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Assessing the sustainability of urban eco-systems through Emergy-based circular economy indicators
Ecological Indicators 109 (2020) 105859
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Assessing the sustainability of urban eco-systems through Emergy-based circular economy indicators ⁎
T
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Remo Santagataa, , Amalia Zucarob, , Silvio Vigliaa,c, Maddalena Ripad, Xu Tiane, Sergio Ulgiatia,f a
Department of Science and Technology, Parthenope University of Napoli, Napoli, Italy ENEA, Laboratory Resources Valorisation, Research Centre of Portici, Portici, Napoli, Italy c Department of Environmental Engineering Sciences, University of Florida, USA d Institute of Environmental Science and Technology (ICTA), Autonomous University of Barcelona (UAB), Bellaterra, Spain e School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China f School of Environment, Beijing Normal University, Beijing, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Emergy accounting Circular economy Sustainability assessment Urban systems Circular indicators
Circular Economy (CE) concepts and tools are getting increasing attention with regard to their implementation in agricultural, urban and industrial sectors towards innovative business models to optimize resource use, process performances and development policies. However, conventional biophysical and economic indicators hardly fit CE characteristics. Life cycle assessment, footprint and economic cost-benefit indicators, do not fully capture the specificity of a closed loop CE framework, characterized by feedbacks and resource use minimization and quality assessment. Commonly used mono-dimensional indicators seem unable to successfully relate the process performance and the use of ecosystem services and natural capital, in that they do not assess the environmental quality and sustainability (renewability, fit to use, recycle potential) of resources and the complexity of interaction between agro/industrial/urban environments and socioeconomic systems, and translate into an incomplete and inadequate picture, far from an effective CE perspective. In this study, Emergy Accounting method (EMA) is used to design an improved approach to CE systemic aspects, focusing on the importance of new indicators capable of capturing both resource generation (upstream), product (downstream) and systems dimensions. This conceptual scheme is built around the case study of the City of Napoli’s economy (Campania region, Southern Italy) considering the surrounding agro-industrial area with its smaller urban settlements. In order to design a reasonable and reliable CE framework, a number of already existing and innovative processes is analyzed and discussed, through a bottom-up procedure capable to account for CE development options based on the recovery of locally available and still usable resources (i.e., conversion of waste cooking oil into biodiesel, conversion of slaughterhouse residues to power and chemicals, recovery and conversion of agro-waste residues, amongst others). The result highlighted that EMA was capable to keep track of the improvement generated by the implemented circularity patterns in terms of reduced total emergy of the system. Moreover, EMA indicators suggested that, in any case, the CE business framework should be intended as a transitional strategy towards more feasible paradigms.
1. Introduction Given that unlimited growth on a limited planet with limited resources is clearly impossible (Brown and Ulgiati, 2011), new production-consumption patterns, that are restorative from both intention and design points of view, become increasingly important if environmental sustainability and human well-being are the goals (Ghisellini et al., 2018,2016). The idea is that rather than discarding products before
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their value is fully utilized, we should use and re-use them. Human population distribution worldwide highlights how more and more people have been moving from countryside to cities. In 2018, 55% of people has been living within urban areas (30% in 1950), a figure that is projected to increase up to 68% in 2050; it is estimated that 43 megacities, i.e. cities with more than 10 million inhabitants, will exist by 2030, most of which in developing countries (United Nations, 2018). Urban systems could be described as entities with their own
Corresponding authors. E-mail addresses: [email protected] (R. Santagata), [email protected] (A. Zucaro).
https://doi.org/10.1016/j.ecolind.2019.105859 Received 16 March 2019; Received in revised form 20 October 2019; Accepted 22 October 2019 1470-160X/ © 2019 Published by Elsevier Ltd.
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conventional energy and material indicators are not able to fully capture the environmental performance generated by closed-loop CE patterns (Geng et al., 2013), in that these indicators are not able to properly keep track of the feedbacks and interconnections within CEoriented systems (Brown and Ulgiati, 2011; Giannetti et al., 2015). Mono-dimensional assessment tools used so far (cumulative energy, exergy, etc.) do not address circularity aspects. On the other side, previous emergy studies were more concerned on environmental cost assessment than on circularity monitoring. Our aim is to recognize to the emergy approach some capabilities that have been ignored until today. As Odum and Odum (2001) suggested, competition and low efficiency must be replaced by collaboration, and high efficiency; quantitative growth must be replaced by qualitative growth. Circular Economy indicates a kind of economy built to be intentionally restorative, to rely on a major share of renewable energy, designed in order to reduce waste. CE aims to resemble living non-linear systems, characterized by the re-use of matter and energy and by the optimization of the entire systems, not only of its singular parts, through a careful management of resources (Ellen MacArthur Foundation, 2012). However, CE should not be considered only as a waste management and recycling option, which may result in a limitation to its implementations. In fact, some waste management strategies do not fit CE objectives (i.e. landfilling and incineration), while CE should be developed as a radically alternative implementation of a systemic point of view (Ghisellini et al., 2016). CE has acquired a significant position in the political agenda, and many implementations from governments, NGOs and relevant stakeholders exist at the present time, but it’s still lacking a unified approach perspective (Kalmykova et al., 2018). According to Geng et al. (2012), critical aspects in the present evaluation of CE supply chains are the lack of: (i) social/environmental indicators; (ii) indicators on urban/industrial synergies; (iii) indicators for responsible and sustainable business; (iv) prevention-oriented indicators (i.e. material/energy reduction, networking rate). Therefore, CE indicators need to incorporate environmental dimension, as conservation of natural capital, decrease of pollution, environmental protection, etc., and social dimension like quality of food and education, jobs, participatory strategies, etc. Also, attention should be given to the different kind of impacts of the alternative options within the surrounding environmental and socio-economic constraints, as highlighted by Kuriqi et al. (2017) and Kuriqi et al. (2019). New CE-oriented indicator systems incorporating network analysis and Emergy Accounting method (EMA) can aid policy makers and provide generalizability to international CE efforts. In this work, EMA is proposed as a comprehensive assessment method to capture and enhance the complex framework of CE. EMA indicators are suggested as valuable tools to evaluate the implementation rate of circular economy patterns within local and regional systems, in order to provide useful information for policy decision making. In this work EMA is applied in order to evaluate the feasibility of a CE scenario within the City of Napoli (Campania region, Italy).
Fig. 1. The four main stages of the pulsing paradigm (After Odum and Odum, 2001).
metabolism, dependent from inputs of energy and materials from external systems at different scales (Zucaro et al., 2014). As resources used by urban inhabitants are mainly generated away from cities, the effects of urbanization affect a wider area beyond city boundaries, in fact, urban growth provides widespread effects on environmental, social and economic features (Verma and Raghubanshi, 2018). In the area of resource efficiency, the European Commission took important initiatives during the years 2011–2015, with a first Circular Economy Package (European Commission, 2015, 2014a, 2014b) further refined to become an ambitious Circular Economy Action Plan (European Commission, 2018a). Circular Economy (CE) lends itself as a model based on closing-the-loop concepts, exploiting the potentiality of secondary raw materials, reducing the extraction of new resources and minimizing waste generation through reuse and recycling strategies (Ghisellini et al., 2016). Contrary to the illusion of unlimited growth, all systems in the biosphere follow oscillating patterns, as highlighted in Fig. 1, (Odum, 1996; Odum and Odum, 2001; Ulgiati, 2004), and so do cities and countries. Oscillations follow the availability of resources. When the resource basis shrinks, also populations, assets and consumption decrease. This pulsing cycle follows four main stages: (i) Growth: abundant resources, increases in population, structure, and assets; low-efficiency and high-competition; (ii) Climax and Transition: maximum size depending on available resources; efficiency increase; collaborative patterns; information storage; (iii) Descent: less resources available, decrease in population and assets, increase in recycling patterns; (iv) Low Energy Restoration: no-growth, consumption smaller than accumulation, and storage of resources for a new cycle ahead. Policies should change accordingly, even if the different size of the oscillation patterns makes difficult to understand the pulsing waves. This means that policies that fit in times of growth may not fit in times of descent. Since industrialized societies are almost exclusively reliant on fossil fuels, a shared consensus exists about a climax stage for urban systems, to be reached when fossil reservoirs are depleted (Agostinho et al., 2018), followed by decline. This context of declining resources represents an opportunity for better explore and understand circular economy potentiality. For this reason, suitable CE indicators are needed for careful monitoring and proper assessing of human systems and economies (Geng et al., 2016). Innovative indicators are required to evaluate the environmental performance for CE-oriented systems and quantify benefits, costs, and bottlenecks over the entire process chain. The
2. Material and methods Emergy is defined as the available energy of one kind directly or indirectly used in a system for transformations leading to a product or a service (Brown and Ulgiati, 2004a; Odum, 1996). Emergy accounts for different categories of supporting contribution to the systems, including renewable and nonrenewable energy and material resources, imported primary resources and manufactured goods, information and knowhow, and finally labor and services (L&S), each one characterized by a different environmental quality. Emergy unit is the solar emjoule (sej) which is the amount of available energy of one kind (solar) converging into a product, resource or service. The total emergy (U) is therefore the total environmental production cost of products and services, obtained multiplying all inflows by an appropriate “environmental cost factor” 2
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Eq. (1) (modified from Ulgiati et al., 2007). A complete key for symbols used in system diagrams is provided as Fig. S1 in Supplementary Materials.
named Unit Emergy Value (UEV, measured as sej/unit-of-inflow) to convert raw resource inflows into the corresponding emergy values and finally summing them into a total emergy U. The UEV of a product is obtained by dividing U by the yield of product delivered. When the unit of a UEV is defined as sej/J, it is named ‘transformity’. All emergy values refer to a Global Emergy Baseline (GEB), representing the total annual emergy driving the biosphere. This work makes reference to the most recent calculation of GEB (12.0E + 24 seJ/yr; Brown et al., 2016) and so all UEVs taken from the literature and calculated with reference to other baselines were converted accordingly. According to Odum (1996), the resources used in a system can be classified as locally available renewable (R) and non-renewable (N), and imported non-renewable (F). Emergy performance indicators (Brown and Ulgiati, 2004b) explored in this work are:
Ulinear =
∗ Ij +
• • •
∑i ∑j=1, ⋯, n cj ∗ m wi + ?
