Search Results (13)

Sustainable intensification requires metrics that are able to capture both economic performance and the often-hidden environmental inputs that support agriculture. Emergy analysis (EmA) meets this need by converting all inputs—free environmental flows and purchased goods/services—into a common unit (solar emjoules, sej). We conducted a PRISMA-documented bibliometric review of EmA in agroecosystems (Web of Science + Scopus, 2000–2022) using Bibliometrix and synthesized farm-scale indicators (ELR, EYR, ESI, %R). Our results show output has grown but is concentrated in a few countries (China, Italy and Brazil) and journals, with farm-level assessments dominating over regional and national assessments. Across cases, mixed crop–livestock systems tend to show lower environmental loading (ELR) and higher sustainability (ESI) than crop-only or livestock-only systems. %R is generally modest, indicating continued reliance on non-renewables, with fertilizers (crops) and purchased feed (livestock) identified as recurrent drivers. Thematic mapping reveals well-developed niche clusters but no single motor theme, consistent with the presence of incongruous baselines, transformities and boundaries that limit comparability. We recommend adoption of the 12.1 × 10²⁴ sej yr⁻¹ baseline, transparent transformity reporting and multi-scale designs that link farm diagnostics to basin and national trajectories. Co-reporting with complementary sustainability assessment methods (such as LCA and carbon footprint), along with appropriate UEV resources, would increase its reputation among policymakers while preserving EmA’s systems perspective, converting dispersed case evidence into cumulative knowledge for circular, resilient agroecosystems. Full article

Fast fashion is a rapidly expanding sector characterized by high production volumes, low costs, and short product lifecycles. While recent efforts have focused on improving sustainability within supply chains, consumer behavior remains a critical yet underexplored driver of environmental impacts. This study presents a web-based calculator tool designed to estimate both the carbon and plastic footprints associated with individual fast fashion consumption, with a particular focus on shopping behaviors, garment disposal, and laundry habits. Adopting a geographical perspective, the analysis explicitly considers the spatial dynamics of consumption and logistics within the urban context of Milan (Italy), a dense metropolitan area representative of high fashion activity and mobility. By incorporating user-reported travel patterns, logistics routes, and localized emission factors, the tool links consumer habits to place-specific environmental impacts. By involving over 360 users, the tool not only quantifies emissions and plastic waste (including microfibers) but also serves an educational function, raising awareness about the hidden consequences of fashion-related choices. Results reveal high variability in environmental impacts depending on user profiles and behaviors, with online shopping, frequent use of private vehicles, and improper garment disposal contributing significantly to emissions and plastic pollution. Our findings highlight the importance of integrating consumer-focused educational tools into broader sustainability strategies. The tool’s dual function as both calculator and awareness-raising platform suggests its potential value for educational and policy initiatives aimed at promoting more sustainable fashion consumption patterns. Full article

This study presents the development of a reliable predictive model for evaluating key physical and mechanical properties of cement-based composites modified with granite powder, a waste byproduct from granite rock cutting. The research addresses the need for more sustainable materials in the concrete industry by exploring the potential of granite powder as a supplementary cementitious material (SCM) to partially replace cement and reduce CO₂ emissions. The experimental program included standardized testing of samples containing up to 30% granite powder, focusing on compressive strength at 7, 28, and 90 days, bonding strength at 28 days, and packing density of the fresh mixture. A multilayer perceptron (MLP) artificial neural network was employed to predict these properties using four input variables: granite powder content, cement content, sand content, and water content. The network architecture, consisting of two hidden layers with 10 and 15 neurons, respectively, was selected as the most suitable for this purpose. The model achieved high predictive performance, with coefficients of determination (R) exceeding 0.9 and mean absolute percentage errors (MAPE) below 6% for all output variables, demonstrating its robustness and accuracy. The findings confirm that granite powder not only contributes positively to concrete performance over time, but also supports environmental sustainability goals by reducing the carbon footprint associated with cement production. However, the model’s applicability is currently limited to mixtures using granite powder at up to 30% cement replacement. This research highlights the effectiveness of machine learning, specifically neural networks, for solving multi-output problems in concrete technology. The successful implementation of the MLP network in this context may encourage broader adoption of data-driven approaches in the design and optimization of sustainable cementitious composites. Full article

