*5.2. Proposed Time-Current Characteristic*

In ac systems selective coordination between upstream and downstream circuit breakers is often achieved in radial systems by using time-current characteristics for the protection devices. Time-current characteristics depict how long a protection device allows a current to flow before it interrupts it, and they are mainly determined by the thermal and magnetic characteristics of the circuit breakers. The upstream and downstream time-current characteristics are chosen in such a way that the downstream circuit breaker clears the fault first, and the upstream circuit breaker only clears the fault when the downstream circuit breaker fails.

It was shown in the previous section that the commutation of inductive currents can cause the undesired tripping of SSCBs in non-faulted sections. Therefore, coordination among downstream circuit breakers is also required, not just between upstream and downstream SSCBs. Furthermore, since this paper aims for a plug-and-play protection scheme, the coordination must also achieve selectivity in meshed low voltage dc grids.

To prevent unnecessary tripping due to commutated currents, the time-current current characteristic must take this current and its decay into account. In the worst-case, the commutated

current is the nominal current and this current decays with the time constant of the line. If the SSCB is carrying the nominal current before commutation, the worst-case current after commutation is characterized by the LR time constant and is given by

$$I\_{\text{total}} = I\_{\text{nom}} \left( \mathbf{1} + e^{-\frac{R\_L}{L\_L}t} \right). \tag{9}$$

Therefore, in order to prevent the SSCB from tripping unnecessarily from commutated inductive currents, the proposed characteristic only interrupts immediately if the current exceeds twice the nominal current. Furthermore, between *I*nom and 2*I*nom, the time-current characteristic is chosen as

$$t\_{\text{clear}} = t\_{\text{max}} - \frac{L\_L}{R\_L} \ln(I+1),\tag{10}$$

where *t*max is the maximum time the SSCB takes to detect and clear an overcurrent, and *I* is the current in the SSCB in multiple of the nominal current.

Note that it is only necessary to know the slowest expected time constant of the lines in the system. Therefore, knowledge about the length of the lines in the system or their interconnection is not required. Moreover, a safety margin can be implemented in order to anticipate uncertainty in the system parameters. The proposed time-current characteristic for SSCBs is shown in Figure 17.

**Figure 17.** Proposed time-current characteristic for the plug-and-play protection scheme, where *t*max is the maximum time that the SSCB needs to detect and interrupt an overcurrent.

Because the time-current characteristic scales with nominal current, the proposed time-current characteristic inherently coordinates upstream and downstream SSCBs. To illustrate this, imagine the system of Figure 12 with an upstream SSCB that has a nominal current of 32 A and two downstream SSCBs that have a nominal current of 16 A. Now if all the SSCBs operate at their nominal current and a fault occurs downstream, the downstream SSCB will trip immediately when the current reaches 32 A. In this case the upstream current is 48 A, for which the upstream breaker will wait a significant time.


2.2.2. Temperature Programmed Reduction (TPR)

TPR measurements were carried out with a lab-made instrument at CATMAT laboratory, Ca' Foscari University of Venice. The analysis was performed under 5% H2/Ar (40 mL/min) from 25 ◦C to 800 ◦C with a heating rate of 10 ◦C/min. The H2 consumption was analyzed by a Micrometrics TPD-TPR 2900 analyzer equipped with a TCD detector (Gow-Mac 24-550 TCD instrument CO, Bethlehem, PA, USA).