(1)
where ∑i Ri represents all locally renewable (R) input flows; ∑i Ni represents locally non-renewable (N) input flows; ∑i∑j=1,…, n Fij is the outside emergy investment as flows Fij (j stands for the different flows of energy, materials and services provided to i processes, represented in the Fig. 2 as A, B, C); ∑j=1,…, n dj*Ij represents the outside emergy investment for fixing damage from emissions, where dj = emergy investment needed for the repair of one unit of the jth damaged storage Ij, Ij = damaged assets (i.e.: environmental issues to be restored, additional costs for effects on human health), the dash-dotted line after the I*d interaction indicates that this is not a recycle giving back restored assets, but it expresses the additional emergy to fix degraded resources; ∑i∑j=1,…, n cj*mwj represents the outside emergy investment for treatment of waste (cj = emergy investment for treatment of the jth waste flow mwj); finally, “?” indicates a not easily accountable emergy loss due to a potential unknown irreversible damage (i.e. loss of biodiversity). The system diagram presented in Fig. 2 changes significantly when introducing circularity patterns, as highlighted in Fig. 3. The total emergy U in this case is calculated as in Eq. (2) (modified from Ulgiati et al., 2007).
• Emergy Yield Ratio: EYR = U/F. It measures the ability of a system •
∑i Ri + ∑i Ni + ∑i ∑j=1, ⋯, n Fij + ∑j=1, ⋯, n dj
in providing a yield by investing outside resources. The lowest value is when a process provides the same amount of emergy invested, and it is equal to 1. Environmental Loading Ratio: ELR = (N + F)/R. This indicator expresses the load of a system on the environment as the amount of non-renewable on renewable resources used. Renewable fraction of emergy used: %REN = R/U. %REN indicates the fraction of emergy from local renewable resources. Empower Density: ED = U/Area. ED is a function of emergy invested per unit of area, reflecting the land development and human activities. Population emergy intensity: EMERGYPerCapita = U/person. It measures the share of resources invested for the support of one average person.
Ucircular =
∑i Ri + ∑i Ni + ∑i ∑j=1, ⋯, n−1 Fi,j + ∑j=1, ⋯, n gj ∑j=1, ⋯, n−1 dj ∗ I j + ∑i ∑j=1, ⋯, n−1 cj ∗ m wi + ?
∗ Wj +
2.1. Increasing local resource reliance
(2) There are no differences in the amount of input flows of R and N (first two terms) in Eqs. (1) and (2). The first difference is in the third term (∑i∑j=1,…, n-1 Fi,j), where the index j = n-1 indicates a smaller emergy investment due to exchanged, reused or recycled resources. The newly added fourth term (∑j=1,…, n gj * Wj) indicates the emergy invested to transfer still usable waste materials from a process to any other process in the system, where gj is the outside additional emergy input to support the j-th waste processing and Wj is the amount of exchanged waste material. ∑ j=1,…, n-1 dj * Ij + ∑i∑ j=1,…, n-1 cj * mwi indicate, respectively, the smaller emergy investment for fixing damages from emissions and the smaller emergy investment for waste treatment and disposal. The question mark “?” refers to the emergy loss due to unknown irreversible damage, but in a circular system it is expected that this loss should be much smaller than in the corresponding linear system and therefore negligible. The total emergy U driving a linear system is generally higher than the total emergy U when circular strategies are implemented. The difference (Δ) between Ulinear and Ucircular can be calculated (Ulgiati et al., 2007), and it should be greater than zero, therefore, being the emergy investment needed for fixing damage from emissions and the emergy investment for waste disposal less in the circular system than in the linear system (Eq. (3)):
In EMA, all resources are referred to the scale of biosphere and their usefulness and quality are quantified on the same value basis and then compared with the product(s) generated. As previously defined (see Section 2), the percentage of renewability of a system is the share of R in the Total Emergy U. As a consequence, every system stores a variable amount of renewable sources depending on their availability, the associated cost for exploitation and the demand by the investigated supply chain. Therefore, the input flows could be splitted into their renewable and non-renewable fractions. However, in this work this is not applied to all inflows but only to local ones, because the (often minimal) fraction of renewable energy in the imported inputs is offset by the much larger emergy costs of transport. Therefore, circular economy strategies, moving from linear to circular patterns entail firstly that production processes are designed in a way to decrease their resource demand, and secondly that the maximum possible effort is displayed in order to increase the fraction of locally available resources. 2.2. Emergy in linear and circular systems The Emergy approach seems to be able to provide a suitable methodology and comprehensive indicators capable to properly deal with circular strategies. The main reasons root in its focus on resource environmental quality and specially featured systems algebra. Different authors (Elia et al., 2017; European Commission, 2018b; Iacovidou et al., 2017; Pauliuk, 2018) assessed conventional methods and indicators (i.e. Life Cycle Assessment, Material Flow Accounting, CostBenefit Analysis, among others) applied to CE, highlighting a general consensus within their inability to capture all aspects of CE and the need for a holistic method. Table 1 lists the main identified differences and fitness to linear and circular systems of respectively mono-dimensional and emergy indicators. Fig. 2 highlights a linear system composed of three main processes (process A, process B and process C); U of this system is calculated as in
Δ > 0requires ∑ Fi, n − i
∑j=1, ⋯, n gj ∗ Wj > 0
(3)
This calculation allows the choice of (i) circular technology and infrastructure, (ii) resources to be exchanged or replaced, (iii) waste management strategies, guiding the choice of policies among different options. 2.3. Developing circularity patterns within a linear system In this work, the effects of the implementation of circular strategies within a linear system are explored. The study is based on the 3
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Table 1 Characteristics of conventional and emergy-based indicators with respect to their fit to Linear and Circular Economy. Linear Economy
Circular Economy
Features
Conventional Indicators
Features
Emergy Indicators
Business Based Stand – Alone (systems not working together) Mono–Criteria (value based on maximized outcome) Design and planning for ever increasing resource use (unlimited growth) Conservative (“more of the same” approach)
Linear Mono-Dimensional (one aspect at a time) User-Side (value in what can be extracted) Non recognized limits to production processes
Network Based Integrative (environment-social-economy nexus) Multi–Criteria (value depending on choices for wellbeing) Preventative eco-design and planning for decreased resource use and easy recovery (resources are limited) Regenerative (improvement instead of exploitation and depletion)
Systemic Comprehensive (global aspects of a system’s performance) Donor-Side (value in what is invested)
Acquisitive (getting more spending less)
Competitive (better performance than other market operators, increase business, displace competitors)
Exploitation-oriented (resource extraction for processing and market)
Redistributive (more jobs supported by better use of resources)
Processes and economies must consider biosphere constraints Enhances appropriate use of natural capital and ecosystem services by attributing environmental value to resources and system’s components Optimization across scales (maximum empower through all levels of a system’s hierarchy)
already implemented and partially to be designed or improved). When adding circular strategies to the system, new connections become evident (Fig. 5). In agreement with the circular economy framework, the assumption that some flows are local instead of being imported from outside the boundaries of the Metropolitan Area, is also made. In fact, recycling decreases the demand for imports and the system may become able to rely on local availability. For the sake of clarity, circular economy strategies aim at decreased resource demand thanks to alternative designs and use patterns; if this is implemented (e.g. less fossil energy), local resources may become the dominant supply (e.g. photovoltaic electricity). The material and energy flows highlighted in red are new established connections or already existing connections that gain a new added value from the implementation of circularity patterns. The circular scenario investigated in this work has
construction of a virtual network involving the municipality of Napoli, Campania region (Southern Italy) and its supporting area. The Municipality of Napoli (hereafter Napoli, for the sake of simplicity) is part of the Metropolitan Area of Napoli (the third largest urban agglomeration in Italy, with a surface of 1.171 km2, 92 urban centers of different size and a large number of agricultural, industrial and commercial firms). The development and sustainability of such a complex agglomeration of different production and consumption processes requires large amounts of material and energy flows, calling for appropriate resource use and decreased waste generation. Figs. 4 and 5 compare how selected processed of livestock and agricultural production (e.g. oil production from olives) from the metropolitan area and beyond contribute to the metabolism of Napoli, respectively via a conventional linear framework and via circular patterns (partially
Fig. 2. System diagram of a linear system composed of processes A, B and C (* indicates the degrading impact of pollution on environmental, social and technological assets, modified from Ulgiati et al., 2007). 4
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Fig. 3. System diagram of a circular system composed of process A, process B and process C (* indicates the degrading interactions between assets and pollution, modified from Ulgiati et al., 2007).
Fig. 4. The investigated linear hypothesis.
products. The electric energy used in livestock production has been replaced by electricity from photovoltaic (replacing fossil energy with renewable sources, as requested by circular economy strategies as preliminary options to reduce demand); the diesel demand has been replaced by bio-diesel from the sub-system generating biodiesel from waste cooking oil (WCO); furthermore, the input flows
been developed with the purpose of maximizing the interconnections and the material and energy exchanges among the different sub-systems involved. In particular:
• The farm sub-system, modeled after Ghisellini et al. (2014), raises livestock and produces meat, milk, dairies, manure and animal-by
5
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Fig. 5. The investigated circular hypothesis (investigated circular options highlighted in red color). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
• •
•
procedure to the global gain. We choose these subsystems and these circles from what we recognized to be important within the city system as final beneficiary of the study, and what we valued as sufficiently strong from our previous studies in order to lead to reliable conclusions. Nevertheless, we are aware that other important “circles” can be added for sectors where circular patterns are likely to be implemented, in order to reach a more complete assessment of potential circularity implementation in an urban system.
of purchased animal feed and animal bedding have been replaced, considering livestock grazing, according to the biological production, self-production of feed and fertirrigation, as highlighted by Buonocore et al. (2018); EU (2008); Ghisellini et al. (2014); Jaklič et al. (2014); The biodiesel production from WCO sub-system, modeled after Ripa et al. (2014), allows biodiesel production from collected WCO. Moreover, in this sub-system the electric energy input flow has been replaced with photovoltaics (PV) electricity; The sub-system of electricity production from animal by-products (ABP), collected within Campania region and properly processed, modeled after Santagata et al. (2017), allows decreased use of fossil powered electricity. In this subsystem, the diesel input flow has been replaced by biodiesel from WCO, while the methane input flows have been significantly reduced by re-circulating the heat self-generated by the power plant. In this work, the emergy of by-products is calculated as indicated by Santagata et al. (2019), allocating the total emergy U to their exergy fraction (Uby-product = Utot * Exergybyproduct/Exergytot). However, when these flows are fed back to earlier steps of the modeled system, their emergy has not been added, in order to avoid double-counting (emergy algebra rules), similarly to the zero-burden LCA rule (Finnveden, 1999); The urban system of Napoli, modeled after Viglia et al. (2018), is the final beneficiary of all the CE simulated hypotheses. The city, considered as a consumer, benefits from the farm products (i.e. meat, milk and dairies characterized by lower emergy cost, due to in-farm circular production); the electricity used is replaced by PV electricity and electricity from ABP; part or diesel used is replaced by biodiesel from WCO; the input of natural gas is lowered by using biogas produced from manure coming from the farm subsystem (energy from agricultural residues). A diminution of Services in accordance with the diminution of imported resources F has been also taken into account (-54%, assuming that less importing would imply less purchase).
