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Review

Industrial Metabolism: A Multilevel Characterization for Designing Sustainable Manufacturing Systems

by
Alejandro M. Martín-Gómez
*,
María Jesús Ávila-Gutiérrez
,
Juan Ramón Lama-Ruiz
and
Francisco Aguayo-González
Design Engineering Department, Higher Polytechnic School, University of Seville, 41011 Seville, Spain
*
Author to whom correspondence should be addressed.
Machines 2024, 12(1), 16; https://0-doi-org.brum.beds.ac.uk/10.3390/machines12010016
Submission received: 27 November 2023 / Revised: 24 December 2023 / Accepted: 26 December 2023 / Published: 27 December 2023
(This article belongs to the Special Issue Sustainable Manufacturing and Green Processing Methods)
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Abstract

:
The development of industrial manufacturing systems has significant implications for society and the environment, often resulting in substantial waste generation. To address this issue and promote sustainable growth, the concept of industrial metabolism offers a promising approach. Industrial metabolism facilitates the circularity of energy and material flows within the industrial environment, contributing to the establishment of more sustainable manufacturing systems. This paper provides a comprehensive analysis of industrial metabolism, highlighting its analogy with natural systems and categorizing models based on their application at different levels: macro (national or regional), meso (eco-industrial park), and micro (manufacturing plant or line). The analysis emphasizes the importance of considering the trophic network and evaluating the efficiency, cyclicality, toxicity, and resilience of industrial metabolic pathways. The proposed characterization of bioinspired industrial metabolism is positioned within the industrial environment. This positioning facilitates the design of manufacturing systems that emphasize circularity, drawing on frameworks applied at different levels within industrial metabolism.

1. Introduction

The concept of metabolism has been used to describe the exchanges of matter and energy between nature and society, providing support for criticism of industrialization centered on the exploitation of wage labor in capitalism [1]. In this sense, Marx and Engels considered labor as the regulation of the society-nature metabolism [2]. Marx coined the term “metabolic rift,” illustrating how industrialization disrupts the centuries-old metabolism between society and nature, creating a gap [3]. Although the metabolic rift has Marxist connotations, it can contribute to the understanding and guidance of social metabolism [4]. Thus, decoupling in the economy is needed towards an inclusive and circular approach [5].
Industrial Metabolism (IM) is a part of social metabolism [6] and refers to this exchange of matter and energy between human society and nature in a manner analogous to the processes and balance of matter and energy in natural organisms and ecosystems [7]. However, this definition lacks geographic, temporal, and material-type dimensions. To address this issue, the relevant boundaries of the system must be explicitly defined in each study, without relying on the society-nature dichotomy [8]. The researcher needs to establish these boundaries and consider the relationships with other elements [9,10]. From another perspective, the field of ecology aims to consider inclusive natural closed cycles [2]. In this context, the use of the term metabolism as a valid concept at scales beyond the individual organism is embraced by industrial ecology [8]. Thus, this analogy between the metabolism of an individual organism and a larger system (the ecosystem) is well received [11].
IM, in relation to the economy, focuses on the consumption of low-entropy resources and the disposal of high-entropy waste into the biosphere [12]. This approach aims to increase resource efficiency through circularity and the utilization of by-products within industrial systems [13]. Sustainable manufacturing systems seek to minimize exchanges with the environment by enhancing internal material circulation through renewable energy flows [14]. Studies are directed towards environmental improvement through technological, economic, and policy instruments that address social and environmental concerns within the Circular Economy (CE) [15,16]. The biological analogy justifies the importance of this approach, as biological metabolism is characterized by high efficiency in energy transformation, material utilization, and circulation [17]. However, IM exhibits a significant difference, as natural systems demonstrate closed loops and nearly universal material recycling. Understanding these energy and material flows in the economy of a community allows for a systemic perspective that facilitates goal setting and the development of indicators [18,19,20].
Therefore, this study aims to investigate the following research inquiries: (a) Can analogies be established between IM and natural systems? (b) Can the characterization of IM from the analogy with ecological systems be used as a framework to guide the design of manufacturing systems for resource circularity? (c) Can specific frameworks for IM be identified at different levels?
The main contributions of the presented work can be summarized as follows. Firstly, it establishes analogies between manufacturing systems and natural systems using the concept of metabolism. By drawing parallels between these two domains, a deeper understanding of industrial processes and their relationship to natural systems is achieved. Secondly, the research provides a comprehensive characterization of IM at multiple levels: macro (nation or region), meso (eco-industrial park), and micro (manufacturing plant or line). Approaching from a bioinspired perspective, characterized by observing and imitating functions in living organisms, this multilevel characterization enables the design of manufacturing systems for resource circularity, identifying the most suitable frameworks in IM.
This work is structured as follows. Section 2 describes the methodology used for the conducted review. Section 3 provides a review of the existing analogies between IM and natural systems, along with an analysis of the works that employ IM at different levels (micro, meso, and macro). Section 4 describes the conceptual characterization of IM from a multilevel bioinspired perspective. Finally, the conclusions are drawn in Section 5.

2. Methodology

In this section, among the existing review methodologies, a Status Quo review [21] will be conducted. This corresponds to a critical and constructive analysis of the existing literature in the field of industrial metabolism through its classification and analysis. The most relevant publications are reviewed with the aim of presenting the most current state of research in the field of industrial metabolism by identifying patterns and trends.
The proposed review methodology consists of four steps [22]. Firstly, the research areas, such as industrial, manufacturing, ecology, and sustainability, are defined and delimited to gather relevant material. Subsequently, a descriptive analysis of the formal aspect of the material is conducted. Following this, the collected material is selected based on structural dimensions and related analytical categories. The first three steps of the methodology are elaborated below, with the fourth step, involving material analysis, extensively developed in Section 3.
The literature review is limited to specific considerations. Only articles from peer-reviewed scientific journals, encompassing research and reviews written in English, are included, except for justified exceptions. The review period spans 20 years, from 2003 to 2023, though earlier works were considered if they were relevant to the field. The search was conducted using keywords in relevant databases such as Scopus, ScienceDirect, SpringerLink, Web of Science, and Taylor & Francis. This delimitation resulted in the identification of 1037 articles with the keyword “industrial metabolism.” Subsequently, detailed searches were conducted to assign each article to specific categories, providing a map of the weight of concepts, as detailed in Table 1.
Among all the identified articles, a selection is made of those that align with the stated objectives. Specifically, the focus is on articles aimed at characterizing industrial metabolism through analogy with natural systems.

3. Background of the Literature

In this section, a review is conducted on the main concepts considered in this study. The aim is to identify and establish the analogies between IM and natural systems, as well as analyze the approaches taken at different levels (micro, meso, and macro) to characterize IM from a bioinspired perspective.

3.1. Analogies between Natural Systems and Manufacturing Systems

3.1.1. Biological Analogy of Industrial Metabolism

The term “metabolism” refers to the internal processes of a living organism, which involve the intake of energy-rich, low-entropy materials to sustain its functions and grow [23]. This process also entails the elimination of waste materials, which are degraded and high-entropy substances. Biological organisms and industrial activities exhibit a complete analogy, as both process materials are driven by a flow of exergy and are self-organizing systems within a stable system [24].
The economic system is governed by rules or human components, involving direct and indirect labor as consumers [25]. Unlike natural systems, productive activity is not self-regulating or self-limiting but is stabilized through mechanisms such as price regulation of inputs and demand. The concept of IM can be applied at different levels, such as nations or regions [26], as long as well-defined boundaries exist to monitor the physical flows of materials and energy [24].
In biology, metabolism encompasses anabolic and catabolic reactions in living beings [23,27]. There are parallels between cellular metabolism and the metabolism of a manufacturing cell. Both involve material transport, product assembly (anabolism), and intermediate material storage. Enzymes catalyze biological reactions in cellular metabolism, while manufacturing cells utilize machinery or workstations [28]. The flow of substances within a cell resembles the flow of material in a manufacturing network [29,30].
However, the definition of metabolism can extend beyond the cellular level, encompassing material and energy flows at different functional levels of living systems [8]. Industrial ecology has led to the study of industrial ecosystem metabolism, focusing on the complexity and balance between synthesis and degradation processes, thus expanding the biological analogy [31]. While direct manufacturing (synthesis and anabolism) has seen significant development, reverse manufacturing (degradation or catabolism) is still a pending challenge, presenting an opportunity within the framework of the CE [32].
The analysis of IM seeks to understand the circulation of materials, water, and energy linked to human activity from their initial extraction to their reintegration into global biogeochemical or technical cycles within the technosphere, as a concept analogous with nature [8,33,34]. The following subsections describe each of these analogies and their relationships.

3.1.2. Ecosystem

An ecosystem includes abiotic and biotic components [27]. Each population in an ecosystem has a range of tolerance to environmental variations, which can limit population growth and survival [23], including productive analogies in the industry [35].
The concept of industrial ecosystem emerges from industrial ecology [36] and is used to model and analyze the energy, material, and economic networks of industrial manufacturing ecosystems [10]. In addition to a positive view, these systems present negative and competitive aspects, such as ecosystem-level competition, predation, parasitism, keystone species, resilience, and system destruction. To establish the analogy, it is necessary to analyze the problems of structural complexity, identify limiting factors of industrial ecosystem evolution, strengthen its capacity for adaptation and self-organization [37], and establish its boundaries [33] for design and modeling purposes [38]. Each actor in the ecosystem possesses different attributes, decision-making principles, and purposes, with coherence being the key concept [33], understood as the proportion of actors whose behavior aligns with their principles and objectives in an ecosystem [39].

