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Abstract: This study evaluates the economic and environmental benefits of implementing the proposed REcovered Nitrogen from manURE (RENURE) criteria as mineral fertiliser into the Nitrates Directive (ND) to facilitate the utilisation of minerals from manure. Implementing the RENURE amendment could significantly contribute to sustainability goals in an economic way, offering a 4.8 % reduction in economic costs in livestock-dense regions including Brittany (-0.7 %), Lombardy (-2.3 %), Flanders (-2.6 %), Lower Saxony (-4.7 %), Catalonia (-4.8 %), North-Rhine Westphalia (-4.8 %), and the Netherlands (-5.0 %). Through spatially explicit multi-agent modeling, the study revealed that the RENURE amendment not only promises economic benefits, but also enhances nitrogen circularity by 1.3 % and reduces greenhouse gas emissions by 6 % in these areas. These findings highlight the potential of nutrient recovery and reuse under RENURE to address both economic and environmental challenges, supporting the European Union's (EU) Farm-to-Fork strategy (F2F) goals of reducing nutrient emissions to the air and fertilizer use.

Abstract: This study evaluates the economic and environmental benefits of implementing the proposed REcovered Nitrogen from manURE (RENURE) criteria as mineral fertiliser into the Nitrates Directive (ND) to facilitate the utilisation of minerals from manure. Implementing the RENURE amendment could significantly contribute to sustainability goals in an economic way, offering a 4.8 % reduction in economic costs in livestock-dense regions including Brittany (-0.7 %), Lombardy (-2.3 %), Flanders (-2.6 %), Lower Saxony (-4.7 %), Catalonia (-4.8 %), North-Rhine Westphalia (-4.8 %), and the Netherlands (-5.0 %). Through spatially explicit multi-agent modeling, the study revealed that the RENURE amendment not only promises economic benefits, but also enhances nitrogen circularity by 1.3 % and reduces greenhouse gas emissions by 6 % in these areas. These findings highlight the potential of nutrient recovery and reuse under RENURE to address both economic and environmental challenges, supporting the European Union's (EU) Farm-to-Fork strategy (F2F) goals of reducing nutrient emissions to the air and fertilizer use.

Network temperatures in district heating systems are important operational factors for obtaining efficient performance. A low network return temperature allows for the recovery of low-grade heat from assets such as condensing boilers, waste incineration, geothermal sources and industrial waste heat. Fluctuations in heating and cooling demands affect the return temperatures of the building substations and in the network. This variability impacts the economic viability and environmental sustainability of the entire system. This paper presents a nonlinear optimization strategy to maintain sufficient energy flows in the network's primary and secondary circuits to achieve low return temperatures from all substations in the network. The defined optimization strategy incorporates the thermodynamic model of the substation and building heating system as opposed to traditional weather-based supply temperature adjustments. The estimated heat demands and tariffs, CO2 penalties are inputs used by the optimizer to find theoptimal solution. The total operational expenditure for electricity and gas consumption shows an 18% reduction with 8% reduction in emissions and 6% efficiency improvement when compared with the measured weather-based approach. The developed strategy will aid the network operators in the economic dispatch of heat generation while ensuring the user's thermal comfort.

Network temperatures in district heating systems are important operational factors for obtaining efficient performance. A low network return temperature allows for the recovery of low-grade heat from assets such as condensing boilers, waste incineration, geothermal sources and industrial waste heat. Fluctuations in heating and cooling demands affect the return temperatures of the building substations and in the network. This variability impacts the economic viability and environmental sustainability of the entire system. This paper presents a nonlinear optimization strategy to maintain sufficient energy flows in the network's primary and secondary circuits to achieve low return temperatures from all substations in the network. The defined optimization strategy incorporates the thermodynamic model of the substation and building heating system as opposed to traditional weather-based supply temperature adjustments. The estimated heat demands and tariffs, CO2 penalties are inputs used by the optimizer to find theoptimal solution. The total operational expenditure for electricity and gas consumption shows an 18% reduction with 8% reduction in emissions and 6% efficiency improvement when compared with the measured weather-based approach. The developed strategy will aid the network operators in the economic dispatch of heat generation while ensuring the user's thermal comfort.

Organic solar cells utilize an energy-level offset to generate free charge carriers. Although a very small energy-level offset increases the open-circuit voltage, it remains unclear how exactly charge generation is affected. Here we investigate organic solar cell blends with highest occupied molecular orbital energy-level offsets (∆EHOMO) between the donor and acceptor that range from 0 to 300 meV. We demonstrate that exciton quenching at a negligible ∆EHOMO takes place on timescales that approach the exciton lifetime of the pristine materials, which drastically limits the external quantum efficiency. We quantitatively describe this finding via the Boltzmann stationary-state equilibrium between charge-transfer states and excitons and further reveal a long exciton lifetime to be decisive in maintaining an efficient charge generation at a negligible ∆EHOMO. Moreover, the Boltzmann equilibrium quantitatively describes the major reduction in non-radiative voltage losses at a very small ∆EHOMO. Ultimately, highly luminescent near-infrared emitters with very long exciton lifetimes are suggested to enable highly efficient organic solar cells. Donor–acceptor systems with low energy-level offset enable high power efficiency in organic solar cells yet it is unclear what drives charge generation. Classen et al. show that long exciton lifetimes enable efficient exciton splitting and thus generation of free charges while also suppressing voltage losses.

Organic solar cells utilize an energy-level offset to generate free charge carriers. Although a very small energy-level offset increases the open-circuit voltage, it remains unclear how exactly charge generation is affected. Here we investigate organic solar cell blends with highest occupied molecular orbital energy-level offsets (∆EHOMO) between the donor and acceptor that range from 0 to 300 meV. We demonstrate that exciton quenching at a negligible ∆EHOMO takes place on timescales that approach the exciton lifetime of the pristine materials, which drastically limits the external quantum efficiency. We quantitatively describe this finding via the Boltzmann stationary-state equilibrium between charge-transfer states and excitons and further reveal a long exciton lifetime to be decisive in maintaining an efficient charge generation at a negligible ∆EHOMO. Moreover, the Boltzmann equilibrium quantitatively describes the major reduction in non-radiative voltage losses at a very small ∆EHOMO. Ultimately, highly luminescent near-infrared emitters with very long exciton lifetimes are suggested to enable highly efficient organic solar cells. Donor–acceptor systems with low energy-level offset enable high power efficiency in organic solar cells yet it is unclear what drives charge generation. Classen et al. show that long exciton lifetimes enable efficient exciton splitting and thus generation of free charges while also suppressing voltage losses.

The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.

The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.