3. Results Table 2 summarizes the EMA results for both a linear, business as usual, evolution of the municipality of Napoli (adjusted after Viglia et al., 2018), and for the implementation of circular patterns scenario within the same urban system. The inventories for this study (Tables S.1 to S.8, in Supplementary Materials) are based on data from the above Table 2 Emergy Accounting and performance indicators, with and without L&S, of the linear and circular hypotheses within the City of Napoli.
The study aims at developing a computation procedure for circular processes. Too many “circles” might have diverted the reader from the
6
Inputs
Linear
Circular
R – Renewable Inputs Locally Available (sej/yr) N – Non-renewable Inputs Locally Available (sej/yr) F – Non-renewable Imported Inputs (sej/yr) L&S (sej/yr) Indicators with L&S U – Total Emergy (sej/yr) EYR = U/F ELR = (N + F)/R %REN = R/U ED = U/area (sej/m2) EMERGYPerCapita (sej/person) Indicators without L&S U – Total Emergy (sej/yr) EYR = U/F ELR = (N + F)/R %REN = R/U ED = U/area (sej/m2) EMERGYPerCapita (sej/person)
3.35E + 19 8.28E + 16 9.24E + 21 5.03E + 21
6.49E + 19 9.42E + 20 4.99E + 21 2.96E + 21
1.43E + 22 1.0 426.2 0.2% 1.22E + 14 1.46E + 16
8.96E + 21 1.1 137.0 0.7% 7.66E + 13 9.16E + 15
9.28E + 21 1.0 276.1 0.4% 7.93E + 13 9.48E + 15
6.00E + 21 1.2 91.4 1.1% 5.13E + 13 6.13E + 15
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mentioned papers ,; as a consequence, all input and output flows (R, N, F, L&S) have been taken into account in the same way as they are included in the original studies. The resulting Total Emergy (a measure of the environmental “size” of the system) for the linear case (Ulinear) is equal to 1.43E + 22 sej/yr with L&S and 9.28E + 21 sej/yr without L& S. Instead, Ucirc calculated for the circular scenario, equal to 8.96E + 21 sej/yr with L&S (a decrease of 38.4%) and 6.00E + 21 sej/yr without L &S (a decrease of 35.3%) as shown in Table 2, translating into a lower emergy demand and suggesting an increased sustainability of the investigated urban system. Considering however that most of the used resources are nonrenewable and imported, the actual sustainability is still questionable and calls for implementation of other aspects (renewability, local reliance) if circular economy actually aims at being an effective business model. This is clearly confirmed by the other emergybased performance indicators in Table 2. The EYR values, very sensitive to the local versus imported characteristics, are very low, and only increases by 10% and 20% respectively with and without L&S inclusion. Similarly, the ELR values decrease (as expected) by 68% and 67% respectively with and without L&S, thanks to increased reliance on renewable and local resources, but remains still very high in absolute terms. The %REN does increase, but hardly moves beyond 1% of total emergy use. Similar yet unsatisfactory improvements are shown by the other indicators in the same Table. Complete EMA tables related to all subsystems, both within the linear and the circular hypotheses, are shown in the Supplementary materials (from Table S.1 to Table S.8), alongside with data and calculations. Fig. 6 highlights the comparison between emergy signatures of both
linear and circular hypotheses, with and without L&S. In the EMA, a signature is a diagram that shows the breakdown of input resources within the total emergy of a process. In so doing, a comparison does not only rely on total use, but also on the different resource categories. In Fig. 6, the input flows are aggregated as macro-categories: (i) L& S, (ii) Fuels, (iii) Electricity, (iv) Food, (v) Metals, (vi) Building materials, (vii) Additional materials, (viii) Others (flows contributing less than 1% to U). The non-negligible decrease in terms of U when converting from the linear system to the circular one is to be attributed to: i) a decrease of the fuel emergy contribution (≈1%); ii) a decrease of the electricity emergy input (≈53%); iii) a decrease of food emergy inputs (≈86%). It should be noted that L&S shows an important fraction of the total emergy U, around 35% in the Business-as-Usual case and 33% in the circular assumption. 4. Discussion The obtained results fulfill the need for comprehensive indicators capable of including the peculiar characteristics of CE framework within CE strategies, policies, decision-making implementation. The Total Emergy U used as a CE indicator can capture the variation of the embodied energy in different processes, due to its unique algebra rules (Odum, 1996), suitable to approach the feedback features of CE, and due to EMA ability to include the contribution of renewable sources and the information embedded within labor and services. Results are shown with and without L&S in order to highlight the important role played by L&S (know-how, infrastructure, standard of living) in a system's dynamics. The calculation of L&S is site specific and depends on lifestyles
Fig. 6. Emergy signature of Linear and Circular hypotheses with and without L&S for the City of Napoli. 7
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Table 3 Δ and %dec values of the investigated subsystems with and without L&S. with L&S
without L&S
Subsystems
Ulin
Ucirc
Δ
%dec
Ulin
Ucirc
Δ
%dec
Farm (sej/yr) Electricity from ABP (sej/MWh) Biodiesel from WCO (sej/kg) City of Napoli (sej/yr)
1.12E + 19 1.02E + 15 1.78E + 12 1.43E + 22
2.08E + 18 5.58E + 14 1.75E + 12 8.96E + 21
9.10E + 18 4.57E + 14 2.72E + 10 5.34E + 21
81% 45% 2% 37%
1.01E + 19 8.33E + 14 7.24E + 11 9.28E + 21
1.02E + 18 3.76E + 14 6.97E + 11 6.00E + 21
9.10E + 18 4.57E + 14 2.72E + 10 3.28E + 21
90% 55% 4% 35%
and market fluctuations as well as on the adopted technology. Nevertheless, in a circular economy situation where the largest possible share of local emergy flows is included, the related fraction of L&S becomes also local, indicating important social (job creation) and economic impacts (e.g. lower cost for transportation). U is an indicator of the ‘size’ of the system under investigation. From this perspective, Δ (Ulinear-Ucircular) expresses the performance of the system in delivering the same output products and services to society by investing a smaller amount of biosphere work. Table 3 sums up Δ values and the percentage decreases (%dec) for the different subsystems investigated in this work, with and without L&S (complete emergy tables for the linear and circular hypotheses of the farm, electricity from ABP and biodiesel from WCO subsystems are provided in the Supplementary materials when implementing CE patterns. The implementation of CE strategies within the investigated circular hypothesis (Fig. 5) have brought to significant reductions in all the subsystem, due also to the ‘magnifying’ effect of the interconnections generated. However, a question of why performance emergy indicators (EYR, ELR, etc) still show partially unsatisfactory values cannot be disregarded. The first answer is that we did not include all the recycling alternatives. Very likely, other resources can be saved and recovered via alternative designs (e.g. demolition and construction materials, minerals and metals from vehicles and computers, municipal and industrial waste management, etc). The more these materials are reused or recycled, the better the performance indicators will appear. A second answer is that the starting point of the investigated system was an almost complete reliance on nonrenewable and imported resources. The decrease of 35% of such amount of resources achieved through recirculation of some material and energy flows does not mean that these resources have been fully replaced by renewables. Iron, even when recycled, is still non-renewable and same applies to exhaust cooking oil (recycled as fuel, but still produced through nonrenewable agricultural patterns). The Circular Economy concept is based on three main pillars:
life time) and the emergy of solar radiation; the UEV of PV electricity is 9.91E + 04 sej/J (after Brown et al., 2012), 47% of the UEV of fossil powered electricity, 2.13E + 05 sej/J, translating into an emergy cost of electricity production of about 8.66E + 20 sej lower, which is only 6% of the total U supporting the city (1.43E + 22 sej). For the sake of clarity, electricity in the business as usual situation accounts for 11% of total emergy; if we apply a 53% improvement of emergy cost, this is the 53% of the 11%, confirming the 6% computed above. If a similar reasoning is applied to all replaced resources, the real sustainability improvement is not so remarkable. This means that the fraction of nonrenewable emergy (i.e. demand for past environmental support) is still dominant and affects the emergy based performance indicators. Unlike energy balances, where joules are added without being weighted through any “conversion factor” (all joules seem equal), in emergy accounting the minerals and fossil inputs are assigned much higher conversion factors (UEV's, Unit Emergy Values) than diluted solar radiation (UEV = 1 sej/J by definition). Energy balances hide the important aspect of the huge biosphere work to generate resources and truncate the computation at the very short supply chain boundary where fossil fuels are burned to extract minerals and generate heat, thus suggesting a low-cost conversion of solar radiation into actual electricity. An emergy-based sustainability assessment requires instead that past biosphere work is accounted for, thus disclosing the actual costs of photovoltaic production as well as any other renewables (biofuels, wind electricity, etc.) or circular patterns (cooking oil recovery as biodiesel). A real sustainability improvement would only be possible if fully renewable materials and energy resources were used to replace non-renewables (e.g. by using recycled silicon and renewable electricity to make the new PV modules). Only if this radical circular economy improvement is implemented, the substitution of fossil electricity with PV electricity will result in a significantly reduced U and better performing indicators as a consequence of the increased % of renewable emergy compared to U. As a consequence, the most important result of this study may be that if circular economy aims at becoming a truly alternative and sustainable business model, most if not all its supporting resources must become renewable and local through appropriate design and lifestyle changes. We cannot state this is going to happen nor can we deny it. In any case, the decreased demand for input resources may at least provide a sufficiently large transition time, in order to search alternative patterns. The simulations generated in this work confirmed how EMA could provide a useful support when dealing with CE decisions, by providing suitable insights from a different perspective, built in environmental, spatial and temporal dimension, compared to conventional user-oriented mono-dimensional indicators. The results seems to be consistent with the general ‘vision for a circular city’ presented by the Ellen MacArthur Foundation (Ellen MacArthur Foundation, 2017) characterized by i) reduced waste and pollution; ii) circulating materials and energy to maximize use; iii) enhanced condition for the regeneration of natural capital. As also highlighted by several researchers (Kalmykova et al., 2018; Petit-Boix and Leipold, 2018), a large number of CE studies and implementations can be observed currently, but a comprehensive organization of strategies and initiatives carried out by the different actors involved (governments, NGOs, industries, etc.) is
1. Preventive design of systems towards lower resource investment for their creation, management and disposal. The increased efficiency of such systems should result in less resource use and less waste to be disposed of (in so decreasing the need for “end of pipe” solutions; 2. Substitution of a wide fraction of non-renewable resources with renewable ones; 3. Feedback (reuse, recycle) of by-products and waste. However, in a business as usual situation similar to the present one, in which CE is still far from being fully implemented, also renewable systems still rely on non-renewable resources (e.g. silicon, aluminum, glass and other minerals as well as fossil electricity to build a photovoltaic generator and storage batteries). Under the important premise that energy and emergy are not the same concept (see previous definitions in Methods), let's look at the replacement of fossil electricity with PV electricity. Two percentages must be kept in mind: (a) a PV module converts into electricity about 10% of the solar energy hitting its surface; (b) the UEV (a different efficiency measure, i.e. the emergy “cost of production”) of photovoltaic electricity is the sum of the emergy of resources invested to build the PV module (discounted over 8
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Acknowledgement
still lacking. The construction of new, understandable indicators could be useful in acknowledging the value of implementations actualized or still in designing phase.