3.1.3. Resilience in the Industrial Ecosystem

Resilience is the capacity of a system to recover its original state after changes in input and state variables while maintaining its essential characteristics and functional relationships [40,41]. Resilience is studied in various fields and disciplines, such as ecology, economics, socio-ecological systems, and engineering [42,43]. Studies on resilience in ecological, complex, and engineering systems have been analyzed [44]. Resilience in complex systems refers to the ability of the industrial system to sustain itself in an unstable environment [45]. Although resilience may lead to inefficiency, system capacities, resource reserves, and critical redundancy for resilience may be in opposition to efficiency logics [46]. In economy, resilience is divided into static [47] and dynamic [42], with established indicators for both [25]. In ecological systems, it is used to guide the design and management of industrial ecosystems to increase adaptability [48]. In engineering systems theory, network structure is important for system resilience [49]. Resilience characteristics include redundancy and flexibility; although they may decrease efficiency, they improve sustainability by allowing the system to handle various disturbances [42]. Thus, emerging countries specializing in natural resource supply are vulnerable and unstable, with high rates of economic growth but lower resilience [50].
Resilience at the eco-industrial park (EIP) level is analyzed through various characteristics and indices of the industrial manufacturing ecosystem, such as network topological characteristics, economical aspects, connectivity among network nodes, the number of roles referred to astrophic levels [51,52], and different indicators of network centrality and efficiency [53]. Additionally, metrics are used for sustainability and resilience in infrastructure construction and its interdependence with society [54,55]. Resilience is also analyzed by considering the removal of an important node (a key species from the natural analogy) and studying the trend of network efficiency change based on load and capacity at a node [56].
Resilience in the industrial manufacturing ecosystem is characterized by its topology, diversity, flexibility, stability, robustness, redundancy, and speed, while at the level of manufacturing systems, it is conceptualized in terms of vulnerability, adaptability, absorptive capacity, and recovery capacity [57,58]. CE can help make the industrial manufacturing ecosystem more resilient, following the analogy of natural systems [59].

3.1.4. Trophic Chain

The trophic chain classifies the biotic components of the ecosystem according to the feeding habits of organisms, dividing them into autotrophs and heterotrophs [27]. Autotrophs, known as producers, manufacture nutrients from compounds and energy in the environment, primarily from the sun, while heterotrophs feed on other living organisms. Heterotrophs are further subdivided into herbivores, carnivores, top predators, omnivores, decomposers, and detritivores [23]. In analogy with the industrial ecosystem, they are classified as producers, consumers, and decomposers [60], with a greater accumulation of energy and matter at higher trophic levels [61]. Industrial producers are companies that produce goods, including water and energy, while industrial consumers are companies that consume the goods produced by producers, and industrial decomposers transform waste into eco-compatible resources [62,63]. This imbalance in the technosphere and the importance of CE and vertical integration among companies emerge as relevant topics. In this line, the concept of a trophic chain contributes significantly to the characterization of IM by providing a structured framework to understand the flow of materials and energy within the industrial ecosystem. The trophic chain allows the modeling of each industry, following the biological analogy, as a producer, consumer, or decomposer.

3.1.5. Trophic Network

Organisms can feed on various prey and be preyed upon by multiple predators, situated at different trophic levels [23]. For greater accuracy, a trophic network can be employed, a graph that shows all the feeding interactions among species in an ecosystem. The arrowhead in the network indicates which organism feeds on another. Trophic networks serve two important objectives [36]: analyzing resource flows in ecosystems and dynamic interactions among species. The network allows for the identification of organisms, their trophic level, and their interactions, enabling the analysis of resource flows. In industry, Ecological Network Analysis (ENA) serves as an analogy to trophic networks [64,65].
In modeling and analyzing IM, ENA is used to characterize the metabolism in the network, its processes, and key pathways [66], identifying effective trophic levels and their efficiency [67], as well as industrial symbiosis relationships [68]. Analogous to ecological systems, it is observed that a network with less diversity has negative repercussions in the form of overdependence and reduced resilience to random perturbations [69]. This analogy becomes particularly insightful when examining cross-sector relationships within the value chain. It sheds light on symbiotic, predatory, and parasitic interactions, offering a succinct comparison between the natural trophic network and the intricate dynamics of interconnection within diverse industrial activities.

3.1.6. Metabolic Pathways

The analogy between cellular and manufacturing metabolic pathways emerges at different levels, highlighting the close connection between trophic networks and specific element pathways [70,71]. The analogy of IM refers to physical processes and resource flows between raw materials and energy. Production factors, such as labor and machinery, act as catalytic enzymes in the conversion of inputs into products [72]. Methodologies are employed to handle the complexity of IM, including temporal dynamics and the quantification of energy and material flows, as well as the embodied energy in facilities and maintenance [73].

3.1.7. Key Species

In ecology, it is hypothesized that certain species act as keystones, having a significant effect on other organisms in an ecosystem [27,74]. In industrial manufacturing, a key species can serve as an indicator of ecosystem stability, as its alteration can impact diversity and production [17,75,76]. Key companies in the industrial ecosystem should share value rather than deplete it [77], and within the supply chain context, identifying the key company is essential for maintaining the stability of the industrial system [78]. Identifying key products in industrial manufacturing is also important [79].
ENA is used to evaluate key species in a network based on their contribution to the overall network and biomass [80,81]. The concept of key species also applies to the value chain and supplier network in industrial manufacturing.

3.1.8. Industrial Habitat

The industrial habitat consists of a specific area with infrastructure and specific resources [82]. The following analogy can be made between natural habitat [27] and industrial habitat based on their components [60]: the soil or territory, water, air, and unlimited solar energy in the natural habitat correspond to the area, air, water supply and sewage, and energy network (different types of energy used, usually limited), respectively. However, the industrial habitat also includes information systems (telecommunications) and transportation (roads, railways, and airports), which do not have a direct analogy in the natural habitat. Although the transfer of information is observed in natural ecosystems and not in habitat [23].

3.1.9. Symbiosis at Eco-Industrial Park Level

The primary means of establishing the industrial habitat, drawing from a biological analogy, is through the implementation of EIP. Industrial symbiosis is a key approach wherein companies collaborate for the exchange of resources, yielding economic, environmental, and social benefits [83,84]. This includes the exchange of materials, energy, water, by-products, and information [85,86]. Industrial symbiosis studies are based on IM and analyze exchanges within a network of industries. The types of industrial symbiosis are classified according to the typology of EIP, as they are the most obvious examples of industrial symbiosis [87,88].
Based on the key species theory from the science of ecology, EIPs are divided into categories based on the presence of a single central company or multiple dominant companies [89], and the relationships among their members are classified into dependence, equality, and hierarchy [90,91]. According to their location, EIPs can be co-located near industries, which reduces costs and risks and optimizes environmental and economic benefits [89], or virtual, with the exchange of by-products and wastes separated by long distances [92]. In terms of formation and origin, EIPs can be promoted by government entities (planned parks) [93] or arise from private initiative (self-organized) [83]. Other approaches to parks include integrated sectors [91], which involve companies from different industrial sectors; specific-sector parks [94,95], with one or more central companies from the same sector and related enterprises; and reuse and recycling parks [92], focused on resource recovery. In terms of complexity, parks are divided into new and existing, with newly planned parks being built from scratch with the goal of connecting companies through shared infrastructure and facilities to reduce environmental impact [66]. Another classification of approaches to industrial symbiosis is based on the dynamics of symbiosis [96].

3.2. Levels of Industrial Metabolism

3.2.1. Micro Level: Industrial Plant

At the micro level, there are studies that analyze the metabolic flows of carbon at the manufacturing plant level [97,98,99], addressing the analysis of processes that compose the metabolic pathway and evaluating the system from an efficiency perspective. At this same level, based on Material Flow Analysis (MFA), company models are established to physically describe IM and the use of natural resources for different materials [100,101]. Another approach to manufacturing plant-level metabolism is through Energy Flow Analysis (EFA) and MFA [70], with the aim of improving flows while considering their effectiveness. At the industrial population level for the coal industry, there are models linked to metabolism and symbiotic relationships [102], as well as the establishment of pathways and their contextualization in the decarbonization-linked territory [103]. In the iron and steel industry, there are models based on IM through EFA [104], where the processes specific to this type of industry are identified, and energy flows are characterized for each activity [105]. Along this line, the IM of pathways is quantified using MFA to comparatively investigate energy efficiency, material efficiency, and emission intensity at the company level [106].