The escalating threat of climate change has intensified the global urgency to accurately predict carbon dioxide (CO₂) emissions for sustainable development, particularly in developing economies experiencing rapid industrialization and globalization. Traditional Extreme Learning Machines (ELMs) offer rapid learning but often yield unstable performance due to random parameter initialization. This study introduces a novel hybrid model, Red-Billed Blue Magpie Optimizer-tuned ELM (RBMO-ELM) which harnesses the intelligent foraging behavior of red-billed blue magpies to optimize input-to-hidden layer weights and biases. The RBMO algorithm is first benchmarked on 15 functions from the CEC2015 test suite to validate its optimization effectiveness. Subsequently, RBMO-ELM is applied to predict Indonesia’s CO₂ emissions using a multidimensional dataset that combines economic, technological, environmental, and globalization-driven indicators. Empirical results show that the RBMO-ELM significantly surpasses several state-of-the-art hybrid models in accuracy (higher R²) and convergence efficiency (lower error). A permutation-based feature importance analysis identifies social globalization, GDP, and ecological footprint as the strongest predictors underscoring the socio-economic influences on emission patterns. These findings offer both theoretical and practical implications that inform data-driven Artificial Intelligence (AI) and Machine Learning (ML) applications in environmental policy and support sustainable governance models. Full article

The fashion industry is one of the world’s largest and third most polluting industries. It produces a carbon footprint of around 10% annually, which is much higher than the footprint produced by flights and shipping. Nowadays, there is an increase in demand for different and new products for people of all ages due to which fast-changing fashion is becoming a trend. But there is a hidden cost in the manufacturing of each material, which is ignored by people and which costs the environment and eventually the health of people. It not only pollutes the air due to the emission of greenhouse gases but also consumes plenty of water along with an increase in plastic and some other waste that pollutes our environment. The solution to the problem is to avoid and move away from this fast fashion trend and subsequently buy a few items of clothing that are good in quality and do not pose a threat to the environment. But this will lower the sales as well as the revenue of the fashion industry, which will eventually affect our economy. The purpose of this study is to construct a novel fractional mathematical programming model that caters to both objectives, i.e., minimizing environmental pollution and maximizing the revenue of the fashion industry with respect to the constraints based on the industry and environment. Full article

Achieving carbon neutrality is widely regarded as a key measure to mitigate climate change. The industrial carbon footprint (ICF) calculation, as a foundation to achieve carbon neutrality, primarily relies on roughly estimating direct carbon emissions based on information disclosed by industries. However, these estimates may not be comprehensive, timely, and accurate. This paper elaborates on the issue of ICF calculation, dividing a factory’s carbon emissions into carbon emissions directly produced by appliances and electricity consumption carbon emissions, to estimate the total carbon emissions of the factory. An appliance identification method is proposed based on a cyclic stacking method improved by Bayesian cross-validation, and an appliance state correction module SHMM (state-corrected hidden Markov model) is added to identify the state of the appliance and then to calculate the corresponding appliance carbon emissions. Electricity consumption carbon emissions come from the factory’s electricity consumption and the marginal carbon emission factor of the connected bus. Regarding the selection of artificial intelligence models and cross-validation technique required in the appliance identification method, this paper compares the effects of 7 cross-validation techniques, including stratified K-fold, K-fold, Monte Carlo, etc., on 14 machine learning algorithms such as AdaBoost, XGBoost, feed-forward network, etc., to determine the technique and algorithms required for the final appliance identification method. Experiment results show that the proposed appliance identification method estimates device carbon emissions with an error of less than 3%, which is significantly superior to other models, demonstrating that the proposed approach can achieve comprehensive and accurate ICF calculation. Full article

