Hasil untuk "Energy industries. Energy policy. Fuel trade"

R. Alizadeh, Leili Soltanisehat, P. Lund et al.

Shifting from fossil to clean energy sources is a major global challenge, but in particular for those countries with substantial fossil-fuel reserves and economies depending on fossil-fuel exports. Here we introduce an improved framework for renewable energy planning and decision-making to help such countries to more effectively harness their abundant renewable energy resources. We use Iran as a case for the analysis. The framework includes identifying and removing barriers that prevent the use of renewables. It is based on combining two models: Benefit, Opportunity, Cost, Risk (BOCR) and Analytic Network Process (ANP) models. In the analyses, the mutual weight of strategic criteria is employed such as technology, economy, energy vulnerability, security, global effects, and human wellbeing. Using the integrated model, we find that solar energy would be the preferential renewable energy source for Iran. Also, the role of infrastructures, policies, and administrative structures in renewable energy to facilitate their development was analyzed. The renewable energy policy-making framework presented is applicable to other countries as well.

Kijeon Nam, Soonho Hwangbo, C. Yoo

Abstract Renewable and sustainable energy systems and policies have globally been promoted to transition from fossil fuel sources to environmentally friendly renewable energy sources such as wind power, photovoltaic energy, and fuel cells. Wind and solar energy sources are erratic and difficult to implement in renewable energy systems, therefore, circumspection is needed to implement such renewable energy systems and policies. Accordingly, this study develops an energy forecasting model with renewable energy technologies on which policy can be based, using the Korean energy policy as a case study. Deep learning-based models forecast fluctuating variation in electricity demand and generation, which are necessary in renewable energy system but not possible with conventional models. The gated recurrent unit shows the best prediction performance among the forecasting models evaluated, and is therefore selected as the base model to evaluate four different renewable energy scenarios. The scenarios are evaluated according to economic-environmental cost assessment. The optimal scenario uses an integrated gasification combined cycle, onshore and offshore wind farms, photovoltaic power stations, and fuel cell plants; in particular, this scenario shows the lowest economic-environmental costs, generates stable electricity for demand, and achieves a policy with 100% renewable energy. The optimal scenario is assessed by considering its strengths, weaknesses, opportunities, and threats analysis while also considering techno-economic-environmental domestic and global energy circumstances.

M. Ridwan, Zulfiquar Ali Antor, Jeremy Ko et al.

The key question for open economies is how to maintain ambitious climate policy while remaining competitive in the industrial sector. Carbon taxes have been accused of increasing production costs while being a driver of efficiency and innovation. The Nordic region offers a severe experiment on this argument, as it has the highest carbon prices globally and depends highly on manufacturing exports. This study examines the relationships among carbon taxation, energy consumption, and industrial competitiveness in Nordic economies from 2000-2024. We estimate the joint effect of environmental policies and structural factors on export performance via panel econometric techniques. To examine this relationship, the study used quantile regression, panel-corrected standard errors, Driscoll–Kraay standard errors, the system generalized method of moments, and panel autoregressive distributive lag model. The results indicate that carbon taxation improves manufacturing exports, implying that tougher climate policies can reinforce competitiveness via induced innovation and efficiency improvements. Exports are boosted by fossil fuel consumption and negatively affect electricity consumption, GDP per capita, and carbon intensity. Value-added manufacturing enhances competitiveness, whereby industrial upgrading and involvement in global value chains have their place. Causality tests revealed a two-way relationship between carbon taxes and exports, and robustness tests confirmed the consistency of the findings. However, the GMM establishes weaker effects of taxation and value added. This study provides evidence that climate leadership and trade competitiveness are not mutually exclusive. Nordic economies demonstrate a viable path to sustainable competitiveness in the global low-carbon transition through carbon pricing within coherent policies supporting innovation, energy efficiency, and low-carbon strategies.

Xinyi Zhu, Sheng Zhou, Fucong Xu et al.

