**4. The Ubiquitous Hemolytic Activity of Fish Cytolysins: From the Definition of the Pore-Forming Mechanism to its Structural Confirmation**

The cytolytic activity of almost all fish cytolysins isolated so far has been extensively characterized against erythrocytes, hence their being referred to as hemolysins in some instances. In spite of some divergence in the literature as to the potency and the species specificity of the hemolytic activity of fish cytolysins, which can by at least partly explained by different experimental conditions—they are all most potent against rabbit erythrocytes [13,24,25], although the reason for such marked sensitivity remains unknown. In addition, a weak hemolytic activity against rat, mice, and cattle erythrocytes has been reported for some fish cytolysins, while those from human, sheep, pig, and chicken appear to be resistant to the lytic activity of these toxins [12,13,25].

There is also some evidence as to the lytic activity of fish cytolysins against other cell types. For instance, the prolonged exposure of neuro-glioblastoma NG108-15 cells to TLY led to an irreversible increase in membrane permeability [57]. In addition, SNTX lysed the membranes of platelets in rabbit blood in a dose-dependent way [58].

Cytolytic activity is often associated with the action of enzymes such as proteases and phospholipases and, in some cases, of C-type lectins such as CEL-III from the sea cucumber *Cucumaria echinata* [59,60]. However, fish venoms lack phospholipase activity [24,26,27,61–63] and, although enzymatic activity such as proteolytic and hyaluronidase has been described in these venoms, no such activity was associated with the purified lethal/hemolytic factors [12]. As to C-type lectins, although such molecules have been described in fish venoms [64], they are not associated with the hemolytic activity induced by these venoms [65]. Therefore, fish cytolysins were very soon believed to lyse erythrocytes through a direct, non-enzymatic mechanism [26].

The formation of non-selective transmembrane pores was soon shown to be the mechanism by which fish cytolysins destroy cells [42,57,66]. The pore-forming activities of SNTX and Sp-CTx were demonstrated through osmotic assays, in which the kinetics of the hemolysis was evaluated in the presence of osmotic protectants of different sizes [42,66]. While the smaller compounds raffinose and saccharose failed to prevent hemolysis, polyethylene glycols (PEGs) of ∼1000–2000 reduced the rate of hemolysis induced by the toxins and PEGs > 3000 conferred almost full protection against it [42,66]. The pores formed by SNTX in the membrane of rabbit erythrocytes were estimated to be 2.5–3.2 nm in diameter [66].

The search for what mediates the interaction between fish cytolysins and the cell membrane began as soon as the first such toxins were purified. For instance, the hemolytic activity of the *T. draco* venom was found to be preceded by the binding of its hemolytic component to a protein receptor (glycophorin) on the surface of erythrocytes [13]. From then on, it became clear that cationic amino acid residues present in fish cytolysins are essential for their interaction with anionic, neutral or zwitterionic lipids in the membrane of erythrocytes, eventually leading to hemolysis.

In 1997, it was observed that SNTX no longer induced hemolysis when positively charged lysine and arginine residues in its surface were chemically modified, although the toxin's secondary structure remained unaffected [66]. The modification of cationic residues also inhibited the lethal activity associated with SNTX [67]. These data are in agreement with structural motifs observed in these toxins, for instance, an amphiphilic, ∼20-residues long α-helix flanked by regions rich in basic residues was predicted in both α- and β-subunits of SNTX, and it is believed to be the cationic site crucial for the toxin's hemolytic activity [52].

As expected, the hemolytic activity of SNTX and neoVTX against rats and rabbit erythrocytes, respectively, was competitively inhibited by negatively charged lipids, most potently by cardiolipin and less so by phosphatidylserine and the gangliosides GM1 and GM2 [39,66]. Although cardiolipin was the most potent inhibitor, it is not present in the erythrocyte membrane, thus the hemolytic activity must be actually triggered by the electrostatic interaction between the toxins' cationic residues and anionic phospholipids such as phosphatidylserine, which is abundant in the membrane of erythrocytes [39]. It stands to reason that the species-specificity of the hemolytic activity of fish cytolysins could be related to the density of these lipids in the erythrocyte membrane.

The electrostatic interaction between anionic lipids and toxins is influenced by the number of negative charges and the pKa of acidic groups in the lipids, as indicated by the different inhibitory potencies of different lipids [66]. This conclusion is further supported by the fact that the maximal hemolytic activity of Sp-CTx is achieved between pH 8 and 9 [43]. Although the neutral lipids phosphatidylcholine, phosphatidylethanolamine, sphingomyelin, and cholesterol did not affect the hemolytic activity of SNTX and neoVTX [39,66], that which was induced by Sp-CTx in rabbit erythrocytes was inhibited by phosphatidylethanolamine, phosphatidylglycerol, and cholesterol [43]. Similar results regarding the role of differently charged lipids on the hemolytic activity were found for the venomous extract of the lionfish *P. volitans* [68], which is yet another indication that this venom contains cytolysins homologous to those identified in other fish species.

The presence of the MACPF/CDC domain in fish cytolysins [53,55] and the inhibitory effect of cholesterol on the hemolytic activity of Sp-CTx [43] suggest that this lipid is also required for the interaction between these toxins and the erythrocyte membrane, much like the MACPF/CDC proteins found in bacteria.

The hemolytic activity of fish cytolysins is influenced by factors other than the cationic residues present in their surfaces. That of Sp-CTx, for instance, is calcium dependent, being abolished by zinc ions [43]. Furthermore, five of the 15 cysteine residues and 10 of the 18 cysteine residues of SNTX and neoVTX, respectively, are free, and these free thiol groups, much like the tryptophan residues in the surface of SNTX, also play an important role in the hemolytic activity induced by these toxins [39,52,69].

All this evidence pointing to a pore-formation mechanism being responsible for the hemolytic activity of fish cytolysins was corroborated by the three-dimensional structure of SNTX. Pore-formation was shown to be the result of an interaction between the αand β-chains along their longitudinal axis through charge complementarity between the MACPF/CDC domains. These domains—which are formed by twisted four-stranded antiparallel β-sheets with two adjacent bundles of α-helices (transmembrane helices— TMH)—interact on the heterodimer to form a soluble early pre-pore. After that, the TMH from each chain unwind to form a large, continuous β-sheet that comprises the β-barrel of the pore (Figure 4).

**Figure 4.** Transmembrane pore formation mechanism. The SNTX heterodimer represents an early and soluble phase of the pore formation mechanism (**A**). After the interaction between chains α and β, the MACPF/CDC domain helices undergo a conformational change to form the continuous β-sheet of the transmembrane pore (**B**). (**A**) was produced on Pymol using the model 4wvm deposited in the Protein Data Bank [55] and (**B**) was adapted from [55]. TMH—transmembrane helices; M—membrane.