Food losses and waste (FLW) reduction and mitigating climate impact in food chains are priorities in achieving sustainable development goals. However, many FLW-reducing interventions induce additional greenhouse gas (GHG) emissions, for example, from energy, fuel, or packaging. The net effect of such interventions (expressed in GHG emissions per unit of food available for consumption) is not obvious, as is illustrated in a number of case studies. We recommend that in the decision to take on FLW-reducing interventions, the trade-offs on sustainability impacts (such as GHG emissions) are taken into consideration. Since FLW induce demand and extra operations in all stages along a supply chain, adequate representation of cumulative GHG emissions along the production and supply chain, including ‘hidden parts’ of the chain, is required, which is challenging in full LCA studies. As a workaround, the case studies in this paper are based on a generic tool, the Agro-Chain greenhouse gas Emission (ACE) calculator that includes metrics and data for common food product categories and supply chain typologies. The calculator represents the structure of a generic (fresh food) supply chain and offers data sets for, amongst others, crop GHG emission factors and FLW in different stages of the production and distribution chain. Through scenario calculations with different chain parameters (describing pre and post-intervention scenarios), the net effects of an intervention on GHG emissions and FLW per unit of food sold to the consumer can be compared with little effort. In the case studies, interventions at the production stage as well as in post-harvest operations, are analyzed. Results show that post-harvest activities (especially FLW) contribute substantially to the carbon footprint of supplied food products. The FLW-reducing interventions are considered to induce additional GHG emissions. In most case studies, FLW-reducing interventions lower total GHG associated with a unit of food supplied to a client or consumer. However, in one case study, the extra emissions due to the intervention were higher than the prevented emission from lowering food losses. Consequently, in the latter case, the intervention is not an effective GHG emission reduction intervention. Full article

Not only can waste rubber enhance the properties of concrete (e.g., its dynamic damping and abrasion resistance capacity), its rational utilisation can also dramatically reduce environmental pollution and carbon footprint globally. This study is the world’s first to develop a novel machine learning-aided design and prediction of environmentally friendly concrete using waste rubber, which can drive sustainable development of infrastructure systems towards net-zero emission, which saves time and cost. In this study, artificial neuron networks (ANN) have been established to determine the design relationship between various concrete mix composites and their multiple mechanical properties simultaneously. Interestingly, it is found that almost all previous studies on the ANNs could only predict one kind of mechanical property. To enable multiple mechanical property predictions, ANN models with various architectural algorithms, hidden neurons and layers are built and tailored for benchmarking in this study. Comprehensively, all three hundred and fifty-three experimental data sets of rubberised concrete available in the open literature have been collected. In this study, the mechanical properties in focus consist of the compressive strength at day 7 (CS7), the compressive strength at day 28 (CS28), the flexural strength (FS), the tensile strength (TS) and the elastic modulus (EM). The optimal ANN architecture has been identified by customising and benchmarking the algorithms (Levenberg–Marquardt (LM), Bayesian Regularisation (BR) and Scaled Conjugate Gradient (SCG)), hidden layers (1–2) and hidden neurons (1–30). The performance of the optimal ANN architecture has been assessed by employing the mean squared error (MSE) and the coefficient of determination ($R^2)$. In addition, the prediction accuracy of the optimal ANN model has ben compared with that of the multiple linear regression (MLR). Full article

The recycled aggregate is an alternative with great potential to replace the conventional concrete alongside with other benefits such as minimising the usage of natural resources in exploitation to produce new conventional concrete. Eventually, this will lead to reducing the construction waste, carbon footprints and energy consumption. This paper aims to study the recycled aggregate concrete compressive strength using Artificial Neural Network (ANN) which has been proven to be a powerful tool for use in predicting the mechanical properties of concrete. Three different ANN models where 1 hidden layer with 50 number of neurons, 2 hidden layers with (50 10) number of neurons and 2 hidden layers (modified activation function) with (60 3) number of neurons are constructed with the aid of Levenberg-Marquardt (LM) algorithm, trained and tested using 1030 datasets collected from related literature. The 8 input parameters such as cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age are used in training the ANN models. The number of hidden layers, number of neurons and type of algorithm affect the prediction accuracy. The predicted recycled aggregates compressive strength shows the compositions of the admixtures such as binders, water–cement ratio and blast furnace–fly ash ratio greatly affect the recycled aggregates mechanical properties. The results show that the compressive strength prediction of the recycled aggregate concrete is predictable with a very high accuracy using the proposed ANN-based model. The proposed ANN-based model can be used further for optimising the proportion of waste material and other ingredients for different targets of concrete compressive strength. Full article