The authors gratefully acknowledge the support from the Italian Ministry of Foreign Affairs and International Cooperation – Ministero degli Affari Esteri e della Cooperazione Internazionale. Direzione Generale per la Promozione del Sistema Paese. Grant number PGR05278.
4.1. Further improvements The circular scenario presented in this work was built on the basis of case studies previously investigated by the authors. It should be clear that a real CE implementation should involve other important supply chains and different solutions within relevant flows and stocks. Cities are to be considered as consumer organisms requiring more inputs from the economy than local resources, in which a relevant emergy stock is stored within the built environments, contributing to enlarge and add complexity to the system (Sevegnani et al., 2018). A survey conducted by the Municipality of Napoli in 2001 highlighted the presence of 38,768 buildings and building complexes, of which about the 88% are for residential use (Comune di Napoli, 2001). This represents of course a massive amount of embodied emergy stored in the urban system, almost locked up for the whole lifetime of the edifices, which could be partially reduced over time by i) substituting buildings that reached their end of life with new ones built with advanced, more efficient materials and technologies; ii) intervening within the normal maintenance using secondary or in general more environmentally sound materials.
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.105859. References Agostinho, F., Sevegnani, F., Almeida, C.M.V.B., Giannetti, B.F., 2018. Exploring the potentialities of emergy accounting in studying the limits to growth of urban systems. Ecol. Indic. 94, 4–12. https://doi.org/10.1016/J.ECOLIND.2016.05.007. Brown, M.T., Campbell, D.E., De Vilbiss, C., Ulgiati, S., 2016. The geobiosphere emergy baseline: a synthesis. Ecol. Modell. 339, 92–95. https://doi.org/10.1016/j.ecolmodel. 2016.03.018. Brown, M.T., Raugei, M., Ulgiati, S., 2012. On boundaries and “investments” in Emergy Synthesis and LCA: a case study on thermal vs. photovoltaic electricity. Ecol. Indic. 15, 227–235. https://doi.org/10.1016/j.ecolind.2011.09.021. Brown, M.T., Ulgiati, S., 2011. Understanding the global economic crisis: a biophysical perspective. Ecol. Modell. 223, 4–13. https://doi.org/10.1016/j.ecolmodel.2011.05. 019. Brown, M.T., Ulgiati, S., 2004a. Emergy analysis and environmental accounting. In: Encyclopedia of Energy. Elsevier, pp. 329–354 doi: 10.1016/B0-12-176480-X/ 00242-4. Brown, M.T., Ulgiati, S., 2004b. Energy quality, emergy, and transformity: H.T. Odum’s contributions to quantifying and understanding systems. Ecol. Modell. 178, 201–213. https://doi.org/10.1016/j.ecolmodel.2004.03.002. Buonocore, E., Mellino, S., De Angelis, G., Liu, G., Ulgiati, S., 2018. Life cycle assessment indicators of urban wastewater and sewage sludge treatment. Ecol. Indic. 94, 13–23. https://doi.org/10.1016/j.ecolind.2016.04.047. Comune di Napoli, 2001. Edifici [WWW Document]. URL http://www.comune.napoli.it/ flex/cm/pages/ServeBLOB.php/L/IT/IDPagina/2848 (accessed 2.7.19). Elia, V., Gnoni, M.G., Tornese, F., 2017. Measuring circular economy strategies through index methods: a critical analysis. J. Clean. Prod. 142, 2741–2751. https://doi.org/ 10.1016/j.jclepro.2016.10.196. Ellen MacArthur Foundation, 2017. Cities in the Circular Economy: An initial exploration. Ellen MacArthur Foundation, 2012. Towards the Circular Economy Vol. 1 – An economic and business rationale for an accelerated transition. European Commission, 2018a. Circular Economy - Implementation of the Circular Economy Action Plan [WWW Document]. URL http://ec.europa.eu/environment/ circular-economy/index_en.htm. European Commission, 2018b. Measuring progress towards circular economy in the European Union – Key indicators for a monitoring framework. European Commission, 2015. Communication from the commission to the European parliament, the council, the European economic and social committee and the committee of the regions. Closing the loop – an eu action plan for the circular economy. [WWW Document]. URL https://eur-lex.europa.eu/legal-content/EN/ TXT/HTML/?uri=CELEX:52015DC0614&from=EN. European Commission, 2014a. Questions and answers on the Commission Communication “Towards a Circular Economy” and the Waste Targets Review [WWW Document]. URL http://europa.eu/rapid/press-release_MEMO-14-450_en. htm. European Commission, 2014b. Communication from the Commission to the European Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions. Towards a circular economy: a zero waste programme for Europe. Brussels. Finnveden, G., 1999. Methodological aspects of life cycle assessment of integrated solid waste management systems. Resour. Conserv. Recycl. 26, 173–187. Geng, Y., Fu, J., Sarkis, J., Xue, B., 2012. Towards a national circular economy indicator system in China: an evaluation and critical analysis. J. Clean. Prod. 23, 216–224. https://doi.org/10.1016/j.jclepro.2011.07.005. Geng, Y., Sarkis, J., Ulgiati, S., 2016. Sustainability, wellbeing, and the circular economy in China and worldwide. Science (80-.) 76–79 doi: sc201255 [pii]\n10.1038/ sc.2012.55. Geng, Y., Sarkis, J., Ulgiati, S., Zhang, P., 2013. Measuring China’s Circular Economy. Science (80-.) 339, 1526–1527. https://doi.org/10.1126/science. Ghisellini, P., Cialani, C., Ulgiati, S., 2016. A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems. J. Clean. Prod. 114, 11–32. https://doi.org/10.1016/j.jclepro.2015.09.007. Ghisellini, P., Ji, X., Liu, G., Ulgiati, S., 2018. Evaluating the transition towards cleaner production in the construction and demolition sector of China: a review. J. Clean. Prod. 195, 418–434. https://doi.org/10.1016/j.jclepro.2018.05.084. Ghisellini, P., Protano, G., Viglia, S., Gaworski, M., Setti, M., 2014. Integrated Agricultural and Dairy Production within a Circular Economy Framework. A Comparison of Italian and Polish Farming Systems. J. Environ. Account. Manag. 2,
5. Conclusions In this work, the case study of the City of Napoli has been used for testing EMA as a suitable method for the implementation of new indicators for CE. EMA seems to be capable of measuring the improvements following the implementation of circular strategies (while common mono-dimensional indicators lack a holistic understanding of CE), by evaluating the difference Δ between the total emergy U of the investigated system in a linear and a circular framework. Δ value is basically grounded in the different emergy investment between 1) disposing all waste generated and importing primary energy and materials and 2) promoting strategies and technology for the recirculation of material and energy within the system, reducing the quantity of waste to be disposed of and the import of energy and materials from the outside. EMA algebra rules has proven to be able to capture the complex features of CE, integrating also environmental and information, delivered within direct and indirect labor, aspects. The implementation of some simulated CE patterns within the framework of the City of Napoli brought to significant reductions in terms of emergy invested in the system, thus further improvements can be achieved by the integration of other CE patterns and the inclusion of relevant stocks of embodied energy (i.e. buildings) contributing to enlarging the complexity of the system. The results achieved in this work suggest that for CE to become a viable and actually alternative business model, preliminary design and planning phases have a paramount importance, while end-of-pipe strategies are only short sight options and do not translate into a sustainable trend. Planning and design are not only technological options, but must look at lifestyle changes as most important turning point for sustainability. As a consequence, when planning and making decisions, questions about what do we actually want to sustain and what lifestyles changes are we actually willing to accept cannot be disregarded. Reliance on technology alone may translate into a temporary improvement followed by increases of the size of the societal assets and resource consumption, so that sustainability would again be in peril. This study provides a framework to: (1) account for the convergence of all supporting energy and material flows towards societal assets and trends; (2) quantify progresses achieved by implementing circular patterns; (3) adopt a set of purposefully designed circular indicators in support to policy making; and finally (4) providing a broad understanding of the interplay of design, technology, and lifestyles for effective sustainability pathways. 9
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019. Petit-Boix, A., Leipold, S., 2018. Circular economy in cities: reviewing how environmental research aligns with local practices. J. Clean. Prod. 195, 1270–1281. https://doi.org/ 10.1016/j.jclepro.2018.05.281. Ripa, M., Buonaurio, C., Fiorentino, G., Mellino, S., Ulgiati, S., 2014. Recycling Waste Cooking Oil into biodiesel: a life cycle assessment. Int. J. Performability Eng. 10, 347–356. Santagata, R., Ripa, M., Ulgiati, S., 2017. An environmental assessment of electricity production from slaughterhouse residues. Linking urban, industrial and waste management systems. Appl. Energy 186, 175–188. https://doi.org/10.1016/j.apenergy. 2016.07.073. Sevegnani, F., Giannetti, B.F., Almeida, C.M.V.B., Agostinho, F., Brown, M.T., 2018. Accounting for internal stocks in assessing the sustainability of urban systems: the case of ABC Paulista. Ecol. Indic. 94, 70–81. https://doi.org/10.1016/j.ecolind.2016. 05.029. Ulgiati, S., Odum, H.T., Odum, E.C., 2004. the prosperous way down. Ecol. Modell. 178, 247–250. https://doi.org/10.1016/j.ecolmodel.2003.12.033. Ulgiati, S., Bargigli, S., Raugei, M., 2007. An emergy evaluation of complexity, information and technology, towards maximum power and zero emissions. J. Clean. Prod. 15, 1359–1372. https://doi.org/10.1016/j.jclepro.2006.07.008. United Nations, 2018. World Urbanization Prospects: The 2018 Revision. Verma, P., Raghubanshi, A.S., 2018. Urban sustainability indicators: challenges and opportunities. Ecol. Indic. 93, 282–291. https://doi.org/10.1016/j.ecolind.2018.05. 007. Viglia, S., Civitillo, D.F., Cacciapuoti, G., Ulgiati, S., 2018. Indicators of environmental loading and sustainability of urban systems. An emergy-based environmental footprint. Ecol. Indic. 94, 82–99. https://doi.org/10.1016/J.ECOLIND.2017.03.060. Zucaro, A., Ripa, M., Mellino, S., Ascione, M., Ulgiati, S., 2014. Urban resource use and environmental performance indicators. An application of decomposition analysis. Ecol. Indic. 47, 16–25. https://doi.org/10.1016/j.ecolind.2014.04.022.