This arrangement is in agreement with the complementarity between MACPF/CDC monomers described for other perforins, which directs initial oligomerization events (prepore assembly). In SNTX, this pre-pore contains 20 SNTX subunits (or 10 SNTX-α/β heterodimers) that line up along a horizontal plane [55].

Furthermore, the PRYSPRY domain was shown to be responsible for the initial interaction of SNTX with the cell surface. Consistent with this function, this domain is located in the solvent-exposed face of SNTX, in a position analogous to the Ig and C2 domains of CDC and perforins, which mediate protein–protein and protein–lipid interactions in the recognition of pathogens by TRIM immune proteins [55].

The high sequence similarity and the presence of conserved domains in the other fish cytolysins identified so far allows us to predict that these proteins perform their cytolytic functions through the same mechanism. For instance, the residues G208 (α-chain) and G209 (β-chain) of SNTX, reported as necessary for pre-pore formation, are present in all cytolysins. So are the residues F206 (α-chain) and F54, I56, and S52 (β-chain), related to the formation of hydrogen bonds between the β4-strand of SNTX-α and the β1-strand of SNTX-β, which are also involved in pre-pore formation (Figure 3).

Moreover, the pairs K205 (α-chain)-E55 (β-chain) and K54 (α-chain)-E206 (β-chain) present in SNTX's β4 (α-chain) and β1 (β-chain) strands form ionic pairs analogous to a partially closed zipper, which contributes to pore formation. These ionic pairs are conserved in almost all cytolysins, in spite of exchanges as to which residue belongs to each chain.

In addition to the structural evidence, the formation of large ring-shaped pores was visualized in rabbit erythrocytes by transmission electron microscopy (outer dimension: 257 ± 5.7 Å (mean ± SEM); lumen i.d.: 117 ± 4.5 Å) [55], in agreement with what had been predicted for the pores formed by SNTX and Sp-CTx [42,66].


1. Introduction

Silicon carbide (SiC), which exhibits a wider band gap as well as a superior breakdown field and thermal conductivity over conventional Si, has gained considerable attention for future power electronics [1]. Among the various types of power devices, metal-oxide-semiconductor field-effect transistors (MOSFETs), which provide a normally-off characteristic, should become a key component for next-generation green electronics. As stated, with the exception of Si, SiC is the only compound semiconductor that yields SiO2 insulators with thermal oxidation. This makes the device fabrication process easier compared with those for other wide band gap semiconductors. It is commonly believed that carbon impurities within the oxides diffuse out in the form of carbon oxides during high temperature oxidation, but a small amount of carbon impurities remains within the oxide and at the SiO2/SiC interface. Consequently, the electrical degradation of SiC-MOS devices causing both deteriorated device performance and reliability is the most crucial obstacle to the implementation of SiCbased power electronics.

Unlike mature Si-MOS technology [2-5], a plausible oxidation model of a SiC surface and physical origin explaining electrical degradation of SiC-MOS devices have yet to be established. High-resolution transmission electron microscopy (TEM) observation reported several nm-thick transition layers with an extremely high excess carbon concentration around 20% beneath the SiO2/SiC interface [6-8]. Although the non-stoichiometric bulk region seems to account for the mobility degradation of SiC-MOSFETs [8], a recent report based on an ion scattering technique pointed out a near-perfect stoichometric SiC region [9].

In addition, there still remain controversial issues of the energy band structure of SiO%SiC interfaces, despite the fact that a small conduction band offset significantly increases the gate leakage current, especially under a high electric field and high operation temperatures [10]. For example, while alternative channels, such as a 4H-SiC(000-1) C-face substrate, have

© 2013 Watanabe and Hosoi; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

proven to provide higher electron mobility than conventional devices on Si-face substrates [11], a further reduction in conduction band offset has been pointed out for the C-face channels [12]. However, details on the physical origins have not been clarified yet.

Furthermore, another important problem of SiC-MOS devices is poor gate oxide reliability, such as low dielectric breakdown field and threshold voltage instability. It has been reported that the location of dielectric breakdown is not correlated with any dislocations in SiC-MOS capacitors with gate oxides formed on 4º-off-angled 4H-SiC(0001) substrates by successive oxidation in N2O and NO ambient [13]. Our conductive atomic force microscopy (AFM) study on a thermally grown SiO /4H-SiC(0001) structure has clearly demonstrated that dielectric breakdowns are preferentially induced at the step bunching [14]. It is generally accepted that the gate oxide breakdown is triggered when electrical defects generated in the oxide by a stress field are connected between the electrode and substrate (percolation model) [15]. Thus, we speculated that a local electric field concentration occurred around the step bunching, resulting in the preferential breakdown due to the acceleration of the defect generation. This suggests that oxidation behavior of step-bunched SiC surface induced by epitaxial growth and high-temperature activation annealing needs to be clarified from the macroscopic point of view, together with the atomic bonding features at SiO%SiC interface mentioned above.

This article provides an overview of our recent studies on the thermal oxidation of 4H-SiC substrates and the energy band structure of SiO%H-SiC fabricated on (0001) Si-face and (000-1) C-face surfaces by means of high-resolution synchrotron x-ray photoelectron spectroscopy (XPS). We investigated the correlation between atomic structure and the electrical properties of corresponding SiC-MOS capacitors and discuss the intrinsic and extrinsic effects of the interface structure and the electrical defects on the band offset modulation. In addition, the surface and interface morphology of a thermally grown SiO%H-SiC(0001) structure were systematically investigated using AFM and TEM to clarify the relation between step bunching and oxidation kinetics.


**3.1 Forced convection experiments**

(dotted) versus KTEG can be seen in **Figure 5**. Three different KS cases have been considered to highlight the fact that, the larger KS, the larger the power output, even for a constant KTEG. It can be seen that when KTEG = KS, the maximum power condition when KLK = 0, then the temperature drop across the μTEG is 50% of the

Similar to the electrical case, many papers discussing thermal matching focus on reaching a temperature drop in the μTEG equal to the temperature drop across KS [5, 24, 25]. When this KS represents a heat exchanger, some authors suggest a low KS heat exchanger to match KTEG and therefore maximize the power output according. While this approach assures operation at the mathematical local maximum for a given KS, it is a bad practice because it ignores the absolute maximum,

Looking at **Figure 5**, if KTEG = 1 W/K, this reasoning would imply that KS = 1 W/K would be necessary, and 50% of the total available temperature difference would drop across the μTEG. However, with a better heat exchanger, KS = 10 W/K or even KS = 100 W/K, then ΔT will asymptotically approach ΔTA, and the power output will

*PL*, *max* ¼ ð Þ *S* � Δ*TA*

*Pmax (left, blue curves) and ΔT/ΔTA (right, red curves) versus KTEG for different KS values. For KTEG = 1, thermal matching conditions would call for KS = 1, but larger Pmax values are possible for larger values of KS.*

In conclusion, both load matching and thermal matching are conditions that are mathematically true, but from a practical point of view, care must be taken when designing a μTEG to maximize its power output. First of all, its electrical internal resistance (RTEG) must be minimized, and after that, power output can be maximized by connecting a load which matches that of the μTEG, or simply an IC implementing an MPPT algorithm. On the thermal side, as RTEG is minimized, the thermal conductance (KTEG) is consequently maximized. Then, as the μTEG is already optimized, and it is not possible to further increase KTEG, the only option is to act on the external components, which in this case is the heat exchanger, and to choose one with an as large as possible KS, so that almost all of the available ΔT will

2

(5)

4*RTEG*

available temperature difference.

asymptotically reach:

**Figure 5.**

**356**

be internally transferred to the μTEG.

which takes place at larger KS values for a given KTEG.

