*Article* **Techno-Economic Viability of Agro-Photovoltaic Irrigated Arable Lands in the EU-Med Region: A Case-Study in Southwestern Spain**

**Guillermo P. Moreda 1 , Miguel A. Muñoz-García 1,\* , M. Carmen Alonso-García 2 and Luis Hernández-Callejo 3**

**Abstract:** Solar photovoltaic (PV) energy is positioned to play a major role in the electricity generation mix of Mediterranean countries. Nonetheless, substantial increase in ground-mounted PV installed capacity could lead to competition with the agricultural use of land. A way to avert the peril is the electricity-food dual use of land or agro-photovoltaics (APV). Here, the profitability of a hypothetical APV system deployed on irrigated arable lands of southwestern Spain is analyzed. The basic generator design, comprised of fixed-tilt opaque monofacial PV modules on a 5 m groundclearance substructure, featured 555.5 kWp/ha. Two APV shed orientations, due south and due southwest, were compared. Two 4-year annual-crop rotations, cultivated beneath the heightened PV modules and with each rotation spanning 24 ha, were studied. One crop rotation was headed by early potato, while the other was headed by processing tomato. All 9 crops involved fulfilled the two-fold condition of being usually cultivated in the area and compatible with APV shed intermitent shading. Crop revenues under the partial shading of PV modules were derived from official average yields in the area, through the use of two alternative sets of coefficients generated for low and high crop-yield shade-induced penalty. Likewise, two irrigation water sources, surface and underground, were compared. Crop total production costs, PV system investment and operating costs and revenues from the sale of electricity, were calculated. The internal rates of return (IRRs) obtained ranged from a minimum of 3.8% for the combination of southwest orientation, early-potato rotation, groundwater and high shade-induced crop-yield penalty, to a maximum of 5.6% for the combination of south orientation, processing-tomato rotation, surface water and low shade-induced crop-yield penalty.

**Keywords:** agrophotovoltaic; agrivoltaic; dual-land use; solar sharing; solar photovoltaic energy; water–food–energy nexus

### **1. Introduction and Objectives**

Nowadays, most countries worldwide are aware of the importance of preserving nature. Environment protection includes, amongst others, measures to limit the use of non-recyclable materials and to reduce the emission of greenhouse-effect gases (GHGs). Reduction in GHGs emission entails burning less fossil fuels and increasing the share of renewable energies in the electricity generation mix. The major renewable sources of electricity, wind and solar, intrinsically non-dispatchable due to the intermittency of their resource, could be backed-up by hydrogen fuel cells in the future. European Union (EU) member states are promoting increases in wind and solar installed capacity. Hereof, Spain and Italy planned national levels of 42% and 30%, respectively, of energy from renewable sources in their gross final energy consumption in 2030 [1,2].

**Citation:** Moreda, G.P.; Muñoz-García, M.A.; Alonso-García, M.C.; Hernández-Callejo, L. Techno-Economic Viability of Agro-Photovoltaic Irrigated Arable Lands in the EU-Med Region: A Case-Study in Southwestern Spain. *Agronomy* **2021**, *11*, 593. https://doi.org/10.3390/ agronomy11030593

Academic Editor: Ajit Govind

Received: 1 February 2021 Accepted: 17 March 2021 Published: 20 March 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Solar power plants produce electricity based on the photovoltaic (PV) effect or by concentrating solar energy onto a heat-transfer-fluid which produces the steam that drives a turbine-generator set. In many countries, ground-mounted solar PV power plants have become familiar in the rural landscape, being popularly known as solar farms. By the end of 2019, installed solar PV power capacity in Spain stood at 8913 MW, representing about 8% of the total installed power capacity in Spain [3].

Substantial further increase in ground-mounted PV power capacity could eventually lead to conflict of interest with the agricultural use of land. This could jeopardize the stability of agricultural produce prices, which should be carefully considered. In 1982, Goetzberger and Zastrow [4] analyzed the possibility of combining agricultural and electricenergy production in the same plot. This dual use of land was later coined in the literature as agrivoltaic or agrophotovoltaic. Here, we use the latter portmanteau, abbreviated as APV. The main differences between a conventional ground-mounted PV power plant and an APV system are:

In 2010, a first APV prototype was erected by Dupraz et al. in Montpellier, France [5], using fixed-tilt PV modules. In their experiments, the main crop cultivated was lettuce [6]. Valle et al. [7] reported on the extension of the Montpellier 2010 prototype with sun-tracking PV modules. In 2016, a fixed-tilt 194.4 kWp APV array with bifacial PV modules and a ground clearance of 5 m was erected in Herdwangen, Germany [8]. The reason for using bifacial PV modules was two-fold: First, to harness snow reflectivity to produce more electricity; and second, to decrease crop shading, thanks to higher transparency of bifacial modules compared to monofacial counterpart. Schindele et al. [8] concluded that their system was profitable for potato but not for wheat. Dinesh and Pearce [9] concluded that PV installed capacity could be increased between 40 and 70 GW if lettuce cultivation alone were converted to APV systems in the United States of America. They recommended exploring the outputs for different crops and geographic areas, to determine the potential of APV farming worldwide. Recently, the consortium SolarPower Europe proposed to integrate a "European Agri-PV strategy" within the future Common Agricultural Policy (CAP) [10]. Hitherto, APV projects in Europe have been of limited acreage. To our knowledge, the largest APV complex, with 2.67 MW, spans 3.2 ha of raspberry near Arnhem, The Netherlands [11].

In APV systems, both the PV array and the understorey crop benefit mutually. For instance, in a watermelon field in the EU-Med region, the shade casted by the heightened PV modules could circumvent the need for anti-fruit-cracking solar protector spraying of the fruits. Apart from the economic saving for the farmer, this is beneficial for the environment. Another example of synergy is the soil moisture condition favored by APV sheds [12] that can save irrigation water. The latter is important for several reasons: First, environmental benefit; second, reduced cultivation cost; third, limited crop yield decrease in case of irrigation water allocation restricted due to drought; fourth, possibility of irrigating an acreage only slightly smaller than that of a non-drought year.

Albeit in Spain average yield per unit area of irrigated crops is 6.5 times greater than that of rainfed agriculture [13], drought episodes make granted water allocations not always deliverable. Irrigation blue water shortage is partly responsible for the difference between irrigable and irrigated area in many countries. Thus, in 2016 the share of irrigable and irrigated areas in the total Utilized Agricultural Area of Spain were of 15.7% and 13.2% respectively [14]. In the same year 2016, the corresponding shares were 32.6% and 20.2% for Italy and of 29.7% and 23.6% for Greece.

The objectives of this work were: The objectives of this work were:

20.2% for Italy and of 29.7% and 23.6% for Greece.

*Agronomy* **2021**, *11*, x 3 of 25

1. to design two irrigated annual crop rotations whereof crops are usually cultivated in the area of study and compatible with partial shading imposed by APV sheds. 1. to design two irrigated annual crop rotations whereof crops are usually cultivated in the area of study and compatible with partial shading imposed by APV sheds.

and irrigated areas in the total Utilized Agricultural Area of Spain were of 15.7% and 13.2% respectively [14]. In the same year 2016, the corresponding shares were 32.6% and

2. to thoroughly determine the stream of expenditure and revenues for both agricultural and electricity production, with the final aim of analyzing the profitability of APV system for each combination of APV shed orientation (due south/ southwest), source of irrigation water (surface/underground), shade-induced crop yield penalty (low/ high) and crop rotation (early potato/processing tomato). 2. to thoroughly determine the stream of expenditure and revenues for both agricultural and electricity production, with the final aim of analyzing the profitability of APV system for each combination of APV shed orientation (due south/ southwest), source of irrigation water (surface/underground), shade-induced crop yield penalty (low/ high) and crop rotation (early potato/processing tomato).

#### **2. Materials and Methods 2. Materials and Methods**

A hypothetical case-study was arranged with annual irrigated crops cultivated under APV sheds in the municipality of Brenes, close to the city of Seville, in southwestern Spain. The centroid of the site sits at 37◦3302200 N and 5◦5008 00 W (Datum ETRS89), standing on average altitude of 40 m a.s.l. Some major woody and arable crops cultivated in the area are: Olive, citrus, almond, peach, alfalfa, early potato, maize, processing tomato, cotton and sunflower. Amongst the annual arable crops, we selected potato to be rotation-head, since under-shading yield data were found in the literature [15] for this crop. Based on agronomical considerations detailed in the next sub-section, a four-year rotation headed by early-potato was designed. Taking into account the 6 ha average size of the agricultural unit plot in Brenes, a total acreage of 24 ha was analyzed. Lettuce, a shade-tolerant crop with documented under-shade yield data, was disregarded in view of its limited cultivation in the area [16]. Conversely, cotton, a traditional local annual crop, was discarded because according to Weselek et al. [17] it does not thrive in shade. For the sake of universality, we considered as if land consolidation had not been implemented in the area of study. Thereby, the spatial distribution considered is fragmented, i.e., the four 6 ha plots are not adjacent to each other (Figure 1). A hypothetical case-study was arranged with annual irrigated crops cultivated under APV sheds in the municipality of Brenes, close to the city of Seville, in southwestern Spain. The centroid of the site sits at 37° 33′ 22″ N and 5° 50′ 8″ W (Datum ETRS89), standing on average altitude of 40 m a.s.l. Some major woody and arable crops cultivated in the area are: Olive, citrus, almond, peach, alfalfa, early potato, maize, processing tomato, cotton and sunflower. Amongst the annual arable crops, we selected potato to be rotation-head, since under-shading yield data were found in the literature [15] for this crop. Based on agronomical considerations detailed in the next sub-section, a four-year rotation headed by early-potato was designed. Taking into account the 6 ha average size of the agricultural unit plot in Brenes, a total acreage of 24 ha was analyzed. Lettuce, a shade-tolerant crop with documented under-shade yield data, was disregarded in view of its limited cultivation in the area [16]. Conversely, cotton, a traditional local annual crop, was discarded because according to Weselek et al. [17] it does not thrive in shade. For the sake of universality, we considered as if land consolidation had not been implemented in the area of study. Thereby, the spatial distribution considered is fragmented, i.e., the four 6 ha plots are not adjacent to each other (Figure 1).

**Figure 1.** Spatial distribution of the four plots totalizing 24 ha, the medium voltage power transmission line budgeted and the pre-existing grid-connection switchyard. **Figure 1.** Spatial distribution of the four plots totalizing 24 ha, the medium voltage power transmission line budgeted and the pre-existing grid-connection switchyard.