3.2.2. Meso Level: Eco-Industrial Park

EIPs are organized as industrial ecosystems that conserve natural and economic resources, reduce production costs, and provide opportunities to gain benefits through the use and sale of waste materials [62,107]. These EIPs are communities of manufacturing and service companies that seek to improve their economic, environmental, and resource performance through collaboration and the pursuit of collective benefits [108]. In this context, it is essential for companies to be able to self-regulate their behavior and self-organize effectively to facilitate self-regulation and information flows [109].
It is important to distinguish the terms industrial symbiosis, eco-industrial network, and EIP, as although they are sometimes used interchangeably, they differ in the scale of analysis, objectives, actors involved, and implementation [110]. The study of metabolism in EIPs aims to create a consortium of companies to strengthen synergies and support decision-making [111]. EIPs act as innovation platforms for environmental management and promote a systemic approach to the system rather than a point-specific treatment of environmental issues [112]. Methodologies are proposed that identify material and energy flows, define collective strategies, and compare scenarios through multi-criteria analysis [113]. In CE, hybrid frameworks are used to assess the benefits of EIPs with multi-criteria approaches based on indices configured using the Delphi method, considering economic, social, and environmental criteria, especially toxicity related to emissions per unit of value added [114]. Likewise, the use of Life Cycle Assessment (LCA) and product value chain assessment within the context of EIPs [115,116].
In the field of EIPs, IM is used to model the processes and metabolic pathways of elements such as chlorine, copper, sulfur, among others [117]. For chlorine, the Substance Flow Analysis (SFA) method is employed to analyze its metabolic pattern and identify the main metabolic pathways in chemical EIPs [9]. Indicators such as resource efficiency, yield rate, conversion rate, and emission factor are established to assess chlorine metabolism in EIPs [115]. In the case of copper, Sankey diagrams are used, and indices such as resource utilization efficiency, production efficiency, and system loss rate are proposed to quantify and evaluate copper metabolism in EIPs [118]. Regarding sulfur, SFA and ENA are employed to analyze the delivery processes, transformation, and internal characteristics of sulfur IM in EIPs [66]. SFA and LCA are also applied to identify impacts and material and energy flows related to sulfur [119].
In relation to carbon and nitrogen metabolism, SFA methods are used to establish IM models at the level of industrial parks, considering metabolic networks [120,121]. Likewise, phosphorus metabolism is analyzed at the EIP level through the use of SFA and the construction of metabolic networks, analyzing the metabolic structure and dynamics to improve water use efficiency and reduce water pollution [122].
There are two main methods for quantitatively analyzing the IM of industrial systems [123]: MFA [124] and SFA [125,126]. Both methods quantify flows and resources in the industrial ecosystem. MFA examines the complete life cycle of a material, but obtaining sufficient data to quantify material flows is challenging [127]. However, SFA traces the pathways of elements in each metabolic process, allowing the specification of flows, stocks, and efficiency. The use of MFA was not found in the reviewed literature, making SFA an important tool for studying the metabolism of specific elements at the meso level, such as copper [118], sulfur [66], or nitrogen [120].

3.2.3. Macro Level: Region

In the analysis of studies on regional IM, the impact of human activity on natural phosphorus cycles is found, which has led to serious environmental problems such as water eutrophication [128,129]. In this context, IM models have been proposed to analyze the metabolic pathways of this element at a regional level, which consider extraction, manufacturing, consumption, waste management, as well as imports, exports, and the biosphere [130,131]. To trace these metabolic pathways and analyze environmental pollutants, the SFA approach is used [132]. Additionally, in the context of chlorine, the MFA is employed to achieve a circular system in a specific region [133]. Regarding the evaluation of IM, studies have compared different input–output models, revealing differences based on residential consumption management, service sectors, waste recycling, and price assumptions [134].
At the national level, MFA is widely used [135,136,137,138,139,140]. This method allows the evaluation of the metabolic transition of regions over a specific period of time while also enabling the observation of metabolic patterns in a specific area [141,142]. In the same vein, other studies expand the scope of the flow accounting approach at the regional level to allow for future decision-making in the management domain [143]. However, it is noted that one of the fundamental aspects lacking in these methods, and many of the previously mentioned studies, is the incorporation of the dynamic aspect. Other approaches establish metabolic models at the national level based on socio-economic models and MFA, where the model is evaluated in economic and environmental terms [138,139]. Alternative approaches employ IM in the analysis and quantification of recycling cycles at the national level [144]. However, one of the main drawbacks of these models is their static nature. Therefore, some authors employ system dynamics to incorporate it into the regional IM model [145].
At the global level, analyses of phosphorus flows and their environmental impacts have been conducted [146]. They use the SFA and a metabolic pathway model to study these flows. They identify impacts such as mineral reserve conservation, soil erosion, fertility degradation, animal waste management, the use of wastewater and detergents, and eutrophication. Other studies propose a macro-model of phosphorus IM based on the CE [147]. SFA and economic, resource use efficiency, and eco-efficiency indicators are considered. In this line, other research develops an IM model that integrates the economic perspective and industrial ecology, based on MFA, input–output analysis, and LCA to model fixed capital stocks within a common framework [148], and others are based on emergy analysis [149].
Regarding the different approaches for quantifying metabolism, Daniels and Moore provide a comparison of various methods. They compare methods and techniques such as MFA, input–output analysis, SFA, ecological footprint analysis, environmental space, material intensity per unit of service, LCA, process sustainability index, and MFA [150]. Among them, MFA stands out as the most suitable for quantifying metabolism at both the micro and macro levels [150]. However, suitability depends on the objective, system boundaries, and its incorporation into a framework that allows for evaluating the goodness of the system. In addition, Sankey diagrams are identified as a useful tool for visualizing IM [151].
Regarding eco-efficiency [152], IM seeks to combine the prudent use of natural resources with economic efficiency to reduce their consumption [153,154]. It is considered a practical and suitable tool for measuring progress toward sustainable development [155]. Some authors define three indices of eco-efficiency for specific cases: resource use efficiency, energy efficiency, and environmental efficiency [156].
Within IM, the main drivers of unsustainable resource use are identified in different sectors [157]. These drivers include the categories of informational behavior, policy and regulatory framework, socio-economic, technological, and infrastructure. Some research delves into the analysis and characterization of endosomatic and exosomatic metabolism [158], linked to complex systems theory [159]. These studies propose controlling the import of advanced products and technology, as well as regulating product export [160], using thermoeconomics [161], and exosomatic metabolism indicators [162]. For endosomatic societal metabolism, the aim is to reduce primary industries, develop industries with high production efficiency, and scale up recycling industries [163].

4. Bioinspired Characterization of Industrial Metabolism

IM is characterized from a biomimetic perspective, emphasizing trophic network analysis. This analysis enables evaluating and managing metabolic pathways for cyclicality, efficiency, toxicity, and resilience, derived from knowledge of the food chain, key products, and companies. The cyclicality of resources through the industrial manufacturing system is achieved through the establishment of metabolic pathways. First, resource cycles are classified based on whether the nutrients belong to biological or technical cycles. Secondly, these metabolic pathways are divided into two main processes: anabolism (or synthesis) and catabolism (or degradation). Finally, both processes will determine the structure of the metabolic pathways, differentiating them as convergent or divergent. The efficiency of metabolism aims to maximize the use of resources and is highly dependent on the associated production process that transforms the resource. From an energy perspective, CE aims to work towards energy use based on renewable sources, made possible by the reduced energy requirements of a restorative CE [164]. A more integrated system allows for a reduction in the use of fossil fuels and an increase in the performance of by-products, which, once their cascade processes are completed, enhance their energy value in decomposition processes and reintegrate into biogeochemical cycles. Viewing the industrial manufacturing system as a living organism, toxicity within the metabolism is influenced by substance properties, dose, and the incorporation of toxic chemicals into products or manufacturing processes through various pathways. This significantly influences their toxicological consequences and pathway predominance. The toxicity perspective encompasses SFA, LCA for human and ecotoxicity, substance analysis from Cradle to Cradle (C2C) [13], and bioinspired/biomimetic design. This underscores the critical need to use toxicity indicators for a thorough assessment and mitigation of the environmental impacts associated with substances [165]. Likewise, the resilience of industrial metabolism refers to its ability to withstand, adapt, and recover from disturbances and changes in the environment.
Figure 1 structures the features of IM based on the review conducted. It establishes relationships that enable modeling IM in a generic manner, allowing for the customization of various aspects depending on the level of observation considered for modeling. Thus, two fundamental aspects of modeling emerge: the habitat and trophic networks. These aspects facilitate the modeling and characterization of industries, their specific relationships, and the features of material pathways (technical and biological nutrients) in terms of cyclicality, efficiency, and toxicity. Additionally, they contribute to understanding the resilience of the industrial network. This characterization is in line with and contributes to the development of the four fundamental objectives of CE [166]: (a) as a regenerative and restorative economic framework; (b) decoupling economic growth from environmental degradation; and (c) seeking to preserve economic, social, and environmental value while (d) contributing to the resilience of the system.
Based on the conducted research, a series of requirements are identified as innovation opportunities that should be incorporated into IM, enabling this approach to be feasible and implementable in sustainable manufacturing systems:
  • Integration of biomimetic approaches: Nature-inspired strategies for efficient and sustainable industrial systems.
  • Industrial trophic network analysis: Optimizing material and energy flows in the industrial supply chain.
  • CE focus: Minimizing waste and maximizing resource value throughout the product life cycle.
  • Efficiency and cyclicality of processes: Enhancing resource consumption and material reuse in manufacturing.
  • Toxicity management: Safeguarding human health and the environment through sustainable material choices.
  • Territorial contextualization: Contextual adaptation; tailoring strategies to local conditions for resource optimization and impact reduction.
  • Continuous evaluation and monitoring: Monitoring performance to drive improvement in environmental, social, and economic aspects.
Incorporating these requirements, at both the physical and virtual levels, and innovation opportunities into IM for CE will contribute to the transition towards more sustainable, efficient, and resilient manufacturing systems that promote resource conservation and sustainable development.
The approach to IM from a biomimetic perspective, inspired by natural ecosystems, allows for the structured development of CE for IM based on an operational approach at micro, meso, and macro levels. Figure 2 aims to illustrate, by way of example, how the characterized approaches to IM can be structured at different levels. In this context, the most suitable frameworks for the circularity of manufacturing systems at various levels are identified (Figure 2), as analyzed in previous studies [167]. The following describes each of these three levels and identifies some of the aspects that contribute to IM:
  • Micro Level: At the micro level, the focus is on the manufacturing company itself, aiming to improve eco-efficiency through clean production practices and frameworks aligned with the product metabolism and manufacturing process in the context of a CE. The company seeks to optimize resource utilization, minimize waste generation, and enhance environmental performance within its own operations.
  • Meso Level: Industrial ecology at the meso level fosters collaborative networks between companies and communities to optimize resource use, enhance energy and water management, and create symbiotic relationships based on the industrial food chain. This approach promotes sustainability, resilience, and reduced environmental impacts within the industrial ecosystem.
  • Macro Level: The macro level aims to restructure regional or national industrial systems for circularity, efficiency, and sustainability. It involves analyzing import/export flows, identifying externalities in other economies, and implementing policies, regulatory frameworks, and strategic planning to support the transition to a circular economy, considering economic, social, and environmental factors.
The IM approach addresses the micro, meso, and macro levels, promoting a holistic transformation towards circular and sustainable manufacturing systems.

