Climate change is increasingly affecting the water cycle, with droughts and floods posing significant challenges for agriculture, hydropower production, and urban water resource management due to growing variability in the factors influencing the water cycle. Reinforcement learning (RL) has demonstrated promising potential in optimization and planning tasks, as it trains models on historical data or through simulations, allowing them to generate new data by interacting with the simulator. This systematic literature review examines the application of reinforcement learning (RL) in water resource management across various domains. A total of 40 articles were analyzed, revealing that RL is a viable approach for this field due to its capability to learn and optimize sequential decision-making processes. The results show that RL agents are primarily trained in simulated environments rather than directly on historical data. Among the algorithms, deep Q-networks are the most commonly employed. Future research should address the challenges of bridging the gap between simulation and real-world applications and focus on improving the explainability of the decision-making process. Future studies need to address the challenges of bridging the gap between simulation and real-world applications. Furthermore, future research should focus on the explainability behind the decision-making process of the agent, which is important due to the safety-critical nature of the application.

Machine Learning (ML) is frequently utilized in prediction tasks; however, its applications in hydropower forecasting,particularly in forecasting hourly power production, has not been thoroughly investigated. In this paper, two Deep Learning (DL) models,namely an autoregressive neural network and Long Short-Term Memory, are compared to a seasonal autoregressive moving average(SARIMA) model to forecast the hourly power production at a hydropower station situated in Linköping, Sweden. Hyperparameteroptimization algorithms are used to identify suitable DL models and algorithms for automatic model identification of SARIMA modelsare utilized. The three models are evaluated using a rolling origin strategy on a test dataset that consists of 10 months (January – October2023) of hourly power production. The DL models provided similarly accurate forecasts as the SARIMA model according to meansquared error and mean absolute error. However, the DL models are poorly calibrated, resulting in lower coverage compared to theSARIMA model. Furthermore, the models are using a univariate time series (i.e., using historical power production to forecast futurepower production) and future studies need to explore additional variables that may be useful in providing a more accurate forecast.

It is likely that different industrial symbiosis collaborations will have different sets of winners and losers when it comes to benefits or costs. In this study we present an analytical framework intended for evaluating a wide-range of industrial symbiosis outcomes that will aid in research design. The framework provide a base for including a broader, but also, specific set of effects and outcomes (economic, environmental and social), including a diverse set of clearly defined actors. Used consistently, the framework can average out costs and benefits across actors in the whole society, so that each actor is more likely to (over time) realize net positive outcomes from a full set of industrial symbiosis applications. The analytical framework is developed by combining theory and concepts from the system of national accounts, the planetary boundaries, and the social foundation. The analytical framework is then applied in a state of the art review, analysing value and benefits in 56 industrial symbiosis research articles. Besides providing a robust model for analysing industrial symbiosis, the results show that private market-based outcomes are the dominant form of economic value and that nonmarket valuations are completely absent. Environmental outcomes mainly consist of decreased CO2 emissions, chemical pollution and water use. Social outcomes include private income and work and network effects for the companies involved in the industrial symbiosis.

Energy management is the most prominent means of improving energy efficiency, and improved energy efficiency constitutes the cornerstone in decarbonization. For successful industrial energy management, defining accurate energy performance indicators (EnPIs) is essential. Energy-intensive industries have previously been found to have an improvement potential regarding the current monitoring of EnPIs, especially at process level. While general models for developing and implementing EnPIs exist, manufacturing industries are diverse in terms of their production processes, which is why industry-tailored models for EnPI development are needed. One major outcome of this paper is a unique model specifically tailored for kraft pulp mills. The model derives from a practice-based approach for EnPI development, building on real-life experiences from a Swedish group of companies. This paper’s developed model, and the validation of the EnPIs, further increase the understanding of the kraft pulp industry’s processes and how to apply descriptive and explanatory indicators. The developed model can potentially be generalized to other sectors.

Svensk industri bör strategiskt arbeta mot ökad energi- och resurseffektivitet på en global marknad med knappare resurser. I detta sammanhang spelar beslutsunderlag och nyckeltal en central roll för att nå ökad effektivitet. Även för tillsynsmyndigheter är rättvisande nyckeltal avseende slutenergianvändning av mycket stor vikt för att kunna bedriva ett rättvist förebyggande och proaktivt arbete med svenska företag. De nyckeltal som finns på internationell och nationell nivå är baserade på tillförd energi och ofta relaterade till en ekonomisk output, till exempel förädlingsvärde. Det saknas emellertid nyckeltal kring slutenergianvändningen inom svensk industri fördelat på energibärare såsom el och olja och fördelat på slutenergiprocesser såsom ugnar, tryckluftskompressorer, etc. De siffror som ibland anges är baserade på grova uppskattningar. Projektets mål har därför varit att generera ett processträd avseende flera av de största, till slutenergianvändning räknat, svenska industribranscherna avseende hur slutenergianvändningen är fördelad på processnivå och olika energibärare, samt att allokera växthusgasutsläpp på dessa olika processer. Resultaten indikerar att nyckeltal baserade på energianvändning och indirekta växthusgasutsläpp på processnivå kan bidra till bättre kunskap om i vilka industriella energianvändande processer den största potentialen för energieffektivisering och minskning av växthusgasutsläpp finns. För att upprätthålla kunskap om var den största potentialen för förbättring finns krävs att energidata regelbundet samlas in efter en standardiserad kategorisering av energianvändande processer. Även om projektet har avgränsats till svensk industri kan resultatet vara till nytta också för andra medlemsstater inom EU liksom globalt.