### *2.1. PV System*

The basic APV shed considered consisted of 22 non-tracking and heightened supporting structures aligned in two parallel swaths of 11 supporting structures each Figure 2. The PV modules are arranged in groups of 24 modules on each supporting structure. The fixed-tilt angle is of 27◦ (the local latitude minus 10◦ ). The PV module considered was the opaque-monofacial-polycrystalline CSP290-60, of 290 Wp [18]. The dimensions of this module are 1640 mm × 992 mm and it weighs 18.2 kg. The supporting structures are

equispaced 9.5 m. The mechanical configuration of the basic APV shed, including lateral (Northeast-Southwest direction for the due SW shed orientation depicted) lattice bracing that leave a ground-clearance of 5 m, is similar to the one reported by Schindele et al. [8]. This substructure is of known cost and would be valid for the location of Seville, where snow and wind loads are less or equal than in Herdwangen. The pillars of the substructure are fixed to the ground by means of a so-called spider-shaped anchor made of an anchoring bush plate with long threaded rods assembled in a circular fashion [19]. module are 1640 mm × 992 mm and it weighs 18.2 kg. The supporting structures are equispaced 9.5 m. The mechanical configuration of the basic APV shed, including lateral (Northeast-Southwest direction for the due SW shed orientation depicted) lattice bracing that leave a ground-clearance of 5 m, is similar to the one reported by Schindele et al. [8]. This substructure is of known cost and would be valid for the location of Seville, where snow and wind loads are less or equal than in Herdwangen. The pillars of the substructure are fixed to the ground by means of a so-called spider-shaped anchor made of an anchoring bush plate with long threaded rods assembled in a circular fashion [19].

*Agronomy* **2021**, *11*, x 4 of 25

The basic APV shed considered consisted of 22 non-tracking and heightened supporting structures aligned in two parallel swaths of 11 supporting structures each Figure 2. The PV modules are arranged in groups of 24 modules on each supporting structure. The fixed-tilt angle is of 27° (the local latitude minus 10°). The PV module considered was the opaque-monofacial-polycrystalline CSP290-60, of 290 Wp [18]. The dimensions of this

*2.1. PV System*

**Figure 2.** Basic Southwest-oriented agrophotovoltaic shed featuring an installed PV capacity of 153.1 kWp and connected to a 150 kW inverter. **Figure 2.** Basic Southwest-oriented agrophotovoltaic shed featuring an installed PV capacity of 153.1 kWp and connected to a 150 kW inverter.

With regard to the PV modules azimuthal angle, two orientations were compared: due southwest and due south. Although the latter is the one that maximizes electric production in the northern hemisphere, Beck et al. [20] concluded that either southeast or southwest is preferable for the crop cultivated beneath the APV shed, since ground radiation distribution is more uniform. The increased radiation uniformity favors crop plants isochronous ripening, which is particularly important for arable crops, usually harvested With regard to the PV modules azimuthal angle, two orientations were compared: due southwest and due south. Although the latter is the one that maximizes electric production in the northern hemisphere, Beck et al. [20] concluded that either southeast or southwest is preferable for the crop cultivated beneath the APV shed, since ground radiation distribution is more uniform. The increased radiation uniformity favors crop plants isochronous ripening, which is particularly important for arable crops, usually harvested in mechanized or semi-mechanized one-single pass. Here, we used SAM 3D scene shade calculator [21] to compare due South (Figure A1, Appendix A) and due Southwest (Figure A2) orientations.

The number of PV modules in the basic APV shed is

$$24 \frac{\text{PV modulus}}{\text{Superting structure}} \cdot 22 \frac{\text{Susporting structures}}{\text{Basic AVV shed}} = 528 \,\text{PV modules/Basis APV shed}$$

The corresponding peak power is

$$1528 \frac{\text{PV modules}}{\text{Basic AVV shed}} \cdot 290 \frac{\text{Wp}}{\text{PV module}} = 153120 \text{ Wp} \cong 153.1 \text{ kWp/Basis APV shed}$$

To calculate the ground area covered by the basic APV shed, we have to multiply its overall width (26 m (Figure 2)) by its overall length. The latter, in turn, is the sum of 95 m (10 × 9.5 m, Figure 2) plus 1.5 m (the horizontal projection of the last module row cantilever 1.64 m to an angle of 27◦ ) plus 9.5 m, i.e., 106 m. Hence,

$$26 \text{ m} \times 106 \text{ m} = 2756 \text{ m}^2 = 0.2756 \text{ ha}$$

To calculate the ground coverage ratio (GCR), the overall module surface area has to be first computed as:

$$1528\text{ PV modulo} \cdot \frac{1.68\text{ m} \cdot 0.992\text{ m}}{\text{PV module}} = 880\text{ m}^2$$

Then, the GCR is

$$\frac{880\text{ m}^2}{2756\text{ m}^2} = 32\%$$

Power density is

$$\frac{153.1 \text{ kWp}}{0.2756 \text{ ha}} \cong 555.5 \text{ kWp/ha}$$

Considering an effective land area of 5.7 ha, obtained by reducing by 5% the size of the agricultural unit plot to account for mismatch between plot legal boundaries and APV shed orientation, the PV capacity installed in each of the four 6 ha plots is

$$555.5 \frac{\text{kWp}}{\text{ha}} \cdot 5.7 \frac{\text{ha}}{\text{plot}} = 3166 \text{ kWp/plot}$$

The inverter considered is the SMA SHP Peak 3, which features a nominal AC power of 150 kW [22]. The number of inverters in the agricultural unit plot would be

$$\frac{3166\text{ kW} \,/\text{air} \,\text{current} \,\text{unit}\,\text{plot}}{153.1\text{ kW} \,\text{p} /\text{inverter}} \cong 21 \text{ invers } /\text{agricultural unit plot}$$

The lifespan considered for the APV system is 25 years, the conventional lifespan of the PV modules. The service life assigned to the inverters is 13 years, which entails inverters replacement in the year 14. Module degradation was computed by means of a degradation coefficient, assigned a value of 1 until year 11 and annually decreased by 0.5% from the year 12 onwards. A simulation was run to estimate annual income from the sale of the energy generated by the PV system. Simulation was done using SISIFO [23], an online free simulation tool for the quality and bankability of PV systems. Table 1 presents a compilation of the main data fed to the SISIFO PV simulator.

### *2.2. Irrigated Crops*

In the EU-Med countries, irrigation water use represents an average 70% of total water withdrawals [24]. On the other hand, more than 80% of the irrigated land-acreage in Spain, involving 7·105 irrigators and 2·106 -hectare, is serviced by an irrigation district [25]. According to Masia et al. [26], surface water from reservoirs represents most of the water stewardshipped and distributed by irrigation districts in the EU-Med region. Concurrently, underground water abstractions in Spain irrigate over one-third of the country irrigable area [27]. Our study is on the arable lands of the Guadalquivir river valley near Seville, where the climate is Mediterranean-oceanic [28]. According to the site coordinates, water would be served by the Comunidad de Regantes del Valle Inferior del Guadalquivir

irrigation district, hereinafter abbreviated as Valle Inferior Irrigation District (VIID). Their irrigation scheme provides surface water with a pressure head (ph) of 441 kPa of water measured at the pumping station [29].

**Table 1.** Input data for PV simulation of a 5.7 ha agro-photovoltaic plant oriented due Southwest, in Brenes (Seville, Spain).

(a) Typical meteorological year. (b) 9.5 m/3.28 m ≈ 3. (c) 26 m/3.28 m ≈ 8. (d) 0 m/26 m = 0.

Instead of continuous mono-cropping, a crop rotation was designed to submit to the principles of sustainable farming. The crop selected as rotation head was early-potato. To do the study more comprehensive, we extended it to an alternative four-year crop rotation. The head-of-rotation in this case was processing-tomato, which in the last years competes with early-potato in local farmers preferences. It is worthy of note that processing-tomato is also mainstream in other EU-Med countries such Italy. The main difference between the two crop rotations is that the early-potato rotation is symmetrical, unlike the processingtomato counterpart. The latter is asymmetrical because tomato withstands well—or indeed "'prefers'"—to be cultivated up to thrice on the same plot, whereas potato is required to be cultivated in a different plot each year.

The crops included in the early-potato rotation, apart from the potato (Solanum tuberosum L.) itself, were: Canola (*Brassica napus*), onion (*Allium cepa* L.), faba bean (*Vicia faba* L.) and forage maize (*Zea mays* L.). The latter two are cultivated on the same plot one after the other in the same year, practice known as sequential cropping or double cropping (Table 2). This scheduling was designed following the sustainable agriculture principles of: (i) avoid cultivating two demanding crops one after the other in the same plot; and (ii) for every plot, avoid repeating the botanic family of the previous year.

**Table 2.** The early-potato rotation designed, wherein potato returns to each plot every four years.

(a) Sequential cropping of faba bean (FB) in first harvest and forage maize (FM) in second harvest.

The crops comprising the processing-tomato rotation (Table 3), apart from tomato (*Lycopersicum esculentum* or *Solanum lycopersicum*) itself, were: Melon (*Cucumis melo* L.), carrot (*Daucus carota* L.), onion and dry peas (*Pisum sativum* L.). Due to the asymmetry of this rotation, its full 25-year scheduling derivation is tedious and is relegated to Table A1 (Appendix B).

**Table 3.** Tomato rotation designed, wherein processing-tomato is cultivated in the same plot for three consecutive years.

### *2.3. Profitability Analysis*

From an entrepreneurship point of view, an APV farm is a business comprised of two activities, namely crop production and electric power generation. To determine the profitability of an APV system, the stream of annual income and expenditure during the project lifetime has to be computed. Expenditure comprise initial investment cost (ascribed to the PV activity, since all the crops considered are annual), plus annual operation and maintenance costs (due to both agricultural and electric energy generation activities). Revenues originate from both activities and go from the second year onwards, since the first year is unproductive and carries only investment costs.

With regard to the APV system investment cost or capital expenditure (CapEx), the main items are the acquisition and installation of the substructure and mounting structures, PV modules, inverters, transformer and the so-called balance-of-system (cables, switchboards, etc.). Table A2 (Appendix C) includes a breakdown of the cost items involved. With regard to the operating expenditures (OpEx), they can be split in two: First, the annual PV OpEx. Second, the annual costs incurred for crop cultivation beneath APV sheds. Table 4 includes estimated crop production costs under the partial shading of an APV shed.

**Table 4.** Crop production cost under full sunlight and under APV partial shading (in both cases assuming irrigation with surface water).

(a) See Table A4 (Appendix D) for details. (b) Due to scarcity of bibliographic data on water saving for some crops under shade, they are assigned the mean between 14% (c) and 9% (d). (c) Assimilated to cucumber as both melon and cucumber belong to the botanical family of *Cucurbitaceae*; data for cucumber available at [30]. (d) Data for cherry tomato available at [31]. In addition, processing-tomato, the same value is assigned to early-potato, since the latter belongs to the same botanical family as tomato, namely, *Solanaceae*. (e) [32].