5. Conclusions

The analysis provides an updated understanding of the IM concept and its applications in manufacturing system modeling. Studies focus on different observation levels (micro, meso, and macro), employing approaches such as the establishment of metabolic pathways at the micro and meso levels and material balance analysis at the macro level. The methods employed highlight the use of SFA for specific elements and MFA for global or aggregated analysis, with a lack of studies integrating the triple environmental, social, and economic perspective. The research emphasizes that each level of IM observation has its own characteristics and study approaches, and together they provide a more comprehensive understanding of IM and its implications. The combination of these levels can contribute to the design of more effective strategies and policies to promote sustainability and efficiency in the industrial realm. Furthermore, the suggested design approach plays a crucial role in fostering the conception and modeling of bioinspired manufacturing systems, deeply anchored in the IM concept. This framework facilitates a unified perspective across diverse levels of approximation, contingent upon specific cases, all under an integrated vision. It not only identifies pertinent natural analogies crucial for designing manufacturing systems but also serves as a unifying structure that consolidates the study domain of IM. This is particularly noteworthy as, historically, the study of IM has often been approached in a fragmented manner, focusing on specific levels and characteristics.
Furthermore, it has been identified that the common methodology for modeling and metabolic analysis of an EIP consists of the following: (1) establishing and defining the system/model boundaries; (2) identifying and grouping types of industries based on interspecies relationships criteria; (3) determining their position in the supply chain; (4) relationship with the process of the analyzed element; (5) identifying and quantifying inputs and outputs of each entity in the system; (6) employing a methodology for quantifying flows; and (7) choosing indicators for assessing the determined metabolism and subsequent decision-making.
In terms of model evaluation, five environmental sustainability indicators integrating industrial ecology principles for circular metabolism have been identified. These indicators mainly consider the following: (1) resource renewability; (2) emission toxicity; (3) input of used materials; (4) product recoverability at the end of use; and (5) process efficiency.
The analyzed contributions focus on the IM of specific substances, delineating their metabolic pathways in the system. Most publications concentrate on the environmental dimension, with some economic considerations. Categorization and evaluation of cyclicality and efficiency are emphasized, while studies addressing toxicity are less common. There is a significant lack of publications that comprehensively integrate the environmental, economic, and social dimensions, using recognized methods to evaluate different parameters.
Regarding the limits of the analogy, the concept of IM is applicable to manufacturing systems and companies, with the company serving as the economic analogy of a living organism. Although interesting differences exist, such as companies producing products or services and their ability to quickly change products or businesses, organisms are highly specialized and require long periods of evolution to alter their behavior.
For future research, exploring successful analogies between industrial metabolism and natural systems in manufacturing practices could offer practical insights. Additionally, investigating challenges and opportunities in applying metabolic concepts to industrial ecosystems, considering human-made complexities and social factors, is a promising area for further study.
Finally, IM presents opportunities for innovation through connectivity, integration, adaptation, and management, with the aim of mitigating and reversing the metabolic rift caused by linear manufacturing systems. For future work, the establishment of a control layer to guide metabolic pathways towards greater efficiency, safety, and circularity of flows is highlighted.

