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Biomethane production from processed industrial food waste (IFW) in admixture with sewage sludge (primary and waste activated sludge: PS and WAS) was evaluated at a range of C:N ratios using a standard biochemical methane potential (BMP) test. IFW alone had a C:N of 30 whereas for WAS it was 5.4 and thus the C:N ratio of the blends fell in that range. Increasing the IFW content in mix improves the methane potential by increasing both the cumulative biogas production and the rate of methane production. Optimum methane yield 239 mL/g VSremoved occurred at a C:N ratio of 15 which was achieved with a blend containing 11 percent (w/w) IFW. As the fraction of IFW in the blend increased, volatile solids (VS) destruction was increased and this led to a reduction in methane yield and amount of production. The highest destruction of volatile solids of 93 percent was achieved at C:N of 20 followed by C:N 30 and 15. A shortened BMP test is adequate for evaluating optimum admixtures.

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.

Results-based financing (RBF) programmes in the clean cooking sector have gained increasing donor interest over the last decade. Although the risks and advantages of RBF have been discussed quite extensively for other sectors, especially health services, there is limited research-documented experience of its application to clean cooking. Due to the sheer scale of the important transition from ‘dirty’ to clean cooking for the 4 billion people who lack access, especially in the Global South, efficient and performance-proven solutions are urgently required. This paper, undertaken as part of the work of the UKAid-funded Modern Energy Cooking Services (MECS) programme, aims to close an important research gap by reviewing evidence-based support mechanisms and documenting essential experiences from previous and ongoing RBF programmes in the clean cooking and other sectors. On this basis, the paper derives key strategic implications and learning lessons for the global scaling of RBF programmes and finds that qualitative key performance indicators such as consumer acceptance as well as longer-term monitoring are critical long-term success factors for RBF to ensure the continued uptake and use of clean cooking solutions (CCS), however securing the inclusion of these indicators within programmes remains challenging. Finally, by discussing the opportunities for the evolution of RBF into broader impact funding programmes and the integration of energy access and clean cooking strategies through multi-sector approaches, the paper illustrates potential steps to enhance the impact of RBF in this sector in the future.

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.

Green ammonia is gaining momentum as a globally significant technology for deep decarbonisation. In this thesis, several models are developed across chemical, techno-economic, and energy system modelling disciplines to explore the future role of green ammonia. First, standalone models of production (i.e., power-to-ammonia) and re-electrification (i.e., ammonia-to-power) are developed and compared to competing technologies. Second, these models are integrated into a planning and dispatch energy system model (ESM) of India to 2050. The ESM has several novel additions including the sector coupling of hydrogen and ammonia, multiple years of granular weather data, and learning-curve-based technology cost forecasts. India is chosen as an ideal case study given its globally unmatched demand growth in all three relevant sectors: electricity, green hydrogen, and green ammonia. The projected electricity demands for green hydrogen and ammonia production account for 25% of the total Indian electricity demand in 2050, underscoring the transformational potential that green hydrogen and ammonia sector coupling can have on the Indian energy system. The results of the state-of-the-art ESM highlight synergistic effects of hydrogen and ammonia sector coupling with the power system. The least-cost system employs seasonal green ammonia production paired with up to 40 million tonnes (i.e., 200 TWh) of ammonia storage, as well as some re-electrification via gas turbines. Sector coupling reduces system curtailment, addresses challenges of long-duration storage, and improves system resilience to interannual weather variations. While India is a crucial case study from a global decarbonisation perspective, the methodology and findings are generally applicable, and it is the aim of this work to motivate and accelerate the wider research community into considering the potential impacts of green ammonia sector coupling on electricity grid design. Finally, this work highlights strategic technology development direction for ammonia producers and gas turbine manufacturers, as well as implications for policymakers.

This paper focuses on the specific growth rate estimation problem in a Polyhydroxybutyrate bioplastic production process by industrial fermentation. The kinetics of the process are unknown and there are uncertainties in the model parameters and inputs. During the first hours of the growth phase of the process, biomass concentration can be measured online by an optical density sensor, but as cell density increases this method becomes ineffective and biomass measurement is lost. An asymptotic observer is developed to estimate the growth rate for the case without biomass measurement based on corrections made by a pH control loop. Furthermore, an exponential observer based on the biomass measurement is developed to estimate the growth rate during the first hours, which gives the initial condition to the asymptotic observer. Error bounds and robustness to uncertainties in the models and in the inputs are found. The estimation is independent of the kinetic models of the microorganism. The characteristic features of the observer are illustrated by numerical simulations and validated by experimental results.