Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production

Machine learning algorithm functional on environmental sustainability assessment in turbomachinery sector: Application on centrifugal compressors

The current government directives have focused industries' attention on environmental sustainability issues in products and processes. There is indeed a growing demand from customers to conduct environmental impact assessments of the products they purchase. This work presents the implementation of a predictive model developed in an industrial context to evaluate the environmental sustainability of a centrifugal compressor rotor assembly. The development of the predictive model arises from the objective of overcoming the limitations of the traditional Life Cycle Assessment approach, which is based on a retrospective evaluation of the product life cycle. The functionality of predictive models is to estimate product environmental sustainability to meet customer demands and guide them toward choices that aim for carbon neutrality. The implementation of the model has been conducted in parallel with a tailored measurement campaign of the primary inventory flows involved in various manufacturing operations. The article details the methodological approach that led to the development of the predictive models and their respective functionality in supporting the design engineer in evaluating the eco-profile of the assembly. In addition to the methodological aspect, the work also includes a case study through which the functionality of the models can be illustrated.

A review of machine learning applications in life cycle assessment studies

Life Cycle Assessment (LCA) is a foundational method for quantitative assessment of sustainability. Increasing data availability and rapid development of machine learning (ML) approaches offer new opportunities to advance LCA. Here, we review current progress and knowledge gaps in applying ML techniques to support LCA, and identify future research directions for LCAs to better harness the power of ML. This review analyzes forty studies reporting quantitative assessment with a combination of LCA and ML methods. We found that ML approaches have been used for generating life cycle inventories, computing characterization factors, estimating life cycle impacts, and supporting life cycle interpretation. Most of the reviewed studies employed a single ML method, with artificial neural networks (ANNs) as the most frequently applied approach. Both supervised and unsupervised ML techniques were used in LCA studies. For studies using supervised ML, training datasets were derived from diverse sources, such as literature, lab experiments, existing databases, and model simulations. Over 70 % of these reviewed studies trained ML models with less than 1500 sample datasets. Although these reviewed studies showed that ML approaches help improve prediction accuracy, pattern discovery and computational efficiency, multiple areas deserve further research. First, continuous data collection and compilation is needed to support more reliable ML and LCA modeling. Second, future studies should report sufficient details regarding the selection criteria for ML models and present model uncertainty analysis. Third, incorporating deep learning models into LCA holds promise to further improve life cycle inventory and impact assessment. Finally, the complexity of current environmental challenges calls for interdisciplinary collaborative research to achieve deep integration of ML into LCA to support sustainable development.

Determination of the most appropriate fertilizing method for apple trees using multi-criteria decision-making (MCDM) approaches

Appropriate tree fertilization with essential nutrients is considered as one of the major factors in enhancing the quality and quantity of horticultural crops. The most efficient way to fertilize trees is to dig holes around the trunks and fill them with appropriate chemical and organic fertilizer. Doing this operation with mechanized methods reduces costs and increases productivity compared to traditional methods. In the present study, multi-criteria decision-making (MCDM) methods, including deterministic analytical hierarchy process (AHP) and fuzzy analytical hierarchy process (FAHP), technique for order of preference by similarity to ideal solution (TOPSIS), fuzzy TOPSIS (FTOPSIS), and analytic network process (ANP), were used to score and select the appropriate fertilizing method for apple trees based on the growers and expert's perspectives. The criteria, including fertilizing operation cost, crop yield, the percentage of tree damages, ease of entering and moving fertilizing equipment in tree rows, field capacity (with or without machinery), comfort and safety of fertilizing operations, after-sales service, access to the required machinery and implements, crop selling price, and crop quality, were used in the above-mentioned methods. The fertilization methods (Hole digging) considered in the present study were traditional fertilization (Shovel), orchard Trencher, motor hole digger, fixed centerline tractor-mounted hole digger, and off-set tractor-mounted hole digger. Based on the results, the priority of mechanized fertilizing methods was determined as tractor-mounted hole diggers (AHP weight of 0.286, FAHP weight of 0.285, TOPSIS relative proximity of 0.65, and FTOPSIS relative proximity of 0.64), fixed centerline tractor-mounted hole diggers (AHP weight of 0.219, FAHP weight of 0.158, TOPSIS relative proximity of 0.56, and FTOPSIS relative proximity of 0.62), motor hole diggers (AHP weight of 0.171, FAHP weight of 0.079, TOPSIS relative proximity of 0.46, and FTOPSIS relative proximity of 0.31), and orchard trenchers (AHP weight of 0.12, FAHP weight of 0.057, TOPSIS relative proximity of 0.19, and FTOPSIS relative proximity of 0.20), respectively. Based on the ANP method, off-set and fixed centerline tractor-mounted hole diggers had the highest priority (weights of 0.43 and 0.27), followed by trencher (weight of 0.16), motor hole diggers (weight of 0.09), and the traditional method (weight of 0.04). Results showed that applying orchard tractors equipped with mounted diggers, especially off-set types, can play an important role in enhancing the quantity and quality of apples produced, as well as reducing the costs of fertilizing operations.

Harnessing synergy: Integrating agricultural waste and nanomaterials for enhanced sustainability

This paper aims to explore the cooperative use of agricultural waste and nanomaterials to improve environmental sustainability. The introduction highlights global environmental challenges and the objectives of integrating the two are highlighted. Valorization of agricultural waste is considered to reduce waste generation, while nanomaterials act as conversion catalysts that help to increase the efficiency of waste conversion and environmental remediation. In addition, synergistic approaches are discussed, including the combination of agricultural waste and nanomaterials, as well as the concept of enhanced resource management. The paper also presents case studies that demonstrate the success of such synergistic applications in pollution control and environmental remediation. Despite the challenges and risks, this approach can provide new ways to create more sustainable and resilient environments through the integration of resources, interdisciplinary cooperation and policy support.

Machine Learning in Environmental Research: Common Pitfalls and Best Practices

Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications.

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002-2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to cost-effectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.

A Recent Digitalization in Recycling Industry Attaining Ecological Sustainability: A Comprehensive Outlook and Future Trend

The management of waste through dual way of recycling (i-e offline and online) is assumed to have a key role in attaining ecological sustainability and enabling circular practices. The research on online recycling is gaining evolution in recent age. Prior literature on the current research theme has failed to provide a comprehensive outlook and future trend. Therefore, the current research intends to elaborate the current research scenario linked with online recycling by critically scrutinizing the prior research over the last 41 years. A comprehensive analysis was conducted using the Scopus database, retrieving a total of 866 articles. These articles were selected to provide a conceptual overview and understanding of the fundamental research conducted in the field. By employing bibliometric analysis this research provides comprehensive detail about evolution, mapping of publications and prominent trends from the year 1981 to 2022 to understand the practices and future trends of online recycling research. The outcomes elucidated that there is exponential increase in research publications relating to online recycling over the last five years. The most influential producer of online recycling research are China, United Kingdom and United States. Chinese Universities has the highest number of publications among all the countries across globe. Moreover, the current research trend is focused on technology based circular economy, industrial ecology, bio-based waste management, dual channel recycling, municipal waste, waste from electrical and electronic equipment (WEEE), environmental impact and lifecycle assessment. Hence, the prominent research perspective and highlighted features could offer recommendation for upcoming studies to contribute in literature and help practitioners, policymakers and professionals move towards circular practices.