Author Contributions

Conceptualization, A.M.M.-G. and F.A.-G.; methodology, A.M.M.-G., M.J.Á.-G. and F.A.-G.; investigation, A.M.M.-G. and J.R.L.-R.; writing—original draft preparation, A.M.M.-G. and M.J.Á.-G.; writing—review and editing, A.M.M.-G. and J.R.L.-R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Foster, J.B. Marx’s Ecology: Materialism and Nature; Monthly Review Press: New York, NY, USA, 2000; ISBN 1583670114. [Google Scholar]
  2. Ayres, R.U.; Ayres, L. A Handbook of Industrial Ecology; Edward Elgar Publishing: Cheltenham, UK, 2002; ISBN 978-1-84064-506-4. [Google Scholar]
  3. Foster, J.B. Marx’s Theory of Metabolic Rift: Classical Foundations for Environmental Sociology. Am. J. Sociol. 1999, 105, 366–405. [Google Scholar] [CrossRef]
  4. Napoletano, B.M.; Urquijo, P.S.; Paneque-Gálvez, J.; Clark, B.; York, R.; Franch-Pardo, I.; Méndez-Lemus, Y.; Vieyra, A. Has (Even Marxist) Political Ecology Really Transcended the Metabolic Rift? Geoforum 2018, 92, 92–95. [Google Scholar] [CrossRef]
  5. Wijkman, A.; Skånberg, K. The Circular Economy and Benefits for Society Jobs and Climate Clear Winners in an Economy Based on Renewable Energy and Resource Efficiency. Available online: https://www.clubofrome.org (accessed on 29 September 2018).
  6. Marx, K. Capital I; Vintage: New York, NY, USA, 1976; ISBN 84-7259-046-1. [Google Scholar]
  7. Wernick, K. Industrial Metabolism. In International Encyclopedia of the Social & Behavioral Sciences; Elsevier: Amsterdam, The Netherlands, 2001; pp. 7331–7333. ISBN 9780080430768. [Google Scholar]
  8. Wassenaar, T. Reconsidering Industrial Metabolism: From Analogy to Denoting Actuality. J. Ind. Ecol. 2015, 19, 715–727. [Google Scholar] [CrossRef]
  9. Ma, L.; Zhao, S.; Shi, L. Industrial Metabolism of Chlorine in a Chemical Industrial Park: The Chinese Case. J. Clean. Prod. 2016, 112, 4367–4376. [Google Scholar] [CrossRef]
  10. Zhou, Y.; Yang, N.; Hu, S. Industrial Metabolism of PVC in China: A Dynamic Material Flow Analysis. Resour. Conserv. Recycl. 2013, 73, 33–40. [Google Scholar] [CrossRef]
  11. Blew, R.D. On the Definition of Ecosystem. Bull. Ecol. Soc. Am. 1996, 77, 171–173. [Google Scholar] [CrossRef]
  12. Ayres, R.U. Industrial Metabolism: Work in Progress. In Theory and Implementation of Economic Models for Sustainable Development. Economy & Environment; Springer: Dordrecht, The Netherlands, 1998. [Google Scholar]
  13. Braungart, M.; McDonough, W.; Bollinger, A. Cradle-to-Cradle Design: Creating Healthy Emissions—A Strategy for Eco-Effective Product and System Design. J. Clean. Prod. 2007, 15, 1337–1348. [Google Scholar] [CrossRef]
  14. Dai, T. A Study on Material Metabolism in Hebei Iron and Steel Industry Analysis. Resour. Conserv. Recycl. 2015, 95, 183–192. [Google Scholar] [CrossRef]
  15. Geissdoerfer, M.; Savaget, P.; Bocken, N.M.P.; Hultink, E.J. The Circular Economy—A New Sustainability Paradigm? J. Clean. Prod. 2017, 143, 757–768. [Google Scholar] [CrossRef]
  16. Kirchherr, J.; Yang, N.H.N.; Schulze-Spüntrup, F.; Heerink, M.J.; Hartley, K. Conceptualizing the Circular Economy (Revisited): An Analysis of 221 Definitions. Resour. Conserv. Recycl. 2023, 194, 107001. [Google Scholar] [CrossRef]
  17. Jensen, P.D.; Basson, L.; Leach, M. Reinterpreting Industrial Ecology. J. Ind. Ecol. 2011, 15, 680–692. [Google Scholar] [CrossRef]
  18. Dewulf, J.; Van Langenhove, H. Integrating Industrial Ecology Principles into a Set of Environmental Sustainability Indicators for Technology Assessment. Resour. Conserv. Recycl. 2005, 43, 419–432. [Google Scholar] [CrossRef]
  19. Albu, A. Business Innovation Using Industrial Ecology. Metal. Int. 2011, 16, 28–31. [Google Scholar]
  20. Faut, L.; Soyeur, F.; Haezendonck, E.; Dooms, M.; de Langen, P.W. Ensuring Circular Strategy Implementation: The Development of Circular Economy Indicators for Ports. Marit. Transp. Res. 2023, 4, 100087. [Google Scholar] [CrossRef]
  21. Mayer, P. Guidelines for Writing a Review Article. Zur.-Based Plant Sci. Cent. 2009, 92, 2084–2090. [Google Scholar] [CrossRef]
  22. Marying, P. Qualitative Content Analysis. Forum Qual. Res. 2000, 1, 10. [Google Scholar] [CrossRef]
  23. Smith, T.M.; Smith, R.L. Ecología; Sexta Edic.; Pearson Educación: London, UK, 2007; ISBN 9788478290840. [Google Scholar]
  24. Ayres, R.U. Industrial Metabolism: Restructuring for Sustainable Development; The United Nations University: Jingumae, Tokyo, 1994; ISBN 92-808-0841-9. [Google Scholar]
  25. Rose, A. Economic Resilience to Natural and Man-Made Disasters: Multidisciplinary Origins and Contextual Dimensions. Environ. Hazards 2007, 7, 383–398. [Google Scholar] [CrossRef]
  26. Klee, R.J. Industrial Metabolism on Ice: A Case Study of Industrial Materials Flows and Environmental Management Alternatives for Scott Base, New Zealand’s Antarctic Research Station. J. R. Soc. N. Z. 2001, 31, 393–409. [Google Scholar] [CrossRef]
  27. Miller, G.T.; Spoolman, S. Essentials of Ecology, 5th ed.; Cengage Learning: Boston, MA, USA, 2011; ISBN 978-0-495-55795-1. [Google Scholar]
  28. Becker, T.; Beber, M.E.; Windt, K.; Hütt, M.-T.; Helbing, D. Flow Control by Periodic Devices: A Unifying Language for the Description of Traffic, Production, and Metabolic Systems. J. Stat. Mech. Theory Exp. 2011, 5, P05004. [Google Scholar] [CrossRef]
  29. Despeisse, M.; Ball, P.D.; Evans, S.; Levers, A. Industrial Ecology at Factory Level—A Conceptual Model. J. Clean. Prod. 2012, 31, 30–39. [Google Scholar] [CrossRef]
  30. Dér, A.; Hingst, L.; Nyhuis, P.; Herrmann, C. A Review of Frameworks, Methods and Models for the Evaluation and Engineering of Factory Life Cycles. Adv. Ind. Manuf. Eng. 2022, 4, 100083. [Google Scholar] [CrossRef]
  31. Jelinski, L.W.; Graedel, T.E.; Laudise, R.A.; McCall, D.W.; Patel, C.K. Industrial Ecology: Concepts and Approaches. Proc. Natl. Acad. Sci. USA 1992, 89, 793–797. [Google Scholar] [CrossRef] [PubMed]
  32. Butzer, S.; Schötz, S.; Petroschke, M.; Steinhilper, R. Development of a Performance Measurement System for International Reverse Supply Chains. Procedia CIRP 2017, 61, 251–256. [Google Scholar] [CrossRef]
  33. Tsujimoto, M.; Kajikawa, Y.; Tomita, J.; Matsumoto, Y. A Review of the Ecosystem Concept—Towards Coherent Ecosystem Design. Technol. Forecast. Soc. Change 2018, 136, 49–58. [Google Scholar] [CrossRef]
  34. Pulido Barrera, P.; Rosales Carreón, J.; de Boer, H.J. A Multi-Level Framework for Metabolism in Urban Energy Systems from an Ecological Perspective. Resour. Conserv. Recycl. 2018, 132, 230–238. [Google Scholar] [CrossRef]
  35. Horodytska, O.; Valdés, F.J.; Fullana, A. Plastic Flexible Films Waste Management—A State of Art Review. Waste Manag. 2018, 77, 413–425. [Google Scholar] [CrossRef] [PubMed]
  36. Graedel, T.E. On the Concept of Industrial Ecology. Annu. Rev. Energy Environ. 1996, 21, 69–98. [Google Scholar] [CrossRef]
  37. Chen, L.; Wang, R.; Yang, J.; Shi, Y. Structural Complexity Analysis for Industrial Ecosystems: A Case Study on LuBei Industrial Ecosystem in China. Ecol. Complex. 2010, 7, 179–187. [Google Scholar] [CrossRef]
  38. Weber, J.L. Implementation of Land and Ecosystem Accounts at the European Environment Agency. Ecol. Econ. 2007, 61, 695–707. [Google Scholar] [CrossRef]
  39. Gu, Y.; Hu, L.; Zhang, H.; Hou, C. Innovation Ecosystem Research: Emerging Trends and Future Research. Sustainability 2021, 13, 11458. [Google Scholar] [CrossRef]
  40. Van Bueren, E.; van Bohemen, H.; Visscher, H. Sustainable Urban Environments; Springer: Berlin/Heidelberg, Germany, 2011; ISBN 9789400712935. [Google Scholar]
  41. Peng, C.; Yuan, M.; Gu, C.; Peng, Z.; Ming, T. A Review of the Theory and Practice of Regional Resilience. Sustain. Cities Soc. 2017, 29, 86–96. [Google Scholar] [CrossRef]
  42. Meerow, S.; Newell, J.P. Resilience and Complexity: A Bibliometric Review and Prospects for Industrial Ecology. J. Ind. Ecol. 2015, 19, 236–251. [Google Scholar] [CrossRef]
  43. Bergström, J.; Van Winsen, R.; Henriqson, E. On the Rationale of Resilience in the Domain of Safety: A Literature Review. Reliab. Eng. Syst. Saf. 2015, 141, 131–141. [Google Scholar] [CrossRef]
  44. Fraccascia, L.; Giannoccaro, I.; Albino, V. Rethinking Resilience in Industrial Symbiosis: Conceptualization and Measurements. Ecol. Econ. 2017, 137, 148–162. [Google Scholar] [CrossRef]
  45. Korhonen, J.; Seager, T.P. Beyond Eco-Efficiency: A Resilience Perspective. Bus. Strategy Env. 2008, 17, 411–419. [Google Scholar] [CrossRef]
  46. Korhonen, J.; Snäkin, J.-P. Quantifying the Relationship of Resilience and Eco-Efficiency in Complex Adaptive Energy Systems. Ecol. Econ. 2015, 120, 83–92. [Google Scholar] [CrossRef]
  47. Gu, X.; Jin, X.; Ni, J.; Koren, Y. Manufacturing System Design for Resilience. Procedia CIRP 2015, 36, 135–140. [Google Scholar] [CrossRef]
  48. Gruner, R.L.; Power, D. Mimicking Natural Ecosystems to Develop Sustainable Supply Chains: A Theory of Socio-Ecological Intergradation. J. Clean. Prod. 2017, 149, 251–264. [Google Scholar] [CrossRef]