> The irrigation district is liable for pressurizing the pipeline network. Therefore, the electricity generated by the APV system would not be partially self-consumed by a pump station, but entirely sold in the electricity wholesale market. Hence, the management

of electricity binomial tariff cost items (contracted power, energy consumption, capacity charge, meter-gauge leasing, irrigators partially exempted electric tax, etc.) is on the irrigation district behalf.

Following the recommendations/prescriptions of the EU Water Framework Directive, many irrigation districts have abandoned the traditional per-hectare flat-rate pricing. The installation of volumetric metering valves has enabled irrigation districts to change to binomial tariffs. These consist of a fixed per-hectare component that is proportional to the area with irrigation rights and a variable or volumetric component that is proportional to the volume of water used [33,34].

The irrigation district determines the volumetric component, based on energy cost. This cost depends on the ph of the irrigation network, the acreage irrigated and the type of crop, since the required hydraulic power is equal to the ph multiplied by the flow rate (in turn, ph depends on whether the water source is underground or surface, the irrigated area topography (plot elevations, size and shape) and the irrigation system service pressure, e.g., sprinklers demand more pressure than drippers) For the VIID, current energy cost is of 0.012 EUR/m3 [35]. Rodríguez-Díaz et al. [36] measured energy consumption and power required per unit of irrigated area for several surface-water irrigation districts in southern Spain. One of them, the Bembézar Margen Derecha (BMDID), featured a ph of 461 kPa, almost identical to the VIID ph of 441 kPa. The crops irrigated are similar in both irrigation districts and similar to those of our study. In a sequel work, Fernández-García et al. [37] reported an energy cost of 0.02 EUR/m3 and a total irrigation cost of 283 EUR/ha for the BMDID. Here, we took the energy cost of 0.02 EUR/m3 [37] instead of the lower 0.012 EUR/m3 [35]. Total irrigation cost in sites where underground water is used is usually two-fold (600 EUR/ha) and sometimes it can reach 900 EUR/ha [38].

Apart from the energy cost, irrigation cost includes water as a fixed-cost levied upon the land. The fixed per-hectare component in turn splits in two: The royalty of the River Basin Organism (Confederación Hidrográfica del Guadalquivir), a public incumbrance for upstream public civil works that allow water disposal to irrigation schemes (42.47 EUR/ha·annum); and the irrigation district fee, for management and maintenance of downstream irrigation district proprietary pipeline network and facilities (63.23 EUR/ha·annum in the case of the VIID [35]). The sum of both amounts equals 105.8 EUR/ha·annum. Further, VIID subscribers are currently, subjected to surcharge disbursement of 84.30 EUR/ha·annum in concept of amortization of pipeline, reservoirs and pumping stations upgrading works commissioned in 2008. Herein, this cost item is not included, since the expected remaining surcharge payment period is shorter than our study lifespan. In addition, regarding water use, Table 5 compiles the volumetric component and the total irrigation cost under full sunlight for the 9 crops considered.

With regard to annual revenues, the main entries are: First, the income from the sale of electricity generated by the PV modules; and second, the income originated from the sale of agricultural produce. With regard to the first, the two variables that intervene are: The annual specific energy yield (kWh/kWp) and the price perceived for the energy generated (EUR/kWh). The annual yield will decrease from year 12 onwards, due to the module degradation coefficient abovementioned. With regard to the wholesale electricity market price perceived for the energy sold, we proceeded as follows: From the future solar contracts due 2026 (FTS YR-26) published in the OMIP 2019 sessions market bulletins [42], one day per month of the year 2019 was selected for averaging. The mean of the 12 prices was 45.02 EUR/MWh (specifically, we took the 11th day of every month, except for May and August, where the 13th and 12th day, respectively, were picked, to skip the eventual Sunday effect). We deliberately dropped the 2020 sessions of the future solar market, because concerns arose about the prices thereof being convoluted with the COVID-19 effect. Afterwards, we divided the 25-year lifespan into three periods: The first one encompassing the first 9 years, while the second and third period spanning the following eight-year each. Finally, we assigned the OMIP FTS mean price previously calculated, 45.02 EUR/MWh, to the first period of 9 years; a price diminished by 5% to the second

period (0.95·45.02 = 42.77 EUR/MWh); and a price diminished by 10% to the last 8-year period (0.90·45.02 = 40.52 EUR/MWh). The reason to assign the foregoing reduced prices to the second and third period is that foreseeably—and unfortunately for generators—increasing solar PV capacity installed in the forthcoming years will lead to decline of wholesale electricity prices [43].

**Table 5.** Irrigation costs under full sunlight.

(a) Following Karkanis et al. [39] and ITACYL [40]. (b) Energy cost translates into a volumetric component of 0.02 EUR/m3 [37]. (c) Fixed component: The sum of the River Basin Organism royalty plus the irrigation district fee equals 105.8 EUR/ha [35]. (d) The fixed cost is halved because faba bean and forage maize share the same field in one year. (e) Assuming a well depth of 100 m, energy cost translates into a volumetric component of 0.04 EUR/m3 [41]. (f) The fixed component is related to the capacity factor charged to the irrigation district (ID) by the electric utility; in turn, the ID apportions this charge to irrigators. It is estimated as 2.3 × 105.8 ∼= 243 EUR/ha; with 105.8 EUR/ha taken from this same table footnote (c) as representative of a surface water ID. The rationale behind the 2.3 coefficient is that, as a rule of thumb, more powerful pumps are required in groundwater IDs compared to surface water IDs; this has a direct effect on the capacity factor charge.

> Table 6 is a compilation of estimated decreases in crop yield due to APV shed shading compared to full-sunlight cultivation. In Table 7, values are five-year (2014–2018) averages calculated from official data of Spain Department of Agriculture [16] compiles crop yields (kg/ha) and prices (EUR/t) under full sunlight.

(a) Rounded to the closest integer. (b) See Appendix E for details on the *uncertainty factors* applied. (c) Assumption: ∆+15% over the values in the second column.

**Table 7.** Crop revenues under full sunlight conditions.

(a) Values calculated as 5-year (2014–2018) averages from [16]. (b) NA: Not applicable.

### **3. Results**

A well-established metric to assess the performance of dual-land use systems like agroforestry [51] and also APV, is the land equivalent ratio (LER). However, the LER exclusively accounts system revenues and not the expenditure. On the other hand, the benchmark yardstick for energy generation systems, the levelized cost of electricity (LCoE), computes cost relative to electricity yield, but does not incorporate the crop production activity. Here, the indicator wherethrough profitability was evaluated was the internal rate of return (IRR). Once income and expenditure from both agricultural and energy generation activity were accounted, their aggregation to obtain the IRR was straightforward.

Figure 3 shows the annual specific yield of 1628 kWh/kWp predicted by the PV simulator for the southwest-oriented APV shed. This value was introduced in Table 8 to compute annual PV income throughout the foreseen lifespan. Likewise, the annual specific yield was introduced in Table A3 to compute the annual PV OpEx. After the entire flow of APV income and expenditure was computed, the IRR was calculated for both early-potato and processing-tomato rotation. The same procedure was followed with the annual specific yield of 1786 kWh/kWp predicted by the SISIFO PV simulator for the south-oriented APV shed.

Unlike Beck et al. [20], we did not find substantial differences between the two orientations. A mean shade factor of 30.6% was calculated for due south orientation (Figure A3), while the counterpart for due SW was of 29.2% (Figure A4). The small difference between both values prevented from matching orientation and shade-induced crop yield penalty. Therefore, we determined that in our case-study APV shed orientation only affects electricity production. With the dichotomist sources of variation considered, namely, crop rotation (potato/tomato), source of irrigation water (surface/underground), level of shadeinduced crop yield penalty (low/high) and APV shed orientation (SW/S), the number of combinations analyzed was of 24 .

The formula for the IRR is given by (Equation (1)):

$$0 = \sum\_{t=1}^{26} \frac{\mathbf{C\_t}}{\left(1 + \text{IRR}\right)^t} - \text{CapEx} \tag{1}$$

where Ct = net cash flow during the year t (calculated as the sum of annual income from electricity sale—Table 8 plus annual income from the sale of agricultural produce harvested, with subtraction of annual PV-OpEx—Table A3 and annual crop production cost; for the early-potato rotation, annual agricultural flow is constant throughout the 25-year lifespan, due to rotation symmetry, whereas in the case of the processing-tomato rotation, the annual agricultural flows vary according to the pattern shown in Table A1); CapEx = total initial investment cost (calculated as 562770 EUR/ha in Table A2 multiplied by 22.8 ha, giving 12,831,156 EUR).

**3. Results**

straightforward.

ented APV shed.

A well-established metric to assess the performance of dual-land use systems like agroforestry [51] and also APV, is the land equivalent ratio (LER). However, the LER exclusively accounts system revenues and not the expenditure. On the other hand, the benchmark yardstick for energy generation systems, the levelized cost of electricity (LCoE), computes cost relative to electricity yield, but does not incorporate the crop production activity. Here, the indicator wherethrough profitability was evaluated was the internal rate of return (IRR). Once income and expenditure from both agricultural and energy generation activity were accounted, their aggregation to obtain the IRR was

Figure 3 shows the annual specific yield of 1628 kWh/kWp predicted by the PV simulator for the southwest-oriented APV shed. This value was introduced in Table 8 to compute annual PV income throughout the foreseen lifespan. Likewise, the annual specific yield was introduced in Table A3 to compute the annual PV OpEx. After the entire flow of APV income and expenditure was computed, the IRR was calculated for both earlypotato and processing-tomato rotation. The same procedure was followed with the annual specific yield of 1786 kWh/kWp predicted by the SISIFO PV simulator for the south-ori-

**Figure 3.** Annual AC energy output as predicted by PV simulator SISIFO [23] for the southwest-**Figure 3.** Annual AC energy output as predicted by PV simulator SISIFO [23] for the southwestoriented APV system in Brenes, Seville, Spain.

(a) (*x* kWh/kWp)·(555.5 kWp/ha) = *y* kWh/ha. (b) 0.95·24 ha = 22.8 ha; (*y* kWh/ha)·(22.8 ha) = *z* kWh. (c) Values in this column are equal to values in the adjacent-left column except for the due year of inverters replacement (year 14), where an income of 10% of inverter purchase price (18,505 EUR/ha, Table A2) is added in concept of old inverters residual value. Hence, the income added in the year 14 is of: 0.1·18,505·22.8 = 42,191 EUR.

> Since the IRR is not explicit in Equation (1), it has to be solved by an iterative method, like the ad hoc function of Microsoft Excel®. Table 9 is a compilation of the IRR for each of the 24 combinations, wherein the minimum and maximum IRR are highlighted.