Artificial neural networks and adaptive neuro-fuzzy inference systems for prediction of soil respiration in forested areas southern Brazil

The purpose of this study was to estimate the temporal variability of CO2 emission (FCO2) from O2 influx into the soil (FO2) in a reforested area with native vegetation in the Brazilian Cerrado, as well as to understand the dynamics of soil respiration in this ecosystem. The database is composed of soil respiration data, agroclimatic variables, improved vegetation index (EVI), and soil attributes used to train machine learning algorithms: artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The predictive performance was evaluated based on the mean absolute error (MEA), root mean square error (RMSE), mean absolute percentage error (MAPE), agreement index (d), confidence coefficient (c), and coefficient of determination (R2). The best estimation results for validation were FCO2 with multilayer perceptron neural network (MLP) (R2 = 0.53, RMSE = 0.967 µmol m-2 s-1) and radial basis function neural network (RBF) (R2 = 0.54, RMSE = 0.884 µmol m-2 s-1) and FO2 with MLP (R2 = 0.45, RMSE = 0.093 mg m-2 s-1) and RBF (R2 = 0.74, 0.079 mg m-2 s-1). Soil temperature and macroporosity are important predictors of FCO2 and FO2. The best combination of variables for training the ANFIS was selected based on trial and error. The results were as follows: FCO2 (R2 = 16) and FO2 (R2 = 29). In all models, FCO2 outperformed FO2. A primary factor analysis was performed, and FCO2 and FO2 correlated best with the weather and soil attributes, respectively.

Production of sustainable biofuels from microalgae with CO2 bio-sequestration and life cycle assessment

Due to anthropogenic emissions, there is an increase in the concentration of carbon dioxide (CO₂) in the atmosphere. Microalgae are versatile, universal, and photosynthetic microorganisms present in nature. Biological CO₂ sequestration using microalgae is a novel concept in CO₂ mitigation strategies. In the current review, the difference between carbon capture and storage (CCS), carbon capture utilization and storage (CCUS), and carbon capture and utilization (CCU) is clarified. The current status of CO₂ sequestration techniques is discussed, including various methods and a comparative analysis of abiotic and biotic sequestration. Particular focus is given to sequestration methods associated with microalgae, including advantages of CO₂ bio-sequestration using microalgae, a summary of microalgae species that tolerate high CO₂ concentrations, biochemistry of microalgal CO₂ biofixation, and elements influencing the microalgal CO₂ sequestration. In addition, this review highlights and summarizes the research efforts made on the production of various biofuels using microalgae. Notably, Chlorella sp. is found to be the most beneficial microalgae, with a sizeable hydrogen (H₂) generation capability ranging from 6.1 to 31.2 mL H₂/g microalgae, as well as the species of C. salina, C. fusca, Parachlorella kessleri, C. homosphaera, C. vacuolate, C. pyrenoidosa, C. sorokiniana, C. lewinii, and C. protothecoides. Lastly, the technical feasibility and life cycle analysis are analyzed. This comprehensive review will pave the way for promoting more aggressive research on microalgae-based CO₂ sequestration.

The nexus between digitalization and sustainability: A scientometrics analysis

Digitalization and sustainability are among the most critical mega-trends in 21st century. The nexus between digitalization and sustainability unfolds exciting opportunities in addressing global challenges, creating a just and sustainable society and laying the groundwork for achieving the Sustainable Development Goals. Several studies have reviewed the link between these two paradigms and how they mutually impact one another. However, most of these reviews are qualitative and manual literature reviews that are prone to subjectivity and so lacking the required rigor. Given the above, this study aims to provide a comprehensive and objective review of the knowledge base on how digitalization and sustainability actually and potentially contribute to each other and highlight the key research that links these two megatrends. A comprehensive bibliometric analysis of academic literature is conducted to objectively visualize the research status quo across time, disciplines, and countries. The Web of Science (WOS) database was searched for relevant publications published between January 1, 1900, and October 31, 2021. The search returned 8629 publications, of which 3405 were identified as primary documents pertaining to the study presented below. The Scientometrics analysis identified prominent authors, nations, organizations, prevalent research issues and examined how they have evolved chronologically. The critical review of results reveals four main domains in research on the nexus of sustainability and digitalization including Governance, Energy, Innovation, and Systems. The concept of Governance is developed within the Planning and Policy-making themes. Energy relates to the themes of emission, consumption, and production. Innovation has associated with the themes of business, strategy, and values & environment. Finally, systems interconnect with networks, industry 4.0, and the supply chain. The findings are intended to inform and stimulate more research and policy-making debate on the potential interconnection between sustainability and digitization, particularly in the post-COVID-19 era.

Machine learning for prediction of soil CO2 emission in tropical forests in the Brazilian Cerrado

Soil CO₂ emission (FCO₂) is a critical component of the global carbon cycle, but it is a source of great uncertainty due to the great spatial and temporal variability. Modeling of soil respiration can strongly contribute to reducing the uncertainties associated with the sources and sinks of carbon in the soil. In this study, we compared five machine learning (ML) models to predict the spatiotemporal variability of FCO₂ in three reforested areas: eucalyptus (RE), pine (RP) and native species (RNS). The study also included a generalized scenario (GS) where all the data from RE, RP and RNS were included in one dataset. The ML models include generalized regression neural network (GRNN), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF). Initially, we had 32 attributes and after pre-processing, including Pearson's correlation, canonical correlation analysis (CCA), and biophysical justification, only 21 variables remained. We used as input variables 19 soil properties and climate variables in reforested areas of eucalyptus, pine and native species. RF was the best model to predict soil respiration to RE [adjusted coefficient of determination (R² adj): 0.70 and root mean square error (RMSE): 1.02 µmol m^-2 s^-1], RP (R² adj: 0.48 and RMSE: 1.07 µmol m^-2 s^-1) and GS (R² adj: 0.70 and RMSE: 1.05 µmol m^-2 s^-1). Our findings support that RF and GRNN are promising for predicting soil respiration of reforested areas which could help to identify and monitor potential sources and sinks of the main additional greenhouse gas over ecosystems.

Silica-derived materials from agro-industrial waste biomass: Characterization and comparative studies

The management and final disposal of agro-industrial wastes are one of the main environmental problems. Due to the presence of silica in some agricultural by-products, it is possible to convert waste into materials with advanced properties. This contribution was aimed to extract and characterize silica materials from various feedstocks including sugarcane bagasse (SCB), corn stalk (CS), and rice husk (RH). Silica yields of 17.91%, 9.39%, and 3.25% were obtained for RH, CS, and SCB. On the other hand, the textural properties show that the siliceous materials exhibited mesoporous structures, with high silica composition in the materials due to the formation of crystalline SiO₂ for SCB and CS and amorphous for RH. XPS spectra demonstrate the presence of Si⁴⁺ species in RH, and Si³⁺/Si⁴⁺ tetrahedra in SCB and CS.

Improved Machine Learning Models by Data Processing for Predicting Life-Cycle Environmental Impacts of Chemicals

Machine learning (ML) provides an efficient manner for rapid prediction of the life-cycle environmental impacts of chemicals, but challenges remain due to low prediction accuracy and poor interpretability of the models. To address these issues, we focused on data processing by using a mutual information-permutation importance (MI-PI) feature selection method to filter out irrelevant molecular descriptors from the input data, which improved the model interpretability by preserving the physicochemical meanings of original molecular descriptors without generation of new variables. We also applied a weighted Euclidean distance method to mine the data most relevant to the predicted targets by quantifying the contribution of each feature, thereby the prediction accuracy was improved. On the basis of above data processing, we developed artificial neural network (ANN) models for predicting the life-cycle environmental impacts of chemicals with R² values of 0.81, 0.81, 0.84, 0.75, 0.73, and 0.86 for global warming, human health, metal depletion, freshwater ecotoxicity, particulate matter formation, and terrestrial acidification, respectively. The ML models were interpreted using the Shapley additive explanation method by quantifying the contribution of each input molecular descriptor to environmental impact categories. This work suggests that the combination of feature selection by MI-PI and source data selection based on weighted Euclidean distance has a promising potential to improve the accuracy and interpretability of the models for predicting the life-cycle environmental impacts of chemicals.