*Heat Transfer - Design, Experimentation and Applications*

As the working scenario for the μTEG devices is dominated by a temperature difference between the hot and the cold parts, heat convection could play also an important role on how these temperatures are established. Convection is a mechanism of heat flux originated from the movement of the surrounding fluid, which will be typically air for the usual applications of the presented devices. Depending on how this movement is induced, convection can be classified as natural or forced.

Natural convection is based on the warming up of the air that is close to a heat source that, due to the lowering of its density, tends to move upwards, giving its place to colder air and so promoting the heat exchange. In forced convection, air is forced to move and then renew by an external force.

In order to demonstrate the improvement in the performance of the device, three different sets of experimental measurements have been performed on a device at three different convection conditions. The first one corresponds to natural convection, which occurs when the device is operated by letting it rest on top of a hot surface exposed to ambient at room conditions. The second and third sets of measurements correspond to forced convection regimes. In the second case, this is accomplished by the use of a standard CPU fan (see **Figure 6**) placed over the device, while in the third, an air jet, obtained through a syringe connected to the compressed air line in the laboratory, is directed towards the device. More details are available at [26].

Such experimental measurements have been performed on two different devices, featuring 30 and 60 μm long silicon NWs (by filling 3 and 6 trenches as described in section 2.1). Consequently, each one of the devices has different electrical and thermal resistances.

The obtained experimental results are shown in **Figure 7**. The devices have been measured at different temperatures of the hot plate (from 50 to 200 °C in 25 °C steps).

The measurement results show a very clear improvement in the performance as a result of forced convection. The maximum output power obtained when the device is mounted under the fan is multiplied by 3 when compared with the natural convection regime, while when under the more directed and higher flow air jet, the performance increases nearly three orders of magnitude: from a few nW to almost 0.7 μW. Moreover, the more performant air convection is, the less relevant become the thermal properties of the thermoelectric material. In natural and air forced convection cases, the larger output power corresponds to the longest nanowires, whereas in the air jet forced convection the opposite is true. This happens because the longer nanowires also have a larger electrical resistance, and its larger thermal

The performance improvement obtained by means of this approach can be observed in **Figure 8**, which shows a plot of the Seebeck voltage output of the device placed on a hot plate at 150 °C when the cold finger is being attached. It can be observed that the voltage increases rapidly after contact, and it rises even more when the applied force to the cold finger is increased slightly, so demonstrating the

reduction of the thermal resistance when the physical contact is improved.

*Managing Heat Transfer Issues in Thermoelectric Microgenerators*

*DOI: http://dx.doi.org/10.5772/intechopen.96246*

**Figure 8.**

**Figure 9.**

**359**

*Voltage evolution while the cold finger is being attached and detached.*

*Power curves for a device, with and without the cold finger, on a hot plate at 250 °C.*

#### **Figure 6.**

*Experimental setup used for the thermoelectrical characterization of μTEG devices under a forced convection regime induced by a CPU fan located on the top. The device is mounted on the thermal chuck of a Linkam station.*

**Figure 7.**

*Maximum power versus chip surface temperature for three different heat convection regimes and on two different devices (adapted from [26]).*

resistance is not significant in this case where the platform to ambient thermal resistance is enough to assure a large ΔT.

#### **3.2 Cold finger approach**

In the previous subsection, it has been experimentally demonstrated how important a good thermal connection with the surrounding ambient is in order to improve the overall performance of μTEG devices. Nevertheless, forced convection scenarios are not always available and artificially forcing them needs additional energy consuming devices. Therefore, in order to explore a passive strategy to diminish the thermal resistance to the surrounding ambient, the effect of contacting the microplatform with a metallic probe has been assessed. With this experiment, the feasibility of the addition of a heat exchanging structure as a general strategy for the reduction of the thermal resistance to the ambient will be demonstrated.

The experimental setup consists of a metallic probe dipped in thermally conducting paste, which is carefully positioned on the micro-platform by the use of a micro-manipulator.

*Managing Heat Transfer Issues in Thermoelectric Microgenerators DOI: http://dx.doi.org/10.5772/intechopen.96246*

The performance improvement obtained by means of this approach can be observed in **Figure 8**, which shows a plot of the Seebeck voltage output of the device placed on a hot plate at 150 °C when the cold finger is being attached. It can be observed that the voltage increases rapidly after contact, and it rises even more when the applied force to the cold finger is increased slightly, so demonstrating the reduction of the thermal resistance when the physical contact is improved.

**Figure 8.** *Voltage evolution while the cold finger is being attached and detached.*

**Figure 9.** *Power curves for a device, with and without the cold finger, on a hot plate at 250 °C.*

resistance is not significant in this case where the platform to ambient thermal

*Maximum power versus chip surface temperature for three different heat convection regimes and on two*

*Experimental setup used for the thermoelectrical characterization of μTEG devices under a forced convection regime induced by a CPU fan located on the top. The device is mounted on the thermal chuck of a Linkam*

In the previous subsection, it has been experimentally demonstrated how important a good thermal connection with the surrounding ambient is in order to improve the overall performance of μTEG devices. Nevertheless, forced convection scenarios are not always available and artificially forcing them needs additional energy consuming devices. Therefore, in order to explore a passive strategy to diminish the thermal resistance to the surrounding ambient, the effect of contacting the microplatform with a metallic probe has been assessed. With this experiment, the feasibility of the addition of a heat exchanging structure as a general strategy for the reduction of the thermal resistance to the ambient will be demonstrated. The experimental setup consists of a metallic probe dipped in thermally conducting paste, which is carefully positioned on the micro-platform by the use of

resistance is enough to assure a large ΔT.

*Heat Transfer - Design, Experimentation and Applications*

**3.2 Cold finger approach**

*different devices (adapted from [26]).*

**Figure 7.**

**Figure 6.**

*station.*

a micro-manipulator.