Abstract The widespread integration of photovoltaic (PV) power, energy storage systems, and other demand‐side resources highlights the importance of optimal dispatching for the PV‐storage‐load virtual power plant (VPP). However, the fluctuation of the PV power generation and the uncertainty of the electricity prices exacerbate the economic operation risks of the VPP. To address these challenges, an optimal dispatching strategy for the PV‐storage‐load VPP is proposed, with due consideration given to the dual uncertainties of electricity prices and PV power output. Firstly, the conditional value‐at‐risk theory is employed to quantify the uncertainty risk of VPP revenue caused by electricity price fluctuations. Secondly, in view of the asymmetric fluctuation intervals of PV power output, a quantification method for PV uncertainty and dispatch robustness is developed using the confidence gap decision theory. Furthermore, by combining the regulation reserve model of multi‐type flexible resources, a robust optimization model for the PV‐storage‐load VPP is constructed with the objective of maximizing comprehensive operational revenue, which includes the provision of upward and downward reserve services. Finally, case studies based on a PV‐storage‐load VPP in a Chinese province are conducted to validate the effectiveness and superiority of the proposed model. The simulation results indicate that the proposed robust optimization strategy effectively reflects the relationship between the uncertainty of PV power output and the risk preference of decision‐maker, mitigates the fluctuation risks of electricity prices to ensure the stability of the power system, and enhances the economic efficiency and flexibility of the PV‐storage‐load VPP operation.

Zhenyu Wang, Jing Sun, Zixiao Guo et al.

Vanadium redox flow batteries (VRFBs) face a significant challenge during high-temperature operation, as the precipitation of V2O5 on the positive side obstructs electrolyte flow, drastically diminishes battery capacity, and eventually leads to battery failure. While various additives have been explored to mitigate V2O5 precipitation, it continues to occur at temperatures exceeding 40 ℃. Unfortunately, there is a lack of research focused on effective methods for removing V2O5 from the battery system. The conventional approach requires disassembling the battery stacks to eliminate V2O5, a process that is not only labor-intensive and costly but also risks damaging the performance and components of the battery. In this study, we introduce an additive free strategy that enables the removal of V2O5 from the battery without the need for disassembly, thereby fully restoring battery capacity. This method is both efficient and simple, offering a cost-effective solution for V₂O₅ dissolution while potentially simplifying VRFB electrolyte manufacturing.

Seongmin Kim, In-Saeng Suh, Travis S. Humble et al.

Developing high-performance materials is critical for diverse energy applications to increase efficiency, improve sustainability and reduce costs. Classical computational methods have enabled important breakthroughs in energy materials development, but they face scaling and time-complexity limitations, particularly for high-dimensional or strongly correlated material systems. Quantum computing (QC) promises to offer a paradigm shift by exploiting quantum bits with their superposition and entanglement to address challenging problems intractable for classical approaches. This perspective discusses the opportunities in leveraging QC to advance energy materials research and the challenges QC faces in solving complex and high-dimensional problems. We present cases on how QC, when combined with classical computing methods, can be used for the design and simulation of practical energy materials. We also outline the outlook for error-corrected, fault-tolerant QC capable of achieving predictive accuracy and quantum advantage for complex material systems.

İbrahim Özaytürk

Due to the contribution of fossil fuels to global warming, nations have begun to explore alternative energy generation techniques. In the new century, energy production has favored renewable energies that do not cause environmental damage as much as fossil fuel energy. The rise in renewable energy generation has correspondingly led to an increased utilization of this energy source. Consequently, researchers have begun the investigation of elements that may influence the utilization of renewable energy. The correlation between renewable energy consumption, the focus of this study, and free trade has emerged as a pertinent inquiry for scholars. This study will examine the correlation between the use of renewable energy and fossil fuels with regard to free trade and will compare the outcomes derived from fossil fuel energy and renewable energy usage. A connection between the utilization of renewable energy, the focus of this study, and free trade has emerged as a topic of inquiry among scholars. This study will examine the correlation between free trade and the utilization of renewable energy vs fossil fuels, comparing the outcomes associated with each energy source. The impact of free trade on both categories of energy use may be determined in this manner. This research examines yearly data spanning the years 1990 to 2022. The study utilized panel data analysis to pick Türkiye's principal trading partners among the European Union (EU) members (Germany, France, Spain, Italy, and the United Kingdom). The panel unit root test was conducted, and based on the findings, the Augmented Autoregressive Distributed Lag (ARDL) bounds test/PMG technique was considered suitable. The research indicates that free trade influences renewable energy consumption in the end, whereas it affects fossil fuel consumption in the short term. Only France, Italy, and the United Kingdom observe the impact of free trade on the use of renewable energy. This condition aligns with the energy policies implemented by the governments. Conversely, the impact of free trade on fossil fuel use in any nation is also significant and aligns with anticipated outcomes. This is also relevant to the advancement and refinement of the related technologies. The study offers advice to researchers and industry professionals seeking cost benefits in free trade, as well as for policymakers about the energy policies they want to enact.Key Words: International Economic, Free Trade, Renewable Energy Consumption, Fossil Fuel ConsumptionJEL Classification: F14, F18, P18