**Table 9.** Internal rate of return for the 16 combinations generated.

To elucidate the profitability associated to the foregoing IRRs, they were confronted with the private investor expected remuneration or annual cost of equity (re). According to Guaita-Pradas and Blasco-Ruiz [52], the cost of equity can be estimated through the capital asset pricing model, (Equation (2)):

$$\mathbf{r\_e} = \mathbf{r\_f} + (\mathbf{r\_m} - \mathbf{r\_f}) \cdot \tag{2}$$

where re = annual cost of equity, i.e.,demanded rate of return on equity; rf = annual risk-free rate of return; rm = annual stock-exchange market rate of return; β = coefficient that reflects the sensitivity of the sector to market fluctuations.

A good representative for rf in Spain is the interest rate of the 30-year maturity Public Treasury bonds, 1.31% [53]. Current market profitability, rm, is of 4.5% [54]. In strict sense, in our case β would be somehow compounded, since the project economic activity sector is not only electric generation but also agricultural production. For the sake of simplicity, we took a PV β of 1.10 [55]. Substituting in (Equation (2)):

$$\mathbf{r\_e} = 0.0131 \,\mathrm{T} + (0.045 - 0.0131) \cdot 1.10 = 0.04819$$

Therefore, the threshold of profitability is 4.8%. The IRRs compiled in Table 9 indicate that some combinations would be profitable from the perspective of a private investor, whereas others would be not.

### **4. Discussion**

Following the mainstream APV philosophy of prioritizing agricultural over power production and based on Beck et al. [20] conclusion, we initially performed calculations for a SW-oriented APV shed. Finally, a comparative shaded-fraction analysis between southwest and south orientation was undertaken. The small difference found (abovementioned values of 29.2% and 30.6%), together with the shape of histograms Figures A3 and A4 suggest little difference between both orientations. Perhaps the subtle difference in ground radiation uniformity in our case was due to the TMY data used. Edge effects could also play a role. This issue deserves more attention and should be further analyzed in a future work.

The result obtained for the early-potato rotation when the APV shed is oriented due Southwest is in line with Trommsdorff [56], who, for organic potatoes cultivated beneath the APV shed described by Schindele et al. [8], obtained an IRR 1.6% lower than WACC. In a broader sense, López Prol et al. [43] wondered if renewable energy generators like PV would ever be competitive considering the faster decline of the wholesale market price compared to the LCoE. From the inception of our study, it was envisaged that a negative factor for APV system profitability would be the high CapEx compared to conventional ground-mounted PV power plants. To restrain APV system CapEx, fixed-tilt PV modules were selected instead of single-axis trackers, which are 7% more expensive in average [57]. Here, the reason to select fixed-tilt PV generator was three-fold: First, to restrain system cost; second, to utilize the substructure described by Schindele et al. [8], which is of known cost; and third, to cast less shading on the understorey crop canopy. With regard to the latter, in an early stage a set of simulations was performed with the dual-use shading analysis tool, an on-line simulator promoted by the Massachusetts government to analyze the technical

viability of APV layouts. Results indicated that single-axis tracking casted more shading, in w% per square meter than fixed-tilt. On the other hand, Amaducci et al. [58] concluded that reduction of global radiation beneath their APV shed was more affected by PV module array GCR than by tilt angle management (fixed tilt/sun-tracking).

In the case that in one of the 6 ha APV plots there existed an authorized underground water well equipped with a submersible pump, a reservoir to store the water abstracted by the well pump and a horizontal-axis pump to pressurize the whole 24 ha farmer irrigation network, the following management strategy could be analyzed: During PV productive hours, a small fraction of the energy produced would be self-consumed by the well pump to replenish the reservoir. To irrigate, i.e., to pressurize the irrigation network preferably during nocturnal hours, energy would be consumed from the grid, through the same HV power transmission line wherethrough PV production is injected. The energy consumed would be registered, for billing purposes, by a bi-directional metering gauge. This management strategy would remove the cost of water delivery charged by the irrigation district. Concurrently, the income from the sale of electricity would be diminished in the amount of the energy self-consumed—and therefore, not sold—by the well pump. Likewise, the farmer would incur in the cost of the nocturnal energy consumed from the grid by the irrigation pump. According to IDAE [59], average installed power pump for pressurized irrigation in Spain is of 2 kW/ha. For a flat topography like the area of study, assuming low pressure drip irrigation and for moderate water depth in the well, the share of installed power could be e.g., 70% for the submersible well pump and 30% for the irrigation pump (the well pump share would increase with increasing depth). Therefore, this results in 0.3 2 kW/ha = 0.6 kW/ha and 0.6 kW/ha 24 ha = 14.4 kW. This is significantly smaller than 450 kW, the minimum contracted power to benefit from the cheapest nocturnal electricity period of the Spanish tariff 6.1.

With regard to the possibility of reducing the 5 m clearance height of APV shed substructure and accordingly save in system CapEx, the following has to be considered: In our study, one head of rotation was first-early potato, planted in late December–January and harvested in late May–early June. In Spain, this type of potato is not harvested with the bulky and tall potato harvester, but with much smaller and shorter machines, namely, potato lifters and windrowers. These machines just dig-up and expose the tubers so that they can be afterwards hand-picked by manual workers. The reason to discard the potato harvester is to preserve tuber quality, since hand-picking is less aggressive. In other parts of Spain, where half-season potatoes are grown, the tubers spend more time within the ground, resulting in a thicker skin that withstands better the abrasions and impacts that occur inside potato harvester. Attending to the potato harvesting machinery used in the area, one could think of saving in substructure height, at least in the case of the earlypotato rotation. However, a two-fold reason dissuade from this: First, canola and faba bean, two of the potato "partners" in the eponymous rotation, are harvested with the bulky combine harvester. Second, the main interest of a high substructure is not only to allow agricultural machinery work beneath, but to provide homogenous light distribution for the crop, casting shade of lower intensity. Analogous considerations apply for the processing-tomato rotation, whereinto processing tomato is harvested with a bulky-tall machine, similar in dimensions to both the potato and combine harvesters.

Among the circumstances that would yield lower IRR are: (i) APV plots remoteness from the grid-connection switchyard (distance higher than the 1 km assumed in Figure 1; (ii) re-activation of Spanish Law 15/2012, under which electricity generators must satisfy a tax of 7% on the value of the energy injected to the grid -this law was challenged before the Constitutional Court of Spain and the final judgment is pending [60].

Among the circumstances that could render higher IRR are: (i) Higher electricity yield due to favorable microclimate condition. Thus, the SISIFO PV simulator used here is not APV-specific but computes yield from site TMY climate data. Cooler temperatures on the back side of the modules, induced by the irrigated understory crop, would improve PV performance, especially in the summer months. (ii) Modification of the PV system electrical design: considering that while the peak power (153.1 kWp) to nominal power (150 kWp) ratio is of approximately 1.02 Wp/Wn; stronger oversizing of the PV array with respect to inverter is recommended [61].

Here, only the quantitative effect of shading on produce yield (t/ha) was considered. More research is needed to investigate the effect of APV shading on produce quality that ultimately affects revenues or even could be a limiting factor for the spread of APV. Nishizawa et al. [62] concluded that severe levels of shading negatively affected melon fruit firmness. Hernández et al. [32] measured not only higher concentration of lycopene, but also lower concentration of vitamin C and phenolic compounds, in tomatoes grown under partial shade compared to the full-sunlight counterpart.

In the authors' opinion, the lack of profitability in some of the combinations of the case-study analyzed herein does not tarnish the potential profitability of APV systems. Higher IRR is envisaged for specialty crops, thanks to extended synergies between the food generator—agricultural crop—and energy generator—PV modules. Savings in fruit orchard hail and bird netting allowed by APV sheds paddle in this direction [63]. Likewise, the utilization of semi-transparent PV modules could increase crop intercepted light without the need for the expensive 5 m ground-clearance substructure that supports conventional opaque PV modules. The fragility of specialties such as raspberry, blackberry and blueberry advises against their mechanized harvesting, contributing to the technical viability of cost-effective limited-height sheds and the subsequent increased profitability of APV systems.

### **5. Conclusions**

In correspondence with objectives (1) and (2) indicated in Section 1, the following conclusions can be drawn:

**Author Contributions:** Conceptualization, M.A.M.-G., G.P.M. and M.C.A.-G.; methodology, G.P.M., M.A.M.-G. and M.C.A.-G.; validation, G.P.M., M.A.M.-G., M.C.A.-G. and L.H.-C.; formal analysis, G.P.M., L.H.-C. and M.C.A.-G.; investigation, G.P.M., M.A.M.-G., M.C.A.-G. and L.H.-C.; data curation, G.P.M.; writing—original draft preparation, G.P.M., M.A.M.-G. and M.C.A.-G.; writing—review and editing, G.P.M., M.A.M.-G., M.C.A.-G. and L.H.-C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** The authors wish to acknowledge the reviewers for their valuable comments.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **Abbreviations**

#### **Appendix A Appendix A**

Comparison between due south and due southwest orientation of APV shed. Comparison between due south and due southwest orientation of APV shed.

**Figure A1.** Due south APV shed. Ground area: 26 m ×106 m. Dimensions and number of PV module supporting structures: 26 m × 3.28 m × 11. Equidistance between supporting structures axes: 9.5 m, as in Figure 2. **Figure A1.** Due south APV shed. Ground area: 26 m × 106 m. Dimensions and number of PV module supporting structures: 26 m × 3.28 m × 11. Equidistance between supporting structures axes: 9.5 m, as in Figure 2.

*Agronomy* **2021**, *11*, x 17 of 25

*Agronomy* **2021**, *11*, x 17 of 25

**Figure A2.** Due southwest APV shed. **Figure A2.** Due southwest APV shed. **Figure A2.** Due southwest APV shed.

**Figure A3.** Time series (1 min step) shaded fraction histogram (zero values excluded) for due south orientation. **Figure A3.** Time series (1 min step) shaded fraction histogram (zero values excluded) for due south orientation. **Figure A3.** Time series (1 min step) shaded fraction histogram (zero values excluded) for due south orientation.

**Figure A4. Figure A4.** Time series (1 min step) shaded fraction histogram (zero-values excluded) for due southwest orientation. Time series (1 min step) shaded fraction histogram (zero-values excluded) for due southwest orientation.

**Figure A4.** Time series (1 min step) shaded fraction histogram (zero-values excluded) for due southwest orientation.

### **Appendix B**

**Table A1.** Processing-tomato rotation scheduling for 25-year lifespan.

### **Appendix C**

**Table A2.** APV system initial investment cost or capital expenditure, CapEx (the delayed CapEx item of inverters replacement incurred in year 14 is included in Table A3).

