Agricultural activities in Spain are increasingly vulnerable to water scarcity and soil degradation, exacerbated by farming intensification and climate change. Alongside, the need for renewable energy generation expansion and negative emission technologies risk increasing land use conflicts. Sustainable strategies to reconcile measures to improve agricultural resilience with the renewable energy transition should be explored. This study evaluates the life-cycle environmental performance of integrating biochar production from residues from the local olive oil value chain with agrivoltaic systems in Andalusian olive groves. Five scenarios consider various constraints for regional biomass availability and for agrivoltaic deployment. A spatial analysis identifies the olive groves that are most suitable for agrivoltaic installation and prioritizes high-erosion groves for biochar application. Integrating these technologies delivers climate change mitigation, in some cases achieving net negative emissions and reducing global warming potential by up to 173% relative to conventional farming. Biochar-induced soil carbon storage transforms the agroecosystem from a net source of emissions (2.14 t CO₂-eq ha^-1) to a carbon sink (-7.90 t CO₂-eq ha^-1), while reducing soil erosion and improving water retention. Agrivoltaic systems further decrease irrigation demand and provide up to 53 TWh of renewable energy. Trade-offs occur with terrestrial ecotoxicity and freshwater eutrophication, but using more advanced and efficient panels can mitigate these burdens. Overall, the combined implementation of biochar and agrivoltaics can co-deliver across multiple environmental challenges, from local valorization of residue streams to enhanced agroecosystem resilience, climate change mitigation, adaptation, and renewable energy generation.

Dynamic Life Cycle Assessment (LCA) ranges in temporal complexity, with fully dynamic approach requiring full life-cycle chronologies for both foreground and background systems. For instance, LCAs based on Environmental Product Declaration (EPDs), required by the French RE2020 regulation, are partially dynamic, since only the foreground timeline is available. The goal of this research is to provide a user-friendly and rigorous method to conduct partially dynamic LCAs for climate change impact based on EPDs. Delay factors are built as coefficient depending on the time distribution of emissions, to define the dynamic characterization factor as a function of the static one. Compatibility constraints between static and dynamic imposes an observation time T equal to the sum of the Life Cycle Duration (LCD) and the Time Horizon of the Impact (THI). Literal mathematical expressions of delay factors and their behaviors are provided for Global Warming Potential (GWP) and Global Temperature Potential (GTP). The application scope of these delay factors covers all greenhouse gases (GHG) and large temporal range of LCD and THI. A case study based on three background products and randomly generated foreground emissions shows that the RE2020 regulation dynamic factors overestimate benefits obtained by delaying emissions compared to present method. This happens because the method ignores compatibility constraints between static and dynamic approaches and because it does not differentiate delay factors for distinct GHGs. However, delaying emissions reduces GWP but still raises GTP, questioning the use of GWP as a dynamic indicator, as it may falsely suggest a declining impact when it is actually increasing.

The carbon footprint (CF) is widely used as a proxy indicator for the overall environmental impacts of entities such as countries, organizations, and products. However, since CF is by definition limited to greenhouse gas emissions, an important question arises: to what extent is CF a reliable predictor of other environmental impacts, including ozone layer depletion, acidification, eutrophication, and toxicity? This question has been examined in previous studies, yielding mixed results. In this paper, we argue that the analytical methods employed in earlier work may lead to misleading conclusions. We therefore propose the use of methods from directional statistics as an alternative approach. We analyze the correlation between life-cycle environmental impacts across approximately 4,000 products using a range of metrics, including linear, logarithmic, and rank-based regression, as well as directional statistics-based measures. Our findings are consistently negative: CF does not serve as a reliable predictor for any of the other environmental impact categories considered. Moreover, we show that traditional metrics used to assess such relationships can be misleading. Overall, relying on CF as a measure of total environmental burden is likely to introduce substantial uncertainty, even in cases where conventional correlation indicators suggest otherwise.

Agricultural soils are vital for reducing atmospheric CO₂; however, the effectiveness of farmland carbon sequestration, including soil organic carbon (SOC) and inorganic carbon (SIC), typically requires a lengthy period and varies with different farming practices. In a long-term study on the North China Plain, SOC and SIC changes due to farming practices involving N fertilization, organic materials, irrigation, and no-tillage were tracked. Four experimental treatments, including no N fertilizer input (CK), local farmer operation (FRM), optimized farming (OPT), and no-tillage (NoT), were selected for the study. From 2008 to 2024, the fertilized treatments sequestered SOC at rates of 0.35-0.63 Mg C ha^-1 yr^-1 in the 0-20 cm layer, which quadrupled in the 0-100 cm layer. Long-term high irrigation with N fertilization accelerated the leaching of SIC into the subsoil, and SIC losses ranged from 0.46 to 0.71 Mg C ha^-1 yr^-1 at 0-20 cm and from 1.88 to 2.42 Mg C ha^-1 yr^-1 at 0-100 cm. Organic materials and N fertilization interactively help sequester SOC, but excessive organic material input results lower conversion efficiency. Crucially, the substantial depletion of SIC across the whole profile largely counteracted the observed SOC gains, leading to a diminished or even negative net carbon balance. Ultimately, this study reveals that failing to account for whole-profile SOC-SIC co-dynamics leads to an overestimation of carbon sequestration in intensive agricultural systems, highlighting the necessity of integrated accounting for accurate climate mitigation assessments.

The cotton and textile sector are among the most energy- and emission-intensive global production systems, but studies on the carbon footprint of cotton and textile products remain fragmented. Substantial inconsistencies exist in system boundaries, methodological choices, and data sources, which limit study results comparability and policy setting. Here, we systematically clarify lifecycle system boundaries spanning cotton cultivation, textile manufacturing, product use, and end-of-life management, with particular attention to the treatment of foreground and background emissions, transportation processes, and soil carbon dynamics. Across stages, we identify consistent emission hotspots driven by fertilizer-induced N₂O emissions, energy-intensive dyeing and finishing processes, consumer laundering behavior, and recycling pathways. Looking ahead, we highlight key priorities for advancing the field, including the development of harmonized global cotton-textile life cycle assessment databases, the adoption of digital and dynamic carbon accounting tools, the establishment of unified product carbon labeling systems, and the integration of circular economy principles toward low- and zero-carbon textile value chains.

Bioenergy from forests (BEF) is widely promoted as a significant contributor to the global renewable energy transition and a primary pathway of achieving climate goals. However, the climate effects of BEF remain deeply contested due to the complexity of the BEF system that requires a multitude of methodological choices and assumptions to contextualize. The resulting divergent conclusions across studies generated scientific disagreement and policy concerns. This review provides a holistic synthesis of the environmental, economic, and social contexts shaping the climate effects of BEF. We first conducted a bibliometric analysis of BEF-related articles to map research trends and dominant paradigms, resulting in four major research clusters spanning forestry systems, bioenergy production, bioeconomy interactions, and emerging climate solutions. Building on this overview, we identifies six key areas of concern and disagreement that critically influence BEF climate assessments: system boundaries, spatial and temporal scales, reference systems, feedstock sourcing, effects of market changes, and social impacts. We provide methodological recommendations for the six aspects. For each area, we articulate contrasting perspectives, underlying assumptions, and empirical evidence, highlighting how methodological choices can lead to fundamentally different conclusions regarding BEF’s climate performance. We provide methodological recommendations to improve comparability, transparency, and policy relevance of BEF assessments. By clarifying sources of disagreement and framing BEF within a broader sustainability context, this work aims to reduce confusion and support more informed, evidence-based decision-making on the future role of BEF in climate mitigation strategies.