**358**

In **Figure 9** the power curves obtained from the same device at a hot plate temperature of 250 °C with and without the cold finger are shown. It can be seen an important improvement in the performance of two orders of magnitude, from 1.3 to 142 nW, so proving the effectiveness of the cold finger approach as a proof of concept validating the further development of more effective heat exchanging structures.

improvement on performance. The thermal and electrical properties of the mate-

The model boundary conditions include a constant hot temperature at the bottom (Thot = 100 °C) and natural convection on the vertical and horizontal walls of the heat exchanger through a heat exchange coefficient directly calculated in COMSOL for an air ambient temperature of 27 °C. When such element is not present, the heat exchange coefficient is applied directly on the platforms surface. **Figure 11** shows the temperature distribution for the whole model under such conditions. As it can be seen, even with a heat exchanger, the lowest temperature reached in the cold part is slightly below 70 °C although the ambient temperature is 27 °C. This is because the thermal resistance from the heat exchanger to the ambient is approximately one third of the total thermal resistance while the thermal resistance from the bottom of the PCB (actually most of this is from the silicon chip) to

rials used in the model are listed in **Table 1**.

*DOI: http://dx.doi.org/10.5772/intechopen.96246*

*Managing Heat Transfer Issues in Thermoelectric Microgenerators*

the heat exchanger is approximately two thirds of it.

**κ (Wm1**

Silicon 150 12103 Silicon oxide 1.4 — Silicon nitride 30 —

Tungsten 174 7.76106 (2) Thermal paste 5 — Copper 401 — FR4 (PCB) 0.3 — Spacer (PMMA) 0.2 —

*Thermal and electrical properties of the materials used in the model.*

*Temperature distribution for the whole model with Thot = 100 °C.*

*been measured to obtain this value.*

**Table 1.**

**Figure 11.**

**361**

**K1**

Silicon NWs 25 (1) 12103 (1) 250106

*(1)Silicon NWs are modeled as a block, not individual nanowires, and the block material properties assume an occupation of only 5% of the total area with nanowires, while the remaining 95% has air material properties. (2)Tungsten electrical conductivity is different from the bulk literature values. The sheet resistance on a real device has*

**) σ (Sm1**

**) S (VK1**

**)**


**3. Findings**

### **3.1 The socio-demographics of food behaviour change**

#### *3.1.1 Food consumption changes*

**Table 5** presents data on changes in four categories of food consumption:

**Table 5** reveals dramatic changes in the types of food consumed during the pandemic. All components of fresh food declined, whilst processed food increased in most *The Geo-Demographics of European Consumers' Food Behaviour in the New Age of Disruption DOI: http://dx.doi.org/10.5772/intechopen.106938*

*Data show the net percentage of households which increased/decreased their consumption from before to during COVID-19. All data are statistically significant at the P < 0.05 level except when marked with X.*

#### **Table 5.**

*The socio-demographics of food consumption change.*

household types, and comfort foods increased dramatically in all. This was probably due to greater opportunities for snacking at home during lockdowns as well as helping to reduce stress. The reduction in fresh food is also likely to be related to supply constraints during COVID-19, given their much shorter shelf lives compared to most non-fresh foods in the context of fewer opportunities to shop. This is also at a time when most households actually increased their overall food intake and the money they spent on food [12].

However, **Table 5** also shows significant differences in household types. Those with children reduced both fresh and processed food intake whilst increasing comfort food consumption more than other households. The presence of children probably boosted the consumption of sweet foods. Households with children also tend to be the largest households with the greatest range of food tastes and needs, so are likely to be the most sensitive to food system shocks. In terms of education, lower secondary level households saw the largest reductions in both fresh and processed foods, as well as the smallest increase in comfort food. Clearly education in this context reflects income levels where saving money during COVID-19 is the priority in terms of the types of food consumed.

Households that lost income during the pandemic, due to losing their job, being laid-off or reduced work activity, changed all types of food consumed much more than no-income-loss households. This study found a significant positive correlation between whether or not households lost income and the PPP per inhabitant of the region in which they live. Thus, it is likely that many income-loss households were also relatively weak financially before COVID-19, which then increased this vulnerability even more. In general, it seems that such households are more sensitive to a shock like COVID-19, and thus need to adapt and change more, whilst non-incomeloss households are likely to have much greater resilience to shock with a correspondingly reduced need to change behaviour.

#### *3.1.2 How food is obtained*

**Table 6** provides data on some aspects of the way households changed how they obtained food from before to during COVID-19. Overall, there was a significant move

*Data show the net percentage of households which increased/decreased their behaviour from before to during COVID-19. All data are statistically significant at the P < 0.05 level except when marked with X.*

#### **Table 6.**

*The socio-demographics of food behaviour change: How obtain food.*

to buying from shops that sold locally produced food, such as farmers' markets, cooperative producers and traditional outlets that also sell local food. This is probably due to the increased interest in food, how it is produced and its quality, during COVID-19, coupled with enhanced awareness of health and of how this links to food (see Sections 3.1.4 and 3.1.5). The two exceptions are single persons who tend to be younger and less concerned with health, and the least educated households which may not have the same level of health and food awareness. It is also noteworthy that households with children and with a university education are most likely to switch to local producers during the pandemic. Similarly with income-loss households although here it is perhaps related to their need to adapt most during a shock (as above), and that their appreciation of food increased at this time more than no-income-loss households in a context where the latter were likely already to have a high appreciation so their measured increase would be less (see Section 3.1.4).

Changes in travel distance to shops show a steep reduction in all household types, as would be expected during the lockdowns, closures and restrictions on people's physical movement and social mixing, and perhaps also reflects the move to buying from local producers. The use of more local shops was greatest amongst households with children, which were more likely to need to work from home during COVID-19 when schools were closed, and those with a university education, which were more likely to be able and willing to engage in hybrid work between locations to the extent permitted by regulations. Interestingly, there is no difference related to income change, perhaps because in the context of the pandemic and its restrictions, financial status has little relevance for travel distance to shops.

The home delivery of food, ordered online or by phone during COVID-19, has increased across all types of household. Although differences are not great, those most likely to be at home most of the time during the pandemic (those with children who were not able to attend school, and with two or more adults who are more likely to be much older and retired) used home delivery more than single households which tend to be by far the youngest and mobile. This probably also applies to households that lost income, typically because they lost their job or were put on furlough during lockdown, so were more often at home most of their time. There is also a very clear tendency for households with a higher level of education to order home delivery, perhaps because (as above) these tend to be able and willing to engage in hybrid work between locations to the extent permitted by regulations.

#### *3.1.3 Food handling and quality*

Aspects of food handling and quality are examined in **Table 7**. Apart from a small decrease in using unpackaged foods by households with a lower secondary education, there was a strong switch to this type of food handling during COVID-19. This is loose food for which customers provide their own bag or which is provided separately by the shop. It mainly relates to fresh everyday food, though some shops sharply increased providing other foodstuffs in bulk for customers to bag themselves during the pandemic, such as rice, pasta, cereals and even drinks. Apart from clear environmental benefits with less packaging, the advantage for the customer is the ability to much better check the quality of individual food items and to precisely calibrate the amount of food they need or can afford. This also relates to heightened health concerns during the pandemic as well as to increased interest in food generally (see Section 3.1.4).