372–391. https://doi.org/10.5890/JEAM.2014.10.007. Giannetti, B.F., Agostinho, F., Moraes, L.C., Almeida, C.M.V.B., Ulgiati, S., 2015. Multicriteria cost-benefit assessment of tannery production: The need for breakthrough process alternatives beyond conventional technology optimization. Environ. Impact Assess. Rev. 54, 22–38. https://doi.org/10.1016/j.eiar.2015.04.006. Iacovidou, E., Velis, C.A., Purnell, P., Zwirner, O., Brown, A., Hahladakis, J., MillwardHopkins, J., Williams, P.T., 2017. Metrics for optimising the multi-dimensional value of resources recovered from waste in a circular economy: a critical review. J. Clean. Prod. 166, 910–938. https://doi.org/10.1016/j.jclepro.2017.07.100. Jaklič, T., Juvančič, L., Kavčič, S., Debeljak, M., 2014. Complementarity of socio-economic and emergy evaluation of agricultural production systems: the case of Slovenian dairy sector. Ecol. Econ. 107, 469–481. https://doi.org/10.1016/j. ecolecon.2014.09.024. Kalmykova, Y., Sadagopan, M., Rosado, L., 2018. Circular economy - From review of theories and practices to development of implementation tools. Resour. Conserv. Recycl. 135, 190–201. https://doi.org/10.1016/j.resconrec.2017.10.034. Kuriqi, A., Pinheiro, A.N., Sordo-Ward, A., Garrote, L., 2019. Influence of hydrologically based environmental flow methods on flow alteration and energy production in a run-of-river hydropower plant. J. Clean. Prod. 232, 1028–1042. https://doi.org/10. 1016/J.JCLEPRO.2019.05.358. Kuriqi, A., Pinheiro, A.N., Sordo-Ward, A., Garrote, L., 2017. Trade-off between environmental flow policy and run-of-river hydropower generation in mediterranean climate. Eur. Water 60, 123–130. https://doi.org/10.13140/RG.2.2.23257.21608. Odum, H.T., 1996. Environmental Accounting: Emergy and Environmental Decision Making. John Wiley & Sons Inc, New York. Odum, H.T., Odum, E.C., 2001. A prosperous way down: principles and policies. University Press of Colorado. Pauliuk, S., 2018. Critical appraisal of the circular economy standard BS 8001:2017 and a dashboard of quantitative system indicators for its implementation in organizations. Resour. Conserv. Recycl. 129, 81–92. https://doi.org/10.1016/j.resconrec.2017.10.
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Assessing the sustainability of urban eco-systems through Emergy-based circular economy indicators ⁎
T
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Remo Santagataa, , Amalia Zucarob, , Silvio Vigliaa,c, Maddalena Ripad, Xu Tiane, Sergio Ulgiatia,f a
Department of Science and Technology, Parthenope University of Napoli, Napoli, Italy ENEA, Laboratory Resources Valorisation, Research Centre of Portici, Portici, Napoli, Italy c Department of Environmental Engineering Sciences, University of Florida, USA d Institute of Environmental Science and Technology (ICTA), Autonomous University of Barcelona (UAB), Bellaterra, Spain e School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China f School of Environment, Beijing Normal University, Beijing, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Emergy accounting Circular economy Sustainability assessment Urban systems Circular indicators
Circular Economy (CE) concepts and tools are getting increasing attention with regard to their implementation in agricultural, urban and industrial sectors towards innovative business models to optimize resource use, process performances and development policies. However, conventional biophysical and economic indicators hardly fit CE characteristics. Life cycle assessment, footprint and economic cost-benefit indicators, do not fully capture the specificity of a closed loop CE framework, characterized by feedbacks and resource use minimization and quality assessment. Commonly used mono-dimensional indicators seem unable to successfully relate the process performance and the use of ecosystem services and natural capital, in that they do not assess the environmental quality and sustainability (renewability, fit to use, recycle potential) of resources and the complexity of interaction between agro/industrial/urban environments and socioeconomic systems, and translate into an incomplete and inadequate picture, far from an effective CE perspective. In this study, Emergy Accounting method (EMA) is used to design an improved approach to CE systemic aspects, focusing on the importance of new indicators capable of capturing both resource generation (upstream), product (downstream) and systems dimensions. This conceptual scheme is built around the case study of the City of Napoli’s economy (Campania region, Southern Italy) considering the surrounding agro-industrial area with its smaller urban settlements. In order to design a reasonable and reliable CE framework, a number of already existing and innovative processes is analyzed and discussed, through a bottom-up procedure capable to account for CE development options based on the recovery of locally available and still usable resources (i.e., conversion of waste cooking oil into biodiesel, conversion of slaughterhouse residues to power and chemicals, recovery and conversion of agro-waste residues, amongst others). The result highlighted that EMA was capable to keep track of the improvement generated by the implemented circularity patterns in terms of reduced total emergy of the system. Moreover, EMA indicators suggested that, in any case, the CE business framework should be intended as a transitional strategy towards more feasible paradigms.
1. Introduction Given that unlimited growth on a limited planet with limited resources is clearly impossible (Brown and Ulgiati, 2011), new production-consumption patterns, that are restorative from both intention and design points of view, become increasingly important if environmental sustainability and human well-being are the goals (Ghisellini et al., 2018,2016). The idea is that rather than discarding products before
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their value is fully utilized, we should use and re-use them. Human population distribution worldwide highlights how more and more people have been moving from countryside to cities. In 2018, 55% of people has been living within urban areas (30% in 1950), a figure that is projected to increase up to 68% in 2050; it is estimated that 43 megacities, i.e. cities with more than 10 million inhabitants, will exist by 2030, most of which in developing countries (United Nations, 2018). Urban systems could be described as entities with their own
Corresponding authors. E-mail addresses: [email protected] (R. Santagata), [email protected] (A. Zucaro).
https://doi.org/10.1016/j.ecolind.2019.105859 Received 16 March 2019; Received in revised form 20 October 2019; Accepted 22 October 2019 1470-160X/ © 2019 Published by Elsevier Ltd.
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conventional energy and material indicators are not able to fully capture the environmental performance generated by closed-loop CE patterns (Geng et al., 2013), in that these indicators are not able to properly keep track of the feedbacks and interconnections within CEoriented systems (Brown and Ulgiati, 2011; Giannetti et al., 2015). Mono-dimensional assessment tools used so far (cumulative energy, exergy, etc.) do not address circularity aspects. On the other side, previous emergy studies were more concerned on environmental cost assessment than on circularity monitoring. Our aim is to recognize to the emergy approach some capabilities that have been ignored until today. As Odum and Odum (2001) suggested, competition and low efficiency must be replaced by collaboration, and high efficiency; quantitative growth must be replaced by qualitative growth. Circular Economy indicates a kind of economy built to be intentionally restorative, to rely on a major share of renewable energy, designed in order to reduce waste. CE aims to resemble living non-linear systems, characterized by the re-use of matter and energy and by the optimization of the entire systems, not only of its singular parts, through a careful management of resources (Ellen MacArthur Foundation, 2012). However, CE should not be considered only as a waste management and recycling option, which may result in a limitation to its implementations. In fact, some waste management strategies do not fit CE objectives (i.e. landfilling and incineration), while CE should be developed as a radically alternative implementation of a systemic point of view (Ghisellini et al., 2016). CE has acquired a significant position in the political agenda, and many implementations from governments, NGOs and relevant stakeholders exist at the present time, but it’s still lacking a unified approach perspective (Kalmykova et al., 2018). According to Geng et al. (2012), critical aspects in the present evaluation of CE supply chains are the lack of: (i) social/environmental indicators; (ii) indicators on urban/industrial synergies; (iii) indicators for responsible and sustainable business; (iv) prevention-oriented indicators (i.e. material/energy reduction, networking rate). Therefore, CE indicators need to incorporate environmental dimension, as conservation of natural capital, decrease of pollution, environmental protection, etc., and social dimension like quality of food and education, jobs, participatory strategies, etc. Also, attention should be given to the different kind of impacts of the alternative options within the surrounding environmental and socio-economic constraints, as highlighted by Kuriqi et al. (2017) and Kuriqi et al. (2019). New CE-oriented indicator systems incorporating network analysis and Emergy Accounting method (EMA) can aid policy makers and provide generalizability to international CE efforts. In this work, EMA is proposed as a comprehensive assessment method to capture and enhance the complex framework of CE. EMA indicators are suggested as valuable tools to evaluate the implementation rate of circular economy patterns within local and regional systems, in order to provide useful information for policy decision making. In this work EMA is applied in order to evaluate the feasibility of a CE scenario within the City of Napoli (Campania region, Italy).