Laëtitia Guilhot

Looking back at four decades of China’s energy policy (1981-2020), three momentous shifts can be said to have taken place. From the Sixth Five-Year Plan (1981) to the Ninth Five-Year Plan (2000), the focus was exclusively on improving energy efficiency. Subsequently, from the Tenth Five-Year Plan (2001) to the Eleventh Five-Year Plan (2010), energy security also became a major objective, as awareness of the gradual depletion of fossil fuels grew. From 2011 onwards (Twelfth and Thirteenth Five-Year Plans), China's energy policy has also aimed to ward off climate change. This policy evolution suggests that the Chinese economy has initiated a low-carbon energy transition since 2011. Nevertheless, this transition cannot be considered sustainable because the local government are reluctant to apply stringent institutional limitations to wealth-creating processes within their jurisdiction and because China’s energy consumption is not decoupled from its economic growth and total environmental costs of renewable energy are not taken account. In seeking to usher in a sustainable energy system, the Chinese government needs to overcome three challenges: 1/ an institutional challenge; 2/ an economic challenge and 3/ an environmental challenge

Debdutta Mandal, Jyoti Bhattacharjee, Debarati Mitra et al.

The energy efficiency and conversion technologies play a vital role for achieving net zero emission as well as environmental sustainability. As our nation and industries attempt to mollify the influence of weather trade, the optimizing the power usage and transition to purifier assets has predominant. Energy performance measures a spectrum of strategies to reduce the electricity consumption as well as easy behavioural modification to advance technology solution. The mission not only for lower green house fuel emission and also the development of energy conversion technologies which are very crucial for decarbonisation energy system via allowing the hybridisation of renewable energy sources such as solar, wind, biomass and hydroelectric power. For innovation in hybrid renewable energy storage system like smart grids, electrification ,batteries and other storage technologies allow to the storage of excess energy generation during peak production time. The transition to net zero is a holistic method that considers not only cost effective technological improvement but also policy frame work as well as financial incentives and public engagement strategies to accelerate the adoption at scale. So we conclude that the race for achieving net zero is not an assignment, however it has a possibility to transform our energy landscape into the cleaner one for our future generation. Keywords: Netzero emissions; Energy efficiency; Energy security; Hybrid Renewable Energy Generation system.