(a) (*x* EUR/kWp)·(555.5 kWp/ha) = *y* EUR/ha. (b) [64]. (c) (528 modules/153.1 kWp)·3166 kWp = 10,919 modules. (d) Calclated from [8]. (e) Calculated from [65]. (f) Adapted from [66]. (g) From [67], (531 £)·(1.10 EUR/£) ∼= 584 EUR. (h) From [68] with consideration of 1000 VDC inverter wire section saving. (i) From [69], (0.034 EUR/WAC)·(150 kWAC) = 5100 EUR. (j) 5100 EUR/153.1 kWp ∼= 33.3 EUR/kWp. (k) 80500 EUR/3166 kWp ∼= 25.4 EUR/kWp. (l) Calculated from [70]. (m) Calculated by introducing 155,241 EUR/ha [8] in the following breakdown model: Construction work, 65% of the installation works cost; electrical installation work, 35% of the installation works cost; labor share within construction work, 40%; labor share within electrical installation work, 70%; ancillary equipment (cranes, welding machines, tools, etc.) share within construction work, 60%; ancillary equipment share within electrical installation work, 30%. Price of construction labor in Spain relative to Germany: 54%; price of electrician labor in Spain relative to Germany: 89% [71]. (n) [72].

**Table A3.** Twenty-four hectare (22.8 ha effective) due SW-oriented PV system annual operating expenditure, OpEx, plus the delayed CapEx of inverters replacement in the year 14.

(a) From Table 8. (b) 0.5 EUR/MWh [73]. (c) p.w.m., power whosale market. Brokerage fee applied: 0.2 EUR/MWh [74], years 2 through 19; years 20 through 26: assumption of 25% price decrease applied [69], resulting in 0.15 EUR/MWh. (d) Years 2 through 19, (2.1 EUR/kWp)·(555.5 kWp/ha)·(22.8 ha) = 26,597 EUR; years 20 through 26: assumption of 25% price decrease applied [32], 0.75 × 26,597 = 19,948 EUR. (e) Years 2 through 19, (1.8 EUR/kWp)·(555.5 kWp/ha)·(22.8 ha) = 22,798 EUR; years 20 through 26: assumption of 25% price decrease applied [32], 0.75 × 22,798 = 17,099 EUR. (f) Years 2 through 19, (0.8 EUR/kWp)·(555.5 kWp/ha)·(22.8 ha) = 10,132 EUR; years 20 through 26: assumption of 25% price decrease applied [32], 0.75 × 10,132 = 7599 EUR. (g) Inverter's replacement cost included (0.95 × 18,505 EUR/ha × 22.8 ha = 400,818 EUR; a 5% price decrease is assumed with respect to the year zero; 18505 EUR/ha taken from Table A2).

*Agronomy* **2021**, *11*, 593


**1. Introduction**

Cities across the globe have undergone notable transformations, especially following the different waves of the industrial revolutions witnessed since the 18th century. Underpinned by these waves, contemporary cities are now experiencing a transformation hinged on widespread technological integration, with diverse and city-specific outcomes being expected. Of this, the most notable objective and outcome being pursued by these cities is the increasing efficiency and performance in different urban frontiers [1]. Technological integration in different elements of cities is enveloped within the Smart City concept. Proponents of the concept envision an urban environment characterised by reduced human

**Citation:** Allam, Z.; Bibri, S.E.; Jones, D.S.; Chabaud, D.; Moreno, C. Unpacking the '15-Minute City' via 6G, IoT, and Digital Twins: Towards a New Narrative for Increasing Urban Efficiency, Resilience, and Sustainability. *Sensors* **2022**, *22*, 1369. https://doi.org/10.3390/s22041369

Academic Editors: Zihuai Lin and Wei Xiang

Received: 21 December 2021 Accepted: 9 February 2022 Published: 10 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

interventions as a result of automation of different urban elements in diverse geographical locations globally. However, automation, being one aspect of 'smartness', is dependent on the amount, quality, and type of data that different urban elements generate [2]. Thus, it is credible to argue that data are becoming a cornerstone of urban planning practice. Therefore, data collection, storage, analysis, and interpretation is critical, especially in helping to understand different urban dynamics and in scaffolding more informed decision making [3].

In addition to data, the smart city concept is further grounded upon the availability of other diverse and advanced technologies that not only allow for data collection, storage, and exploitation but also permit for technologies that help in implementing decisions and insights after data are synthesised, analysed, and interpreted. Such technologies include Artificial Intelligence (AI), Machine Learning (ML), Crowd Computing (CC), connectivity technologies such as 5G (including anticipated 6G), Robotics, and many others [4]. When compounded, the bulk of these technologies, plus different urban elements are seen to make smart cities and their market attractiveness very lucrative.

Currently, the smart cities 'industry' is valued at approximately USD 741.6 billion. With the attention on its sustained implementation amid the prevailing global pandemic challenges, this industry is expected to grow substantially to over USD 2.5 trillion by 2026 [5]. While there are other sources that estimate the market value of this industry differently (for example, Marekts and Markets [6] estimates it to be currently worth USD 457 billion and to grow to USD 873.7 billion by 2026), it is evident that regardless of different market valuations, the concept is promising and is expected to contribute to strategic urban planning trends globally. In particular, beyond the economic frontier, the smart city concept is expected to continue providing opportunities for increased liveability standards, increased sustainability prospects, and improved social dimensions amongst other unparralleled benefits [7–9].

While the smart cities concept is still gaining traction, the COVID-19 pandemic has prompted the emergence of a new urban planning concept—the '15-minute city'. This city type focuses more on promoting social dimensions, urban proximity, and diversity via increasing use of technologies [10]. While the '15-minute city' concept will be described comprehensively in the next section, it is worth noting that the concept has only been described in the literature since 2016, with authors noting commonalities including social distancing, work-from-home concepts, and reduced travel movements, and the concept is increasingly becoming associated in tandem with the smart city concept in crafting and structuring more liveable and human scale cities [11]. However, this urban planning model promises to introduce new characteristics, such as proximity-based approaches to the planning of urban amenities, and urban restructuring, especially in relation to existing urban infrastructures and other elements, so that ultimately, there is a need to mediate these new aspects to human dimensions and values.

The 15-minute city concept, when unpackaged, both in the global north and south, is expected to transform urban areas and allow them to become more human-friendly, especially in the shadow of the post-pandemic 'new normal'. Furthermore, this concept will prompt urban areas to better align for prospective future post-pandemic urban morphologies, especially with prospects that new concepts and technologies (e.g., metaverse) may have in prompting urban restructuring, greenfield policy reinvention, and regeneration generally. The contributions of this paper to the knowledge on 15-minute city concept include the following:

• Highlights some possible obstacles that will need to be overcome to ensure that the anticipated benefits, especially on the social front, are realised.

Within this context, this paper seeks to explore the technological dimensions that are influencing the adoption and implementation of the 15-minute city concept in different global cities. This appraisal includes introducing the '15-minute city' concept in Section 2; Section 3 situates its relationship within Smart Cities practices and relevant techniques and technologies; Section 4 unpacks the concept's technological dimensions; and Section 5 presents a discussion and conclusions.

#### **2. The '15-Minute City'**

The '15-minute city' concept is a new urban planning model conceived in 2016 by Franco-Colombian scientist Carlos Moreno, a specialist in intelligent control of complex systems, who envisioned the need for urban environments to be people-centred [12,13]. Moreno acknowledges that he drew inspiration from Jane Jacobs' writings [14]. His model gained prominence with its electoral advancement by Paris Mayor Anne Hidalgo within her "living smart city" initiative called the "Ville du quart d'heure"—the 15-minute city [15].

Moreno's concept is that within an urban area, where human aspects such as socialisation, self-actualisation, cultural demand, and health, among others, the time required for people to access different nodes within the space is given precedence and priority during city planning. This policy empowers that the placement of essential urban amenities, infrastructures, and opportunities is deliberately actioned to facilitate enhanced accessibility. With policy implementation, it becomes possible for residents within given urban areas to comfortably walk or cycle to any given node within a city in a timeframe not exceeding 15 minute [16]. Thus, the demand for the use of automobiles to travel within the city is reduced, providing room for opportunities to create walkways and bicycle lanes that would have been otherwise suppressed in conventional urban planning models that prioritise vehicular flows' efficiencies. Therefore, the 15-minute city concept seeks to bring a paradigm shift in the way urban planning has been previously practiced, shifting it from one focused upon vehicular flows, resulting in gridlocked cities, being a deterrent to the human societal endeavours and city liveability.

Moreno's '15-minute city' concept is inspired from 'Chrono-urbanism', where the aspect of time is believed to be a key factor to consider relative to space [10,13]. That is, the act of placing of different urban amenities and different elements needs to be guided by how much time it would take a walking or a cycling resident to move from one node to the next. In essence, even in urban areas endowed with maximum space, the proximity of different urban elements needs to be a critical consideration.

Within this concept, it is possible to structure a number of nodes within a city, as long as all these observe the four key characteristics (as shown in Figure 1 below) that Moreno, Allam, Chabaud, Gall and Pratlong [11] argue are key in driving urban liveability. These include proximity, diversity, ubiquitousness, and density. In regard to diversity, the vision is to render urban areas accommodating of people from given backgrounds, thus promoting cultural vibrancy, while ensuring that there is diversity in terms of urban structures. That is, planners need to ensure that each of the urban structures, infrastructure, and elements could be used utilised for multiple purposes, hence allowing for their maximum utilisation [12]. For instance, in the case of neighbourhoods, there are opportunities for building urban structures such as car parks that would have capacities for multiple use. Such a move would ensure that there is maximum utility derived from buildings and urban public spaces. Therefore, there is the capacity to craft sufficient urban spaces for the creation of other critical amenities within the same neighbourhoods. Another example is the utilisation of school playing grounds for other purposes including parking, and recreation centres, especially external to school time whether on the surface, above, or below.

**Figure 1.** The 15-minute city framework as introduced by Moreno, Allam, Chabaud, Gall, and Pratlong [11].

In terms of density, the 15-minute city concept espouses that cities should have an optimal number of residents. This assumption thereby ensures that it is possible to facilitate quality resource and service provision, without over-consumption or under-utilisation. In respect to the theme of this paper, the density dimension is critical in terms of data generation, which in turn helps not only with influencing how resources are utilised but also in feeding the virtual model of the city, thereby helping in rendering improved urban dimensions. The ubiquitousness principle advances the need for 15-minute cities' requirements to be in large supply in all geographies, thereupon making them available for everyone and at an affordable cost. This aspect will greatly benefit from the deployment of the three technologies (IoT, Digital Twins, and 6G) being advanced in this paper. We argue that with these technologies, it will be possible for urban planners to contextualise and implement 15-minute city models. Additionally, through using the aforementioned technologies, it will be possible to customise each model to varying geographies to fully address place-relevant human dimensions. Thus, such technologies will help in fast tracking those customisations and implementation as is already being done in cities such as Paris, where the agenda is to reduce private cars with a target of 50%, while ensuring cycling-friendly environments through the creation of more bicycle lanes in the city. Another city implementing this concept is Bellevue through its *Environmental Stewardship Plan 2021–2025*.

