Green doi: 10.3390/green1020008

Authors: Bhanu Sharma Amit Tiwari Rashanjot Kaur Kathleen Marshall Park Eugene Pinsky

Large-scale artificial intelligence systems increasingly disclose energy and carbon metrics, but their freshwater costs remain less consistently measured. This paper introduces the Water Cost of Intelligence (WCI), a per-query metric combining direct water consumed for on-site data-center cooling with indirect water consumed during electricity generation, weighted by local scarcity using Aqueduct 4.0 Baseline Water Stress (BWS) scores. We first reconstruct Google’s disclosed Gemini direct-water figure from Google’s own reported parameters, an internal consistency check on the implementation rather than an independent validation. Expanding the accounting boundary to include electricity-generation water raises the estimate for a median large language model (LLM) prompt by 179% under a uniform national water-intensity value. Parameterizing that intensity by the regional generation mix instead changes the estimate substantially and reverses the regional ordering, depending on whether hydroelectric reservoir evaporation is allocated to generation: the same grid is the least water-intensive of those studied under one convention and the most water-intensive under the other. A region cannot be characterized as water-efficient in terms of electricity without first establishing that convention. Direct-only reporting can be internally accurate yet boundary-incomplete, and regional scarcity can change the interpretation of identical physical water use by an order of magnitude.

Green doi: 10.3390/green1020007

Authors: Omotayo Emmanuel Ojewumi Gang Chen Modupe Elizabeth Ojewumi

Biochar, a carbon-rich product resulting from the thermochemical transformation of organic biomass under limited oxygen condition, is currently drawing much worldwide attention due to its multiple applications in carbon sequestration, soil improvement, environmental remediation, and biomass waste management. Initially, the focus of research was primarily on the technical possibilities of biochar production, its economic aspects, and its contribution to climate change mitigation through carbon sequestration and the promotion of sustainable agriculture. Nevertheless, recent research indicates the high complexity and dynamics of biochar interactions with the environment, driven by a combination of factors like feedstock type, process conditions, biochar properties, and other factors. While biochar exhibits multiple beneficial effects, including improving soil structure, enhancing nutrient retention, promoting microbial activities, and remediating contaminants, several environmental risks associated with biochar application have also been identified, namely the formation of polycyclic aromatic hydrocarbons (PAHs), heavy metal contamination, creation of persistent free radicals, changes in soil chemistry, and modification of soil microbial community structure. Such risks are greatly related to production process parameters, treatment methods, and biochar application practices. Moreover, differences in feedstock choice, pyrolysis temperature, reactor design, biochar application rate, and analytical methods used make comparative analysis of results difficult.

Green doi: 10.3390/green1020006

Authors: Manuel Escourido-Calvo

The fashion industry faces growing pressure to demonstrate credible sustainability performance across environmental, social and economic dimensions, yet integrated approaches remain underdeveloped, particularly for small and medium-sized enterprises and social enterprises operating in circular textile systems. This article examines the case of INS3RTEGA, a Spanish textile social enterprise that combines post-consumer textile waste management with protected employment for people with disabilities. Using an exploratory single-case design informed by Triple Bottom Line thinking, the study develops a pragmatic assessment strategy that combines a partial Social Return on Investment (SROI)-style calculation, a simplified carbon-based estimation informed by life cycle thinking, and a set of non-monetised social inclusion and employment indicators. The results reveal a distinctive configuration of integrated sustainability performance: substantial avoided climate-related burden and high textile recovery rates, strong structural inclusion outcomes through disability-inclusive employment, and a modest partial monetised return under conservative and incomplete valuation assumptions. The case illustrates how socially inclusive circular fashion models may generate meaningful sustainability value that remains only partially visible through conventional financial or fully monetised metrics. The article discusses methodological implications for hybrid impact assessment in resource-constrained organisations and policy implications for the emerging European framework on sustainable and circular textiles.