*3.4. Gallium Oxide Power Rectifiers*

Once the device-grade epitaxial layers have been grown either homo- (bulk Ga2O3) hetero- (e.g., sapphire, silicon), or both, the simplest electronic devices one can define are rectifiers. In a Schottky rectifier, the counter-electrode (cathode) is processed to allow low resistance Ohmic contact while the anode contact is intended as a Schottky junction over a lightly doped epitaxy; it conducts electrons in the forward mode while sustaining large electric fields (by the creation of a depletion space charge region) in the reverse mode. As mentioned previously, devices using Ga2O3 are primarily limited to unipolar devices and Schottky diodes are made, in general, on *n*-type semiconductor layers as electrons are lighter than holes. However, it is also important to consider the appropriate metal contacts to Ga2O3 as they are responsible for connecting the semiconductor to the surrounding electrical circuit/system and parameters such as the Schottky barrier height are crucial. For different contacts to Ga2O3, such as in GaN and AlGaN, which utilize stacks of different metals [132], this decision can make an important difference to the nature of the contact. Regarding Schottky contacts to Ga2O3,Ni/Au is a common choice (see Table 2). Other Schottky contacts investigated include Pt, Ni, Cu, W, Ir, TiN/Au, Pt/Ti/Au, Ni/Au, ndPt/Au [133–136]. Very recently, an ultra-large Schottky barrier of ~1.8 eV was extracted for all-oxide PdCoO2/*β*-Ga2O3 Schottky diodes [137]. The polar layered structure of PdCoO2 generates electric dipoles, realizing a large Schottky barrier height of ~1.8 eV (well beyond the 0.7 eV expected from the basal Schottky–Mott relation) along with a large on/off ratio approaching 108 , even at a high temperature of 350 ◦C (Figure 5c). As there are a number of polar oxides, this is a promising approach to increase the reverse blocking voltage of Ga2O3 diodes [138].

**Figure 5.** Schematics of (**a**) vertical Ga2O3 Schottky diodes and (**b**) *p*-*n* heterojunction diodes. (**c**) A PdCoC2/Ga2O3 exhibiting the ultra-large Schottky barrier of 1.8 eV. (**d**) Baliga's FOM for selected Schottky and p-n HJ diodes from the literature. Panel (**c**) adapted with permission from Harada et al. [137] © 2022 AAAS 4.0 (CC BY-NC). Adapted with permission from [12] © 2021 copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

In the counter-electrode, highly doped regions beneath the metallization are deployed to assist ohmicity of the contacts [139]. The dopants for this have previously been discussed. Another approach to this is using thin films of highly-conducting oxides [140].

Ohmic contacts to *β*-Ga2O3 are commonly based on Ti/Au, however other metal contacts have been utilized, such as In, Ti, Ti/Al/Au, In/Au, and Ti/Al/Ni/Au. Besides, there are other metals which have exhibited pseudo Ohmic behavior including Zr, Ag, and Sn [132]. This pseudo nature meant that, initially, ohmicity was observed but, after annealing, rectifying behavior became dominant. Therefore, the Schottky/Ohmic nature is also dependent upon the Ga2O3 ′ s surface/interface states together with the exact choice of metal stack, explaining, in turn, the varying contact resistivity of certain metals. While delivering low contact resistance, it is worth mentioning that Au is not considered a CMOS-compatible metal. This is an issue shared with GaN-based technology [148].

For the continued development of high voltage *β*-Ga2O3devices, edge termination is an important aspect as it is with its Si, GaN, and 4H-SiCcounterparts. Edge termination in *β*-Ga2O3is being explored and focused specifically on field plates (FP), imparted edge termination (ET), guard ring field plates, thermally oxidized termination, beveled mesas, and trench. These techniques are all deployed to further manage the electrical field to reduce the electric field crowding at the diode edges to increase its blocking capabilities. SBD devices can be made with either a vertical architecture, using homoepitaxial Ga2O3 or with a lateral architecture using either homo- or heteroepitaxial (e.g., on sapphire) Ga2O3. In general, the vertical structure is preferred as the device pitch is reduced and the encapsulation is simpler. Hu et al. [141] demonstrated a field-plated lateral β-Ga2O3 SBD on a sapphire substrate with a reverse blocking voltage of more than 3 kV, an *Ron*