#### **3. The '15-Minute City' via the Smart City Network**

One of the key dimensions of the 15-minute city is digitalisation. This entails the use of digital technologies to influence how the city functions and thus deliver services as well as provide value-producing opportunities in relation to various urban systems and domains. The latter relate to the overall landscape of the 15-minute city in terms of its underlying components, including ICT infrastructure, built infrastructure, green/blue infrastructure, transport infrastructure, energy infrastructure, economic infrastructure, and social infrastructure. Against the backdrop of this perspective paper, the focus is mainly on the ICT infrastructure and the built infrastructure given their particular relevance to the dimensions of the 15-minute city. The built infrastructure denotes the following:

" ... the patterns of the physical objects in the city pertaining to the built-up areas as well as those areas planned for new development and redevelopment ... The compact and ecological dimensions of urban design characterize most of the built infrastructure as regards its buildings, blocks, streets, open space, public space, green space, and essential infrastructure" [17].

The core design strategies shared by both the compact city and the eco-city are density, diversity, and proximity enabled by mixed land use—which are strategies that also characterise the 15-minute city. ICT infrastructure enables a 15-minute city to move to a data-driven form of urbanism by leveraging advanced data and information technologies to entirely transform its processes and practices—evaluating, analysing, re-engineering, and envisioning the way urban infrastructures and services can be designed, developed, managed, and planned in line with the vision of sustainability. ICT infrastructure digitally consists of those components that power the technology that pervades the fabric of the city (e.g., sensors, smart devices, systems, software programs, networks, data storage facilities, data processing platforms, cloud and fog computing, policies, and standards) and thus permeates urban life, providing support for the management of the city. To coordinate the many different components that comprise the digitalisation dimension of the 15-minute city requires a much stronger function of intelligence. This brings together what government and business have to offer in terms of engaging users of services and communities and providing hardware, software, and solutions enabling smartness, respectively. In this respect, among the issues deemed important are the ways in which ICT infrastructure can be integrated and coordinated, how the data can be analysed and harnessed, and how services can be delivered in a more efficient way. With respect to the latter, digital infrastructure is critical in remote areas in improving not only the efficiency of infrastructure networks but also their sustainability and services (e.g., energy, mobility, transport).

ICT infrastructure can be deployed within the 15-minute city's own facilities (or cloud computing) in order to deliver solutions to different stakeholders with respect to services and applications. In this regard, ICT infrastructure should initiate innovative approaches to the use and integration of IoT, AI, AIoT, big data analytics, simulation models, and intelligent decision-support systems as part of urban computing to enable urban intelligence (e.g., enhancing mobility, reducing congestion, lowering energy use, reducing air pollution, improving planning, optimising governance, etc.). The purpose of this approach will be towards solving problems and issues related to the 15-minute city's operational management and development planning. As related to urban computing, the efforts " ... dedicated to connecting unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualisation methods to structure intelligent urban computing systems for smart cities ... " [18] can be utilised to develop innovative solutions in the form of applied urban intelligence for the management, planning, and governance of the 15-minute city.

Unsurprisingly, urban computing and intelligence is increasingly gaining momentum in academic circles and policy debates as a policy agenda for integrated advanced technologies and their novel smart applications for tackling many of the contemporary complex problems and challenges associated with urbanisation and sustainability.

[As] " ... a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings and humans, to tackle the major issues that cities face ... " [urban computing] " ... create win–win–win solutions that improve urban environment, human life quality, and city operation systems ... [and] also helps us understand the nature of urban phenomena [and urban dynamics] and even predict the future of cities . . . " [19].

As an integrated and holistic approach, urban computing and intelligence makes it possible to generate well-informed decisions concerning a wide range of city services and operations, and it can also enable feedback loops between urban environments, human activities, and physical movements [20]. The analytical process in this approach enables the creation of knowledge services required for enhancing decision making based on the design of the components and their relationships, as illustrated in Figure 2.

**Figure 2.** General architecture for urban computing and intelligence based on big data analytics. Illustration by authors.

With the escalating rate of urbanisation and mounting challenges of sustainability, it has become of crucial importance to develop a new urban fabric that can deal effectively with urban development in regard to its dimensions—namely land use change, population increase, cultural change, and economic growth, through such design strategies as compactness, density, diversity, and mixed land use. In this context, an urban fabric refers to

" ... the physical characteristics of urban areas in terms of components, buildings, spatial patterns, scales, streetscapes, infrastructure, networks, and functions, as well as socio-cultural, ecological, economic, and organizational structures . . . " [21].

This also involves making the best use of the digital and informational assets to ensure that the city is sustainable in its approaches to integrating new technologies and their novel applications with compact design strategies. This requires implementing an advanced form of urban computing for monitoring, measuring, analysing, evaluating, designing, and planning urban systems, thereby enabling many functions of urban intelligence for the purpose of improving the sustainability, efficiency, resilience, and life quality in the 15-minute city.

Within this context, IoT has recently become the predominant paradigm of urban computing and intelligence, shifting from a vision of ICT of ubiquitous computing towards one of a deployable paradigm. This shift heralds a new wave of city analytics whose basic ingredient is big data analytics [22–24], which is fostered by the proliferation and widespread diffusion of wireless communication technologies on a hard-to-imagine scale. This is manifested mainly in the quantity and scale of Wi-Fi hotspots covering many urban areas to form a dense multi-faceted IoT network necessitating a large number of sensors exhaustively deployed across the city in order to enhance their communication capabilities and data transfer processes.

Given the wide array of its network in urban areas, via smart city networks, IoT has been extensively installed and used in cities without many engineering obstacles as regards

resources, buildings, and infrastructures. IoT infrastructure, involving a myriad of devices seamlessly connected for information exchange, is used to collect vast troves of data to aid in enhancing and optimising urban operations, functions, designs, and policies in relation to various urban domains. IoT when coupled with the data deluge flowing through its multiple networks of sensors plays a key role in the development and implementation of the 15-minute city as a new concept, serving as a technological backbone to the city's attempts to address its goals of sustainability with respect to its underlying dimensions. As an unprecedented planning effort, the 15-minute city initiative is a response to the deconcentration of land use, and as such, it emphasises density, diversity, and mixed land use as key strategies for ensuring liveability, vitality, affordability, energy conservation, and environmental quality. This emerging approach to urban development seeks to deliver more efficient land use, build a resilient and adaptable urban community, lower per capita rates of energy usage and per capita infrastructure provision, and thereby reduce pollution thanks to density and proximity.

Further, IoT infrastructure is necessary to fulfil the needs and visions of the 15-minute city as a smart sustainable approach to urban development. IoT is seen as key to enabling both the smart city [1] and sustainable city infrastructure [25], as it provides a flexible infrastructure that is of crucial importance to deal with the myriad of interconnected devices. It is important for the 15-minute city to have IoT infrastructure in place, where enddevice connectivity is monitored, communication reliability is assured, and its sub-systems are intelligent enough to communicate and exchange information with one another while forming a large-scale digital system with widely deployed devices to enable services [26–28] associated with sustainable urban living. A successful implementation of IoT in the 15- minute city means

" ... supporting the complexity of different sensors and their networks set up in urban environments as well as simplifying the composition of interoperable services and applications. Sensor–enabled smart objects are regarded as the essential feature of the interconnected infrastructures of the future" [29].

IoT is an advanced form of ICT of ubiquitous computing. It includes an array of ICT architectures that are fundamentally aimed at describing and providing the relevant infrastructure that underlie the functioning of the digital ecosystem of the city—urban computing and intelligence—within both smart cities [30] and sustainable cities [29]. Thus, ICT architecture denotes a framework for the design of the components and their relationships, functioning as a kind of a roadmap to a city's ICT aspects: for example, what needs to be done to respond to the city's digital needs. ICT infrastructure, in contrast, includes the assets themselves that are used in the city, such as hardware, software, networks, computers, towers, servers, and so forth. Accordingly, the architectural design of ICT determines the variety and number of technologies that can be included in the ICT infrastructure. In essence, a digital ecosystem is built on an infrastructure that has a particular architecture. Therefore, it is impossible to use a particular architecture or infrastructure as a blueprint for all possible implementations in real-world settings. In other words, there is no single consensus on architecture for ICT or infrastructure for ICT that can be agreed upon universally. Different cities have different architectures and different infrastructures, such as planning-based architecture, governance-based architecture, operations-based architecture, healthcare-based architecture, and smart home-based architecture and others.

Bibri and Krogstie [29] offer a detailed review of the key technological and computational components of IoT, including its relationship with big data technology and analytics, sensors and things, big data analytics as a holistic digital system, the core enabling technologies of big data ecosystem, big data analytics solutions, ICT architecture, and IoT infrastructure. Nevertheless, as an advanced approach to ICT design, IoT architecture tends to converge on the number of layers with regard to the design of the components that make up a technological system and their relationships. This still depends on the application domain [24,31–34]. Sometimes, the architectural layers are combined depending on the complexity of the application domain while using different, and sometimes overlapping,

labels, such as the physical layer, perception layer, information source layer, middleware layer, network layer, technology layer, application layer, service layer, and domain layer. In the context of the 15-minute city, the four layers of IoT architecture include the following:


**5. Conclusions**

We have examined the emergence of quantum Darwinism for a composite system consisting of two qubits interacting with a *N*-partite bath. For an excitation-preserving interaction between the system qubits, we established that the system information is faithfully, redundantly encoded throughout the environment; therefore, we see the emergence of clear Darwinistic signatures. Nevertheless, a decoherence-free subspace permits the system to create and maintain significant non-classical features in the form of quantum discord. Employing the framework of strong quantum Darwinism, which insists that in addition to a mutual information plateau, the discord between the system and an environment fragment must vanish, we have shown that whether or not this state is interpreted as objective and classical depends on how the discord is evaluated. Following the framework of Ref. [6], for measurements on the system, the sizable non-zero coherence present in the decoherence-free subspace implies that this state is definitively not objective. However, as quantum Darwinism posits that classicality and objectivity are dictated by what information can be learned by measuring the environment, and due to the asymmetric nature of the quantum discord when measurements are made on the environment, we find that the discord is vanishing and therefore conclude a classically objective state. To better understand this point, we demonstrated that redundant encoding at the level of the composite system does not imply the same for the individual constituents. Specifically, when non-classical correlations are established between the system qubits, there is still a redundant proliferation of *some* of the system information into the environment; however, the correlations between the two system qubits prevent all of the system's information from being redundantly shared with the environment.