Machine learning for surrogate process models of bioproduction pathways

Technoeconomic analysis and life-cycle assessment are critical to guiding and prioritizing bench-scale experiments and to evaluating economic and environmental performance of biofuel or biochemical production processes at scale. Traditionally, commercial process simulation tools have been used to develop detailed models for these purposes. However, developing and running such models can be costly and computationally intensive, which limits the degree to which they can be shared and reproduced in the broader research community. This study evaluates the potential of an automated machine learning approach to develop surrogate models based on conventional process simulation models. The analysis focuses on several high-value biofuels and bioproducts for which pathways of production from biomass feedstocks have been well-established. The results demonstrate that surrogate models can be an accurate and effective tool for approximating the cost, mass and energy balance outputs of more complex process simulations at a fraction of the computational expense.

Big data and artificial intelligence application in energy field: a bibliometric analysis

This paper uses bibliometrics to characterize the knowledge systems of big data, artificial intelligence (AI), and energy based on the Science Citation Index Extension (SCI-E) and Social Science Citation Index (SSCI) of the Web of Science from 2001 to 2020. Results show that China is the country with the highest number of publications (1115), accounting for 29% of the total; however, the most influential country in the field is the USA, with an h-index of 75. The Chinese Academy of Sciences publishes the largest number of papers (104) and plays a vital role in the collaboration network. The study also reveals that the IEEE Access is the most productive journal (195) in terms of the number of publications, and engineering is the most popular discipline (1526). The key theoretical foundation includes deep learning (293), big data (105), energy consumption (79), and reinforcement learning (40). The application of big data and AI in the field of energy focuses on smart grid, energy consumption, and renewable energy. Early research frontiers involve optimization and prediction of energy-related problems using the genetic algorithm and neural networks. Since 2013, energy big data have gained prominence. At present, machine learning, deep learning, and fog computing are frequently combined with energy saving. In the future, big data and AI will be utilized to promote the application of renewable energy and energy-saving renovation of buildings. These findings can help researchers understand the developmental trends and correctly grasp the research direction and method of the emerging interdisciplinary field.

Eco-energy and environmental evaluation of cantaloupe production by life cycle assessment method

Environmental crises and resource depletion have adversely affected the food security around the world. Food security in the future can be guaranteed by sustainable agriculture that respects the environment. So, it is necessary to decrease the energy consumption of resources for agricultural productions to achieve the maximum sustainability. For agricultural productions, environmental and energy issues are completely interrelated, and a comprehensive evaluation is necessary to manage them in all productions. In this study, energy, environmental, and economic indicators in cantaloupe production were studied. The studied energy indices included energy efficiency, energy productivity, net energy gain, and energy intensity. Life cycle method based on ISO 14040 standard was used to evaluate the environmental impacts. This method includes goal statement, identification of inputs and outputs, and a system for assessing and interpreting the environmental impacts of various agricultural productions. Also, for economic analysis, the average prices of inputs and outputs and also net return (NR), gross return (GR), and profit-to-cost ratio were used. The results showed that nitrogen fertilizer (32.28%) and diesel fuel (30.52%) had the highest and cantaloupe seeds (0.39%), and oil consumption in tractor engines (0.43%) had the lowest share of energy consumption, respectively. Energy efficiency, energy productivity, energy intensity, and net energy gain were estimated 0.56, 0.70 kg MJ^-1, 1.41 MJ kg^-1, and - 11,775.86 MJ ha^-1, respectively. The results of the present status of environmental impacts showed that the most effective factor in climate change is direct emissions from the diesel fuel. Also, indirect emissions from phosphorus and urea fertilizers had the highest effect on ecosystem quality. Various machine operations such as primary and secondary plowing, spraying, and transportation were the main causes of high diesel fuel consumption. Economic analysis showed that the profit-to-cost ratio and the productivity values were calculated about 1.6 and 7.27, respectively, which means that for every dollar spent in cantaloupe farms, it produced 7.27 kg of cantaloupe production. The variable costs were estimated at 1154.5 and fixed cost was 1487 $ha^-1. Among the variable costs, transportation and fuel costs were the highest with 64.3%. Decreasing the diesel fuel consumption by using appropriate farm management methods and using the reduce tillage methods can play an effective role in reducing the consumption of this input and improving the energy, environmental, and economic indicators in cantaloupe production.

Environmental impacts of corn silage production: influence of wheat residues under contrasting tillage management types

The intensification of specific land management operations (tillage, herbicide, etc.) is increasing land degradation and contributing to ecosystem pollution. Mulches can be a sustainable tool to counter these processes. This is particularly relevant for rural areas in low-income countries where agriculture is a vital sector. In this research, the environmental impact of different rates of wheat residues (no residues, 25, 50, 75, and 100%) in corn silage cultivation was evaluated using the life cycle assessment (LCA) method under conventional tillage (CT) and no-tillage (NT) systems in a semi-arid region in Karaj, Iran. Results showed that in both tillage systems, marine aquatic ecotoxicity (ME) and global warming potential (GWP) had the highest levels of pollution among the environmental impact indicators. In CT systems, the minimum (17,730.70 kg 1,4-dichlorobenzene (DB) eq.) and maximum (33,683.97 kg 1,4-DB eq.) amounts of ME were related to 0 and 100% wheat residue rates, respectively. Also, in the CT system, 0 and 100% wheat residue rates resulted in minimum (176.72 kg CO₂ eq.) and maximum (324.95 kg CO₂ eq.) amounts of GWP, respectively. However, in the NT system, the 100% wheat residue rate showed the minimum amounts of ME (11,442.39 kg 1,4-DB eq.) and GWP (120.21 kg CO₂ eq.). Also, in the NT system, maximum amounts of ME (17,174 kg 1,4-DB eq.) and GWP (175.60 kg CO₂ eq.) were observed with a zero wheat residue rate. On-farm emissions and nitrogen fertilizers were the two factors with the highest contribution to the degradation related to environmental parameters at all rates of wheat residues. Moreover, in the CT system, the number of environmental pollutants increased with the addition of a higher wheat residue rate, while in the NT system, increasing residue rates decreased the amount of environmental pollutants. In conclusion, this LCA demonstrates that the NT system with the full retention of wheat residues (100%) is a more environmentally sustainable practice for corn silage production. Therefore, it may be considered one of the most adequate management strategies in this region and similar semi-arid conditions. Further long-term research and considering more environmental impact categories are required to assess the real potential of crop residues and tillage management for sustainable corn silage production.

