Cleaner Environmental Systems最新文献

Life Cycle Inventory (LCI) analysis is an essential and time-consuming phase of life cycle assessment (LCA). While primary data is among the most reliable and desirable data source types, it is often challenging to collect for industry-specific processes due to confidentiality concerns, in particular with respect to unique proprietary processes. In such cases, computer-based process simulation software can be used to fill gaps in inventory data based on mass and energy balances. While building process simulation models, engagement with industry is essential for verification of process models and validation of simulated data. Although process simulation-based life cycle inventory modelling is not a new research area, there has been no systematic review on this topic with respect to common methodological choices. To fill this gap, this systematic review aims to identify common practices in simulating LCI data using process simulation. Studies that used process simulation for LCI modelling were reviewed to identify the reasons for using process simulation, approaches for simulating LCI, software employed, validation processes, and processes to calculate and report uncertainty. Based on the review findings, a methodological framework was proposed to explain how process simulation-based LCI can be integrated with conventional LCA, specifically for industrial processes.

A Life Cycle Assessment (LCA) study was carried out to assess the environmental profile of the main Sardinian dairy sheep farming systems, with the scope to provide a detailed and robust baseline for the identification of effective mitigation solutions at farm level and to develop environmental strategies at regional scale. Both product- and area-based functional units (FUs) were adopted, considering sixteen impact categories and soil carbon sequestration estimates as well. Water Use, Climate Change, Land Use, Ecotoxicity Freshwater, Marine Eutrophication and Fossils Resource Use resulted the main impact categories, cumulatively contributing over 80% of the total environmental impact (single score). Environmental performances significantly varied according to the geo-pedological traits of the different sheep milk production areas and were driven by the farming systems’ structure and production level. The group of farms located in less fertile areas showed significantly worse environmental performance per kg of normalized milk for the impact categories Climate Change and Land Use, whereas no significant differences were observed for the remaining main impact categories. Considering the area-based FU, this farm group resulted less impacting for all main categories compared to the group of farms located in more plain and productive soils, with a significantly lower impact observed for Marine Eutrophication and Fossils Resource Use. Regardless of the FU used, feed supply management represented a key area of improvement, and soil carbon sequestration impact compensated the high GHG emission intensity of grassland-based farms despite the limited nutritional value of natural pasture. Regional strategies should be based on ecosystem services optimization and eco-innovative solutions tailored according to both the specific geo-pedological conditions and the production level of each farming system.

To achieve the ambitious climate targets set for 2050, it is essential to understand the energy footprints resulting from different lifestyles. This research aims to analyse the variation in direct and embedded energy consumption across different Spanish autonomous communities and socio-economic segments. To do so, we combine the Global Multi-Regional Input-Output methodology (GMRIO) with microdata from Household Budget Surveys (HBS). The findings show that income, household size, and nationality significantly affect the energy footprint of individuals. High-income households have an energy footprint of up to 63.3 MWh·cap⁻¹·yr⁻¹, 75.0% higher than national average. Furthermore, individuals living alone show a 41.4% larger consumption than the national average. In contrast, households with foreign nationalities show an energy footprint of 24.7 MWh·cap⁻¹·yr⁻¹, a 31.8% reduction over national average. On the other hand, differences in gender, age, or municipality size do not play a significant role in energy footprint variations. The energy footprint and the Gross Domestic Product are significantly correlated, as wealthier regions have a TPEF of 17.6% above the national average, while poorer regions show a 31.6% decrease in footprint. Altogether, this work suggests ways to reduce energy consumption in lifestyles, providing specific actions in policy-making.

Material flow analysis is an important tool for estimating material flows and embedded emissions of transport infrastructure. Missing attributes tend to be a major barrier to accurate estimates. In this study a machine learning model is developed to estimate the missing data in a statistics dataset of roads, to enable a bottom-up material stock and flow analysis. The proposed approach was applied to the Swedish road network to predict missing data for road width in the statistical dataset. The predicted hybrid dataset was then used to estimate material stocks, flows, and embodied emissions from Year 2020 to Year 2045 using decarbonization scenarios with a supply chain perspective. The study demonstrates that machine learning models can be used to enable national-level material stock and flow analyses of roads. Multiple machine learning algorithms were tested, and the best performing model achieved an R² value of 0.784. In the scenario-based analysis, the embodied emissions of Swedish roads could be reduced by up to 51% using available materials.

Increasing demands for limited natural resources accelerate rethinking their usage and processing. A focal point of interest lies in the steel industry, given its substantial contribution to emissions and the notable attribute of steel being fully recyclable. Hence, closing material loops to ensure the preservation of material value and supply security is of substantial importance. Yet, until today, supply chains are still characterized by interrupted information streams that prevent circular material flows. Digital platforms are attributed to overcoming these shortcomings due to their ability to moderate ecosystems and render technological connectivity. However, industrial companies lack knowledge of how digital platforms can be used to design closed material loops. Therefore, this paper is built on a longitudinal case study of a joint venture between a recycling and steel melting plant company aiming at creating a digital platform to close the material flow of steel. Six design priorities, structured along the three core building blocks of digital platform ecosystems and the four core dimensions of collective action theory (CAT), have been derived to guide managers in designing digital platforms for the CE-specific context. This study presently contributes to understanding the relationship between digital platforms and CE.