Shilpa Mesineni, Ala E. Omrani, Nawaf Nazir

The shipping industry, particularly containerized cargo transport, plays a vital role in global trade, but it is also a significant contributor to greenhouse gas (GHG) emissions. With increasing pressure to reduce carbon footprints and achieve sustainability targets, the U.S. maritime sector is seeking innovative solutions to lower its environmental impact. Optimizing fuel usage, improving operational efficiencies, and adopting alternative fuels are essential strategies to decarbonize maritime transport. However, these complex challenges require sophisticated tools for simulating and analyzing different fuel and routing strategies. The US Department of Energy, Advanced Research Projects Agency - Energy funded a project to focuses on developing an advanced decision-support tool to optimize fuel deployment and intermodal freight routing for domestic container shipping in the U.S., aiming to reduce GHG emissions and improve operational efficiency. This project aims to develop an advanced decision-support tool that leverages modeling and simulation to optimize fuel deployment and intermodal freight route planning for domestic container shipping across U.S. maritime corridors. The tool's central goal is to simulate and assess vessel operations, considering both environmental impact and operational efficiency, with a focus on reducing greenhouse gas (GHG) emissions through intelligent fuel selection and optimized routing strategies. The project will integrate traditional oil-based vessel technologies with alternative/Low emission fuels, specifically methanol and ammonia/hydrogen, to evaluate these technologies' potential for decarbonizing the maritime sector. The modeling scope encompasses several critical aspects of container shipping operations: it will simulate container and containership movements across key U.S. maritime routes, including both open sea and inland waterways (e.g., from Los Angeles to Seattle). The simulation will track vessel movements in real-time, incorporating key operational events such as departures, arrivals, refueling, and port dwell times. The model will identify and analyze a range of domestic vessel types, initially using baseline information on oil-powered ships and comparing them with vessels using alternative fuels. This comparison will allow for an evaluation of energy consumption, operational efficiency, and emissions reductions achievable with different fuel mixes. A key feature of the model will be its ability to estimate energy consumption and emissions under various operating conditions. The model will take into account a broad range of factors, such as cargo weight, vessel type, sea currents, wind conditions, and route specifics to generate highly accurate simulations of energy usage and emissions per voyage. By using advanced modeling techniques, the tool will create a detailed reduced-order model, informed by real-world operational data, which can be generalized and applied across various domestic corridors and routes. This will allow for scalable insights and flexible application, making the tool useful for a variety of real-world shipping scenarios. The role of advanced modeling and simulation is crucial in enabling the detailed analysis required to understand complex maritime operations. The model will simulate dynamic operational events (such as docking, refueling, or delays) and intermodal transport interactions, optimizing freight routing across different transport modes (water, rail, and truck) to minimize overall GHG emissions. This approach allows for a comprehensive, whole-system analysis that considers both direct emissions from maritime transport and indirect emissions from associated modes of transport. Moreover, the model will interface with external systems simulators, to incorporate data on port-to-port container origin-destination (OD) flows, port storage, and dwell times. These external inputs will be integrated into the model to ensure that port operations, cargo handling, and intermodal logistics are optimized for efficiency and emissions reduction. This integration of multiple data sources and transport modes will enable the simulation to reflect the complexities of the real-world intermodal network, offering insights on how best to allocate freight across water, rail, and road to achieve the lowest possible emissions. Through the use of advanced modeling and simulation, the tool will generate actionable insights to guide policy makers, shipping operators, and environmental regulators in their efforts to decarbonize U.S. domestic shipping. The ability to simulate and compare different fuel types, vessel configurations, and routing strategies will help stakeholders make data-driven decisions that balance economic feasibility with environmental sustainability. Ultimately, the tool will help identify pathways to reduce the carbon footprint of domestic container shipping, offering a roadmap for adopting cleaner, more efficient maritime transport practices while improving operational performance. By enabling the detailed modeling and simulation of complex maritime systems, this project will contribute significantly to the development of sustainable maritime transport policies and technologies, providing a critical resource for advancing the transition to a low-carbon economy in the U.S. shipping sector.

Gloria Siwe Usiagu, Oladipupo Olugbenga Adekoya, Chinelo Emilia Okoli et al.

The global energy landscape stands at the precipice of transformation, with a growing consensus on the imperative to shift towards sustainable and renewable sources. Amidst this transition, Liquefied Natural Gas (LNG) emerges as a pivotal bridge fuel, strategically positioned to facilitate the journey towards renewable energy. This review encapsulates the essence of the comprehensive review, delving into the global perspective on LNG's role as a bridge fuel in the renewable energy transition. The narrative unfolds with a contextualization of the pressing need for a renewable energy transition, acknowledging the escalating environmental concerns and the imperative to reduce carbon footprints. It then seamlessly transitions to the concept of LNG as a bridge fuel, elucidating its capacity to serve as a cleaner alternative to traditional fossil fuels and its role in mitigating the challenges associated with intermittent renewable energy sources. Examining LNG's significance on a global scale, the review navigates through key aspects. It scrutinizes the environmental considerations and carbon reduction goals that position LNG as a pragmatic choice in the decarbonization journey. The analysis extends to policy factors influencing LNG demand, unraveling the intricate tapestry of governmental initiatives supporting its adoption and the dynamic interplay with renewable energy policies. Economic drivers take center stage in the exploration of LNG's role as a bridge fuel. The review investigates the cost competitiveness, market dynamics, and the industrial demand propelling its consumption. The comprehensive examination encompasses the expansion of global LNG production capacity, featuring key exporting nations and the technological innovations transforming LNG production processes. Regional dynamics further enrich the narrative, providing insights into LNG's impact on major economies across Asia-Pacific, Europe, and North America. The review culminates in a nuanced exploration of the geopolitical implications surrounding LNG trade, emphasizing its role in geopolitical events, trade agreements, and potential risks for stakeholders. In essence, this review serves as a guide to stakeholders, policymakers, and industry players navigating the complex interplay between LNG and renewable energy in the global quest for a sustainable energy future.

Farah Durani, Ahsan Anwar, Arshian Sharif et al.