  49. Fan, Y.; Qiao, Q.; Fang, L. Network Analysis of Industrial Metabolism in Industrial Park—A Case Study of Huai’an Economic and Technological Development Area. J. Clean. Prod. 2017, 142, 1552–1561. [Google Scholar] [CrossRef]
  50. Muradian, R.; Walter, M.; Martinez-Alier, J. Hegemonic Transitions and Global Shifts in Social Metabolism: Implications for Resource-Rich Countries. Introduction to the Special Section. Glob. Environ. Change 2012, 22, 559–567. [Google Scholar] [CrossRef]
  51. Kharrazi, A.; Kraines, S.; Rovenskaya, E.; Avtar, R.; Iwata, S.; Yarime, M. Examining the Ecology of Commodity Trade Networks Using an Ecological Information-Based Approach: Toward Strategic Assessment of Resilience. J. Ind. Ecol. 2015, 19, 805–813. [Google Scholar] [CrossRef]
  52. Pan, S.C.; Hu, T.S.; You, J.X.; Chang, S.L. Characteristics and Influencing Factors of Economic Resilience in Industrial Parks. Heliyon 2023, 9, e14812. [Google Scholar] [CrossRef] [PubMed]
  53. Chopra, S.S.; Khanna, V. Understanding Resilience in Industrial Symbiosis Networks: Insights from Network Analysis. J. Env. Manag. 2014, 141, 86–94. [Google Scholar] [CrossRef]
  54. Marjaba, G.E.; Chidiac, S.E. Sustainability and Resiliency Metrics for Buildings—Critical Review. Build. Env. 2016, 101, 116–125. [Google Scholar] [CrossRef]
  55. Shah, S.S.; Babiceanu, R.F. Resilience Modeling and Analysis of Interdependent Infrastructure Systems. In Proceedings of the 2015 Systems and Information Engineering Design Symposium, SIEDS 2015, Charlottesville, VA, USA, 24 April 2015; pp. 154–158. [Google Scholar]
  56. Li, X.; Xiao, R. Analyzing Network Topological Characteristics of Eco-Industrial Parks from the Perspective of Resilience: A Case Study. Ecol. Indic. 2017, 74, 403–413. [Google Scholar] [CrossRef]
  57. Cerѐ, G.; Rezgui, Y.; Zhao, W. Critical Review of Existing Built Environment Resilience Frameworks: Directions for Future Research. Int. J. Disaster Risk Reduct. 2017, 25, 173–189. [Google Scholar] [CrossRef]
  58. Francis, R.; Bekera, B. A Metric and Frameworks for Resilience Analysis of Engineered and Infrastructure Systems. Reliab. Eng. Syst. Saf. 2014, 121, 90–103. [Google Scholar] [CrossRef]
  59. Aguiñaga, E.; Henriques, I.; Scheel, C.; Scheel, A. Building Resilience: A Self-Sustainable Community Approach to the Triple Bottom Line. J. Clean. Prod. 2017, 173, 186–196. [Google Scholar] [CrossRef]
  60. Liwarska-Bizukojc, E. The Conceptual Model of an Eco-Industrial Park Based upon Ecological Relationships. J. Clean. Prod. 2009, 17, 732–741. [Google Scholar] [CrossRef]
  61. Ruth, M. Advanced Introduction to Ecological Economics; Edward Elgar Publishing Ltd.: Cheltenham, UK, 2018; ISBN 1786433524. [Google Scholar]
  62. Côté, R.; Hall, J. Industrial Parks as Ecosystems. J. Clean. Prod. 1995, 3, 41–46. [Google Scholar] [CrossRef]
  63. Martín-Gómez, A.M.; Aguayo-González, F.; Marcos-Bárcena, M. Smart Eco-Industrial Parks: A Circular Economy Implementation Based on Industrial Metabolism. Resour. Conserv. Recycl. 2018, 135, 58–69. [Google Scholar] [CrossRef]
  64. Ulanowicz, R.E. Quantitative Methods for Ecological Network Analysis. Comput. Biol. Chem. 2004, 28, 321–339. [Google Scholar] [CrossRef] [PubMed]
  65. Borrett, S.R.; Sheble, L.; Moody, J.; Anway, E.C. Bibliometric Review of Ecological Network Analysis: 2010–2016. Ecol. Model. 2018, 382, 63–82. [Google Scholar] [CrossRef]
  66. Zhang, Y.; Zheng, H.; Yang, Z.; Liu, G.; Su, M. Analysis of the Industrial Metabolic Processes for Sulfur in the Lubei (Shandong Province, China) Eco-Industrial Park. J. Clean. Prod. 2014, 96, 126–138. [Google Scholar] [CrossRef]
  67. Fiscus, D.A. Comparative Network Analysis toward Characterization of Systemic Organization for Human-Environmental Sustainability. Ecol. Model. 2009, 220, 3123–3132. [Google Scholar] [CrossRef]
  68. Song, X.; Geng, Y.; Dong, H.; Chen, W. Social Network Analysis on Industrial Symbiosis: A Case of Gujiao Eco-Industrial Park. J. Clean. Prod. 2018, 193, 414–423. [Google Scholar] [CrossRef]
  69. Layton, A.; Bras, B.; Weissburg, M. Improving Performance of Eco-Industrial Parks. Int. J. Sustain. Eng. 2017, 10, 250–259. [Google Scholar] [CrossRef]
  70. Rodríguez, M.T.T.; Andrade, L.C.; Bugallo, P.M.B.; Long, J.J.C. Combining LCT Tools for the Optimization of an Industrial Process: Material and Energy Flow Analysis and Best Available Techniques. J. Hazard. Mater. 2011, 192, 1705–1719. [Google Scholar] [CrossRef]
  71. Zhao, G.; Chen, J.; Zhang, H.; Jiang, Z. Industrial Metabolic Pathway Analysis and Flux Control for the Metallurgical System. Smart Innov. Syst. Technol. 2019, 155, 421–431. [Google Scholar] [CrossRef]
  72. Mustafin, A.; Kantarbayeva, A. Supply Chain Modeled as a Metabolic Pathway. Math. Model. Anal. 2018, 23, 473–491. [Google Scholar] [CrossRef]
  73. Pierie, F.; Bekkering, J.; Benders, R.M.J.; van Gemert, W.J.T.; Moll, H.C. A New Approach for Measuring the Environmental Sustainability of Renewable Energy Production Systems: Focused on the Modelling of Green Gas Production Pathways. Appl. Energy 2016, 162, 131–138. [Google Scholar] [CrossRef]
  74. Paine, R.T. A Note on Trophic Complexity and Community Stability. Am. Nat. 1969, 103, 91–93. [Google Scholar] [CrossRef]
  75. Reed, B. Shifting from ‘Sustainability’ to Regeneration. Build. Res. Inf. 2007, 35, 674–680. [Google Scholar] [CrossRef]
  76. Wells, P.E. Re-Writing the Ecological Metaphor: Part 1. Prog. Ind. Ecol. Int. J. 2006, 3, 114. [Google Scholar] [CrossRef]
  77. Sriram, K.; Ganesh, L.S.; Madhumathi, R. Inferring Principles for Sustainable Development of Business through Analogies from Ecological Systems. IIMB Manag. Rev. 2013, 25, 36–48. [Google Scholar] [CrossRef]
  78. Zhang, H.; Sun, X.; Zhu, X.; Li, Y. Structure Stability Analysis of Industrial System and Construction of Eco-Industrial Chain: A Case Study of Lianyungang Xuwei New Area, China. Acta Ecol. Sin. 2014, 34, 255–260. [Google Scholar] [CrossRef]
  79. Levine, S.H. Products and Ecological Models A Population Ecology Perspective. J. Ind. Ecol. 2000, 3, 47–62. [Google Scholar] [CrossRef]
  80. Libralato, S.; Christensen, V.; Pauly, D. A Method for Identifying Keystone Species in Food Web Models. Ecol. Model. 2006, 195, 153–171. [Google Scholar] [CrossRef]
  81. Mukherjee, J.; Karan, S.; Chakrabarty, M.; Banerjee, A.; Rakshit, N.; Ray, S. An Approach towards Quantification of Ecosystem Trophic Status and Health through Ecological Network Analysis Applied in Hooghly-Matla Estuarine System, India. Ecol. Indic. 2018, 100, 55–68. [Google Scholar] [CrossRef]
  82. Cerceau, J.; Mat, N.; Junqua, G. Territorial Embeddedness of Natural Resource Management: A Perspective through the Implementation of Industrial Ecology. Geoforum 2018, 89, 29–42. [Google Scholar] [CrossRef]
  83. Albino, V.; Fraccascia, L.; Giannoccaro, I. Exploring the Role of Contracts to Support the Emergence of Self-Organized Industrial Symbiosis Networks: An Agent-Based Simulation Study. J. Clean. Prod. 2016, 112, 4353–4366. [Google Scholar] [CrossRef]
  84. Demartini, M.; Tonelli, F.; Govindan, K. An Investigation into Modelling Approaches for Industrial Symbiosis: A Literature Review and Research Agenda. Clean. Logist. Supply Chain. 2022, 3, 100020. [Google Scholar] [CrossRef]
  85. Chertow, M.R. Industrial Symbiosis. In Encyclopedia of Energy; Elsevier: Amsterdam, The Netherlands, 2004; pp. 407–415. ISBN 9780121764807. [Google Scholar]
  86. Butturi, M.A.; Lolli, F.; Sellitto, M.A.; Balugani, E.; Gamberini, R.; Rimini, B. Renewable Energy in Eco-Industrial Parks and Urban-Industrial Symbiosis: A Literature Review and a Conceptual Synthesis. Appl. Energy 2019, 255, 113825. [Google Scholar] [CrossRef]
  87. Zhang, Y.; Zheng, H.; Chen, B.; Su, M.; Liu, G. A Review of Industrial Symbiosis Research: Theory and Methodology. Front. Earth Sci. 2015, 9, 91–104. [Google Scholar] [CrossRef]
  88. Perrucci, D.V.; Aktaş, C.B.; Sorentino, J.; Akanbi, H.; Curabba, J. A Review of International Eco-Industrial Parks for Implementation Success in the United States. City Environ. Interact. 2022, 16, 100086. [Google Scholar] [CrossRef]
  89. Jacobsen, N.B. Industrial Symbiosis in Kalundborg, Denmark: A Quantitative Assessment of Economic and Environmental Aspects. J. Ind. Ecol. 2008, 10, 239–255. [Google Scholar] [CrossRef]
  90. Wang, G.; Feng, X.; Khim Hoong, C. Symbiosis Analysis on Industrial Ecological System. Chin. J. Chem. Eng. 2014, 22, 690–698. [Google Scholar] [CrossRef]
  91. Fan, Y.; Qiao, Q.; Fang, L.; Yao, Y. Emergy Analysis on Industrial Symbiosis of an Industrial Park- A Case Study of Hefei Economic and Technological Development Area. J. Clean. Prod. 2017, 141, 791–798. [Google Scholar] [CrossRef]