Utilization and life cycle assessment of low activity solid waste as cementitious materials: A case study of titanium slag and granulated blast furnace slag

The dumping of cement production and industrial solid waste can cause severe environmental impact. In order to reduce the environmental impact of cement production and reasonably dispose of solid waste, a new type of cementing material was developed using industrial solid waste as raw materials. It solves the problem that low activity solid waste is difficult to reuse and makes up for the less research, which considered both preparation and environmental evaluation. The orthogonal tests of cement mortar strength as well as life cycle assessment were carried out. The results from variance and range analyses of the orthogonal tests revealed that the fraction of solid waste mainly affected the compressive strength of the solid waste cement mortar, and its specific surface area primarily influenced the flexural strength. After curing for 28 days, the compressive and flexural strength values of the developed cementing material were 40.6 MPa and 8.6 MPa, respectively. The results of life cycle impact assessment indicated that the developed solid waste cement had more environmental advantages than ordinary cement in 18 midpoints environmental impact types, and could diminish environmental impact by 16.1 % on the whole. The solid waste cement has achieved great environmental gains in the human toxicity, natural land transformation, metal depletion, climate change and other environmental impact categories. In addition, the clinker calcination, transportation and material mining were identified as critical processes responsible for the human toxicity, natural land transformation and metal depletion. Through sensitivity and uncertainty analyses, the development of the solid waste cement was proved to be the most effective method to decrease the environmental impact of cement. Finally, the methods of further reducing the environmental impact of cement were proposed.

A Path Toward Systemic Equity in Life Cycle Assessment and Decision-Making: Standardizing Sociodemographic Data Practices

Social equity has been a concept of interest for many years, gaining increased focus from energy and environmental communities. The equitable development, collection, and reporting of sociodemographic data (e.g., data related to socioeconomic status, race, and ethnicity) are needed to help meet several of the United Nations Sustainable Development Goals (i.e., Affordable and Clean Energy; Reduce Inequalities; Peace, Justice and Strong Institutions; and Partnerships for the Goals). Yet, there has not been a consolidation of relevant concepts and application framing in energy and environmental life cycle assessment and decision-making practices. Our study aims to help fill this gap by consolidating existing knowledge on relevant equity applications, providing examples of sociodemographic data needs, and presenting a path toward a more holistic equity administration. In this critique, we present a framework for integrating equity in energy and environmental research and practitioner settings, which we call systemic equity. Systemic equity requires the simultaneous and effective administration of resources (i.e., distributive equity), policies (i.e., procedural equity), and addressing the cultural needs of the systematically marginalized (i.e., recognitional equity). To help provide common language and shared understanding for when equity is ineffectively administered, we present ostensible equity (i.e., when resource and policy needs are met, but cultural needs are inadequately met), aspirational equity (i.e., when policy and cultural needs are met, but resources are inadequate), and exploitational equity (i.e., when resource and cultural needs are met, but policies are inadequate). We close by establishing an adaptive 10-step process for developing standard sociodemographic data practices. The systemic equity framework and 10-step process are translatable to other practitioner and research communities. Nonetheless, energy and environmental scientists, in collaboration with transdisciplinary stakeholders, should administer this framework and process urgently.

Tools Towards the Sustainability and Circularity of Data Centers

We are living in an age when data centers are expanding, require abundant spaces, and are an integral part in the urban communities, using massive amounts of environmental resources, and remains in the foreseeable future as the primary driver of the global energy consumption. This demand is disruptive and at times of both peril and opportunity due to impacts such as the COVID-19 pandemic, which is altering the demand of digital infrastructure around the world. With the global call for zero carbon emissions, there needs to be solutions put in place for the de-carbonization of data centers. New innovations are made available, which will have an economic, social, and environmental impact on data centers. Concepts such as circular economy and fourth industrial revolution technologies are useful procedural tools that can be used to systematically analyze data centers, control their mining and critical raw materials, can be utilized in the transition towards a sustainable and circular data center, by objectively assessing the environmental and economic impacts, and evaluating alternative options. In this paper, we will look at the current research and practice, the impact on the United Nations Sustainable Development goals, and look at future strides being taken towards more sustainable and circular data centers. We had discovered that decreasing the environmental effect and energy consumption of data centers is not sufficient. When it comes to data center architecture, both embodied and operational emissions are critical. Data centers also have a vital societal role in our daily lives, enabling us to share data and freely communicate via social media, transacting on the blockchain with cryptocurrencies, free online education, and job creation. As a result, sustainability and efficiency measures have expanded in a variety of ways, including circularity and its associated tools, as well as newer technologies.

Principal of environmental life cycle assessment for medical waste during COVID-19 outbreak to support sustainable development goals

Disposal of medical waste (MW) must be considered as a vital need to prevent the spread of pandemics during Coronavirus disease of the pandemic in 2019 (COVID-19) outbreak in the globe. In addition, many concerns have been raised due to the significant increase in the generation of MW in recent years. A structured evaluation is required as a framework for the quantifying of potential environmental impacts of the disposal of MW which ultimately leads to the realization of sustainable development goals (SDG). Life cycle assessment (LCA) is considered as a practical approach to examine environmental impacts of any potential processes during all stages of a product's life, including material mining, manufacturing, and delivery. As a result, LCA is known as a suitable method for evaluating environmental impacts for the disposal of MW. In this research, existing scenarios for MW with a unique approach to emergency scenarios for the management of COVID-19 medical waste (CMW) are investigated. In the next step, LCA and its stages are defined comprehensively with the CMW management approach. Moreover, ReCiPe2016 is the most up-to-date method for computing environmental damages in LCA. Then the application of this method for defined scenarios of CMW is examined, and interpretation of results is explained regarding some examples. In the last step, the process of selecting the best environmental-friendly scenario is illustrated by applying weighting analysis. Finally, it can be concluded that LCA can be considered as an effective method to evaluate the environmental burden of CMW management scenarios in present critical conditions of the world to support SDG.

Artificial Neural Network for Predicting Building Energy Performance: A Surrogate Energy Retrofits Decision Support Framework

Assessing the energy performance of existing residential buildings (ERB) has been identified as key to improving building energy efficiency and reducing associated greenhouse gas emissions in Canada. However, identifying optimal retrofit packages requires a significant amount of knowledge of building energy modelling, and it is a time-consuming and laborious process. This paper proposed a data-driven framework that combines machine learning, multi-objective optimization, and multi-criteria decision-making techniques to evaluate the energy performance of ERB and thereby formulate optimal retrofit plans. First, an artificial neural network (ANN) was developed to predict the energy performance of a wide range of retrofit packages. A genetic algorithm was employed to determine the best structure and hyperparameters of the ANN model. Then, the energy consumption results were integrated with environmental and economic impact data to evaluate the environmental and economic performance of retrofit packages and thereby identify Pareto optimal solutions. Finally, a multi-criteria decision-making method was used to select the best retrofit packages among the optimal solutions. The proposed framework was validated using data on a typical residential building in British Columbia, Canada. The results indicated that this framework could effectively predict building energy performance and help decision-makers to make an optimal decision when choosing retrofit packages.