Swedish industry should strategically work towards improved energy and resource efficiency. In this context, decision making and key performance indicators (KPIs) play a central role in achieving improved efficiency. Even for regulation authorities, fair KPIs of energy end-use are very important to be able to perform excellent, preventive and proactive work towards Swedish companies. KPIs at international and national levels are based on energy supplied, normally related to an economic output, such as value added. However, there are no key figures about the energy end-use in Swedish industry, distributed on energy carriers such as electricity and oil, and in turn allocated on energy end-using processes such as furnaces, air compressors, etc. The existing figures regarding this are based on rough estimates. The goal of the project has therefore been to generate a process tree for several of the largest, energy end-using Swedish manufacturing industries, as regards how energy end-use is distributed at the process level and for different energy carriers, and in turn allocate greenhouse gas emissions for these different processes. The results indicate that energy KPIs based on energy use and indirect carbon greenhouse gas emissions at process level can contribute to better knowledge of the industrial energy end-use processes that have the greatest potential for energy efficiency improvements as well as greenhouse gas abatement. In order to continuously know the processes with the greatest potential for improvement, energy end-use data should be collected regularly and follow a standardized categorization of energy end-use processes. The project has been limited to Swedish industry, but the results can be useful for other EU member states as well as globally.

Increasing energy efficiency within the industrial sector is one of the main approachesin order to reduce global greenhouse gas emissions. The production and processing of aluminiumis energy and greenhouse gas intensive. To make well-founded decisions regarding energy effi-ciency and greenhouse gas mitigating investments, it is necessary to have relevant key performanceindicators and information about energy end-use. This paper develops a taxonomy and key perfor-mance indicators for energy end-use and greenhouse gas emissions in the aluminium industry andaluminium casting foundries. This taxonomy is applied to the Swedish aluminium industry andtwo foundries. Potentials for energy saving and greenhouse gas mitigation are estimated regardingstatic facility operation. Electrolysis in primary production is by far the largest energy using andgreenhouse gas emitting process within the Swedish aluminium industry. Notably, almost half of thetotal greenhouse gas emissions from electrolysis comes from process-related emissions, while theother half comes from the use of electricity. In total, about 236 GWh/year (or 9.2% of the total energyuse) and 5588–202,475 tonnes CO2eq/year can be saved in the Swedish aluminium industry and twoaluminium casting foundries. The most important key performance indicators identified for energyend-use and greenhouse gas emissions are MWh/tonne product and tonne CO2-eq/tonne product.The most beneficial option would be to allocate energy use and greenhouse gas emissions to boththe process or machine level and the product level, as this would give a more detailed picture of thecompany’s energy use and greenhouse gas emissions.

In the transition towards fossil-free transports, there is an increasing interest in upgraded biogas, or biomethane, as a vehicle fuel. Liquefied biogas has more than twice as high energy density as compressed biogas, which opens up the opportunity for use in heavy transports and shipping and for more efficient distribution. There are several ways to produce and distribute compressed and liquefied biogas, but very few studies comparing them and providing an overview. This paper investigates the energy balance, environmental impact and economic aspects of different technologies for upgrading, liquefaction and distribution of biogas for use as a vehicle fuel. Furthermore, liquefaction is studied as a method for efficient long-distance distribution.

The results show that the differences between existing technologies for upgrading and liquefaction are small in a well-to-tank perspective, especially if the gas is transported over a long distance before use. Regarding distribution, liquefaction can pay back economically after 25–250 km compared to steel container trailers with compressed gas, and reduce the climate change impact after 10–30 km. Distribution in gas grid is better in all aspects, given that it is available and no addition of propane is required. Liquefaction can potentially expand the geographical boundaries of the market for biogas as a vehicle fuel, and cost reductions resulting from technology maturity allow cost-effective liquefaction even at small production capacities.

As a result of rapid development in China and the growth of megacities, large amounts of organic wastes are generated within relatively small areas. Part of these wastes can be used to produce biogas, not only to reduce waste-related problems, but also to provide renewable energy, recycle nutrients, and lower greenhouse gases and air polluting emissions. This article is focused on the conditions for biogas solutions in Guangzhou. It is based on a transdisciplinary project that integrates several approaches, for example, literature studies and lab analysis of food waste to estimate the food waste potential, interviews to learn about the socio-technical context and conditions, and life-cycle assessment to investigate the performance of different waste management scenarios involving biogas production. Xiaoguwei Island, with a population of about 250,000 people, was chosen as the area of study. The results show that there are significant food waste potentials on the island, and that all studied scenarios could contribute to a net reduction of greenhouse gas emissions. Several socio-technical barriers were identified, but it is expected that the forthcoming regulatory changes help to overcome some of them.

Research has revealed the existence of an energy-efficiency gap – the difference between optimal and actual energy end-use, suggesting that energy efficiency can be improved. Energy management (EnM) is a means for improving industrial energy efficiency. However, due to various barriers, the full potential of EnM is not realised. Several studies have addressed drivers and barriers to energy efficiency but few to EnM. This study aims to identify EnM practices, the most important perceived drivers and barriers for EnM, and relations among them in the energy-intensive Swedish pulp and paper industry (PPI), which has the longest experience internationally of practising EnM systems, and has worked according to the standards since 2004. Our results show that, altogether, the PPI works regularly and continuously with EnM, with a clear division of responsibilities. The highest maturity for EnM practices was for energy policy, followed by organization, investments, and performance measurement. The study also shows that communication between middle management and operations personnel has potential for improvement. The most important categories of drivers were economic, whereas for barriers they were organizational. Nevertheless, knowledge-related barriers and drivers were amongst the most important, suggesting that the absorptive capacity for energy issues could be improved.