of 24.3 mΩcm2 (anode–cathode spacing 24 µm), and an FOM >0.37 GWcm−2 (while an FOM of ~500 GWcm−2 was achieved as the anode-cathode spacing (and *Vbr*) was reduced). Zhou et al. [149] implemented a Mg implanted ET device on a vertical β-Ga2O3 SBD with a reverse blocking voltage of 1.55 kV and a low specific on-resistance of 5.1 mΩcm2 (epi thickness 10 µm) and an FOM of 0.47 GWcm−2 . Analogously, Lin et al. [150] implemented a guard ring with or without an FP on vertical SBDs. The terminated devices exhibited a specific on-resistance of 4.7 mΩcm2 and a Vbr of 1.43 kV. Wang et al. [151] implemented a thermally oxidized termination on a vertical SBD with a Vbr of 940 V, a specific on-resistance of 3.0 mΩcm2 , and an FOM of 0.295 GWcm−2 . Allen et al. [152] implemented a small-angle beveled field plate (SABFP), on thinned Ga2O3 substrates and a non-punch-through vertical SBD design rendering a Vbr of 1100 V, a peak electric field of 3.5 MVcm−1 , and an FOM of 0.6 GWcm−2 .

Somehow the state of the art is given by Li et al. [153]. They demonstrated an FP vertical Ga2O3 trench SBDs with a Vbr of 2.89 kV (which is ~500 V higher than those without FPs). The trench SBDs exhibited a differential specific on-resistance of 10.5 (8.8) mΩcm2 from DC (pulsed) measurements leading to an FOM of 0.80 (0.95) GWcm−2 . This Baliga's power FOM is approaching that for the best vertical SBD GaN devices (e.g., 1.7 GWcm−2 [154]) but is still several times smaller than lateral AlGaN/GaN SBD (e.g., 3.6 GWcm−2 [155]) and bipolar *p*-*n* vertical GaN diodes (e.g., ~4.6 GWcm−2 [156]). Both, the 2D gas formed at the AlGaN/GaN interface and the bipolar injection are effective ways of further reducing the on-resistance in these devices while keeping the breakdown voltage high. The lack of low resistivity *p*-type layer for the anode has to date, prevented a competitive homojunction p-n Ga2O3 diode, but *p*-*n* heterojunction diodes have been realized by integrating *n*-type Ga2O3 with *p*-type semiconductors, such as CuO (1.49 kV) [157] and NiO (1.06 kV/1.86kV) [158,159]. Nickel oxide as the *p*-type blocking layer in heterojunction power diodes resulted in a particularly promising approach with this NiO/Ga2O3device [160] yielding a Baliga's FOM of 0.33 GWcm−2 (Figure 5c,d).

Recently, extremely high-*k* dielectrics have been explored for electric field management in WBG semiconductor-based lateral and vertical device structures [160–164]. According to the TCAD simulations of Roy et al. [165], a super-dielectric Ga2O3 SBD with practically achievable device dimensions with extremely high FOM should be possible; e.g., 20kVcanbeachievedforan *Ron* of 10 mΩ-cm2 with a dielectric constant of 300, a Ga2O3 width/dielectric width ratio of 0.2, and an aspect ratio (drift layer length (anode to cathode spacing)/drift layer width ratio) of 10 resulting in a PFOM of 40 GWcm−2 (surpassing the theoretical unipolar FOM of *β*-Ga2O3SBD by four times).


*3.1. Intercomparison of InSAR and In Situ Displacement Time Series*

The InSAR LoS measurements are independently projected to the vertical as well as combined to calculate the actual vertical motion component, using the ascending track and descending track (VERT-ASC-DES) at the West Point (WP) (see Materials and Methods section). Qualitatively, there is a positive correlation between the vertical displacement VERT-ASC-DES at WP, as calculated by InSAR using the satellite observations and as measured by ground-based extensometers EXT1 and EXT2 (Figure 2). Quantitatively, the least squares correlation coefficient (cc) is much higher for EXT2 (cc = 0.69) than for EXT1 (cc = 0.47). The relatively low cc values are expected given the non-linearity phenomenon and the heterogeneities of the clay layer as described in Section 3.2.