Life cycle assessment of tiger puffer (Takifugu rubripes) farming: A case study in Dalian, China

In China, energy consumption and carbon emission by the aquaculture industry have become major problems. The tiger puffer (Takifugu rubripes) is an emerging aquaculture species in China, but its environmental impact during the farming process has not yet been evaluated systematically. To the best of our knowledge, this is the first life cycle assessment (LCA) of tiger puffer land-sea relay strategy in Dalian, China. To analyze the environmental impact of the tiger puffer farming process, the following four stages were considered: seed rearing, deep-sea cage farming-1, industrial recirculating aquaculture, and deep-sea cage farming-2. The LCA software GaBi 10.5 academy version and CML-IA-Jan. 2016-world method were used to calculate the environmental impacts. According to the LCA results, marine aquatic ecotoxicity potential was the largest contributor to the environmental impact, and industrial recirculating aquaculture was the largest farming stage in the whole tiger puffer farming process. Energy in the form of electricity, coal, and gasoline was consumed to maintain the power supply in the tiger puffer farming process, and it was a key factor that influenced the environmental performance. Based on the sensitivity and energy analyses, energy consumption for equipment operation at the industrial recirculating aquaculture stage, feed consumption, and gasoline consumption for transportation at the deep-sea cage farming-2 stage need to be carefully considered. The following improvement measures were suggested to improve the environmental performance of tiger puffer farming and the aquaculture industry: establish electricity, wind power, and solar energy integrated management systems; ex-ante LCA for parameter optimization in future technology research and development; and new production strategies such as aquaponics and integrated multi-trophic aquaculture. Moreover, life cycle inventory (LCI) of tiger puffer land-sea relay farming was established to obtain essential information, enrich aquaculture LCI databases, and support aquaculture LCA research.

Application of classic and soft computing for modeling yield and environmental final impact in vegetable production (a case study: transplanting onion in Isfahan province, Iran)

This study aimed to develop a precision model between inputs and yield, and also between inputs (indirect emissions) and environmental final index (EFI) in onion farms through regression models (classic computing) and artificial intelligence models (soft computing). Required data were collected through direct measurement and questionnaire. To this end, 85 and 70 questionnaires were distributed among onion farmers in Fereydan and Falavarjan regions (Isfahan province, center of Iran), respectively. In the Fereydan region, the total energy input, onion yield, and water use efficiency (WUE) were obtained as 239496 MJ.ha^-1, 97658 kg.ha^-1, and 9.08 kg.m^-3, respectively, while for Falavarjan region, these were obtained as 232221 MJ.ha^-1, 94485 kg.ha^-1, and 10.8 kg m^-3, respectively. Electricity and diesel fuel were the most widely used inputs in the study areas. Based on the results related to the environmental indices, EFI was obtained as 547.38 and 363.54 pPt.t^-1 for Fereydan and Falavarjan regions, respectively. The contribution of direct (such as CO₂ and NH₃) and indirect emissions (especially electricity) to the total EFI was 74 and 26% in Fereydan and 63 and 37% in Falavarjan region, respectively. Results related to the Cobb-Douglas regression model (CDR) showed that the effects of seed, manure, and labor on the onion yield were significant at 1% level of confidence. However, despite meeting the regression assumptions, the CDR model has predicted the yield and EFI with lower accuracy and higher error compared to artificial neural network models (ANNs), multi-layer perceptron (MLP), and adaptive neuro-fuzzy inference system (ANFIS). Soft computing (artificial intelligence) modeling showed that the ANFIS model (Grid Partitioning (GP)) has higher computational speed an lower error compared to multi-layer perceptron (MLP) models. Therefore, the comparison of the best GP and MLP models showed that the root-mean-square-error (RMSE) was obtained as 10.649 and 52.321 kg.ha^-1 for yield and 25.08 and 40.94 pPt.ha^-1 for EFI, respectively.

Theoretical Perspectives on Sustainable Supply Chain Management and Digital Transformation: A Literature Review and a Conceptual Framework

In an era where environmental and social pressures on companies are increasing, sustainable supply chain management is essential for the efficient operation and survivability of the organizations (members of the chain). Digital transformation and the adoption of new technologies could support the development of sustainable strategies, as they support supply chain processes, decrease operational costs, enable control and monitoring of operations and support green practices. The purpose of this paper is to explore the relationship between sustainable supply chain management and digital transformation through the adoption of specific technologies (Blockchain technology, big data analytics, internet of things). It aims at theory building and the development of a conceptual framework, enabling the explanation of under which circumstances the above combination could lead to the development of sustainable performances. It also aims to examine how companies can increase their competitive advantage and/or increase their business performance, contributing both to academics and practitioners. After conducting a literature review analysis, a significant gap was detected. There are a few studies providing theoretical approaches to examining all three pillars of sustainability, while at the same time analyzing the impact of big data analytics, internet of things and blockchain technology on the development of sustainable supply chains. Aiming to address this gap, this paper primarily conducts a literature review, identifies definitions and theories used to explain the different pillars of flexibility, and examines the effect of different technologies. It then develops a theoretical conceptual framework, which could enable both academics and practitioners to examine the impact of the adoption of different technologies on sustainable supply chain management. The findings of this research reveal that digital transformation plays an important role to companies, as the combination of different technologies may lead to the development of significant capabilities, increasing sustainable performances and enabling the development of sustainable strategies, which can improve companies’ position in the market.

Agricultural waste management strategies for environmental sustainability

Globally, abundant agricultural wastes (AWs) are being generated each day to fulfil the increasing demands of the fast-growing population. The limited and/or improper management of the same has created an urgent need to devise strategies for their timely utilization and valorisation, for agricultural sustainability and human-food and health security. The AWs are generated from different sources including crop residue, agro-industries, livestock, and aquaculture. The main component of the crop residue and agro-industrial waste is cellulose, (the most abundant biopolymer), followed by lignin and hemicellulose (lignocellulosic biomass). The AWs and their processing are a global issue since its vast majority is currently burned or buried in soil, causing pollution of air, water and global warming. Traditionally, some crop residues have been used in combustion, animal fodder, roof thatching, composting, soil mulching, matchsticks and paper production. But, lignocellulosic biomass can also serve as a sustainable source of biofuel (biodiesel, bioethanol, biogas, biohydrogen) and bioenergy in order to mitigate the fossil fuel shortage and climate change issues. Thus, valorisation of lignocellulosic residues has the potential to influence the bioeconomy by producing value-added products including biofertilizers, bio-bricks, bio-coal, bio-plastics, paper, biofuels, industrial enzymes, organic acids etc. This review encompasses circular bioeconomy based various AW management strategies, which involve 'reduction', 'reusing' and 'recycling' of AWs to boost sustainable agriculture and minimise environmental pollution.

A Sustainable Approach towards Disposable Face Mask Production Amidst Pandemic Outbreaks

SARS-CoV-2 has become a global pandemic, causing many disruptions in multiple sectors. The World Health Organization has urged the public to wear face masks as part of the countermeasure. As the demand for face masks increased, research on the environmental sustainability of face masks production started to emerge. However, the scope of the prior studies is limited to environmental impacts during the manufacturing process. Broadening the research scope is critical to acquire a comprehensive environmental impact analysis. Therefore, this study investigates the life cycle impact assessment of disposable face mask production, from raw material extraction to the point of sale, by adopting the life cycle assessment method. Disposable face masks are assessed for a single person, over one functional unit (FU) of 30 12-h days. The ReCiPe approach was used with a Hierarchist perspective. The results reveal that disposable face mask manufacture contributes significantly to enormous environmental impact categories. As a solution, this study proposes a reconfiguration of the manufacturing process, by altering the design and material proportion of the earloop to minimise the environmental impact. The investigation indicates that the proposed design might decrease the global warming contribution, from 1.82593 kg CO2 eq. to 1.69948 kg CO2 eq.