Despite this greater interest in health and its relationship with food during COVID-19, there was a general decrease in the consumption of organic food. However, many of the comments made by survey respondents referred to reduced availability due to supply chain issues and related price increases. Interestingly, the only exceptions were households with children, where parents are likely to have even greater health concerns, as well as in households with higher educational levels, where it is likely health and food awareness is greater and that incomes are higher. This perhaps also explains why income-loss households had the greatest decrease in organic food consumption across all types of household.

Dramatic reductions in food waste were seen across all types of household, most tellingly in households with children and with higher education levels. Here, it is probable there was heightened awareness about the environmental and health problems caused by food waste, as well as more motivation not to throw good food away

*Data show the net percentage of households which increased/decreased their behaviour from before to during COVID-19. All data are statistically significant at the P < 0.05 level.*

#### **Table 7.**

*The socio-demographics of food behaviour change: Food handling and quality.*

for both economic and ethical reasons. The fact that income-loss households had the greatest reduction in food waste of all household types is likely to relate to their greater financial concerns given that wasted food is also wasted money.

### *3.1.4 Food preparation and importance*

**Table 8** shows various aspects of the preparation of food revealing dramatic increases across almost all types of household in meal planning and in the range of food types, ingredients and recipes used in preparing meals. This is also directly reflected in the steep rise of households' understanding of the importance of food.

As would be expected given their generally greater sensitivity to food and health, households with children show the largest increases of any household type across all these variables. When looking at educational level, the higher this is the greater is the interest in food preparation and importance. It may also be relevant to note that households on the lowest level were the only household type that marginally lost interest in any of these issues. The fact that income-loss households increased interest in food preparation and importance more than no-income-loss households probably reflects their greater food sensitivity and more free time due to the increased chance they were confined at home during COVID-19. This must be seen in a context where no-income-lost households already had relatively high food awareness and interest before the pandemic, so the change these households report is not so great.

## *3.1.5 Food vulnerability*

The pandemic led to different types of food vulnerability, and/or exposed preexisting food challenges, as depicted in **Table 9**. In terms of household composition, the use of free food from food banks or elsewhere is greatest by households with two or more adults which, as noted above, tend to be the oldest and least financially welloff. Households with children were more likely to grow their own food, perhaps due to their need to feed a larger family and activate children not able to attend school.

*Data show the net percentage of households which increased/decreased their behaviour from before to during COVID-19. All data are statistically significant at the P < 0.05 level.*

#### **Table 8.**

*The socio-demographics of food behaviour changes: Food preparation and importance.*

*The Geo-Demographics of European Consumers' Food Behaviour in the New Age of Disruption DOI: http://dx.doi.org/10.5772/intechopen.106938*

*Data show the percentage of households experiencing food vulnerability during COVID-19. All data are statistically significant at the P < 0.05 level except when marked with X.*

#### **Table 9.**

*The socio-demographics of food vulnerability.*

In **Table 9**, note, that the rates of both free food use and growing own food, although greater during the first wave of COVID-19, when this survey was undertaken, by respectively 27% and 12% compared to before the pandemic, also grew substantially after the first wave. On average, European food banks redistributed 68% more food in the whole of 2020 than in 2019 [13].

Educational level also seems to be related to the use of free food with the lowest level having the highest usage, although they grew their own food less. There are clear differences between households that lost income during the pandemic and those that did not, with the former much more likely to use free food and grow own food, almost certainly due to their greater financial vulnerability.

In the context of food vulnerability, it is useful to look at households' COVID anxiety, i.e. whether any household member felt anxious about infection. **Table 9** shows that both households with children and those with two or more adults, typically those with respectively much younger and much older people, had the greatest anxiety. No clear pattern is visible in terms of education but one can be seen in households' income change with those that lost income having significantly more levels of COVID anxiety than those that did not.

#### *3.1.6 Food stress*

**Table 10** provides data on food stress and shows that, like food vulnerability and for probably similar reasons, both households with children and with two or more adults had the greatest anxiety regarding acquiring food during COVID-19. These were also the households that stocked-up most and had greater special dietary needs, both of which are more likely as there are more persons than in single households.

In terms of education, and although the data for anxiety about acquiring food is inconclusive, a similar pattern of more missed meals at lower levels with fewer at higher levels is very clear. The data also show that food stocking-up increases with higher educational levels, which perhaps reflects the ability to do so related to typically larger incomes and more opportunity. Special dietary needs also increase with

*Data show the percentage of households experiencing food stress during COVID-19. All data are statistically significant at the P < 0.05 level except when marked with X.*

#### **Table 10.**

*The socio-demographics of food stress.*

educational level which is more difficult to explain, but may be related to greater health and food awareness in households with more education and thus increased likelihood that diet is prioritized. Cutting across this latter point are the data for income change that consistently show the almost doubling of each stress variable amongst income-lost households. This is almost certainly related to these households' sudden reduction of financial stability regardless of their income, education and household type prior to the pandemic.

#### **3.2 The geography of food behaviour change**

#### *3.2.1 Food consumption changes*

**Table 11** shows how food consumption changed during COVID-19 across the regions defined in Section 2.3. All regions saw large decreases in fresh food, but with significantly lower decreases in capitals amongst the metros and in urban regions generally for fresh meat and fish, probably due to the relatively better logistics and supply infrastructures there. Processed and comfort food consumption also increased more in urban regions due to their better logistics and higher incomes, whilst processed foods actually slightly reduced in rural regions. Examining the metro hierarchy shows that capitals changed the types of food consumed significantly less than other metros, almost certainly due to their better food supply logistics. Capitals also have the highest mean incomes than other regions so their purchasing power is higher. However, this is not reflected in differences between urban and rural areas which are more mixed, with the latter reducing fresh everyday foods less than the former perhaps related to better in-situ supply, whilst reducing fresh meat and fish more maybe because supplies tend to rely on longer supply chains.

The geography of consumption change is also distinctive when looking at the national level using AIC data. The Low group of countries with the lowest consumption expenditure decreased all food types during COVID-19, with the marginal exception of only a very small increase in comfort food, probably indicating their

*The Geo-Demographics of European Consumers' Food Behaviour in the New Age of Disruption DOI: http://dx.doi.org/10.5772/intechopen.106938*

*Data show the net percentage of households which increased/decreased their consumption from before to during COVID-19. All data are statistically significant at the P < 0.05 level.*

#### **Table 11.**

*The geography of food consumption change.*

greater financial strain. In contrast the High group experienced lower fresh food decreases and much higher processed and comfort food increases, indicating their relative financial resilience. This supports the view that national AIC does seem to impact important aspects of an individual household's financial situation and that the High group is more financially resilient, less subject to stress and thereby also more able to withstand the food shock of COVID-19.

