to complete the study with a full picture of the consequences for other countries emissions because of lack of consistent data. • Power redispatching in Germany in 2015–2019 did not increase GHG and air pollutants emissions. • The impact of redispatching on emissions from neighbouring countries has not been assessed because of lack of data. • Curtailment of wind and solar power is increasing: Opportunities for further potential emissions reductions are lost. • Congestion in the power network remains an issue in Europe. [ABSTRACT FROM AUTHOR]

Organisations attempt to contribute their share towards fighting the climate crisis by trying to reduce their emission of greenhouse gases effectively towards net zero. An instrument to guide their reduction efforts is internal carbon pricing. Next to choosing the right pricing tool, defining the exact value of an internal carbon price, especially against the background of potential regulatory external carbon prices, and assessing its impact on business units' energy systems poses a challenge for organisations. The academic literature has so far not examined the impact differences of an internal carbon price across different countries, which this paper addresses by using an optimisation model. First, it analyses the energy system cost increase of a real-world facility based on an internal carbon price compared to a potential regulatory carbon price within a country. Second, we evaluate the energy system cost increase based on an internal carbon price across different countries. The results show that with regard to internal carbon prices the additional total system cost compared to potential external carbon prices stays within 9%, 15%, and 59% for Germany, Japan, and the United Kingdom, respectively. The increase in the energy system cost in each country varies between 3% and 93%. For all countries, the cost differences can be reduced by allowing the installation of renewables. The integration of renewables via energy storage and power-to-heat technologies depends on the renewable potentials and the availability of carbon capture and storage. If organisations do not account for these differences, it might raise the disapproval of internal carbon prices within the organisation. [ABSTRACT FROM AUTHOR]