**Author Contributions:** All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.

**Funding:** S.C. gratefully acknowledges the Science Foundation Ireland Starting Investigator Research Grant "SpeedDemon" (No. 18/SIRG/5508) for financial support. B.Ç. is supported by the BAGEP Award of the Science Academy and by The Research Fund of Bahçe¸sehir University (BAUBAP) under project No. BAP.2019.02.03. B.V. and acknowledges the UniMi Transition Grant H2020. M.P. is supported by the H2020-FETOPEN-2018-2020 project TEQ (grant No. 766900), the DfE-SFI Investigator Programme (grant 15/IA/2864), the Royal Society Wolfson Research Fellowship (RSWF\R3\183013), the Leverhulme Trust Research Project Grant (grant No. RGP-2018-266), and the U.K. EPSRC (grant No. EP/T028106/1).

**Institutional Review Board Statement:** Not Applicable.

**Informed Consent Statement:** Not Applicable.

**Data Availability Statement:** Not Applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A. Derivations of Equations (6) and (7)**

*Appendix A.1. Direct Approach*

Using the definition of mutual information, one has I(*ρS*1*S*2:E*f*) = *S*(*ρS*1*S*2 ) + *S*(*ρ*E*f*) − *S*(*ρS*1*S*2E*f*) and I(*ρS*1:E*f*) = *S*(*ρS*1 ) + *S*(*ρ*E*f*) − *S*(*ρS*1E*f*), from which ΔI = I(*ρS*1*S*2:E*f*) − I(*ρS*1:E*f*) can be calculated as the following:

$$\begin{split} \Delta \mathcal{L} &= \left[ S(\rho\_{S\_1 \mathcal{E}\_f}) - S(\rho\_{S\_1}) \right] - \left[ S(\rho\_{S\_1 S\_2 \mathcal{E}\_f}) - S(\rho\_{S\_1 S\_2}) \right] \\ &= \left[ S(\rho\_{S\_2 \mathcal{E}\_f}) - S(\rho\_{\mathcal{E}\_f}) \right] - \left[ S(\rho\_{S\_2 \mathcal{E}}) - S(\rho\_{\mathcal{E}}) \right]. \end{split} \tag{A1}$$

In passing to the second line, we assume that the total *S*1*S*2E system starts from a pure state and E *f* denotes the part of the environment that is not included in the fraction E*f* , i.e., E *f* is the complement of E*f* . We continue by adding and subtracting *S*(*ρS*2 ) to the right hand side of the above equation and rearranging to obtain the following:

$$\begin{split} \Delta \mathcal{L} &= \left[ S(\rho\_{\mathcal{S}\_{2}}) + S(\rho\_{\mathcal{E}}) - S(\rho\_{\mathcal{S}\_{2}\mathcal{E}}) \right] - \left[ S(\rho\_{\mathcal{S}\_{2}}) + S(\rho\_{\mathcal{E}\_{f}}) - S(\rho\_{\mathcal{S}\_{2}\mathcal{E}\_{f}}) \right] \\ \Delta \mathcal{L} &= \mathcal{Z}(\rho\_{\mathcal{S}\_{2}\mathcal{E}}) - \mathcal{Z}(\rho\_{\mathcal{S}\_{2}\mathcal{E}\_{f}}). \end{split} \tag{A2}$$

We note that this result is completely independent of the nature of the dynamics and valid for arbitrary fractions at any given instant. Considering the fraction to be the whole environment, i.e., E *f* is an empty set, the second term in the above equation vanishes and reduces to the following simple form:

$$
\Delta T = \mathcal{T}(\rho\_{\mathbb{S}\_2; \mathcal{E}}),
\tag{A3}
$$

which corresponds to the gap at the end of the curves.

#### *Appendix A.2. Koashi–Winter Relation*

Given an arbitrary tripartite quantum system *ρABC*, the Koashi–Winter (KW)relation [36] states the following inequality:

$$E\_f(\rho\_{AB}) \le \mathcal{S}(\rho\_A) - f^{\leftarrow}(\rho\_{AC}),\tag{A4}$$

with equality attained if *ρABC* is pure and *Ef*(·) denotes the entanglement of formation. Here, we will try to exploit this inequality to present bounds on the mutual information shared between the different fractions of the system and environment.

In addition to our usual assumption of a pure *S*1*S*2E state, we also introduce a pure auxiliary unit A that serves only as a mathematical tool to ensure that our set up fits within the framework of the KW relation. Identifying *A* → *S*1*S*2, *B* → A and *C* → E*f* , and noting that by definition *Ef*(*ρS*1*S*2A) = 0, we have the following:

$$J^{\leftarrow}(\rho\_{S\_1S\_2;\mathcal{E}\_f}) \le S(\rho\_{S\_1S\_2}) \tag{A5}$$

$$\mathcal{Z}(\rho\_{S\_1S\_2;\mathcal{E}\_f}) \le S(\rho\_{S\_1S\_2}) + D^{\leftarrow}(\rho\_{S\_1S\_2;\mathcal{E}\_f})$$

which provides an upper bound on the information that we can obtain on the total system by probing a fraction of the environment.

We now use the KW relation to bound the mutual information between a single system qubit and fractions of the environment. To that end, we shift our labeling to *A* → *S*1, *B* → *S*2 and *C* → E*f* again with the assumption that *S*1, *S*2 and the whole environment E is in a pure state. The KW relation gives the following:

$$E\_f(\rho\_{S\_1S\_2}) \le S(\rho\_{S\_1}) - f^{\leftarrow}(\rho\_{S\_1\mathcal{E}\_f}).\tag{A6}$$

Adding I(*ρS*1:E*f*) to both sides we get

$$\mathcal{E}\_f(\rho\_{\mathcal{S}\_1\mathcal{S}\_2}) + \mathcal{Z}(\rho\_{\mathcal{S}\_1\mathcal{E}\_f}) \le \mathcal{S}(\rho\_{\mathcal{S}\_1}) + D^{\leftarrow}(\rho\_{\mathcal{S}\_1\mathcal{E}\_f}) \tag{A7}$$

$$\mathcal{Z}(\rho\_{\mathcal{S}\_1\mathcal{E}\_f}) \le \mathcal{S}(\rho\_{\mathcal{S}\_1}) + D^{\leftarrow}(\rho\_{\mathcal{S}\_1\mathcal{E}\_f}) - \mathcal{E}\_f(\rho\_{\mathcal{S}\_1\mathcal{S}\_2}).$$

Comparing Equations (A5) and (A7) can help us to understand the discrepancy between the mutual information curves presented in Figure 2d. Naturally, we have both I(*ρS*1*S*2:E*f*) and I(*ρS*1:E*f*) greater than zero, and moreover, we know that discarding a subsystem never increases the mutual information, thus I(*ρS*1*S*2:E*f*) ≥ I(*ρS*1:E*f*). Together, this allows us to restrict the gap between the curves as the following:

$$0 \le \Delta T \le S(\rho\_{\mathcal{S}\_1 \mathcal{S}\_2}) + D^{\epsilon -}(\rho\_{\mathcal{S}\_1 \mathcal{S}\_2 \mathcal{E}\_f}).\tag{A8}$$

Finally, let us also specifically look at the gap at the end of the curves in Figure 2d, i.e., E*f* = E, where we can use the equality in the KW relations given in Equations (A5) and (A7), and obtain a more precise expression. A pure *S*1*S*2E state implies that I(*ρS*1*S*2:E ) = 2*S*(*ρS*1*S*2), and we have the following:

$$\begin{split} \Delta \mathcal{Z} &= 2S(\rho\_{S\_1 \mathcal{S}\_2}) - S(\rho\_{S\_1}) - D^{\leftarrow}(\rho\_{S\_1 \mathcal{E}}) + E\_f(\rho\_{S\_1 \mathcal{S}\_2}) \\ &= 2S(\rho\_{S\_1 \mathcal{S}\_2}) - S(\rho\_{S\_1}) - S(\rho\_{\mathcal{E}}) + S(\rho\_{S\_1 \mathcal{E}}) \\ &= S(\rho\_{\mathcal{S}\_2}) + S(\rho\_{\mathcal{E}}) - S(\rho\_{S\_2 \mathcal{E}}) \\ &= \mathcal{Z}(\rho\_{\mathcal{S}\_2 \mathcal{E}}). \end{split} \tag{A9}$$

In passing from the first to the second line we resort to the basic definition of I(*ρS*1:E ). Note that this is exactly the same result we obtain in Equation (A3) using the direct approach.

#### **Appendix B. Testing the Strong Quantum Darwinism Criteria**

We would like to assess whether the mutual information between the system particles and a single environment state I(*ρS*1*S*2:*E*1 ) is purely classical. To that end, we need to check whether the state *ρS*1*S*2*E*1 has a vanishing quantum discord or not, for which we have two options to consider: measurements performed on the system or environment side. The former is the condition of *strong Darwinism* introduced in [6] and the latter is an alternative constraint recently put forward in Ref. [37].

Here, we present the explicit calculation of the nullity condition for quantum discord introduced in [40,41] considering both measurement scenarios mentioned in the paragraph above. An arbitrary state *ρAB* has a vanishing quantum discord with measurements on *A* or *B* if and only if one can find an orthonormal basis {|*n*-} or {|*m*-} in the Hilbert space of *A* or *B* such that the total state can be written in block-diagonal form in this basis. Mathematically, it is possible express this condition as follows:

$$D^{\rightarrow}(\rho\_{A:B}) = 0 \iff \rho\_{AB} = \sum\_{n} p\_n |n\rangle\langle n| \otimes \rho\_{n'}^{B} \tag{A10}$$

$$D^{\leftarrow}(\rho\_{A:B}) = 0 \iff \rho\_{AB} = \sum\_{m} q\_{m} \rho\_{m}^{A} \circledast |m\rangle\langle m|.\tag{A11}$$

Note that in our case, we make the identification *A* → *S*1*S*2 and *B* → *E*1.

Let us start with checking the former condition. We pick an arbitrary orthogonal basis in the Hilbert space of the environmental qubit as {|*ei*-} and express the state at hand as follows:

$$
\rho\_{S\_1 S\_2 E\_1} = \sum\_{i,j} \rho\_{ij}^{S\_1 S\_2} \circledast \left| e\_i^E \right\rangle \left\langle e\_j^E \right|, \tag{A12}
$$

In order for the state in Equation (A12) to be written as the one given in Equation (A11), all *ρS*1*S*2 *ij* 's must be simultaneously diagonalizable and, if it exists, the basis in which they are diagonal is then {|*n*-}. It was shown in [40,41] that mathematically, this implies the following: *D* →(*ρS*1*S*2:*E*1 ) = 0 if and only if one has *ρS*1*S*2 *ij* , *ρS*1*S*2 *ij* = 0. Similarly, this condition can be stated as *ρS*1*S*2 *ij* 's must be normal matrices such that *ρS*1*S*2 *ij* , *ρS*1*S*2 *ij* † = 0, and also commute with each other [40,41].