  92. Cecelja, F.; Raafat, T.; Trokanas, N.; Innes, S.; Smith, M.; Yang, A.; Zorgios, Y.; Korkofygas, A.; Kokossis, A. E-Symbiosis: Technology-Enabled Support for Industrial Symbiosis Targeting Small and Medium Enterprises and Innovation. J. Clean. Prod. 2015, 98, 336–352. [Google Scholar] [CrossRef]
  93. Park, H.S.; Rene, E.R.; Choi, S.M.; Chiu, A.S.F. Strategies for Sustainable Development of Industrial Park in Ulsan, South Korea-From Spontaneous Evolution to Systematic Expansion of Industrial Symbiosis. J. Env. Manag. 2008, 87, 1–13. [Google Scholar] [CrossRef]
  94. Wen, Z.; Meng, X. Quantitative Assessment of Industrial Symbiosis for the Promotion of Circular Economy: A Case Study of the Printed Circuit Boards Industry in China’s Suzhou New District. J. Clean. Prod. 2015, 90, 211–219. [Google Scholar] [CrossRef]
  95. Illsley, B.; Jackson, T.; Lynch, B. Addressing Scottish Rural Fuel Poverty through a Regional Industrial Symbiosis Strategy for the Scottish Forest Industries Sector. Geoforum 2007, 38, 21–32. [Google Scholar] [CrossRef]
  96. Boons, F.; Chertow, M.; Park, J.; Spekkink, W.; Shi, H. Industrial Symbiosis Dynamics and the Problem of Equivalence. J. Ind. Ecol. 2016, 21, 938–952. [Google Scholar] [CrossRef]
  97. Liu, L.; Cui, Z.; Zhu, L. Industrial Metabolism of Carbon in an Alcohol Fermentation Factory. Fresenius Env. Bull. 2009, 18, 2040–2047. [Google Scholar]
  98. Yu, S.; Lin, F.; Zhao, G.; Chen, J.; Zhang, Z.; Zhang, H. Accurate Carbon Accounting Based on Industrial Metabolism for the Lean Management of Carbon Emission. Energy Rep. 2023, 9, 3872–3880. [Google Scholar] [CrossRef]
  99. Chen, J.; Zhang, H.; Zhao, G.; Qureshi, A.S. A Novel Method for Estimating Carbon Emission Based on Industrial Metabolism: Blast Furnace Iron-Making with Micro Mechanism Model. Energy Rep. 2022, 8, 10125–10133. [Google Scholar] [CrossRef]
  100. Kósi, K.; Torma, A. Tracing Material Flows on Industrial Sites. Period. Polytech. Soc. Manag. Sci. 2005, 13, 133–155. [Google Scholar]
  101. de la Torre de Palacios, L.; Espí Rodríguez, J.A. Dematerialisation and Material Flow Analysis as Sustainability Indicators in the Extraction of Mineral Resources. Resour. Policy 2022, 79, 103131. [Google Scholar] [CrossRef]
  102. Wang, D.; Wang, Y.; Song, X. Evolution Model with Time Lag Effects for the Coal Industrial Symbiosis System: A Case Study of Ordos, China. J. Clean. Prod. 2018, 187, 863–876. [Google Scholar] [CrossRef]
  103. Schneider, C.; Lechtenböhmer, S. Concepts and Pathways towards a Carbon-Neutral Heavy Industry in the German Federal State of North Rhine-Westphalia. In Proceedings of the Eceee Industrial Summer Study Proceedings; European Council for an Energy Efficient Economy (ECEEE), Berlin, Germany, 11–13 June 2018; pp. 443–453. [Google Scholar]
  104. Sun, Q.; Li, H.; Xu, B.; Cheng, L.; Wennersten, R. Analysis of Secondary Energy in China’s Iron and Steel Industry—An Approach of Industrial Metabolism. Int. J. Green. Energy 2016, 13, 793–802. [Google Scholar] [CrossRef]
  105. Hou, F.; Zhao, G.; Zhang, H.; Zhang, H.; Huang, C. Evaluating the Energy Properties of the Short-Flow Process Based on the Industrial Metabolism: A Case Study on the Metal 3D Printing of Hybrid Deposition and Micro Rolling (HDMR). J. Manuf. Process 2023, 97, 125–136. [Google Scholar] [CrossRef]
  106. Li, X.; Sun, W.; Zhao, L.; Cai, J. Material Metabolism and Environmental Emissions of BF-BOF and EAF Steel Production Routes. Miner. Process. Extr. Metall. Rev. 2018, 39, 50–58. [Google Scholar] [CrossRef]
  107. Chertow, M. The Source of Value: An Executive Briefing and Sourcebook on Industrial Ecology. J. Ind. Ecol. 1997, 1, 151–152. [Google Scholar] [CrossRef]
  108. Roberts, B.H. The Application of Industrial Ecology Principles and Planning Guidelines for the Development of Eco-Industrial Parks: An Australian Case Study. J. Clean. Prod. 2004, 12, 997–1010. [Google Scholar] [CrossRef]
  109. Batten, D.F. Fostering Industrial Symbiosis with Agent-Based Simulation and Participatory Modeling. J. Ind. Ecol. 2009, 13, 197–213. [Google Scholar] [CrossRef]
  110. Winans, K.; Kendall, A.; Deng, H. The History and Current Applications of the Circular Economy Concept. Renew. Sustain. Energy Rev. 2017, 68, 825–833. [Google Scholar] [CrossRef]
  111. Harpet, C.; Gully, E.; Blavot, C.; Mehu, J.; Bonnet, J. Seeking Industrial Synergies in the French Chemical Valley Territory: A Methodological Approach for Decision Support. Prog. Ind. Ecol. Int. J. 2013, 8, 92. [Google Scholar] [CrossRef]
  112. Dong, L.; Fujita, T.; Dai, M.; Geng, Y.; Ren, J.; Fujii, M.; Wang, Y.; Ohnishi, S. Towards Preventative Eco-Industrial Development: An Industrial and Urban Symbiosis Case in One Typical Industrial City in China. J. Clean. Prod. 2016, 114, 387–400. [Google Scholar] [CrossRef]
  113. Tröger, D.; Araneda, A.A.B.; Busnelli, R.; Yajnes, M.; Williams, F.; Braun, A.C. Exploring Eco-Industrial Development in the Global South: Recognizing Informal Waste-Picking as Urban-Industrial Symbiosis? Clean. Waste Syst. 2023, 5, 100096. [Google Scholar] [CrossRef]
  114. Zhao, H.; Zhao, H.; Guo, S. Evaluating the Comprehensive Benefit of Eco-Industrial Parks by Employing Multi-Criteria Decision Making Approach for Circular Economy. J. Clean. Prod. 2016, 142, 2262–2276. [Google Scholar] [CrossRef]
  115. Han, F.; Li, W.; Yu, F.; Cui, Z. Industrial Metabolism of Chlorine: A Case Study of a Chlor-Alkali Industrial Chain. Env. Sci. Pollut. Res. 2014, 21, 5810–5817. [Google Scholar] [CrossRef]
  116. Hu, Q.; Huang, H.; Kung, C.C. Ecological Impact Assessment of Land Use in Eco-Industrial Park Based on Life Cycle Assessment: A Case Study of Nanchang High-Tech Development Zone in China. J. Clean. Prod. 2021, 300, 126816. [Google Scholar] [CrossRef]
  117. Náray-Szabó, G.; Mika, L.T. Conservative Evolution and Industrial Metabolism in Green Chemistry. Green. Chem. 2018, 20, 2171–2191. [Google Scholar] [CrossRef]
  118. Han, F.; Yu, F.; Cui, Z. Industrial Metabolism of Copper and Sulfur in a Copper-Specific Eco-Industrial Park in China. J. Clean. Prod. 2016, 133, 459–466. [Google Scholar] [CrossRef]
  119. Tian, J.; Shi, H.; Chen, Y.; Chen, L. Assessment of Industrial Metabolisms of Sulfur in a Chinese Fine Chemical Industrial Park. J. Clean. Prod. 2012, 32, 262–272. [Google Scholar] [CrossRef]
  120. Wu, J.; Shi, L. Nitrogen Metabolism in Industrial Parks: A Case of Yixing Economic Development Zone. Acta Ecol. Sin. 2010, 30, 6208–6217. [Google Scholar]
  121. Tian, J.; Guo, Q.; Chen, Y.; Li, X.; Shi, H.; Chen, L. Study on Industrial Metabolism of Carbon in a Chinese Fine Chemical Industrial Park. Env. Sci. Technol. 2013, 47, 1048–1056. [Google Scholar] [CrossRef]
  122. Wu, J.; Shi, L. Phosphorus Metabolism in Industrial Parks: A Case Study of Yixing Economic Development Zone. Acta Ecol. Sin. 2010, 30, 2397–2405. [Google Scholar]
  123. Janssen, M.A.; Van Den Bergh, J.C.J.M.; Van Beukering, P.J.H.; Hoekstra, R. Changing Industrial Metabolism: Methods for Analysis. Popul. Env. 2001, 23, 139–156. [Google Scholar] [CrossRef]
  124. Brunner, P.H.; Helmut, R. Practical Handbook of Material Flow Analysis; CRC/Lewis: Boca Raton, FL, USA, 2004; ISBN 9781566706049. [Google Scholar]
  125. Loiseau, E.; Junqua, G.; Roux, P.; Bellon-Maurel, V. Environmental Assessment of a Territory: An Overview of Existing Tools and Methods. J. Env. Manag. 2012, 112, 213–225. [Google Scholar] [CrossRef]
  126. Bai, L.; Qiao, Q.; Li, Y.; Xie, M.; Wan, S.; Zhong, Q. Substance Flow Analysis of Production Process: A Case Study of a Lead Smelting Process. J. Clean. Prod. 2015, 104, 502–512. [Google Scholar] [CrossRef]
  127. Sendra, C.; Gabarrell, X.; Vicent, T. Material Flow Analysis Adapted to an Industrial Area. J. Clean. Prod. 2007, 15, 1706–1715. [Google Scholar] [CrossRef]
  128. Yuan, Z.; Sun, L.; Bi, J.; Wu, H.; Zhang, L. Phosphorus Flow Analysis of the Socioeconomic Ecosystem of Shucheng County, China. Ecol. Appl. 2011, 21, 2822–2832. [Google Scholar] [CrossRef]
  129. Yuan, Z.; Shi, J.; Wu, H.; Zhang, L.; Bi, J. Understanding the Anthropogenic Phosphorus Pathway with Substance Flow Analysis at the City Level. J. Env. Manag. 2011, 92, 2021–2028. [Google Scholar] [CrossRef]
  130. Bi, J.; Chen, Q.; Zhang, L.; Yuan, Z. Quantifying Phosphorus Flow Pathways Through Socioeconomic Systems at the County Level in China. J. Ind. Ecol. 2013, 17, 452–460. [Google Scholar] [CrossRef]
  131. Yamano, H.; Sakata, I. Supply Chain Dynamics beyond Optimization: Metabolism of Regional Inter-Firm Networks. Heliyon 2023, 9, e16104. [Google Scholar] [CrossRef]
  132. Yuan, Z.; Liu, X.; Wu, H.; Zhang, L.; Bi, J. Anthropogenic Phosphorus Flow Analysis of Lujiang County, Anhui Province, Central China. Ecol. Model. 2011, 222, 1534–1543. [Google Scholar] [CrossRef]
  133. Kumagai, S.; Lu, J.; Fukushima, Y.; Ohno, H.; Kameda, T.; Yoshioka, T. Diagnosing Chlorine Industrial Metabolism by Evaluating the Potential of Chlorine Recovery from Polyvinyl Chloride Wastes—A Case Study in Japan. Resour. Conserv. Recycl. 2018, 133, 354–361. [Google Scholar] [CrossRef]