During the three-year period, the ground displacement time-series may be decomposed with the addition of a linear trend component T, a seasonal component S and an irregular residual component. There is a linear trend with a shrinking of about −2.2 mm/yr at EXT1, while the measured motion at EXT2 is merely a cyclic component with a negligible shrinking of about −0.3 mm/yr (Figure 2). During the same period, the linear trend measured by Sentinel-1A/B lies within in situ rates at EXT1 and EXT2, with a global shrinking of about −0.55 mm/yr. Concerning the cyclic part, the expansion is up to three times higher at EXT2 than at EXT1, with average swelling of 9.4 ± 2.0 mm and 3.1 ± 1.0 mm, respectively (Figure 2). Over the same period, the swelling magnitude measured by VERT-ASC-DES is 4.5 ± 0.5 mm.

#### *3.2. Electrical Tomography Survey*

Electrical resistive tomography has been carried out in order to image the lithology stratification and the heterogeneity of the clay layer (i.e., depth, thickness, and fraction of clay) (see Materials and Methods section). Along three 28.5 m long profiles in the WP cell (Figure 1), the resistivity values range from 8 to 100 Ω.m and a three-layer stratified subsurface is highlighted (Figures 3 and 4).

**Figure 2.** Comparison of the in situ vertical displacement of both extensometers (EXT1 and EXT2) to VERT-ASC-DES, the vertical displacement using the ascending and the descending track of Sentinel-1 at West Point (WP). The linear trendlines of the displacements are also shown (dotted lines).

**Figure 3.** (**a**) Vertical cross section of resistivity along P1 profile (EXT1-EXT2). (**b**) Clay depth and thickness at EXT1 and EXT2 deduced from the resistivity profile (see text). E1 sensor (near EXT1, Figure 1) and W5 (near EXT2) are both located at the same 1.2 m depth inside the clay layer.

**Figure 4.** (**a**) Vertical cross section of resistivity along P2 profile (SC1-SC2) showing the SC1 and SC2 geological logs. (**b**) Vertical cross section of resistivity along P3 profile (South-North) showing TR1 drilling.

We used two drill cores along the SC1-SC2 profile to define the clay subsurface material by the resistivity value [30] (Figure 4). A first layer above the depth of 0.59 m ± 0.22 m corresponds to topsoil and backfill with some clay lenses (Figures 3 and 4). A second layer with a mean thickness of 1.5 m ± 0.42 m corresponds to the clay material with a resistivity lower than 17 Ω.m ± 1 Ω.m. Below 2.09 m ± 0.42 m, a third layer corresponds to sandy limestones with a resistivity gradient due to the weathering process.

Along the EXT1-EXT2 resistivity profile (Figure 3), the mean depth and thickness of the clay material layer is 0.46 m ± 0.05 m (min-max, 0.34–0.55) and 1.97 m ± 0.18 m (minmax, 1.70–2.39), respectively. The mean depth of the carbonate layer is 2.43 m ± 0.16 m (min-max, 2.15–2.73). The depth (approximately at 0.5 m) and thickness (about 2.3 m) of the clay layer at EXT1 and EXT2 are very similar (Figure 3). However, the resistivity of the clay layer at EXT2 is much lower compared to EXT1, indicating that the clay fraction is much higher beneath EXT2 [20]. That is also consistent with the expansion difference between the extensometers showing a much higher swelling magnitude at EXT2.

Along the three profiles, the mean depth and thickness of the clay material layer in the West Point (WP) cell is 0.59 m ± 0.22 m and 1.5 m ± 0.42 m.