Implementing Artificial Intelligence Techniques to Predict Environmental Impacts: Case of Construction Products

Nowadays, product designers, manufacturers, and consumers consider the environmental impacts of products, processes, and services in their decision-making process. Life Cycle Assessment (LCA) is a tool that assesses the environmental impacts over a product’s life cycle. Conducting a life cycle assessment (LCA) requires meticulous data sourcing and collection and is often time-consuming for both practitioner and verifier. However, predicting the environmental impacts of products and services can help stakeholders and decision-makers identify the hotspots. Our work proposes using Artificial Intelligence (AI) techniques to predict the environmental performance of a product or service to assist LCA practitioners and verifiers. This approach uses data from environmental product declarations of construction products. The data is processed utilizing natural language processing (NLP) which is then trained to random forest algorithm, an ensemble tree-based machine learning method. Finally, we trained the model with information on the product and their environmental impacts using seven impact category values and verified the results using a testing dataset (20% of EPD data). Our results demonstrate that the model was able to predict the values of impact categories: global warming potential, abiotic depletion potential for fossil resources, acidification potential, and photochemical ozone creation potential with an accuracy (measured using R2 metrics, a measure to score the correlation of predicted values to real value) of 81%, 77%, 68%, and 70%, respectively. Our method demonstrates the capability to predict environmental performance with a defined variability by learning from the results of the previous LCA studies. The model’s performance also depends on the amount of data available for training. However, this approach does not replace a detailed LCA but is rather a quick prediction and assistance to LCA practitioners and verifiers in realizing an LCA.

Prediction of Almond Nut Yield and Its Greenhouse Gases Emission Using Different Methodologies

The evaluation of a production system to analyze greenhouse gases is one of the most interesting challenges for researchers. The aim of the present study is to model almond nut production based on inputs by employing artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) procedures. To predict the almond nut yield with respect to the energy inputs, several ANN and ANFIS models were developed, evaluated, and compared. Among the several developed ANNs, a network with an architecture of 8-12-1 and a log-sigmoid, and a linear transfer function in the hidden and output layers, respectively, is found to be the best model. In general, both approaches had a good capability for predicting the nut yield. The comparison results revealed that the ANN procedure could predict the nut yield more precisely than the ANFIS models. Furthermore, greenhouse gas (GHG) emissions in almond orchards are determined where the total GHG emission is estimated to be about 2348.85 kg CO2eq ha−1. Among the inputs, electricity had the largest contribution to GHG emissions, with a share of 72.32%.

Comparisons between main and ratoon crops in resource use efficiencies, environmental impacts, and economic profits of rice ratooning system in central China

Rice production in China is constrained by excessive water consumption, labor shortage, large environmental footprint, and low economic profit. Rice ratooning is a promising practice to increase famers' profit with higher resource use efficiency and less environmental impact compared with other rice cropping systems. However, there is limited information on the differences in energy use efficiency, water and labor productivity, environmental footprint, and economic return between main crop (MC) and ratoon crop (RC) in this cropping system. This study was conducted to compare the system performance between the two crops of ratoon rice using on-farm survey data. Average grain yield was 8.40 and 4.55 t ha^-1 for MC and RC, respectively. Although RC produced 45.9% lower grain yield, it had 57.3% less total energy input and 71.0% lower total production cost than MC, which resulted in a significantly higher energy use efficiency, net energy ratio, net economic return and benefit-to-cost ratio. Lower total energy input and production cost of RC was mainly attributed to the reduction in fertilizer application and labor input, respectively compared with MC. In addition, both labor and water productivity of RC was significantly higher than those of MC. Furthermore, the global warming potential (GWP) and yield-scaled GWP of RC was 59.3% and 23.4% lower than those of MC, respectively, due to lower agronomic inputs and GHGs emissions. Overall, our results suggested that RC had higher resource use efficiency, better economic performance, and less environment impact compared with MC.

Energy, environment and economy assessment of medical waste disposal technologies in China

Medical waste (MW) has exploded since the COVID-19 pandemic and aroused great concern to MW disposal. Meanwhile, the energy recovery for MW disposal is necessary due to high heat value of MW. Harmless disposal of MW with economically and environmentally sustainable technologies along with higher energy recovery is urgently required, and their energy recovery efficiencies and environmental impacts reduction due to energy recovery are key issues. In this study, five MW disposal technologies, i.e. rotary kiln incineration, pyrolysis incineration, plasma melting, steam sterilization and microwave sterilization, were evaluated and compared via energy recovery analysis (ERA), life cycle assessment (LCA), and life cycle costing (LCC) methods. Furthermore, three MW incineration technologies with further energy recovery and two sterilization followed by co-incineration technologies were analyzed to explore their improvement potential of energy recovery and environment benefits via scenario analysis. ERA results reveal that the energy recovery efficiencies of "steam and microwave sterilization + incineration" are the highest (≥83.4%), while that of the plasma melting is the lowest (19.2%). LCA results show that "microwave sterilization + landfill" outperforms others while the plasma melting exhibits the worst, electricity is the most significant contributor to the environmental impacts of five technologies. Scenario analysis shows that the overall environmental impact of all technologies reduced by at least 45% after further heat utilization. LCC results demonstrate that pyrolysis incineration delivers the lowest economic cost, while plasma melting is the highest. Co-incineration of sterilized MW and municipal solid waste could be recommended.

Selection of Renewable Energy in Rural Area Via Life Cycle Assessment-Analytical Hierarchy Process (LCA-AHP): A Case Study of Tatau, Sarawak

With a growing global population and energy demand, there is increasing concern about the world’s reliance on fossil fuels, which have a negative impact on the climate, necessitating the immediate transition to a cleaner energy resource. This effort can be initiated in the rural areas of developing countries for a sustainable, efficient and affordable energy source. This study evaluated four types of renewable energy (solar, wind, biomass, and mini-hydro energy) using the integrated Life Cycle Assessment (LCA) and Analytical Hierarchy Process (AHP) approaches to select the best renewable energy source in Tatau, Sarawak. The criteria under consideration in this study included the environment, engineering and economics. The LCA was used to assess the environmental impact of renewable energies from gate-to-grave boundaries based on 50 MJ/day of electricity generation. The AHP results showed that solar energy received the highest score of 0.299 in terms of the evaluated criteria, followed by mini-hydro, biomass and wind energy, which received scores of 0.271, 0.230 and 0.200, respectively. These findings can be used to develop a systematic procedure for determining the best form of renewable energy for rural areas. This approach could be vital for the authorities that are responsible for breaking down multi-perspective criteria for future decision making in the transition into renewable energy.

Technical optimization and life cycle assessment of environment-friendly superplasticizer for concrete engineering

With the rapid development of the construction industry, it is necessary to synthesize environment-friendly functional polymers, especially when developing "green" construction industry types. Herein a novel solid-state polycarboxylate superplasticizer (PCE) with low energy-consumption was designed and synthesized. In industrial application, solid-state PCE has exhibited better cement paste fluidity and concrete slump compared to liquid-state PCE. A life cycle assessment (LCA) of the PCE synthesis, the packaging materials used, and the transportation of the PCE were conducted based on the ReCiPe method. The results indicated that liquid-state PCE has a far greater environmental impact at >60% than solid-state PCE, which is less significant at <40%. The inventory data that are associated with the production of the new polymer are disclosed for the first time to enrich the related database in this field. This study demonstrates the optimization of the state and synthesis technique of a functional polymer, improving the performance and lowering the environmental impacts involved in producing the polymer, while reducing the risks to human health and protecting the ecosystem at the same time.