Fortune Ganda

By employing the GMM panel VAR framework, we examine the interplay among natural resource rents, technological innovation, financial development, and energy consumption in the BRICS from 1990 to 2020 on an annual basis. The findings of the study demonstrate a significant negative association between natural resources and technical innovation, as well as a negative relationship with financial development. While the notion of the natural resource curse is deemed invalid, the present study asserts that natural resources do indeed cause financial development. There is an insignificantly positive relationship between natural resources and energy use. There exists a significant negative association between financial development and technical innovation, while a positive association is shown between financial development and energy use. Primary energy consumption is negative (positive) and statistically significantly associated with natural resources (financial development), although that link is simply negative in the case of technological innovation. Technological innovation is positive and significantly related to variables (natural resources and energy consumption), while the link is insignificantly positive to financial development. The results of the causality test reveal a bidirectional relationship between energy consumption and technological innovation, with all variables showing a significant influence on each parameter. There exists a unidirectional causal relationship wherein natural resources influence financial development, natural resources influence technological innovation, and financial development influences technological innovation. Moreover, there is a unidirectional correlation that may be observed from energy use towards natural resources, financial progress, and technological innovation. The findings from the impulse response function indicate that there is a substantial increase in the proportion of each variable that can be explained by other parameters as we transition from the short-term to the long-term. The implications of the study findings are also presented.

Guozhi Li, Yidan Yuan, Xunuo Chen et al.

Abstract To gain a deeper understanding of the carbon emission mechanism from transportation facilities, all system elements affecting carbon emissions from regional transportation facilities are identified and analyzed according to panel data from 30 regions in China. A spatial econometric model for carbon emissions from transportation facilities is constructed using the Spatial Dolbin model from 2004 to 2022 as the research period. From the results, the carbon dioxide emissions from transportation facilities added from 318 million tons in 2004 to 752 million tons in 2022, with an average annual growth rate of 4.9%. The global spatial auto-correlation coefficient was significant at the 5%, with an obvious spatial correlation between carbon dioxide emissions within a geographical range. In addition, through stability testing, the model showed high stability in both spatial lag testing and spatial error testing, demonstrating strong ability to interpret data. The research shows that the carbon emission is affected by independent variables, including population, economy, technology, and transportation, and exhibit significant spatial distribution characteristics in different regions and years, providing a basis for policy formulation and carbon emission management.

yinuo wang, Muhammad Umair, Assilova Aizhan et al.

The persistent disparity between urban and rural incomes in China poses a critical challenge to alleviating energy poverty in rural areas. This study investigates how the income gap between urban and rural regions exacerbates rural energy poverty, focusing on the period from 2005 to 2023, utilizing data from 30 provinces. By employing a two-way fixed-effects model and asymmetry analysis, the research reveals that an increase in the urban-rural income disparity significantly intensifies rural energy poverty. Notably, at higher income quantiles, the gap's effect on energy poverty is more pronounced, while at lower quantiles, its impact is less severe. Financial development, rather than alleviating the situation, is positively associated with rural energy poverty, highlighting an unintended consequence of unequal access to financial services. The results further show that rural regions with limited financial inclusion experience a deepening of energy poverty, with financial service accessibility benefiting wealthier demographics more than the impoverished rural population. These findings imply that targeted policies promoting equitable financial access, narrowing income disparities, and integrating energy poverty reduction strategies are essential to achieving China's Rural Revitalization Strategy.

Ning Qi, Kaidi Huang, Zhiyuan Fan et al.

This paper studies the long-term energy management of a microgrid coordinating hybrid hydrogen-battery energy storage. We develop an approximate semi-empirical hydrogen storage model to accurately capture the power-dependent efficiency of hydrogen storage. We introduce a prediction-free two-stage coordinated optimization framework, which generates the annual state-of-charge (SoC) reference for hydrogen storage offline. During online operation, it updates the SoC reference online using kernel regression and makes operation decisions based on the proposed adaptive virtual-queue-based online convex optimization (OCO) algorithm. We innovatively incorporate penalty terms for long-term pattern tracking and expert-tracking for step size updates. We provide theoretical proof to show that the proposed OCO algorithm achieves a sublinear bound of dynamic regret without using prediction information. Numerical studies based on the Elia and North China datasets show that the proposed framework significantly outperforms the existing online optimization approaches by reducing the operational costs and loss of load by around 30% and 80%, respectively. These benefits can be further enhanced with optimized settings for the penalty coefficient and step size of OCO, as well as more historical references.