Green doi: 10.3390/green1010005

Authors: Eduardo Vidoretti Argenton Everton Gomede Leonardo de Souza Mendes

Context: Accurate citrus crop mapping is essential for agricultural monitoring, production planning, and supply-chain management, particularly in Brazil, one of the world’s leading orange producers and the leading orange-juice exporter. Satellite image time series from Sentinel-2 provide rich spectral and temporal information for crop identification. However, citrus mapping remains challenging due to fragmented agricultural landscapes, cloud contamination, class imbalance, and spectral overlap with other vegetation classes. Problem: Conventional machine learning models often depend on handcrafted vegetation indices, while attention-based deep learning models may require larger datasets and can become unstable under geographically constrained conditions. Therefore, there is a need for a compact and robust deep learning architecture capable of extracting citrus phenological signatures directly from multispectral time-series data. Methods: This study evaluates a Spatio-Temporal Pixel-Set Encoder Convolutional Neural Network (PSE-CNN) for citrus crop classification in the immediate geographic regions of São João da Boa Vista and Mogi Guaçu, São Paulo, Brazil. MapBiomas Collection 10.1 data from 2019 to 2024 were used to derive reference polygons, and Sentinel-2 imagery was processed into cloud-masked, 15-day temporal composites using ten spectral bands. The proposed PSE-CNN was benchmarked against PSE-TAE, PSE-Transformer, Random Forest, and XGBoost using spatially grouped data partitioning and temporal test years. Results: The proposed PSE-CNN achieved the highest Unified F1-Score of 0.704 and the lowest coefficient of variation of 3.03%, indicating stronger inter-annual stability across test years and random seeds among the evaluated models. It also outperformed classical models that relied on handcrafted vegetation indices and demonstrated greater overall stability than attention-based deep learning alternatives. Conclusions: The results indicate that combining pixel-set encoding with temporal convolution provides a resource-aware and stable framework for retrospective citrus crop mapping from Sentinel-2 satellite image time series. These findings suggest that PSE-CNN can support scalable agricultural monitoring, contributing to sustainable crop inventory systems in regions where labeled data and computational infrastructure are limited.

Green doi: 10.3390/green1010004

Authors: Martino Oliboni Giampaolo Oliviero Anna Mazzi

This study analyses the availability of ISO standards to support companies in achieving climate neutrality. A systematic review, conducted in accordance with the PRISMA methodology, examined ISO standards published between 2015 and April 2025 containing at least one of the four keywords in the title or abstract: carbon capture, carbon footprint, carbon storage and greenhouse gas. The analysis selected 43 documents, developed mainly by the ISO Technical Committees 207 and 265, highlighting growing interest in the topic in recent years. The standards identified are primarily related to Target 13.2 of the 2030 Agenda, concerning the integration of climate change measures into national strategies and policies. Support for the other targets of Sustainable Development Goal 13 is limited or absent. Furthermore, the standards mainly support the monitoring and evaluation strategy defined by the IPCC: the analysis reveals an abundance of standards related to measurement and reporting, while international guidance dedicated to mitigation measures remains limited.

Green doi: 10.3390/green1010003

Authors: Bonface O. Manono

Despite producing much of the world’s food, small-scale farms face severe resource shortages, climate risks, and infrastructure gaps. While digital advances ranging from IoT sensing to AI-driven analytics offer pathways to improve productivity, adoption remains uneven. This integrative review synthesizes evidence on smart-farming technologies specifically for smallholders, identifying primary barriers, enabling conditions, and design principles for successful deployment. Unlike broader smart-farming reviews, the article explicitly evaluates small-farm suitability, evidence quality, and implementation architecture rather than technological capability alone. The synthesis shows that adoption is consistently constrained by clustered barriers, notably high capital and maintenance costs, limited technical capacity, and unreliable electricity or internet access. It also finds that evidence is strongest for modular, offline-capable monitoring and alerting tools, while evidence for durable gains from highly integrated full-platform systems remains thinner and more pilot-dependent. To advance equitable innovation, the review proposes a fit-for-context deployment logic centered on co-design, local repair and advisory capacity, and financing and policy support aligned with small-farm realities. Overall, smart farming can strengthen productivity, resilience, and environmental performance on small farms, but only when technologies are embedded in inclusive service models and implementation systems.

Green doi: 10.3390/green1010002

Authors: Soroush Khakpour Francesco Nocera Alberta Latteri Claudio Tosto Lorena Saitta

In this study, the effect of temperature on thermal-assisted glass delamination was investigated using two treatment conditions differing in the set temperature of the process (100 °C vs. 140 °C). Thermogravimetric Analysis (TGA) confirmed that ethylene-vinyl acetate (EVA) remains thermally stable up to about 280 °C, with degradation onset near 300 °C, ensuring that both treatments operate below decomposition. Differential Scanning Calorimetry (DSC) analysis identified an endothermic transition attributable to the melting of crystalline regions in EVA within the thermal range of 35–65 °C, indicating enhanced polymer chain mobility at elevated temperatures. This endothermic transition corresponds to the melting of polyethylene crystallites within the EVA copolymer and should not be interpreted as a glass transition, since the Tg of EVA is typically located at approximately −30 to −35 °C. Fourier Transform Infrared (FTIR) analysis verified preservation of ester functional groups, confirming the absence of chemical degradation. The morphological analysis performed via Scanning Electron Microscopy (SEM) revealed a clear temperature-dependent morphology of EVA after thermal-assisted delamination. At 140 °C, enhanced polymer softening and viscous flow led to smoother surfaces and more uniform interfacial separation, whereas at 100 °C, limited mobility resulted in heterogeneous, fragmented residues and predominantly cohesive failure. These results highlight that optimizing temperature is key to balancing effective delamination with residue minimization, supporting more sustainable PV recycling.

Green doi: 10.3390/green1010001

Authors: Janusz A. Kozinski

It is with great pleasure that I introduce Green (ISSN 3042-9242) [...]