#### *3.2.2 How food is obtained*

Examining aspects of how households changed their acquisition of food during COVID-19 in **Table 12** also shows important differences between the regional types but, unlike with food consumption, reveals important and significant contrasts between the three metros that reflect the characteristics presented in Section 2.3. Capitals and smaller metros, as relatively wealthy regions, both increase their move to local producers whilst second-tier metros, typically as relatively more socioeconomically challenged, slightly decrease this behaviour. This is only one aspect that both reflects capitals' economic predominance as well as the increasing population and economic growth of the smaller metros, mainly at the expense of the second-tier metros and rural regions, which is also seen in many of the data presented below. (See also Section 4.2) A similar distinction amongst the metros is also seen in the increase in home delivery ordered online or by phone, whereas here this is likely to be related more to the state of the market in terms of the density of suitable shops. There are, however, no significant differences in travel distance to shops probably because this variable is likely to be directly dependent on the status of movement restrictions in different areas rather than geo-demographic factors.

Looking at the urban-rural continuum also shows a stronger move to local producers and to home delivery in urban regions, given their greater wealth and higher population densities providing better market opportunities for these outlets. Again, the reduced distance to shops is hardly significant due both to local movement regulations and the existing spacing of shops. In terms of national AIC differences, similar

*Data show the net percentage of households which increased/decreased their behaviour from before to during COVID-19. All data are statistically significant at the P < 0.05 level except when marked with X.*

#### **Table 12.**

*The geography of food behaviour change: How obtain food.*

conclusions can be drawn from the much greater move to local producers by the High group but much less use of home delivery. The latter is difficult to explain but is perhaps related to the much stricter lockdown and closure rules in the Low AIC group, that mainly consists of the more southern and eastern European countries, during the first wave thereby increasing the need for home delivery.

#### *3.2.3 Food handling and quality*

As shown in **Table 13** there was a strong move to unpackaged food in all regions, again in capitals and smaller metros much more than second-tier metros and in urban more than rural regions, probably linked to the patterns of increased interest in health and food shown in **Table 14**.

Comparing the two AIC groups, the countries in the Low AIC group made a much bigger switch to unpackaged food than the High group, perhaps as a form of catch-up but also, as with the income-loss households in **Table 7**, a great increase in health-food awareness. Regarding the overall decrease in the purchase of organic food, capitals and High AIC countries do best, probably mainly due to more acute supply and price issues elsewhere.

In terms of the dramatic decrease in food waste in all regions and countries, the likely reasons for which are explained in Section 3.1.3, smaller metros stand out. As described in Section 3.2.2, this is the regional type with, overall, relatively high wealth coupled with the most cohesive socio-demographic which is also growing relatively fast in both population and economic terms. These characteristics lend themselves to high awareness of both health and food, as well as the links between them, and this is reflected in many of the food behaviour changes during COVID-19. Where there are exceptions (such as organic food) there are likely to be very specific reasons. Capital city regions, of course, have many households that fit this description, but most capitals tend to house significant low-wage as well as high-wage sectors, so are much more diverse and often less cohesive, unlike the smaller metros.

*The Geo-Demographics of European Consumers' Food Behaviour in the New Age of Disruption DOI: http://dx.doi.org/10.5772/intechopen.106938*

*Data show the net percentage of households which increased/decreased their behaviour from before to during COVID-19. All data are statistically significant at the P < 0.05 level except when marked with X.*

#### **Table 13.**

*The geography of food behaviour change: Food handling and quality.*

Looking at food waste reduction in the two country AIC groups, the High group is a little ahead of the Low group, perhaps because the countries in this group overall had slightly greater awareness of the problem.

#### *3.2.4 Food preparation and importance*

There were also huge increases in households' engagement in, and the awareness and thus assessment of, the importance of food in all regions and countries during COVID-19, as shown in **Table 14**. Both capitals and the smaller metros again have significantly greater positive changes than the second-tier, with smaller metros markedly ahead of capitals in terms of new recipes and ingredients and overall food

*Data show the net percentage of households which increased/decreased their behaviour from before to during COVID-19. All data are statistically significant at the P < 0.05 level except when marked with X.*

#### **Table 14.**

*The geography of food behaviour change: Food preparation and importance.*

importance, almost certainly for similar reasons as in Section 3.2.3. Along the urbanrural continuum it difficult to see any real pattern in changes in meal planning and the types of food used, perhaps because these aspects are likely to be less dependent on market dynamics. However, clear differences between urban and rural regions emerge again in terms of new recipes and ingredients and overall food importance. Perhaps here market dynamics play a bigger role through higher densities of supply and demand leading to greater diversity of ingredients and recipes, in turn promoting increased interest in food.

Looking at the two AIC country groups, the Low group has consistently greater changes than the High group. This is almost certainly for reasons similar as for the two income change groups in Section 3.1.4, i.e. their greater sensitivity to food during a shock related to their smaller available consumption income. However, it is clear that both groups significantly increased across all variables and it is noteworthy that the difference in their assessment of food importance is not significant.

#### *3.2.5 Food vulnerability*

**Table 15** shows data that, in many ways, rests on similar explanations as in **Table 9**, but this time in terms of geography rather than only socio-demographics given that these two factors are clearly related.

In **Table 15**, the use of free food is lowest in the smaller metros as the most cohesive socio-demographic regional type. As seen in Section 2.3, both capitals and second-tier metros have many more low-wage jobs as well as pockets of poverty. In contrast the smaller metros have a higher incidence of growing own food, perhaps related to their greater physical space due to lower population densities amongst the metros and the likelihood of more modern town planning. Some similar reasons seem to apply along the urban-rural continuum. Rural areas often tend to have lower incomes and greater poverty, although this is only seen in terms of food banks rather than free meals, perhaps because the logistics for the latter are more demanding than for the former. In the case of growing own food, however, rural areas saw much

*Data show the percentage of households experiencing food vulnerability during COVID-19. All data are statistically significant at the P < 0.05 level except when marked with X.*

#### **Table 15.**

*The geography of food vulnerability.*

*The Geo-Demographics of European Consumers' Food Behaviour in the New Age of Disruption DOI: http://dx.doi.org/10.5772/intechopen.106938*

greater increases than the other regions, given the substantially greater opportunities in these areas.

Similar explanations are likely for the differences between the two AIC groups in relation to free food and growing own food. The Low group has much less consumption income, it has been impacted more by the COVID-19 shock, and there is often more physical space for food growing in these countries. It is of interest that there are only small significant geographic distinctions in COVID-anxiety, maybe because this tends to be engendered by the media more than actual infections which have been higher in regions of greater population density. However, capitals, urban regions and Low AIC countries score a little higher than the other regions. This is almost certainly due to the impact greater population densities have in the two former regional types as well as the overall higher likelihood of contracting COVID-19 in the Low AIC countries during the first wave [22].