Fig. 1. The four main stages of the pulsing paradigm (After Odum and Odum, 2001).
metabolism, dependent from inputs of energy and materials from external systems at different scales (Zucaro et al., 2014). As resources used by urban inhabitants are mainly generated away from cities, the effects of urbanization affect a wider area beyond city boundaries, in fact, urban growth provides widespread effects on environmental, social and economic features (Verma and Raghubanshi, 2018). In the area of resource efficiency, the European Commission took important initiatives during the years 2011–2015, with a first Circular Economy Package (European Commission, 2015, 2014a, 2014b) further refined to become an ambitious Circular Economy Action Plan (European Commission, 2018a). Circular Economy (CE) lends itself as a model based on closing-the-loop concepts, exploiting the potentiality of secondary raw materials, reducing the extraction of new resources and minimizing waste generation through reuse and recycling strategies (Ghisellini et al., 2016). Contrary to the illusion of unlimited growth, all systems in the biosphere follow oscillating patterns, as highlighted in Fig. 1, (Odum, 1996; Odum and Odum, 2001; Ulgiati, 2004), and so do cities and countries. Oscillations follow the availability of resources. When the resource basis shrinks, also populations, assets and consumption decrease. This pulsing cycle follows four main stages: (i) Growth: abundant resources, increases in population, structure, and assets; low-efficiency and high-competition; (ii) Climax and Transition: maximum size depending on available resources; efficiency increase; collaborative patterns; information storage; (iii) Descent: less resources available, decrease in population and assets, increase in recycling patterns; (iv) Low Energy Restoration: no-growth, consumption smaller than accumulation, and storage of resources for a new cycle ahead. Policies should change accordingly, even if the different size of the oscillation patterns makes difficult to understand the pulsing waves. This means that policies that fit in times of growth may not fit in times of descent. Since industrialized societies are almost exclusively reliant on fossil fuels, a shared consensus exists about a climax stage for urban systems, to be reached when fossil reservoirs are depleted (Agostinho et al., 2018), followed by decline. This context of declining resources represents an opportunity for better explore and understand circular economy potentiality. For this reason, suitable CE indicators are needed for careful monitoring and proper assessing of human systems and economies (Geng et al., 2016). Innovative indicators are required to evaluate the environmental performance for CE-oriented systems and quantify benefits, costs, and bottlenecks over the entire process chain. The
2. Material and methods Emergy is defined as the available energy of one kind directly or indirectly used in a system for transformations leading to a product or a service (Brown and Ulgiati, 2004a; Odum, 1996). Emergy accounts for different categories of supporting contribution to the systems, including renewable and nonrenewable energy and material resources, imported primary resources and manufactured goods, information and knowhow, and finally labor and services (L&S), each one characterized by a different environmental quality. Emergy unit is the solar emjoule (sej) which is the amount of available energy of one kind (solar) converging into a product, resource or service. The total emergy (U) is therefore the total environmental production cost of products and services, obtained multiplying all inflows by an appropriate “environmental cost factor” 2
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Eq. (1) (modified from Ulgiati et al., 2007). A complete key for symbols used in system diagrams is provided as Fig. S1 in Supplementary Materials.
named Unit Emergy Value (UEV, measured as sej/unit-of-inflow) to convert raw resource inflows into the corresponding emergy values and finally summing them into a total emergy U. The UEV of a product is obtained by dividing U by the yield of product delivered. When the unit of a UEV is defined as sej/J, it is named ‘transformity’. All emergy values refer to a Global Emergy Baseline (GEB), representing the total annual emergy driving the biosphere. This work makes reference to the most recent calculation of GEB (12.0E + 24 seJ/yr; Brown et al., 2016) and so all UEVs taken from the literature and calculated with reference to other baselines were converted accordingly. According to Odum (1996), the resources used in a system can be classified as locally available renewable (R) and non-renewable (N), and imported non-renewable (F). Emergy performance indicators (Brown and Ulgiati, 2004b) explored in this work are:
Ulinear =
∗ Ij +
• • •
∑i ∑j=1, ⋯, n cj ∗ m wi + ?
(1)
where ∑i Ri represents all locally renewable (R) input flows; ∑i Ni represents locally non-renewable (N) input flows; ∑i∑j=1,…, n Fij is the outside emergy investment as flows Fij (j stands for the different flows of energy, materials and services provided to i processes, represented in the Fig. 2 as A, B, C); ∑j=1,…, n dj*Ij represents the outside emergy investment for fixing damage from emissions, where dj = emergy investment needed for the repair of one unit of the jth damaged storage Ij, Ij = damaged assets (i.e.: environmental issues to be restored, additional costs for effects on human health), the dash-dotted line after the I*d interaction indicates that this is not a recycle giving back restored assets, but it expresses the additional emergy to fix degraded resources; ∑i∑j=1,…, n cj*mwj represents the outside emergy investment for treatment of waste (cj = emergy investment for treatment of the jth waste flow mwj); finally, “?” indicates a not easily accountable emergy loss due to a potential unknown irreversible damage (i.e. loss of biodiversity). The system diagram presented in Fig. 2 changes significantly when introducing circularity patterns, as highlighted in Fig. 3. The total emergy U in this case is calculated as in Eq. (2) (modified from Ulgiati et al., 2007).
• Emergy Yield Ratio: EYR = U/F. It measures the ability of a system •
∑i Ri + ∑i Ni + ∑i ∑j=1, ⋯, n Fij + ∑j=1, ⋯, n dj
in providing a yield by investing outside resources. The lowest value is when a process provides the same amount of emergy invested, and it is equal to 1. Environmental Loading Ratio: ELR = (N + F)/R. This indicator expresses the load of a system on the environment as the amount of non-renewable on renewable resources used. Renewable fraction of emergy used: %REN = R/U. %REN indicates the fraction of emergy from local renewable resources. Empower Density: ED = U/Area. ED is a function of emergy invested per unit of area, reflecting the land development and human activities. Population emergy intensity: EMERGYPerCapita = U/person. It measures the share of resources invested for the support of one average person.
Ucircular =
∑i Ri + ∑i Ni + ∑i ∑j=1, ⋯, n−1 Fi,j + ∑j=1, ⋯, n gj ∑j=1, ⋯, n−1 dj ∗ I j + ∑i ∑j=1, ⋯, n−1 cj ∗ m wi + ?