Evaluating the energy use, economic and environmental sustainability for smoked fish production from life cycle assessment point of view (case study: Guilan Province, Iran)

The purpose of this study is to determine energy use patterns, evaluating the environmental impacts and economic evaluation of smoked fish production in the Guilan Province. The initial data were collected from seven smokehouses in the north of Iran through face-to-face questionnaire method, while the required data related to the background system were extracted from the Ecoinvent 2.2 database. The total input and output energy result showed the use is examined to be 98,143.29 and 60,048 MJ ton^-1, respectively. About 79.31% of this was generated by fish, 7.7% from electricity, and 6.7% from wood chip. Life cycle assessment results showed that fish and salt emissions have the most notable effects. Among damage categories, the largest emissions were related to human health (37.77%). Climate change (30.35%), resource (28.78%), and ecosystem quality (0.92%) are next phases. The rates of nonrenewable, fossil in CExD method calculated 35,426.61 MJ ton^-1. Economic analysis of production was carried out. Some economic indicators have been calculated, and the benefit to cost ratio was obtained as 3.09. Due to the justification of fisheries management, these issues merit further exploration.

A Framework for Evaluating and Disclosing the ESG Related Impacts of AI with the SDGs

Artificial intelligence (AI) now permeates all aspects of modern society, and we are simultaneously seeing an increased focus on issues of sustainability in all human activities. All major corporations are now expected to account for their environmental and social footprint and to disclose and report on their activities. This is carried out through a diverse set of standards, frameworks, and metrics related to what is referred to as ESG (environment, social, governance), which is now, increasingly often, replacing the older term CSR (corporate social responsibility). The challenge addressed in this article is that none of these frameworks sufficiently capture the nature of the sustainability related impacts of AI. This creates a situation in which companies are not incentivised to properly analyse such impacts. Simultaneously, it allows the companies that are aware of negative impacts to not disclose them. This article proposes a framework for evaluating and disclosing ESG related AI impacts based on the United Nation’s Sustainable Development Goals (SDG). The core of the framework is here presented, with examples of how it forces an examination of micro, meso, and macro level impacts, a consideration of both negative and positive impacts, and accounting for ripple effects and interlinkages between the different impacts. Such a framework helps make analyses of AI related ESG impacts more structured and systematic, more transparent, and it allows companies to draw on research in AI ethics in such evaluations. In the closing section, Microsoft’s sustainability reporting from 2018 and 2019 is used as an example of how sustainability reporting is currently carried out, and how it might be improved by using the approach here advocated.

Estimation of Unit Process Data for Life Cycle Assessment Using a Decision Tree-Based Approach

Lacking unit process data is a major challenge for developing life cycle inventory (LCI) in life cycle assessment (LCA). Previously, we developed a similarity-based approach to estimate missing unit process data, which works only when less than 5% of the data are missing in a unit process. In this study, we developed a more flexible machine learning model to estimate missing unit process data as a complement to our previous method. In particular, we adopted a decision tree-based supervised learning approach to use an existing unit process dataset (ecoinvent 3.1) to characterize the relationship between the known information (predictors) and the missing one (response). The results show that our model can successfully classify the zero and nonzero flows with a very low misclassification rate (0.79% when 10% of the data are missing). For nonzero flows, the model can accurately estimate their values with an R² over 0.7 when less than 20% of data are missing in one unit process. Our method can provide important data to complement primary LCI data for LCA studies and demonstrates the promising applications of machine learning techniques in LCA.

The structural equivalence of tourism cooperative network in the Belt and Road Initiative Area

This study constructed the international tourism cooperation network (ITCN) in the Belt and Road Initiative area and further analyzed the structural equivalence of the tourism cooperative network by block-modeling approach through Convergence of iterated Correlations CONCOR algorithm in UCINET 6 data set. The results displayed the layout of subgroups: The East Europe, North Asia-Pacific and South Asia were in core positions; Middle East, the Americas and Africa were at margins of the network. Besides, each inter-block relational pattern and the status of each block had been presented. The sociogram of inter-block density highlighted the importance of reciprocal ties. These ties were mainly constructed between core blocks, but seldom constructed between peripheral blocks. The degree of competition derived from structural equivalence can be balanced through the implementation of intra-block differential strategy and the design of inter-block relational patterns.

Identifying environmental hotspots and improvement strategies of vanillin production with life cycle assessment

Vanillin, an important aroma chemical, can be synthesized through industrial oxidation processes and biotechnological processes. Studying the environmental impacts of synthetic vanillin production processes is fundamental to making these processes feasible and sustainable; however, few studies have focused on such analyses. This study involved performing a life cycle assessment (LCA) to evaluate multiple industrial synthesis and biosynthesis processes for producing synthetic vanillin. The results indicated that human toxicity potential (HTP) appeared to be the most affected indicator among all the impact categories considered. The dominant drivers of the HTP of the vanillin synthesis process were electricity consumption and ultrapure water consumption. Improvement strategies were then proposed to investigate the possibility of reducing the environmental burdens created by vanillin synthesis. Natural gas power generation was determined to be the best choice for replacing traditional coal-fired power generation, thus reducing the negative impacts of these processes on the environment. The best ways to reduce chemical consumption were to recover organic solvents and to replace ultrapure water with industrial or distilled water. All these improvement strategies were demonstrated to be able to effectively reduce the HTP. In addition, suggestions for evaluating scaled-up vanillin production, increasing the LCA coverage to include technological advancements in biosynthesis techniques, and introducing cost-benefit analysis into the LCA were discussed.

Integration of life cycle assessment (LCA) and modeling methods in investigating the yield and environmental emissions final score (EEFS) of carp fish (Cyprinus carpio) farms

This study was conducted to investigate and predict the yield and environmental emissions final score (EEFS) of common carp fish farms in Shushtar county of Khuzestan province. The required data was collected from 115 carp fish farms selected by random sampling using face-to-face questionnaire and interview. The total input energy, the yield, and energy ratio were obtained as 293,127.95 MJ ha^-1, 3389.28 kg ha^-1, and 0.30, respectively. Electricity and feed consumption had the highest contributions to total input energy and environmental emissions. The normalization results showed that the marine aquatic ecotoxicity (MAET) and freshwater aquatic ecotoxicity (FAET) had the highest values among all impact categories with 671.50×10^-9 and 152.60×10^-9, respectively. Also, the EEFS was calculated per tons of carp fish as 7793.09 pPt. The comparison of results between the regression model and adaptive neuro-fuzzy inference system (ANFIS) indicated that in prediction of the yield, the accuracy values (R²) of regression and ANFIS models were 0.87 and 0.99, respectively, while in prediction of EEFS, R² of regression and ANFIS models were 0.98 and 0.99, respectively. In total, it was concluded that ANFIS model can predict the yield better than regression model.

Evaluation of environmental consequences affecting human health in the current and optimal cropping patterns in the eastern Lorestan Province, Iran

Planning for optimal use of resources and reduction of environmental impacts, in addition to resource protection, is associated with increasing farmers' revenues and boosting the regional economy. Given the limited resources and environmental impacts of agricultural activities on human health, it is necessary to determine an appropriate cropping pattern. The present research aimed to maximize net profit and minimize environmental impacts, including the releases of carcinogens, noncarcinogens, ozone layer depletion, ionizing radiation, and respiratory inorganics and organics on human health. In this study, an optimal cropping pattern of irrigated and rainfed crops was proposed for the east of Lorestan Province using multi-objective nonlinear programming (MOP). Results showed that the cropping areas of chickpea, rapeseed, and potatoes decreased by 50% in the irrigated crop of MOP model and that of lentil in the MOP model of rainfed crops compared with the current pattern. Another important result was increases in the cropping areas of lentil and bean in the MOP pattern of irrigated crops and wheat in the rainfed MOP model. The environmental impacts of agricultural sector on human health can be reduced by determining an optimal cropping pattern. The implementation of this model in the region reduced the emissions of carcinogens (4%), noncarcinogens (9%), respiratory inorganics (17%), ionizing radiation (14%), ozone layer depletion (17%), and respiratory organics (15%) compared with the existing situation along with an increased net profit of $968,483. According to the findings, consideration of environmental objectives affecting human health is essential in the optimization of the cropping pattern. In addition to optimal use of water and land resources, using the proposed model helps to increase profits and reduce environmental consequences on human health.