  134. Liang, S.; Zhang, T. Investigating Reasons for Differences in the Results of Environmental, Physical, and Hybrid Input-Output Models. J. Ind. Ecol. 2013, 17, 432–439. [Google Scholar] [CrossRef]
  135. Ščasný, M.; Kovanda, J.; Hák, T. Material Flow Accounts, Balances and Derived Indicators for the Czech Republic during the 1990s: Results and Recommendations for Methodological Improvements. Ecol. Econ. 2003, 45, 41–57. [Google Scholar] [CrossRef]
  136. Kovanda, J.; van de Sand, I.; Schütz, H.; Bringezu, S. Economy-Wide Material Flow Indicators: Overall Framework, Purposes and Uses and Comparison of Material Use and Resource Intensity of the Czech Republic, Germany and the EU-15. Ecol. Indic. 2012, 17, 88–98. [Google Scholar] [CrossRef]
  137. Kovanda, J.; Hak, T. Historical Perspectives of Material Use in Czechoslovakia in 1855–2007. Ecol. Indic. 2011, 11, 1375–1384. [Google Scholar] [CrossRef]
  138. Kovanda, J.; Havranek, M.; Hak, T. Calculation of the“Net Additions to Stock” Indicator for the Czech Republic Using a Direct Method. J. Ind. Ecol. 2007, 11, 140–154. [Google Scholar] [CrossRef]
  139. Xu, M.; Zhang, T. Material Flows and Economic Growth in Developing China. J. Ind. Ecol. 2007, 11, 121–140. [Google Scholar] [CrossRef]
  140. Infante-Amate, J.; Soto, D.; Aguilera, E.; García-Ruiz, R.; Guzmán, G.; Cid, A.; de Molina, M.G. The Spanish Transition to Industrial Metabolism: Long-Term Material Flow Analysis (1860–2010). J. Ind. Ecol. 2015, 19, 866–876. [Google Scholar] [CrossRef]
  141. Soto, D.; Infante-Amate, J.; Guzmán, G.I.; Cid, A.; Aguilera, E.; García, R.; González de Molina, M. The Social Metabolism of Biomass in Spain, 1900–2008: From Food to Feed-Oriented Changes in the Agro-Ecosystems. Ecol. Econ. 2016, 128, 130–138. [Google Scholar] [CrossRef]
  142. Schaffartzik, A.; Mayer, A.; Gingrich, S.; Eisenmenger, N.; Loy, C.; Krausmann, F. The Global Metabolic Transition: Regional Patterns and Trends of Global Material Flows, 1950–2010. Glob. Environ. Change 2014, 26, 87–97. [Google Scholar] [CrossRef]
  143. McMillan, C.A.; Moore, M.R.; Keoleian, G.A.; Bulkley, J.W. Quantifying U.S. Aluminum in-Use Stocks and Their Relationship with Economic Output. Ecol. Econ. 2010, 69, 2606–2613. [Google Scholar] [CrossRef]
  144. Ciacci, L.; Werner, T.T.; Vassura, I.; Passarini, F. Backlighting the European Indium Recycling Potentials. J. Ind. Ecol. 2018. [CrossRef]
  145. Lennox, J.A.; Turner, G.; Hoffman, R.; McInnis, B. Modeling Basic Industries in the Australian Stocks and Flows Framework. J. Ind. Ecol. 2004, 8, 101–120. [Google Scholar] [CrossRef]
  146. Liu, Y.; Villalba, G.; Ayres, R.U.; Schroder, H. Global Phosphorus Flows and Environmental Impacts from a Consumption Perspective. J. Ind. Ecol. 2008, 12, 229–247. [Google Scholar] [CrossRef]
  147. Ma, S.; Hu, S.; Chen, D.; Zhu, B. A Case Study of a Phosphorus Chemical Firm’s Application of Resource Efficiency and Eco-Efficiency in Industrial Metabolism under Circular Economy. J. Clean. Prod. 2015, 87, 839–849. [Google Scholar] [CrossRef]
  148. Pauliuk, S.; Wood, R.; Hertwich, E.G. Dynamic Models of Fixed Capital Stocks and Their Application in Industrial Ecology. J. Ind. Ecol. 2015, 19, 104–116. [Google Scholar] [CrossRef]
  149. Gan, L.; Wan, X.; Ma, Y.; Lev, B. Efficiency Evaluation for Urban Industrial Metabolism through the Methodologies of Emergy Analysis and Dynamic Network Stochastic Block Model. Sustain. Cities Soc. 2023, 90, 104396. [Google Scholar] [CrossRef]
  150. Daniels, P.L. Approaches for Quantifying the Metabolism of Physical Economies: A Comparative Survey: Part II: Review of Individual Approaches. J. Ind. Ecol. 2002, 6, 65–88. [Google Scholar] [CrossRef]
  151. Schmidt, M. The Sankey Diagram in Energy and Material Flow Management. J. Ind. Ecol. 2008, 12, 82–94. [Google Scholar] [CrossRef]
  152. Keffer, C.; Shimp, R.; Lehni, M. Eco-Efficiency Indicators & Reporting, Report on the Status of the Project’s Work in Progress and Guideline for Pilot Application; WBCSD, Working Group on Eco-efficiency Metrics and Reporting: Geneva, Switzerland, 1999. [Google Scholar]
  153. Hoffren, J.; Apajalahti, E.L. Development of Eco-Efficiency in Metal Production in Finland. Prog. Ind. Ecol. Int. J. 2009, 6, 153. [Google Scholar] [CrossRef]
  154. Hoffren, J. Reconsidering Quantification of Eco-Efficiency: Application to a National Economy. Prog. Ind. Ecol. Int. J. 2006, 3, 538. [Google Scholar] [CrossRef]
  155. Hoffren, J. Development of Eco-Efficiency in Finnish Forest Industry: 1997-2007. Prog. Ind. Ecol. Int. J. 2010, 7, 163. [Google Scholar] [CrossRef]
  156. Dai, T.; Lu, Z. Analysis of Eco-Efficiency of Steel Industry. Dongbei Daxue Xuebao/J. Northeast. Univ. 2005, 26, 1168–1173. [Google Scholar]
  157. Hirschnitz-Garbers, M.; Tan, A.R.; Gradmann, A.; Srebotnjak, T. Key Drivers for Unsustainable Resource Use—Categories, Effects and Policy Pointers. J. Clean. Prod. 2016, 132, 13–31. [Google Scholar] [CrossRef]
  158. González de Molina, M.; Toledo, V. The Social Metabolism—A Socio-Ecological Theory of Historical Change; Springer: Cham, Switzerland, 2014; ISBN 978-3-319-06358-4. [Google Scholar]
  159. Zhang, Y. Urban Metabolism: A Review of Research Methodologies. Env. Pollut. 2013, 178, 463–473. [Google Scholar] [CrossRef] [PubMed]
  160. Infante Amate, J.; González De Molina, M. “Sustainable de-Growth” in Agriculture and Food: An Agro-Ecological Perspective on Spain’s Agri-Food System (Year 2000). J. Clean. Prod. 2013, 38, 27–35. [Google Scholar] [CrossRef]
  161. Herrmann-Pillath, C. Energy, Growth, and Evolution: Towards a Naturalistic Ontology of Economics. Ecol. Econ. 2015, 119, 432–442. [Google Scholar] [CrossRef]
  162. Lu, Y.; Geng, Y.; Qian, Y.; Han, W.; McDowall, W.; Bleischwitz, R. Changes of Human Time and Land Use Pattern in One Mega City’s Urban Metabolism: A Multi-Scale Integrated Analysis of Shanghai. J. Clean. Prod. 2016, 133, 391–401. [Google Scholar] [CrossRef]
  163. Li, Y.; Zhang, Y.; Yang, N. Ecological Network Model Analysis of China’s Endosomatic and Exosomatic Societal Metabolism. Proc. Procedia Environ. Sci. 2010, 2, 1400–1406. [Google Scholar] [CrossRef]
  164. Ellen Macarthur Foundation. The Circular Model—Brief History and Schools of Thought; Ellen Macarthur Foundation: Isle of Wight, UK, 2013; pp. 1–4. [Google Scholar]
  165. Ávila-Gutiérrez, M.J.; Martín-Gómez, A.; Aguayo-González, F.; Córdoba-Roldán, A. Standardization Framework for Sustainability from Circular Economy 4.0. Sustainability 2019, 11, 6490. [Google Scholar] [CrossRef]
  166. Masi, D.; Day, S.; Godsell, J. Supply Chain Configurations in the Circular Economy: A Systematic Literature Review. Sustainability 2017, 9, 1602. [Google Scholar] [CrossRef]
  167. Ávila-Gutiérrez, M.J.; Martín-Gómez, A.; Aguayo-González, F.; Lama-Ruiz, J.R. Eco-Holonic 4.0 Circular Business Model to Conceptualize Sustainable Value Chain towards Digital Transition. Sustainability 2020, 12, 1889. [Google Scholar] [CrossRef]
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Figure 1. Bioinspired characterization of Industrial Metabolism.
Figure 1. Bioinspired characterization of Industrial Metabolism.
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Figure 2. Multilevel characterization of industrial metabolism for design for circularity in manufacturing systems.
Figure 2. Multilevel characterization of industrial metabolism for design for circularity in manufacturing systems.
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Table 1. Number of documents that include key concepts alongside the term industrial metabolism.
Table 1. Number of documents that include key concepts alongside the term industrial metabolism.
Concept Concept
Circular economy423Inter-/Intra-specific3
Industrial ecology894Eco-industrial park286
Analogy48Resilience135
Eco-efficiency258Ecological network65
Toxicity69Trophic chain24
Cyclicity/Circularity124Metabolic pathway25
Ecosystem597Habitat66
Symbiosis319Keystone species5
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Martín-Gómez, A.M.; Ávila-Gutiérrez, M.J.; Lama-Ruiz, J.R.; Aguayo-González, F. Industrial Metabolism: A Multilevel Characterization for Designing Sustainable Manufacturing Systems. Machines 2024, 12, 16. https://0-doi-org.brum.beds.ac.uk/10.3390/machines12010016

AMA Style

Martín-Gómez AM, Ávila-Gutiérrez MJ, Lama-Ruiz JR, Aguayo-González F. Industrial Metabolism: A Multilevel Characterization for Designing Sustainable Manufacturing Systems. Machines. 2024; 12(1):16. https://0-doi-org.brum.beds.ac.uk/10.3390/machines12010016

Chicago/Turabian Style

Martín-Gómez, Alejandro M., María Jesús Ávila-Gutiérrez, Juan Ramón Lama-Ruiz, and Francisco Aguayo-González. 2024. "Industrial Metabolism: A Multilevel Characterization for Designing Sustainable Manufacturing Systems" Machines 12, no. 1: 16. https://0-doi-org.brum.beds.ac.uk/10.3390/machines12010016

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