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SummaryEnvironmentally extended multiregional input‐output (EE MRIO) tables have emerged as a key framework to provide a comprehensive description of the global economy and analyze its effects on the environment. Of the available EE MRIO databases, EXIOBASE stands out as a database compatible with the System of Environmental‐Economic Accounting (SEEA) with a high sectorial detail matched with multiple social and environmental satellite accounts. In this paper, we present the latest developments realized with EXIOBASE 3—a time series of EE MRIO tables ranging from 1995 to 2011 for 44 countries (28 EU member plus 16 major economies) and five rest of the world regions. EXIOBASE 3 builds upon the previous versions of EXIOBASE by using rectangular supply‐use tables (SUTs) in a 163 industry by 200 products classification as the main building blocks. In order to capture structural changes, economic developments, as reported by national statistical agencies, were imposed on the available, disaggregated SUTs from EXIOBASE 2. These initial estimates were further refined by incorporating detailed data on energy, agricultural production, resource extraction, and bilateral trade. EXIOBASE 3 inherits the high level of environmental stressor detail from its precursor, with further improvement in the level of detail for resource extraction. To account for the expansion of the European Union (EU), EXIOBASE 3 was developed with the full EU28 country set (including the new member state Croatia). EXIOBASE 3 provides a unique tool for analyzing the dynamics of environmental pressures of economic activities over time.

River deltas and estuaries are disproportionally-significant coastal landforms that are inhabited by nearly 600 M people globally. In recent history, rapid socio-economic development has dramatically changed many of the World's mega deltas, which have typically undergone agricultural intensification and expansion, land-use change, urbanization, water resources engineering and exploitation of natural resources. As a result, mega deltas have evolved into complex and potentially vulnerable socio-ecological systems with unique threats and coping capabilities. The goal of this research was to establish a holistic understanding of threats, resilience, and adaptation for four mega deltas of variable geography and levels of socio-economic development, namely the Mekong, Yellow River, Yangtze, and Rhine deltas. Compiling this kind of information is critical for managing and developing these complex coastal areas sustainably but is typically hindered by a lack of consistent quantitative data across the ecological, social and economic sectors. To overcome this limitation, we adopted a qualitative approach, where delta characteristics across all sectors were assessed through systematic expert surveys. This approach enabled us to generate a comparative assessment of threats, resilience, and resilience-strengthening adaptation across the four deltas. Our assessment provides novel insights into the various components that dominate the overall risk situation in each delta and, for the first time, illustrates how each of these components differ across the four mega deltas. As such, our findings can guide a more detailed, sector specific, risk assessment or assist in better targeting the implementation of risk mitigation and adaptation strategies.

We introduce a novel simulation tool capable of calculating the energy yield of a PV system based on its fundamental material properties and using self-consistent models. Thus, our simulation model can operate without measurements of a PV device. It combines wave and ray optics and a dedicated semiconductor simulation to model the optoelectronic PV device properties resulting in the IV-curve. The system surroundings are described via spectrally resolved ray tracing resulting in a cell resolved irradiance distribution, and via the fluid dynamics-based thermal model, in the individual cell temperatures. A lumped-element model is used to calculate the IV-curves of each solar cell for every hour of the year. These are combined factoring in the interconnection to obtain the PV module IV-curves, which connect to the inverter for calculating the AC energy yield. In our case study, we compare two types of 2 terminal perovskite/silicon tandem modules with STC PV module efficiencies of 27.7% and 28.6% with a reference c-Si module with STC PV module efficiency of 20.9%. In four different climates, we show that tandem PV modules operate at 1–1.9 °C lower yearly irradiance weighted average temperatures compared to c-Si. We find that the effect of current mismatch is significantly overestimated in pure optical studies, as they do not account for fill factor gains. The specific yields in kWh/kWp of the tandem PV systems are between −2.7% and +0.4% compared to the reference c-Si system in all four simulated climates. Thus, we find that the lab performance of the simulated tandem PV system translates from the laboratory to outdoors comparable to c-Si systems. Photovoltaic Materials and Devices Electrical Sustainable Energy Energie and Industrie

Photoexcitation and charge carrier thermalization inside semiconductor photocatalysts are two important steps in solar fuel production. Here, photoexcitation and charge carrier thermalization in a silicon wafer are for the first time probed by a novel, yet simple and user-friendly Attenuated Total Reflectance Infrared spectroscopy (ATR-IR) system.

District heating is a network of pipes through which heat is delivered from a centralised source. It is expected to play an important role in the decarbonisation of the energy sector in the coming years. In district heating, heat is traditionally generated through fossil fuels, often with combined heat and power (CHP) units. However, increasingly, waste heat is being used as a low carbon alternative, either directly or, for low temperature sources, via a heat pump. The design of district heating often has competing objectives: the need for inexpensive energy and meeting low carbon targets. In addition, the planning of district heating schemes is subject to multiple sources of uncertainty such as variability in heat demand and energy prices. This paper proposes a decision support tool to analyse and compare system designs for district heating under uncertainty using stochastic ordering (dominance). Contrary to traditional uncertainty metrics that provide statistical summaries and impose total ordering, stochastic ordering is a partial ordering and operates with full probability distributions. In our analysis, we apply the orderings in the mean and dispersion to the waste heat recovery problem in Brunswick, Germany.