Comparative analysis on environmental and economic performance of agricultural cooperatives and smallholder farmers: The case of grape production in Hebei, China

Agricultural modernization and intensification have been regarded as a significant way to support agricultural development and improve farm income in China. Agricultural cooperatives have played an important role in promoting the modernization and intensification of Chinese agricultural sector. Given the increasing concerns about environmental harm, however, it still remains unclear whether and the extent to which agricultural cooperatives contributes to reducing environmental impacts of agricultural production. Hence, this study performed an environmental evaluation using life cycle assessment for three different organization forms of grape production in Changli County, Hebei Province, China: smallholder farmers, farmer-owned cooperatives and investor-owned firm-led cooperatives. Then the results of life cycle assessment were monetarized and cost benefit analysis was used to evaluate the economic performance of these three organization forms of grape production. The results demonstrate that investor-owned firm-led cooperatives present an overall improvement in environmental and economic performance with the lowest weighted environmental index (integrating all impact categories into a single score), the highest net profit and the highest total net benefit. The results also show a difference in potential improvement in environmental impacts and economic returns between cooperatives and smallholder farmers. Additionally, the production and application of organic and chemical fertilizer and pesticide have been identified as major contributors to total environmental damage.

Application of soft computing to predict water quality in wetland

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.

Determination of the most effective control methods of SO2 Pollution in Tehran based on adaptive neuro-fuzzy inference system

Air pollution in metropolises is one of the serious problems of human life. Tehran is one of the cities facing air pollution problem. Urban managers concern about choosing different management methods to control air pollution. In this study, a combination of fuzzy systems and neural networks has been used to select the most suitable scenario for controlling SO₂ pollution. According to the method presented in this paper, 8 input data categories such as wind speed, precipitation, temperature, pressure, humidity, gas oil consumption, gasoline consumption and urban green space levels have been used as independent parameters and SO₂ pollutant concentration has been considered as the dependent parameter. The contribution of each meteorological station to the meteorological data was determined by Thiessen Polygon Method. Then, using adaptive neural fuzzy inference systems, modeling was done in Sugeno Method and the least root mean square error (3.19) was determined for the model. Then, by changing each of the independent parameters, the effect of each of these independent parameters on SO₂ pollutant was measured. The results showed that the parameters of pressure, urban green space, gasoline consumption, gas oil consumption, temperature, wind speed and humidity, respectively, had the greatest effect on reducing the SO₂ concentration. Since the parameters of gasoline and gas oil consumption as well as the area of green space are changeable by different policies and by human decisions, the concentration of SO₂ pollutant can be controlled by reducing the consumption of gasoline and gas oil and increasing the green space in Tehran.

Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

The unprecedented urban growth of recent years requires improved urban planning and management to make urban spaces more inclusive, safe, resilient and sustainable. Additionally, humanity faces the COVID pandemic, which especially complicates the management of Smart Cities. A possible solution to address these two problems (environmental and health) in Smart Cities may be the use of Machine Learning techniques. One of the objectives of our work is to thoroughly analyze the link between the concepts of Smart Cities, Machine Learning techniques and their applicability. In this work, an exhaustive study of the relationship between Smart Cities and the applicability of Machine Learning (ML) techniques is carried out with the aim of optimizing sustainability. For this, the ML models, analyzed from the point of view of the models, techniques and applications, are studied. The areas and dimensions of sustainability addressed are analyzed, and the Sustainable Development Goals (SDGs) are discussed. The main objective is to propose a model (EARLY) that allows us to tackle these problems in the future. An inclusive perspective on applicability, sustainability scopes and dimensions, SDGs, tools, data types and Machine Learning techniques is provided. Finally, a case study applied to an Andalusian city is presented.

Sugarcane Industry Waste Recovery: A Case Study Using Thermochemical Conversion Technologies to Increase Sustainability

The sugarcane industry has assumed an increasingly important role at a global level, with countries such as Brazil and India dominating the field. However, this causes environmental problems, since the industry produces large amounts of waste, such as sugarcane bagasse. This by-product, which is energetically partially recovered in sugar mills and in the pulp and paper industry, can make a significant contribution to the general use of biomass energy, if the usual disadvantages associated with products with low density and a high moisture content are overcome. From this perspective, thermochemical conversion technologies, especially torrefaction, are assumed to be capable of improving the fuel properties of this material, making it more appealing for potential export and use in far-off destinations. In this work, sugarcane samples were acquired, and the process of obtaining bagasse was simulated. Subsequently, the bagasse was dried and heat-treated at 200 and 300 °C to simulate the over-drying and torrefaction process. Afterward, product characterization was performed, including thermogravimetric analysis, elemental analysis, calorimetry, and energy densification. The results showed significant improvements in the energy content, from 18.17 to 33.36 MJ·kg−1 from dried bagasse to torrefied bagasse at 300 °C, showing that despite high mass loss, there is potential for a future value added chain for this waste form, since the increment in energy density could enhance its transportation and use in locations far off the production site.

Systems thinking on the resource nexus: Modeling and visualisation tools to identify critical interlinkages for resilient and sustainable societies and institutions

Achieving the UN Sustainable Development Goals depends on using resources efficiently, avoiding fragmentation in decision-making, recognising the trade-offs and synergies across sectors and adopting an integrated Nexus thinking among policymakers. Nexus Informatics develops the science of recognising and quantifying nexus interlinkages. Nexus-coherent solutions enhance the effect of policymaking in achieving adequate governance, leading to successful strategic vision and efficient resource management. In this article, we present the structure of a System Dynamics Model-the Nexus_SDM-that maps sector-specific data from major databases (e.g., EUROSTAT) and scenario models (e.g., E3ME-FTT OSeMOSYS and SWIM) for the national case study of Greece. Disaggregation algorithms are employed on annual national-scale data, turning them into detailed spatial and temporal datasets, by converting them to monthly values spread among all 14 River Basin Districts (RBDs). The Nexus_SDM calculates Nexus Interlinkage Factors and quantifies interlinkages among Water, Energy, Food, Built Environment, Natural Land and greenhouse gas (GHG) emissions. It simulates the nexus in the national case study of Greece as a holistic multi-sectoral system and provides insights into the vulnerability of resources to future socio-economic scenarios. It calculates the link between crop type/area, irrigation water and agricultural value, revealing which crops have the highest agricultural value with the least water and crop area. It demonstrates that fossil fuel power generation and use of oil for transportation are responsible for the most GHG emissions in most RBDs and presents projections for years 2030 and 2050. The analysis showcases that to move from a general nexus thinking to an operational nexus concept, it is important to focus on data availability and scale. Advanced Sankey and Chord diagrams are introduced to show distribution of resource use among RBDs and an innovative visualisation tool is developed, the Nexus Directional Chord plot, which reveals Nexus hotspots and strong interlinkages among sectors, facilitating stakeholder awareness.