#### *3.2.6 Food stress*

Again, in some reflection of Section 3.1.6 and well as food vulnerability above, the geographic expressions of food stress in **Table 16** show some similar patterns. Despite logistics ensuring more stable food supplies, anxiety acquiring food and the incidence of missed meals seems to be greatest in capitals and urban regions generally, although in the latter it is only marginally significant. It is also in these regions that other data from the survey show there are more income-loss households than average, thus adding a financial element to this anxiety. This is probably also related to the actual incidence of COVID-19 infection which is higher in more densely populated areas and reflects the COVID-19 risk anxiety in **Table 15**.

In terms of stocking-up food, a similar pattern is seen, although there is no apparent difference between capitals and smaller metros, probably because households in the latter have equally sufficient financial resources and food awareness as the former. The increase in special dietary needs up the metro hierarchy is, however, more difficult to explain though may again reflect the greater incidence of COVID-19

*Data show the percentage of households experiencing food stress during COVID-19. All data are statistically significant at the P < 0.05 level except when marked with X.*

#### **Table 16.**

*The geography of food stress.*

infections. Examining the data for the two AIC groups shows very significant differences across all variables and is again probably related to differences in their expenditure incomes and higher rates of COVID-19 infection during the first wave [22].

#### **4. Discussion**

Food is arguably our most vital strategic commodity. It is central to our existence, not just for sustenance and survival but as a huge contributor to our cultural, social and economic lives. The food industry also has huge environmental impacts. So, when food systems and patterns of food preparation, consumption and diet are disrupted by large scale social and economic shocks like the financial crisis, COVID-19 and the war in Ukraine, this is of utmost importance. This research shows that the majority of European households significantly changed important aspects of their food behaviour, many of which are likely to persist into the future. It is also clear that, although this chapter has taken food consumption as its example, the lessons are likely to apply to most, if not all, types of consumption in the age of disruption.

This chapter clearly shows that the food system is multidimensional, that both socio-demographic determinants and geography strongly affect both food behaviours and changes in food behaviours, and that there is a highly significant alignment and interaction between geography and society. It has confirmed that the most important predictors of food behaviour change during a significant disruption are household composition, education, income and geography, and that these are closely interdependent. In times of food system disruption during a crisis, both sociodemographics and geography are increasingly transforming the market system heretofore based on large centralized organizations, long supply chains and everincreasing globalization.

#### **4.1 Socio-demographics**

There are very many highly significant differences in changes to food consumption during COVID-19 between different types of household, especially in terms of household composition, educational level achieved and income change. Households with children tend to be the largest households with the greatest range of food tastes and needs, so are likely to be the most sensitive to food system shocks. They have typically changed their food behaviour much more than other households. Their size often means tighter budgets and this shows in their relatively large reduction of fresh food but also largest increase in comfort food consumption, no doubt because of their children's demand for sugary foods whilst stuck at home during school shutdowns. Household with two or more adults were also relatively responsive to the shock, although these households tend to consist of much older, often retired, people many of whom are living on small incomes and are very concerned about their health and, especially since COVID-19, about the impact of food on this. Both households with children and with two or more adults also generally suffer much more than single households from food vulnerability and stress and this was magnified if the household lost income during the pandemic.

Somewhat similar patterns are seen in relation to the educational level of the household, so that the higher levels are the most resilient and least likely to change their food behaviours during the pandemic. The less educated the household, the more

#### *The Geo-Demographics of European Consumers' Food Behaviour in the New Age of Disruption DOI: http://dx.doi.org/10.5772/intechopen.106938*

likely it is to decrease fresh food consumption and increase processed food consumption, although the university educated households did increase their consumption of comfort food more, probably because of their typically higher incomes and access to shops. The less educated households also switched less to local producers, used home delivery, unpackaged and organic food much less and engaged less often in enhanced food preparation behaviours, as well as decreasing their food waste the least. It seems likely that awareness of the links between food and health were less clear to these households, a supposition reinforced by their much lower increase in evaluating the overall importance of food. These households were also more likely to revert to foodbanks during the pandemic as well as obtain free food from other sources, a situation reinforced by the fact that many more of their households lost income during the pandemic.

Households' income change is a good surrogate for household income which, in turn, is significantly correlated with Actual Individual Consumption (AIC) data even though this is generalised to the national level. The results presented above clearly show that households losing income during Covid-19 were likely to be fragile even before the pandemic which then made their situation worse. They nearly always experienced food behaviour changes arising from Covid-19 much more than noincome-loss households. Interestingly, the proportion of such households is also significantly lower in the smaller metros than in other regional types.

Overall, there are often quite stark food behaviour inequalities between household types. These show up, for example, in a significant trend to less healthy eating during the pandemic away from fresh food to more processed and sugary foods and alcohol, especially amongst households with children and income-loss households whose vulnerability has been further exposed. These differences existed before COVID-19, but the system shock has further exacerbated them. On the positive side, however, the pandemic has also dramatically accelerated the previous slow trend towards more local and seasonal food delivered along short supply chains, a move to smaller independent retail outlets, and much greater food awareness and interest in trying new types of food and recipes.

#### **4.2 Geography and hybrid work**

There is a strong tendency for more or less regular changes in food behaviour outwards from a country's capital city center to its rural periphery, i.e. down the metropolitan hierarchy and along the urban-rural continuum. These changes are directly related to decreasing population density and of economic activities which symbiotically both attracts and is dependent upon each region's distinctive sociodemographic characteristics. However, there is also an important counter trend with some movement of population out of both the largest, typically capital, metropolitan areas and from the so-called second-tier, typically older industrial, metro cities towards smaller metros in or beyond the suburbs and in peri-urban and adjacent rural areas. These smaller metros are also growing in size, also fed by some rural depopulation as more desirable destinations than the larger metros with their higher rents and living expenses.

This is a spatial dynamic, long recognized by geographers (for example refs. [23, 24]), as the current counter-urbanization phase of urban development in more economically developed countries. It has been ongoing since the 1990s and has been given added impetus by the disruptions of the early twenty-first century. For example, in Denmark there is an ongoing movement of population out of the five largest

Danish Cities, including Copenhagen, to fast-growing smaller provincial cities in their hinterlands as part of a development that is likely to continue to 2040 and beyond, also propelled by movements from rural areas [25]. This trend towards the growth of smaller metros has been ongoing for about 30 years but has now been sharply intensified by the accelerated adoption of digital technology during the pandemic. This is dramatically increasing online shopping, working from home and hybrid work in general, as well as online gatherings rather than meeting in person.