∗ Wj +
2.1. Increasing local resource reliance
(2) There are no differences in the amount of input flows of R and N (first two terms) in Eqs. (1) and (2). The first difference is in the third term (∑i∑j=1,…, n-1 Fi,j), where the index j = n-1 indicates a smaller emergy investment due to exchanged, reused or recycled resources. The newly added fourth term (∑j=1,…, n gj * Wj) indicates the emergy invested to transfer still usable waste materials from a process to any other process in the system, where gj is the outside additional emergy input to support the j-th waste processing and Wj is the amount of exchanged waste material. ∑ j=1,…, n-1 dj * Ij + ∑i∑ j=1,…, n-1 cj * mwi indicate, respectively, the smaller emergy investment for fixing damages from emissions and the smaller emergy investment for waste treatment and disposal. The question mark “?” refers to the emergy loss due to unknown irreversible damage, but in a circular system it is expected that this loss should be much smaller than in the corresponding linear system and therefore negligible. The total emergy U driving a linear system is generally higher than the total emergy U when circular strategies are implemented. The difference (Δ) between Ulinear and Ucircular can be calculated (Ulgiati et al., 2007), and it should be greater than zero, therefore, being the emergy investment needed for fixing damage from emissions and the emergy investment for waste disposal less in the circular system than in the linear system (Eq. (3)):
In EMA, all resources are referred to the scale of biosphere and their usefulness and quality are quantified on the same value basis and then compared with the product(s) generated. As previously defined (see Section 2), the percentage of renewability of a system is the share of R in the Total Emergy U. As a consequence, every system stores a variable amount of renewable sources depending on their availability, the associated cost for exploitation and the demand by the investigated supply chain. Therefore, the input flows could be splitted into their renewable and non-renewable fractions. However, in this work this is not applied to all inflows but only to local ones, because the (often minimal) fraction of renewable energy in the imported inputs is offset by the much larger emergy costs of transport. Therefore, circular economy strategies, moving from linear to circular patterns entail firstly that production processes are designed in a way to decrease their resource demand, and secondly that the maximum possible effort is displayed in order to increase the fraction of locally available resources. 2.2. Emergy in linear and circular systems The Emergy approach seems to be able to provide a suitable methodology and comprehensive indicators capable to properly deal with circular strategies. The main reasons root in its focus on resource environmental quality and specially featured systems algebra. Different authors (Elia et al., 2017; European Commission, 2018b; Iacovidou et al., 2017; Pauliuk, 2018) assessed conventional methods and indicators (i.e. Life Cycle Assessment, Material Flow Accounting, CostBenefit Analysis, among others) applied to CE, highlighting a general consensus within their inability to capture all aspects of CE and the need for a holistic method. Table 1 lists the main identified differences and fitness to linear and circular systems of respectively mono-dimensional and emergy indicators. Fig. 2 highlights a linear system composed of three main processes (process A, process B and process C); U of this system is calculated as in
Δ > 0requires ∑ Fi, n − i
∑j=1, ⋯, n gj ∗ Wj > 0
(3)
This calculation allows the choice of (i) circular technology and infrastructure, (ii) resources to be exchanged or replaced, (iii) waste management strategies, guiding the choice of policies among different options. 2.3. Developing circularity patterns within a linear system In this work, the effects of the implementation of circular strategies within a linear system are explored. The study is based on the 3
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Table 1 Characteristics of conventional and emergy-based indicators with respect to their fit to Linear and Circular Economy. Linear Economy
Circular Economy
Features
Conventional Indicators
Features
Emergy Indicators
Business Based Stand – Alone (systems not working together) Mono–Criteria (value based on maximized outcome) Design and planning for ever increasing resource use (unlimited growth) Conservative (“more of the same” approach)
Linear Mono-Dimensional (one aspect at a time) User-Side (value in what can be extracted) Non recognized limits to production processes
Network Based Integrative (environment-social-economy nexus) Multi–Criteria (value depending on choices for wellbeing) Preventative eco-design and planning for decreased resource use and easy recovery (resources are limited) Regenerative (improvement instead of exploitation and depletion)
Systemic Comprehensive (global aspects of a system’s performance) Donor-Side (value in what is invested)
Acquisitive (getting more spending less)
Competitive (better performance than other market operators, increase business, displace competitors)
Exploitation-oriented (resource extraction for processing and market)
Redistributive (more jobs supported by better use of resources)
Processes and economies must consider biosphere constraints Enhances appropriate use of natural capital and ecosystem services by attributing environmental value to resources and system’s components Optimization across scales (maximum empower through all levels of a system’s hierarchy)
already implemented and partially to be designed or improved). When adding circular strategies to the system, new connections become evident (Fig. 5). In agreement with the circular economy framework, the assumption that some flows are local instead of being imported from outside the boundaries of the Metropolitan Area, is also made. In fact, recycling decreases the demand for imports and the system may become able to rely on local availability. For the sake of clarity, circular economy strategies aim at decreased resource demand thanks to alternative designs and use patterns; if this is implemented (e.g. less fossil energy), local resources may become the dominant supply (e.g. photovoltaic electricity). The material and energy flows highlighted in red are new established connections or already existing connections that gain a new added value from the implementation of circularity patterns. The circular scenario investigated in this work has
construction of a virtual network involving the municipality of Napoli, Campania region (Southern Italy) and its supporting area. The Municipality of Napoli (hereafter Napoli, for the sake of simplicity) is part of the Metropolitan Area of Napoli (the third largest urban agglomeration in Italy, with a surface of 1.171 km2, 92 urban centers of different size and a large number of agricultural, industrial and commercial firms). The development and sustainability of such a complex agglomeration of different production and consumption processes requires large amounts of material and energy flows, calling for appropriate resource use and decreased waste generation. Figs. 4 and 5 compare how selected processed of livestock and agricultural production (e.g. oil production from olives) from the metropolitan area and beyond contribute to the metabolism of Napoli, respectively via a conventional linear framework and via circular patterns (partially
Fig. 2. System diagram of a linear system composed of processes A, B and C (* indicates the degrading impact of pollution on environmental, social and technological assets, modified from Ulgiati et al., 2007). 4
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Fig. 3. System diagram of a circular system composed of process A, process B and process C (* indicates the degrading interactions between assets and pollution, modified from Ulgiati et al., 2007).
Fig. 4. The investigated linear hypothesis.
products. The electric energy used in livestock production has been replaced by electricity from photovoltaic (replacing fossil energy with renewable sources, as requested by circular economy strategies as preliminary options to reduce demand); the diesel demand has been replaced by bio-diesel from the sub-system generating biodiesel from waste cooking oil (WCO); furthermore, the input flows
been developed with the purpose of maximizing the interconnections and the material and energy exchanges among the different sub-systems involved. In particular:
• The farm sub-system, modeled after Ghisellini et al. (2014), raises livestock and produces meat, milk, dairies, manure and animal-by
5
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Fig. 5. The investigated circular hypothesis (investigated circular options highlighted in red color). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
• •
•
procedure to the global gain. We choose these subsystems and these circles from what we recognized to be important within the city system as final beneficiary of the study, and what we valued as sufficiently strong from our previous studies in order to lead to reliable conclusions. Nevertheless, we are aware that other important “circles” can be added for sectors where circular patterns are likely to be implemented, in order to reach a more complete assessment of potential circularity implementation in an urban system.
of purchased animal feed and animal bedding have been replaced, considering livestock grazing, according to the biological production, self-production of feed and fertirrigation, as highlighted by Buonocore et al. (2018); EU (2008); Ghisellini et al. (2014); Jaklič et al. (2014); The biodiesel production from WCO sub-system, modeled after Ripa et al. (2014), allows biodiesel production from collected WCO. Moreover, in this sub-system the electric energy input flow has been replaced with photovoltaics (PV) electricity; The sub-system of electricity production from animal by-products (ABP), collected within Campania region and properly processed, modeled after Santagata et al. (2017), allows decreased use of fossil powered electricity. In this subsystem, the diesel input flow has been replaced by biodiesel from WCO, while the methane input flows have been significantly reduced by re-circulating the heat self-generated by the power plant. In this work, the emergy of by-products is calculated as indicated by Santagata et al. (2019), allocating the total emergy U to their exergy fraction (Uby-product = Utot * Exergybyproduct/Exergytot). However, when these flows are fed back to earlier steps of the modeled system, their emergy has not been added, in order to avoid double-counting (emergy algebra rules), similarly to the zero-burden LCA rule (Finnveden, 1999); The urban system of Napoli, modeled after Viglia et al. (2018), is the final beneficiary of all the CE simulated hypotheses. The city, considered as a consumer, benefits from the farm products (i.e. meat, milk and dairies characterized by lower emergy cost, due to in-farm circular production); the electricity used is replaced by PV electricity and electricity from ABP; part or diesel used is replaced by biodiesel from WCO; the input of natural gas is lowered by using biogas produced from manure coming from the farm subsystem (energy from agricultural residues). A diminution of Services in accordance with the diminution of imported resources F has been also taken into account (-54%, assuming that less importing would imply less purchase).
3. Results Table 2 summarizes the EMA results for both a linear, business as usual, evolution of the municipality of Napoli (adjusted after Viglia et al., 2018), and for the implementation of circular patterns scenario within the same urban system. The inventories for this study (Tables S.1 to S.8, in Supplementary Materials) are based on data from the above Table 2 Emergy Accounting and performance indicators, with and without L&S, of the linear and circular hypotheses within the City of Napoli.
The study aims at developing a computation procedure for circular processes. Too many “circles” might have diverted the reader from the
6
Inputs
Linear
Circular
R – Renewable Inputs Locally Available (sej/yr) N – Non-renewable Inputs Locally Available (sej/yr) F – Non-renewable Imported Inputs (sej/yr) L&S (sej/yr) Indicators with L&S U – Total Emergy (sej/yr) EYR = U/F ELR = (N + F)/R %REN = R/U ED = U/area (sej/m2) EMERGYPerCapita (sej/person) Indicators without L&S U – Total Emergy (sej/yr) EYR = U/F ELR = (N + F)/R %REN = R/U ED = U/area (sej/m2) EMERGYPerCapita (sej/person)
3.35E + 19 8.28E + 16 9.24E + 21 5.03E + 21
6.49E + 19 9.42E + 20 4.99E + 21 2.96E + 21
1.43E + 22 1.0 426.2 0.2% 1.22E + 14 1.46E + 16
8.96E + 21 1.1 137.0 0.7% 7.66E + 13 9.16E + 15
9.28E + 21 1.0 276.1 0.4% 7.93E + 13 9.48E + 15
6.00E + 21 1.2 91.4 1.1% 5.13E + 13 6.13E + 15
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mentioned papers ,; as a consequence, all input and output flows (R, N, F, L&S) have been taken into account in the same way as they are included in the original studies. The resulting Total Emergy (a measure of the environmental “size” of the system) for the linear case (Ulinear) is equal to 1.43E + 22 sej/yr with L&S and 9.28E + 21 sej/yr without L& S. Instead, Ucirc calculated for the circular scenario, equal to 8.96E + 21 sej/yr with L&S (a decrease of 38.4%) and 6.00E + 21 sej/yr without L &S (a decrease of 35.3%) as shown in Table 2, translating into a lower emergy demand and suggesting an increased sustainability of the investigated urban system. Considering however that most of the used resources are nonrenewable and imported, the actual sustainability is still questionable and calls for implementation of other aspects (renewability, local reliance) if circular economy actually aims at being an effective business model. This is clearly confirmed by the other emergybased performance indicators in Table 2. The EYR values, very sensitive to the local versus imported characteristics, are very low, and only increases by 10% and 20% respectively with and without L&S inclusion. Similarly, the ELR values decrease (as expected) by 68% and 67% respectively with and without L&S, thanks to increased reliance on renewable and local resources, but remains still very high in absolute terms. The %REN does increase, but hardly moves beyond 1% of total emergy use. Similar yet unsatisfactory improvements are shown by the other indicators in the same Table. Complete EMA tables related to all subsystems, both within the linear and the circular hypotheses, are shown in the Supplementary materials (from Table S.1 to Table S.8), alongside with data and calculations. Fig. 6 highlights the comparison between emergy signatures of both