Global environmental concerns are leading to paradigm shift in the automobile eco-system. This comprehensive study of the automobile ecosystem, incorporates technical, economic, and environmental-social-governance (ESG) dimensions. An “integrated multi-criteria decision-making” (MCDM) methodology using “interpretive structural modelling” (ISM) and “fuzzy analytical hierarchy process” (FAHP) is deployed, The ISM models highlights prominent drivers and critical barriers, establishes their hierarchy, and depicts interrelationships. The FAHP rankings for drivers favours electric vehicles and hydrogen fuel vehicles over others, while barriers promote hybrids and alternate fuels like Biofuel, CNG/LNG. Unlike previous works, this study considers all eco-system factors, both critical drivers and barriers, and demonstrates hierarchy using a unique V-model. This is an innovative extension of V-model from traditional systems engineering to strategic project management domain. This holistic approach, in sync with the natural state of things, will enable government, industry, and academia in formulating a strategic framework towards future automobile sustainability.

Sicily ranks as the third-largest region in Italy for olive growing and olive oil production, holding the second position nationally regarding the number of active olive oil mills. This pioneering study integrates spatial and economic analyses to examine the geographical distribution of olive oil mills in Sicily and their relationship with the localization of olive groves. Using Local Indicators of Spatial Association (LISA), we conducted an advanced analysis of spatial patterns of olive oil mills, considering travel time on the road network. The adopted methodology addresses issues related to overestimation based on straight-line assumptions and the neglect of travel speed. Unlike traditional Euclidean distance approaches, our methodology provides a detailed understanding of the spatial relationships between olive oil mills and olive groves, revealing distinct patterns linked to elevation and proximity to olive groves. By linking profitability indicators with spatial clusters, we identify different thresholds of economic sustainability. Consequently, these findings contribute to a more comprehensive understanding of the olive oil industry, suggesting more environmentally sustainable practices. Policymakers, researchers, and industry stakeholders can leverage this knowledge to make informed decisions that foster the long-term sustainability of the olive oil sector.

This research evaluates the economic and environmental feasibility of extracting dietary fibre (DF) from a by-product such as soybean hulls. Techno-economic (TEA) and life cycle assessment (LCA) were carried out to identify the critical factors that may limit the implementation of a potential biorefinery plant. The modelling of the process was carried out on the basis of mass and energy balances, as well as the characteristics of the required equipment. TEA indicators such as minimum selling price (MSP), fixed capital investment, manufacturing costs were evaluated. A cradle-to-gate LCA approach and a functional unit (FU) of 1 kg of product (85% DF content) were considered. Impact categories such as global warming (GW), eutrophication, eco-toxicity, among others, were analysed. The results indicate that the production capacity achieves the plateau at about 56 kt y⁻¹, with an MSP value of 2.6 $·kg⁻¹. Furthermore, the GW profile was 8.76 kg CO₂eq per FU, and the main hotspot is the alkaline digestion stage due to the use of potassium hydroxide (KOH). Nevertheless, the management of the hulls from multi-product food plants and switching KOH production to renewable sources may reduce the profile in almost all categories analysed.

In viticulture, the risk of spring frost is mainly due to earlier budbreak, increasing the vulnerability of buds and green organs to freezing temperatures. Active Spring Frost Protection Methods (ASFPMs) aim to mitigate this risk by increasing the temperature in the bud area. ASFPMs are often seen as highly labour-intensive and resource consuming practices. ASFPM technologies are diverse and influenced by different external drivers, affecting differently their application strategies and the required equipment for efficiency. This study proposes a framework for analysing and comparing ASFPMs’ potential environmental impacts using Life Cycle Assessment (LCA) methodology. We illustrate this proposal with an example: Winter cover, wind machine, sprinkler and anti-frost candles comparison in Loire Valley conditions. We modeled the attributional LCAs with Impact world + characterisation method using Abribalyse 3.1 and Ecoinvent 3.8 databases. Application and climatic scenarios were elaborated to set conditions of ASFPMs use. The overall combination of attributional LCAs and external scenarios designs a context-specific LCA. Required time of application for each ASFPM to protect 1 ha during frost hours was determined using linear regression of ASFPM application time in function of total seasonal frost hours based on a recent decade (2013–2023). Sensitivity analysis consisted in varying frost hours theoretically with a step of 1 unit, using the lowest and highest frost hour numbers from 2013 to 2023 as boundaries. Overall, the ranking between ASFPM environmental scores changes based on the theoretical frost duration. The implementation of context-specific elements allowed for the development of system boundaries in attributional LCA, enabling the analysis and comparison of different types of technologies. The framework of this study showed its relevance in the context of ASFPM technologies through a concrete example in Loire Valley viticulture. Future research may consider other contextual elements and ASFPM technologies. This framework could be used in different fields of study to analyse and compare contrasted technologies in terms of environmental impacts.

Cement production significantly contributes to climate change, necessitating alternatives to mitigate the environmental impacts of this essential construction material. This study evaluates 18 environmental impacts of producing Ordinary Portland Cement (OPC) and Graphene (Gr) using life cycle assessment (LCA). Additionally, we explore whether mixing OPC and Gr can lower the life cycle environmental impacts of the final product (OPC_Gr). Our results show that OPC production in the United Kingdom generates 775 kg CO₂ eq./t, 57% only from geogenic CO₂ emissions. Gr production via electrochemical exfoliation in Australia results in 121,000–143,000 kg CO₂ eq./t, primarily due to electricity generation. Using hydro and nuclear power (e.g., in Brazil and France) can sharply reduce these impacts (global warming potential in the range of 11,000–35,000 kg CO₂ eq./t). Adding 0.02 wt% of Gr in powder form (Gr_powder) from Australia to the OPC and assuming a 16.5% reduction in its usage due to increased strength, results in 674 kg CO₂ eq./t OPC_Gr (a 13% reduction). However, some impact categories like marine eutrophication and freshwater ecotoxicity potentials increase sharply (>28%). Using Gr_powder from Brazil and France further reduces the OPC_Gr global warming potential and the overall environmental footprint.