Using our analytics, we can write the general form of *ρS*1*S*2*E*1 at the instant we observe Darwinism, i.e., *JSEt* = *π*/4, as follows:

$$
\rho\_{S\_1S\_2E\_1} = \begin{pmatrix} a & -a & 0 & 0 \\ -a & a & 0 & 0 \\ 0 & 0 & c & c \\ 0 & 0 & c & d \\ \hline \\ 0 & 0 & d^\* & d^\* & e & 0 & 0 \\ \hline \\ 0 & 0 & d^\* & d^\* & e & e & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & f & -f \\ b^\* & -b^\* & 0 & 0 & 0 & 0 & -f & f \\ \end{pmatrix}, \tag{A13}
$$

where *a* = *α*2/2, *b* = ( −1)*Nαδ*/2, *c* = *β*2 cos2 *Jπ* 2 + *γ*2 sin2 *Jπ* 2 /2, *d* = [2*βγ* + *i*(*β* − *γ*)*β* + *γ*) sin(*Jπ*)]/4, *e* = *γ*2 cos2 *Jπ* 2 + *β*2 sin2 *Jπ* 2 /2 and *f* = *δ*2/2. Recall that parameters *α* = cos *θ*1 cos *θ*2, *β* = cos *θ*1 sin *θ*2, *γ* = sin *θ*1 cos *θ*2, *δ* = sin *θ*1 sin *θ*2 are dependent on the initial states of the system qubits. The dimensions of our the system particles and the environmental qubit are *dS*1*S*2 =4 and *dE*1 =2, respectively, which means that the set {|*ei*-} is composed of two elements and we have 4 *ρS*1*S*2 *ij* matrices that are 4 × 4 in size. Horizontal and vertical lines dividing the density matrix in Equation (A13) in fact denote these 4 matrices. Explicitly, we have the following:

$$\begin{aligned} \rho\_{S\_1S\_2}^{11} &= \begin{pmatrix} a & -a & 0 & 0 \\ -a & a & 0 & 0 \\ 0 & 0 & c & c \\ 0 & 0 & c & c \end{pmatrix}, & \rho\_{S\_1S\_2}^{12} &= \begin{pmatrix} 0 & 0 & b & -b \\ 0 & 0 & -b & b \\ d & d & 0 & 0 \\ d & d & 0 & 0 \end{pmatrix}, \\\ \rho\_{S\_1S\_2}^{21} &= \begin{pmatrix} 0 & 0 & d^\* & d^\* \\ 0 & 0 & d^\* & d^\* \\ b^\* & -b^\* & 0 & 0 \\ -b^\* & b^\* & 0 & 0 \end{pmatrix}, & \rho\_{S\_1S\_2}^{22} &= \begin{pmatrix} c & e & 0 & 0 \\ e & e & 0 & 0 \\ 0 & 0 & f & -f \\ 0 & 0 & -f & f \end{pmatrix}. \end{aligned}$$

The diagonal matrices are clearly normal matrices, i.e., they satisfy *ρii S*1*S*2 , *ρii S*1*S*2 † = 0. However, the off-diagonal ones are not normal in general. In fact, using the parameters we use in our simulations (setting *S*1-*S*2 interaction *J* = 10), they do not commute independentlyoftheinitialstateofthesystem.Asaresult,itisnotpossibletowrite*ρS*1*S*2*E*1inthe

form given in Equation (A11), and thus *D*→(*ρS*1*S*2:*E*1 ) > 0 implying that the condition for strong Darwinism, as defined in [6], is not satisfied.

Considering the alternative approach of checking the condition of vanishing discord with measurements on the environment side, *D*←(*ρS*1*S*2:*E*1 ) > 0, similar to the previous case, we start by expressing our total state in an arbitrary orthogonal basis in the Hilbert space of the system qubits {*sk*} as the following:

$$
\rho\_{S\_1 S\_2 E\_1} = \sum\_{k,l} \left| s\_k^S \right\rangle \left\langle s\_l^S \right| \circledast \rho\_{E\_1}^{kl}.\tag{A14}
$$

Recalling that *dS*1*S*2 = 4, it is possible to identify the set {*sk*} consists of four elements and we have 16 *ρkl E*1 matrices which are 2 × 2 in size, denoted by the horizontal and the vertical lines below:

Following [40,41] again, checking the nullity condition amounts to checking whether the commutators satisfy the condition *ρkl E*1 , *ρkl E*1 = 0. It is possible to show that these commutators indeed vanish, which implies that from the point of view put forward in Ref. [37], all mutual information we have between the system qubits and environment fractions at the instant we observe the plateau is classical, and therefore objective.


*2.1. Goal and Scope*

In this study, four electric vehicle types representing different vehicle technologies (BEV and PHEV) and battery options (LFP and NMC) have been discussed. Qin 300 (BEV-LFP), Qin 80 (PHEV-LFP), Qin 450 (BEV-NMC), and Qin 100 (PHEV-NMC) were chosen as the representative vehicles and the related information is mainly provided by its manufacturer, BYD, a major leading electric vehicle maker in China [14]. The choice of Qin series is due to its high market share, which contributed to 7% of the total new electric vehicles in the first half year of 2018. Especially in the PHEV market, Qin PHEV models account for 23.8% in the same period. Besides, choosing the vehicles from one plant allows a comparable basis for comparison, such as the comparative size and class of vehicles, the same modeling approach of energy efficiency, and unwanted variations in the production line are greatly avoided.


*Edited by Muhammad Mohiuddin, Bilal Khalid, Md. Samim Al Azad and Slimane Ed-dafali*

*Leadership in a Changing World - A Multidimensional Perspective* investigates the multidimensional aspect of leadership by exploring different perspectives and practices as well as existing theories of effective leadership in a changing world. Chapters address such topics as the connection between leadership, innovation, and creativity, venture leadership, e-leadership, digital leadership, and more. Beyond understanding the nature of effective leadership, this book examines the nature of leadership focusing on what we know and how we know it.

*Taufiq Choudhry, Business, Management and Economics Series Editor*

Published in London, UK © 2022 IntechOpen © Igor Kutyaev / iStock

Leadership in a Changing World - A Multidimensional Perspective

IntechOpen Series

Business, Management and Economics,

Volume 1

Leadership in a

Changing World

A Multidimensional Perspective

*Edited by Muhammad Mohiuddin, Bilal Khalid,*

*Md. Samim Al Azad and Slimane Ed-dafali*


**3. Demonstration (microscopy) and isolation of parasite (culture) (Figure 6)**

Direct visualization of amastigotes in clinical specimens is the diagnostic gold standard in regions where tissue aspiration is feasible and microscopy and technical skill are available. [24] Microscopy of bone marrow aspirates is the safest diagnostic approach for paediatric patients, with amastigotes seen in more than 90% of cases by an experienced observer. The higher diagnostic efficacy of the bone marrow examination in children is probably related to the heavier parasitisation encountered in children. Microscopic examination of splenic aspirates offers the highest sensitivity (up to 98%), but is assoiated with the risk of lfe-threatening haemorrhage in cases with profound thrombocytopenia. [1] The results of culture in Novy-McNeal-Nicole (NNN) and RPMI 1640 media have been disappointing in one study. [24]

**Figure 6.** Amastigotes forms of *Leishmania spp* by Giemsa stain


**1. Introduction**

Fibrosis is characteristically defined as the thickening and scarring of tissues from a pathological repair process. While often fibrosis is thought to occur following an injury, in adipose tissue changes in metabolism can trigger an inflammatory cascade that initiates the maladaptive fibrotic repair process. Once initiated, fibrotic changes in adipose tissue have devastating effects for patients. In most cases, adipose tissue fibrosis is undetected, difficult to reverse, and interferes with treatment options for obesity and other comorbidities.

White adipose tissue is responsible for storing and releasing energy in the body by regulating lipogenesis and lipolysis, respectively [1]. Lipogenesis is the process where free fatty acids and glycerol are taken up from the blood stream and stored in adipocytes as triglycerides. Conversely, lipolysis is the process in which triglycerides are catabolized into free fatty acids and glycerol that are released into the blood stream where they are used as an energy source by other organs [2]. An imbalance between these two processes leads to obesity and metabolic diseases, such as type 2 diabetes [3].

Obesity is associated with adipocyte enlargement (hypertrophy) and formation of new adipocytes (hyperplasia) [4]. This increase in adipocyte volume and tissue mass triggers inflammation, further dysregulation in adipose tissue, and eventually adipose tissue fibrosis [5]. Like other fibrotic diseases, adipose tissue fibrosis is the accumulation and increased production of extracellular matrix (ECM) proteins [6,7]. In healthy adipose tissue, the ECM can be remodeled to accommodate normal fluctuations in adipocyte size. However, when adipose tissue becomes fibrotic the stiff ECM cannot be dynamically remodeled.

Historically, obesity has been linked to an overall increase in lipolysis that results in ectopic accumulation of lipids in other tissues and insulin resistance [8,9]. An increase is observed when lipolysis is normalized to overall lean body mass; however, when normalized to lipid content there is a

decrease in basal lipolysis rate per cell [10–12]. Furthermore, starved hypertrophic adipocytes only lose a portion of their stored triglycerides (<50%) without supplemental factors [13]. There is limited literature investigating the e ffect adipose tissue fibrosis has on lipolysis, so most studies assume the same trends as obesity. There are several theories as to why this may not be valid. One being that as adipocytes shrink, stress between the ECM and cells can increase, eventually inhibiting lipolysis, leading to enlarged adipocytes without a mechanism for depleting their lipid stores [7].

 For example, gastric bypass surgery, which generally causes rapid fat loss and increases in insulin sensitivity, is less e ffective for patients with high degrees of adipose tissue fibrosis [14,16]. Specifically, insulin resistance is linked to higher levels of adipose tissue inflammation and increased ECM deposition [15]. Initially, as insulin sensitivity decreases, adipocytes begin to undergo enhanced lipolysis and release free fatty acids into the environment. These free fatty acids cause further insulin resistance and can lead to inflammation by signaling macrophages and other immune cells to the area [17]. Over time this inflammation increases ECM protein production leading to interstitial fibrosis in adipose tissue. The increased ECM sti ffness prevents the adipocytes from expanding in a healthy manner, causing the tissue to be metabolically dysfunctional, including adipocyte death, decreased lipolysis, and disrupted cell-cell interactions [6,14].

Adipose tissue fibrosis has a complicated and critical role in metabolic dysfunction and obesity. With the growing obesity epidemic, it is important to understand the mechanisms behind the development and progression of adipose tissue fibrosis. Furthermore, targeted therapeutic treatments are required for patients with fibrotic adipose tissue. This article discusses recent research in modeling adipose tissue fibrosis using in vitro (2D and 3D) and in vivo methods with considerations for biomaterial selections. Additionally, this article will outline the importance of adipose tissue in treating other fibrotic diseases and methods used to detect and characterize adipose tissue fibrosis.