It is clear that the COVID-19 pandemic has already led to a strong overall increase of the digitalisation of society, and there are predictions that people are unlikely to return to the old ways of doing things. With fewer people coming into very large cities to work and shop, that leaves a big space in areas that were once characterised by bustling shops and offices. Consumption is now shifting significantly to these smaller cities and suburban centres. Those places that are most at risk are those that have little else to attract locals and visitors from further afield. According to a UK report [26], there has been a loss of commuter flow into these cities of between over a tenth to almost a third of commuter footfall seen pre-COVID-19. Apart from the largest, mainly capital, cities like London [26], contends that it is unlikely there will be a return to old commuting habits in most very large cities. Those able to telework from home are now doing so for at least part of the week or shifting to hybrid-working that mixes working from home, working closer to home in more convenient local suburban centres with much less frequent commuting to the large city. This is already leading to further significant reductions in office space in large cities and some collapse in their central retail areas.

This pandemic-induced 'zoomshock transformation' has magnified the existing counter-urbanization trend that, during COVID-19, led to a major shutdown of city and suburban centres with plummeting city-centre commercial real-estate values. Now, largely beyond the pandemic, 'zoomshock'seems likely to see an acceleration of the spatial mixing of population, consumption and work especially away from the very large city centres that questions the benefits of agglomeration [27].

The main mechanism seems to be that, although tele- and hybrid-work only increased slowly from 5.4% of the EU-27's employed population in 2009 to about 9% in 2019, since the outbreak of COVID-19 this rose so that over 40% of those currently working in the EU began to telework fulltime [28]. Almost all experience since the 1990s has shown multiple benefits of these new ways of working that directly impact socio-economic and consumption geographies, such as better compatibility between work and family obligations, more flexibility, more recreation and leisure time, increased local community activity, saving costs on commuting, and increased performance and employee satisfaction. However, difficulties include pressure to work more intensely and longer hours when employees are always online, collaboration and communication with colleagues and co-workers can be difficult, employees find it hard to separate work and private life, poorly equipped workplaces may lead to health impairments and psychological problems and organisations become highly dependent on technical equipment which does not always function well [29]. These problems impact vulnerable populations in general, and this polarization remains the main challenge, as clearly seen in many of the food consumption results reported in this chapter.

Notwithstanding this issue, the newer, smaller but growing metros tend to be the most socio-economically vibrant and, from this evidence, the sociability of smaller cities does seem to provide some solutions. They are arguably at the 'sweet-spot' between being too large and too small whilst retaining significant political and

*The Geo-Demographics of European Consumers' Food Behaviour in the New Age of Disruption DOI: http://dx.doi.org/10.5772/intechopen.106938*

economic resources. Compared to all other regional types, their populations show the greatest egalitarian and cohesive profiles in terms of income, age, education and family size, as well as the lowest proportion of households that lost income during the pandemic. Across nearly all types of food behaviour, these smaller metros directly reflect this regional geography by displaying many of the advantages of capital metros while foregoing some of the disadvantages. Capital cities often contain significant enclaves of poverty as well as very wealthy households, whilst second-tier metros tend to be former industrial cities now economically retarded with the lowest metro incomes and relatively high levels of social exclusion, unemployment and poverty.

This overall dynamic is being driven by a better quality of life in smaller metros that are able to balance urban and rural advantages with high service levels, as well as continued good connectivity to the larger metros when desired. Their relative lack of physical connectivity compared to the larger metros has been decisively countered by the dramatically increased take up of digital technology during the pandemic, including the strong moves to online shopping and working from home. For example, smaller metros typically change their food behaviour significantly less than all other regional types during Covid-19, showing them to be the most resilient to the disruptions of the pandemic. They also exhibit much smaller food behaviour differences between households that lost income during the pandemic and those that did not. This means that their overall food vulnerability is much lower.

#### **5. Conclusion**

Overall, more vulnerable households (however measured) suffered more during the COVID-19 shock, but this also meant that both their behaviour and awareness around the health-food relationship also improved significantly, though clearly from a low base. Less vulnerable and stressed households already had relatively high levels of behaviour change and awareness around the health-food relationship and, although this increased during the pandemic, this increase tended to be less given it was starting from a higher level. So, in many ways, the COVID-19 food shock pushed more vulnerable households to catch-up to some extent with the already relatively high levels of food-health awareness in less vulnerable households, despite the serious difficulties they encountered. Indeed, data from ref. [12] also indicates that more vulnerable households say their changed food behaviour is more likely to continue beyond COVID-19 than do less-vulnerable households. This includes, for example, greater increases in shopping with local producers and in more local shops, growing own food, using a wider range of food dishes and recipes, greater use of unpackaged food and much less food waste. Thus, a useful policy guide during this age of disruption would be to put in place measures to support the positive food behavioural changes of all households with a specific focus on the more vulnerable households and with lower incomes.

How all these impacts will play out over the longer term is a critical issue and needs focused research and policy action, especially because the likelihood of other shocks in future with similar effects is high. These could include new pandemics, the ongoing and increasingly alarming climate crisis, new disruptive technologies, geo-political and economic-trade tensions, etc. The recent Russian invasion of Ukraine and the growing disruption of both energy and food systems is but the latest example.

### **Acknowledgements**

This chapter has been entirely prepared and written by the author, but he recognizes and is warmly appreciative of the tireless work, dedication and collaboration in preparing the data and some parallel studies of, especially, Alberto Sturla (Department of Policies and Bioeconomy, Council for Agricultural Research and Economics, Savona, Italy), Meike Janssen (Consumer and Behavioural Insights Group, Department of Management, Society and Communication, Copenhagen Business School, Denmark), Jan Vávra (Department of Local and Regional Studies, Institute of Sociology of the Czech Academy of Sciences, Prague, Czechia), Zdeňka Smutná (Department of Geography, Faculty of Science, Jan Evangelista Purkyně University in Ústí nad Labem, Ústí nad Labem, Czechia, and Faculty of Social and Economic Studies, Jan Evangelista Purkyně University in Ústí nad Labem, Ústí nad Labem, Czechia), Barbora Duží (Department of Environmental Geography, Institute of Geonics of the Czech Academy of Sciences, Brno, Czechia), Hristo Hristov (Nutrition Institute, Nutrition and Public Health research group, Ljubljana, Slovenia) and Igor Pravst (Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, SI-1000 Ljubljana, Slovenia, and VIST– Faculty of Applied Sciences, Gerbičeva cesta 51A, SI-1000 Ljubljana, Slovenia).

The author also wishes to acknowledge the work of the following organisations which participated in the design and promotion of the questionnaire and assisted in preparing their own country's data for the dataset (in alphabetical order):

*The Geo-Demographics of European Consumers' Food Behaviour in the New Age of Disruption DOI: http://dx.doi.org/10.5772/intechopen.106938*

Support was also provided by the European Food Information Council (EUFIC) and the European network for COmmunity-Led Initiatives climate change and Sustainability (ECOLISE).

See also https://www.food-covid-19.org.