#### *3.3. Intercomparison of In Situ Soil Moistures and SMOS Satellite Surface Soil Moistures*

We investigated here the soil moisture variations acquired by E1 sensor (near EXT1) and W5 (near EXT2) that are both located at the same 1.2 m depth inside the clay layer (Figure 3). The temporal variations of E1 and W5 are strongly correlated (cc = 0.73) during the three-year period (Figure 5). For the seasonal one-year period, there is a slight leading of E1 moisture relative to W5 (about 0.75 months) as calculated by the Cross Wavelet Transform (XWT) (see Materials and Methods Section 2.5). Indeed, the infiltration time of the meteoric water inside the clay layer at 1.2 m depth is lower in the E1 case, since the clay fraction of low permeability is shallower at EXT1 compared to EXT2 (Figure 3).

**Figure 5.** Comparison of three soil moistures at about 1.2 m depth (E1, E5, W5) to the Surface Soil Moisture SSM-ASC and SSM-DES using the SMOS ascending and descending track, respectively.

We use here the term Surface Soil Moisture (SSM) to refer to the volumetric soil moisture in the first few centimeters (0–5 cm) of the soil. We investigated also the SSM acquired by the ascending (SSM-ASC) and descending (SSM-DES) orbits of the SMOS satellite, showing rather positive correlation (cc = 0.48). For the one-year period, SSM-ASC and SSM-DES are both in-phase and there is a slight leading of SSM-ASC relative to SSM-DES for the 5-month period (about 0.9 months). Hence, the SSM-ASC dataset was considered more appropriate for calculating the phase differences to other time series in the discussion. For the one-year period, the phase lead of the surface soil moisture SSM-ASC relative to the in-ground soil moistures E1 and W5 calculated by XWT is about 0.16 yr and 0.22 yr, respectively.

#### **4. Discussion**

#### *4.1. Intercomparison of InSAR and In Situ Displacement Time Series*

Displacements measured by InSAR are in the direction of the Line-of-Sight (LoS) of the satellite, while displacements measured by extensometers depict motion in the vertical direction. However, the combination of ascending and descending InSAR measurements for the calculation of the actual vertical motion component (VERT-ASC-DESC) allows the direct inter-comparison of satellite and in situ observations.

When exposed to moisture, the magnitude of the vertical expansion of soils will depend on the amount of expansive clay minerals in the subsurface. In addition to the vertical displacement, the subsurface heterogeneities (i.e., clay depth and thickness and percentage of sand) may introduce spatial variability to the observed ground displacements. The above-mentioned factors need to be considered when examining the consistency of our measurements. In our case, the vertical displacements VERT-ASC-DESC from remote sensing are within the motion values of both extensometers, not only for linear displacement rates but also in terms of observed seasonality (see Section 3.1). This result is in agreement with the assumption that the effect of the spatial resolution of the remote sensing measurements (90 m by 90 m cell) represents an average of the vertical displacements within that resolution cell.

#### *4.2. Shrinking and Swelling Periods Using Fourier Power Spectra and Continuous Wavelet Transform*

We use first the Fourier spectra of vertical displacements to calculate the main frequency components of time series of both extensometers as well as at WP and East Point (EP) cells of the P-SBAS grid. The one-year seasonal period is found in the power spectrum of the four displacement time series at EXT1, EXT2, WP, and EP (Figure 6). In the frequency band between 2 and 4 yr−1 (corresponding to a period between 0.5 and 0.25 yr), there are

three other frequencies which are noted F1, F21, and F22 (order of increasing frequency) and P1, P21, and P22 periods (order of decreasing period). It should be noted that the frequency values for all displacement time series are close to the first three sub-annual components of the Fourier transform of the soil moistures E1 and W5 at 1.2 m depth (Figure 6a) and the two ascending and descending time series of the surface soil moisture (SSM-ASC and SSM-DES) as measured by SMOS (Figure 6b).