The Kaya identity is a powerful index displaying the influence of individual carbon dioxide (CO₂) sources on CO₂ emissions. The sources are disaggregated into representative factors such as population, gross domestic product (GDP) per capita, energy intensity of the GDP, and carbon footprint of energy. However, the Kaya identity has limitations as it is merely an accounting equation and does not allow for an examination of the hidden causalities among the factors. Analyzing the causal relationships between the individual Kaya identity factors and their respective subcomponents is necessary to identify the real and relevant drivers of CO₂ emissions. In this study we evaluated these causal relationships by conducting a parallel multiple mediation analysis, whereby we used the fossil fuel CO₂ flux based on the Open-Source Data Inventory of Anthropogenic CO2 emissions (ODIAC). We found out that the indirect effects from the decomposed variables on the CO₂ flux are significant. However, the Kaya identity factors show neither strong nor even significant mediating effects. This demonstrates that the influence individual Kaya identity factors have on CO₂ directly emitted to the atmosphere is not primarily due to changes in their input factors, namely the decomposed variables. Full article

Nowadays, to ensure sustainability of smart materials, it is imperative to eliminate or reduce carbon footprint related to nano material production. The concept of design of experiment to provide an optimal synthesis process, with a desired yield, is indispensable. It is the researcher’s goal to get optimum value for experiments that requires multiple runs and multiple inputs. Herein, is a reliable approach of utilizing design of experiment (DOE) for response surface methodology (RSM). Thus, to optimize a facile and effective synthesis process for fluorescent carbon dots (CDs) derived from tapioca that is in line with green chemistry principles for sustainable synthesis. The predictions for fluorescent CDs synthesis from RSM were in excellent agreement with the artificial neural network (ANN) model prediction by the Levenberg–Marquardt back propagation (LMBP) algorithm. Considering R², root mean square error (RMSE) and mean absolute error (MAE) have all revealed a positive hidden layer size. The best hidden layer of neurons were discovered at point 4-8, to confirm the validity of carbon dots, characterization of surface morphology and particles sizes of CDs were conducted with favorable confirmations of the unique characteristics and attributes of synthesized CDs by hydrothermal route. Full article

In a world dominated by linear economic systems, the road to improving resource use is multi-faceted. Whilst public and private organisations are making progress in introducing sustainable practices, we ask ourselves the extent to which education providers are contributing to the circular economy. As engines for skills and knowledge, universities play a primary role in propelling circular economy approaches into reality and, as such, hold the potential for raising the bar on sustainable performance. A rapid evidence assessment (REA) was therefore undertaken to examine the interactions between university estate management and the circular economy. This assessment identified six pertinent themes: campus sustainability, the hidden curriculum, environmental governance, local impact, university material flows, and the role of universities as catalysts for business and examined 70 publications. A second part of the study reviewed the environmental activities of 50 universities ranked highly in terms of their environmental credentials or their environmental science courses. The results are presented and then discussed in terms of how universities can affect material flows, promote sustainability outside of the formal curriculum, and act as catalysts with business. The economic significance of universities provides an appreciable demand for circular products and services. Universities should develop “hidden curriculum” plans to promote improved environmental behaviours of staff and students. Universities can also catalyse a circular economy by working with business to improve eco-effectiveness as well as eco-efficiency. For example, projects should extend the focus from decreasing carbon footprint to achieving carbon positivity, from improving water efficiency to treating wastewater, and from recycling to reverse logistics for repurposing. Pilot projects arising from such work could provide valuable research bases and consultancy opportunities. Full article

What potentials do manufacturing companies have for identifying inefficiencies in their use of resources? Assessing the products with regard to their durability, functional usefulness, use of materials, etc. is only one aspect of the exercise. The actual production operations and the search for in-plant inefficiencies represent the other. In Germany, the material flow cost accounting (MFCA) method was developed years ago to tackle this requirement. It evaluates material losses in the company in monetary terms and thus points up the economic benefit of resource efficiency. MFCA first achieved practical relevance and large-scale application in Japan. Now there is even an ISO standard on the method. The article outlines the process and presents typical examples. It explains how a methodological bridge can be built to assess the loss of material in ecological terms too. Full article