linear and circular hypotheses, with and without L&S. In the EMA, a signature is a diagram that shows the breakdown of input resources within the total emergy of a process. In so doing, a comparison does not only rely on total use, but also on the different resource categories. In Fig. 6, the input flows are aggregated as macro-categories: (i) L& S, (ii) Fuels, (iii) Electricity, (iv) Food, (v) Metals, (vi) Building materials, (vii) Additional materials, (viii) Others (flows contributing less than 1% to U). The non-negligible decrease in terms of U when converting from the linear system to the circular one is to be attributed to: i) a decrease of the fuel emergy contribution (≈1%); ii) a decrease of the electricity emergy input (≈53%); iii) a decrease of food emergy inputs (≈86%). It should be noted that L&S shows an important fraction of the total emergy U, around 35% in the Business-as-Usual case and 33% in the circular assumption. 4. Discussion The obtained results fulfill the need for comprehensive indicators capable of including the peculiar characteristics of CE framework within CE strategies, policies, decision-making implementation. The Total Emergy U used as a CE indicator can capture the variation of the embodied energy in different processes, due to its unique algebra rules (Odum, 1996), suitable to approach the feedback features of CE, and due to EMA ability to include the contribution of renewable sources and the information embedded within labor and services. Results are shown with and without L&S in order to highlight the important role played by L&S (know-how, infrastructure, standard of living) in a system's dynamics. The calculation of L&S is site specific and depends on lifestyles
Fig. 6. Emergy signature of Linear and Circular hypotheses with and without L&S for the City of Napoli. 7
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Table 3 Δ and %dec values of the investigated subsystems with and without L&S. with L&S
without L&S
Subsystems
Ulin
Ucirc
Δ
%dec
Ulin
Ucirc
Δ
%dec
Farm (sej/yr) Electricity from ABP (sej/MWh) Biodiesel from WCO (sej/kg) City of Napoli (sej/yr)
1.12E + 19 1.02E + 15 1.78E + 12 1.43E + 22
2.08E + 18 5.58E + 14 1.75E + 12 8.96E + 21
9.10E + 18 4.57E + 14 2.72E + 10 5.34E + 21
81% 45% 2% 37%
1.01E + 19 8.33E + 14 7.24E + 11 9.28E + 21
1.02E + 18 3.76E + 14 6.97E + 11 6.00E + 21
9.10E + 18 4.57E + 14 2.72E + 10 3.28E + 21
90% 55% 4% 35%
and market fluctuations as well as on the adopted technology. Nevertheless, in a circular economy situation where the largest possible share of local emergy flows is included, the related fraction of L&S becomes also local, indicating important social (job creation) and economic impacts (e.g. lower cost for transportation). U is an indicator of the ‘size’ of the system under investigation. From this perspective, Δ (Ulinear-Ucircular) expresses the performance of the system in delivering the same output products and services to society by investing a smaller amount of biosphere work. Table 3 sums up Δ values and the percentage decreases (%dec) for the different subsystems investigated in this work, with and without L&S (complete emergy tables for the linear and circular hypotheses of the farm, electricity from ABP and biodiesel from WCO subsystems are provided in the Supplementary materials when implementing CE patterns. The implementation of CE strategies within the investigated circular hypothesis (Fig. 5) have brought to significant reductions in all the subsystem, due also to the ‘magnifying’ effect of the interconnections generated. However, a question of why performance emergy indicators (EYR, ELR, etc) still show partially unsatisfactory values cannot be disregarded. The first answer is that we did not include all the recycling alternatives. Very likely, other resources can be saved and recovered via alternative designs (e.g. demolition and construction materials, minerals and metals from vehicles and computers, municipal and industrial waste management, etc). The more these materials are reused or recycled, the better the performance indicators will appear. A second answer is that the starting point of the investigated system was an almost complete reliance on nonrenewable and imported resources. The decrease of 35% of such amount of resources achieved through recirculation of some material and energy flows does not mean that these resources have been fully replaced by renewables. Iron, even when recycled, is still non-renewable and same applies to exhaust cooking oil (recycled as fuel, but still produced through nonrenewable agricultural patterns). The Circular Economy concept is based on three main pillars:
life time) and the emergy of solar radiation; the UEV of PV electricity is 9.91E + 04 sej/J (after Brown et al., 2012), 47% of the UEV of fossil powered electricity, 2.13E + 05 sej/J, translating into an emergy cost of electricity production of about 8.66E + 20 sej lower, which is only 6% of the total U supporting the city (1.43E + 22 sej). For the sake of clarity, electricity in the business as usual situation accounts for 11% of total emergy; if we apply a 53% improvement of emergy cost, this is the 53% of the 11%, confirming the 6% computed above. If a similar reasoning is applied to all replaced resources, the real sustainability improvement is not so remarkable. This means that the fraction of nonrenewable emergy (i.e. demand for past environmental support) is still dominant and affects the emergy based performance indicators. Unlike energy balances, where joules are added without being weighted through any “conversion factor” (all joules seem equal), in emergy accounting the minerals and fossil inputs are assigned much higher conversion factors (UEV's, Unit Emergy Values) than diluted solar radiation (UEV = 1 sej/J by definition). Energy balances hide the important aspect of the huge biosphere work to generate resources and truncate the computation at the very short supply chain boundary where fossil fuels are burned to extract minerals and generate heat, thus suggesting a low-cost conversion of solar radiation into actual electricity. An emergy-based sustainability assessment requires instead that past biosphere work is accounted for, thus disclosing the actual costs of photovoltaic production as well as any other renewables (biofuels, wind electricity, etc.) or circular patterns (cooking oil recovery as biodiesel). A real sustainability improvement would only be possible if fully renewable materials and energy resources were used to replace non-renewables (e.g. by using recycled silicon and renewable electricity to make the new PV modules). Only if this radical circular economy improvement is implemented, the substitution of fossil electricity with PV electricity will result in a significantly reduced U and better performing indicators as a consequence of the increased % of renewable emergy compared to U. As a consequence, the most important result of this study may be that if circular economy aims at becoming a truly alternative and sustainable business model, most if not all its supporting resources must become renewable and local through appropriate design and lifestyle changes. We cannot state this is going to happen nor can we deny it. In any case, the decreased demand for input resources may at least provide a sufficiently large transition time, in order to search alternative patterns. The simulations generated in this work confirmed how EMA could provide a useful support when dealing with CE decisions, by providing suitable insights from a different perspective, built in environmental, spatial and temporal dimension, compared to conventional user-oriented mono-dimensional indicators. The results seems to be consistent with the general ‘vision for a circular city’ presented by the Ellen MacArthur Foundation (Ellen MacArthur Foundation, 2017) characterized by i) reduced waste and pollution; ii) circulating materials and energy to maximize use; iii) enhanced condition for the regeneration of natural capital. As also highlighted by several researchers (Kalmykova et al., 2018; Petit-Boix and Leipold, 2018), a large number of CE studies and implementations can be observed currently, but a comprehensive organization of strategies and initiatives carried out by the different actors involved (governments, NGOs, industries, etc.) is
1. Preventive design of systems towards lower resource investment for their creation, management and disposal. The increased efficiency of such systems should result in less resource use and less waste to be disposed of (in so decreasing the need for “end of pipe” solutions; 2. Substitution of a wide fraction of non-renewable resources with renewable ones; 3. Feedback (reuse, recycle) of by-products and waste. However, in a business as usual situation similar to the present one, in which CE is still far from being fully implemented, also renewable systems still rely on non-renewable resources (e.g. silicon, aluminum, glass and other minerals as well as fossil electricity to build a photovoltaic generator and storage batteries). Under the important premise that energy and emergy are not the same concept (see previous definitions in Methods), let's look at the replacement of fossil electricity with PV electricity. Two percentages must be kept in mind: (a) a PV module converts into electricity about 10% of the solar energy hitting its surface; (b) the UEV (a different efficiency measure, i.e. the emergy “cost of production”) of photovoltaic electricity is the sum of the emergy of resources invested to build the PV module (discounted over 8
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Acknowledgement
still lacking. The construction of new, understandable indicators could be useful in acknowledging the value of implementations actualized or still in designing phase.
The authors gratefully acknowledge the support from the Italian Ministry of Foreign Affairs and International Cooperation – Ministero degli Affari Esteri e della Cooperazione Internazionale. Direzione Generale per la Promozione del Sistema Paese. Grant number PGR05278.
4.1. Further improvements The circular scenario presented in this work was built on the basis of case studies previously investigated by the authors. It should be clear that a real CE implementation should involve other important supply chains and different solutions within relevant flows and stocks. Cities are to be considered as consumer organisms requiring more inputs from the economy than local resources, in which a relevant emergy stock is stored within the built environments, contributing to enlarge and add complexity to the system (Sevegnani et al., 2018). A survey conducted by the Municipality of Napoli in 2001 highlighted the presence of 38,768 buildings and building complexes, of which about the 88% are for residential use (Comune di Napoli, 2001). This represents of course a massive amount of embodied emergy stored in the urban system, almost locked up for the whole lifetime of the edifices, which could be partially reduced over time by i) substituting buildings that reached their end of life with new ones built with advanced, more efficient materials and technologies; ii) intervening within the normal maintenance using secondary or in general more environmentally sound materials.
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5. Conclusions In this work, the case study of the City of Napoli has been used for testing EMA as a suitable method for the implementation of new indicators for CE. EMA seems to be capable of measuring the improvements following the implementation of circular strategies (while common mono-dimensional indicators lack a holistic understanding of CE), by evaluating the difference Δ between the total emergy U of the investigated system in a linear and a circular framework. Δ value is basically grounded in the different emergy investment between 1) disposing all waste generated and importing primary energy and materials and 2) promoting strategies and technology for the recirculation of material and energy within the system, reducing the quantity of waste to be disposed of and the import of energy and materials from the outside. EMA algebra rules has proven to be able to capture the complex features of CE, integrating also environmental and information, delivered within direct and indirect labor, aspects. The implementation of some simulated CE patterns within the framework of the City of Napoli brought to significant reductions in terms of emergy invested in the system, thus further improvements can be achieved by the integration of other CE patterns and the inclusion of relevant stocks of embodied energy (i.e. buildings) contributing to enlarging the complexity of the system. The results achieved in this work suggest that for CE to become a viable and actually alternative business model, preliminary design and planning phases have a paramount importance, while end-of-pipe strategies are only short sight options and do not translate into a sustainable trend. Planning and design are not only technological options, but must look at lifestyle changes as most important turning point for sustainability. As a consequence, when planning and making decisions, questions about what do we actually want to sustain and what lifestyles changes are we actually willing to accept cannot be disregarded. Reliance on technology alone may translate into a temporary improvement followed by increases of the size of the societal assets and resource consumption, so that sustainability would again be in peril. This study provides a framework to: (1) account for the convergence of all supporting energy and material flows towards societal assets and trends; (2) quantify progresses achieved by implementing circular patterns; (3) adopt a set of purposefully designed circular indicators in support to policy making; and finally (4) providing a broad understanding of the interplay of design, technology, and lifestyles for effective sustainability pathways. 9
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