Chun Fu, Hussain Kazmi, Matias Quintana et al.

Advances in machine learning and increased computational power have driven progress in energy-related research. However, limited access to private energy data from buildings hinders traditional regression models relying on historical data. While generative models offer a solution, previous studies have primarily focused on short-term generation periods (e.g., daily profiles) and a limited number of meters. Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata. Using a dataset comprising 1,828 power meters from various buildings and countries, this model is compared with traditional methods like Conditional Generative Adversarial Networks (CGAN) and Conditional Variational Auto-Encoders (CVAE). It explicitly handles long-term annual consumption profiles, harnessing metadata such as location, weather, building, and meter type to produce coherent synthetic data that closely resembles real-world energy consumption patterns. The results demonstrate the proposed diffusion model's superior performance, with a 36% reduction in Frechet Inception Distance (FID) score and a 13% decrease in Kullback-Leibler divergence (KL divergence) compared to the following best method. The proposed method successfully generates high-quality energy data through metadata, and its code will be open-sourced, establishing a foundation for a broader array of energy data generation models in the future.

Shakeel Ahmad, Haifeng Jia, Anam Ashraf et al.

Water is one of the essential natural resources for human beings. However, rising worldwide water demand and a significant decline in availability due to a lack of dynamic management and over-extraction have resulted in a complex scenario in terms of water availability. The current paper examines water resources and their management, methodologies, aims, and scope. Through the perspective of water resources and their management in Pakistan, 93 research publications were critically analyzed using a systematic review technique. The technique includes a systematic review of existing literature on water resource management, with particular emphasis on policy, governance, and environmental challenges. The study results demonstrate gaps and weaknesses in existing laws and regulations, alongside the threats to water resource management due to population expansion, urban development, climate change, and water contamination. To properly address these problems, the current study proposed a comprehensive framework for water resource management. This framework includes a national water policy that argues for sustainability and improves institutional strength. Infrastructure development, climate change adaptation, and examining social and environmental variables are all emphasized as important problems. Furthermore, it is essential to emphasize the importance of education and raising knowledge about water resource management among the general public and relevant stakeholders. By following these recommendations and the proposed OECD key principles on water governance, Pakistan may make significant progress towards achieving sustainable water management, aligning with its development objectives, and ensuring clean and safe water availability for future generations.

Federico Capra, Diego Abalos, Stefania Codruta Maris et al.

Abstract Digestate, a by‐product of biogas production, is widely recognized as a promising renewable nitrogen (N) source with high potential to replace synthetic fertilizers. Yet, inefficient digestate use can lead to pollutant N losses as ammonia (NH3) volatilization, nitrous oxide (N2O) emissions and nitrate (NO3−) leaching. Cover crops (CCs) may reduce some of these losses and recycle the N back into the soil after incorporation, but the effect on the N balance depends on the CC species. In a one‐year field study, we tested two application methods (i.e., surface broadcasting, BDC; and shallow injection, INJ) of the liquid fraction of separated co‐digested cattle slurry (digestate liquid fraction [DLF]), combined with different winter cover crop (CC) options (i.e., rye, white mustard or bare fallow), as starter fertilizer for maize. Later, side‐dressing with urea was required to fulfil maize N‐requirements. We tested treatment effects on yield, N‐uptake, N‐use efficiency parameters, and N‐losses in the form of N2O emissions and NO3− leaching. CC development and biomass production were strongly affected by their contrasting frost tolerance, with spring‐regrowth for rye, while mustard was winter killed. After the CCs, injection of DLF increased N2O emissions significantly compared with BDC (emission factor of 2.69% vs. 1.66%). Nitrous oxide emissions accounted for a small part (11%–13%) of the overall yield‐scaled N losses (0.46–0.97 kg N Mg grain−1). The adoption of CCs reduced fall NO3− leaching, being 51% and 64% lower for mustard and rye than under bare soil. In addition, rye reduced NO3− leaching during spring and summer after termination by promoting N immobilization, thus leading to −57% lower annual leaching losses compared with mustard. DLF application method modified N‐loss pathways, but not the cumulative yield‐scaled N losses. Overall, these insights contribute to inform an evidence‐based design of cropping systems in which nutrients are recycled more efficiently.

Walter Leal Filho, Diogo Guedes Vidal, Maria Alzira Pimenta Dinis et al.