**Figure 6.** In the top, the magnitude of the Fast Fourier Transform (FFT) of different time series between frequencies 0.5 and 5 year−1 are shown: (**a**) vertical displacement of both extensometers (EXT1 and EXT2) and the Soil Moisture at E1 and W5 (about 1.2 m depth), (**b**) VERT-ASC-DES for vertical displacement using ascending and descending S-1A/1B tracks at West Point (WP) and East Point (EP), and SSM-ASC and SSM-DES for the Surface Soil Moisture using the ascending and descending SMOS track, respectively. In the bottom, the phase angle difference in degrees between the FFT of the SSM-ASC using SMOS ascending track and the FFT of the displacement of four time series are shown: (**c**) EXT1 and EXT2 and (**d**) VERT-ASC-DES at WP and EP.

The Continuous Wavelet Transform (CWT) tool permits the recognition of power in time-frequency space, along with assessing confidence levels against red noise backgrounds (see Materials and Methods section). We use now the continuous wavelet power spectrum in order to unravel the intermittent physical mechanisms of the shrink/swell process at EXT2 and WP cell during 3.3 years (September 2016–December 2020):

**Figure 7.** The continuous wavelet transform (CWT) during 3.3 years (September 2016–December 2020) are shown for two vertical displacements: (**a**) EXT2 and (**b**) VERT-ASC-DES at WP. The thick contour designates the 5% significant level against red noise (see Materials and Methods section). The Cone of Influence (COI) where edge effects might distort the picture is shown as a lighter shadow.

In conclusion, the CWT analysis highlights two sub-annual periods that reflect both average ground shrinking (P1) and swelling timeframes (P21 and P22). A similar behavior is observed at EXT1 extensometer and within EP cell (data not shown).

### *4.3. Estimating Variations of Expansive Clay Depth and Thickness Using the Time Series Phase Difference*

The phase difference between the Surface Soil Moisture (SSM) and InSAR displacement time series indicates the time lag between the cause and the effect in the shrink/swell process. We test the assumption that depth and thickness of expansive clays are linked to the phase difference between both time series for the shrinkage and swelling periods, respectively. We use and compare the Fourier and XWT analyses to calculate this phase difference (see Materials and Methods section). The XWT spectra will be high in the time-frequency areas where both CWTs display high values, so this helps identify common time patterns in the two data sets. The XWT permits also the recognition of relative phase lags in time-frequency space (noted ΔΦ). While P1 and P21 are the shrinking and swelling periods found by the Fourier method (as described above), P1\* and P21\* are the same similar periods we found using the thick contours of XWT that designate the 5% significant level against red noise (Figure 8). XWT tool is used as well to calculate the circular standard deviation of this phase difference ΔΦ (see Materials and Methods section) and all these values are reported in Table 2.

No significant difference is found between ΔΦ values of both extensometers EXT1 and EXT2 for the shrinking period P1\* and the swelling period P21\* (Table 2). This result is consistent with the tomography results which show that both depth and thickness of the clay is indeed comparable at the location of the extensometers (Figure 3b). For the shrinking period P1\*, the phase difference in degrees is three times higher at EXT2 (ΔΦ = 107◦) than at WP (ΔΦ = 34◦) (Table 2). The same phase difference for the shrinking period is observed between EXT1 and EP (Table 2 and Figure 9). This result is further consistent with the tomographic findings indicating clays lenses in the first 0.5 m subsurface layer of the three 28m-wide profiles in the WP cell (Figures 3 and 4). Conversely, ΔΦ of the swelling periods is higher at EP cell (ΔΦ = 110◦) in comparison to the WP cell (ΔΦ = 72◦) (Table 2 and Figure 9). A higher clay thickness in the EP cell in comparison to the WP cell may be an explanation of this result (there is a need of electrical tomography data to check this assumption).

**Figure 8.** In the top, the cross wavelet transform (XWT) between SSM-ASC and two times series is shown: (**a**) EXT2 and (**b**) VERT-ASC-DES at WP. The relative phase relationship is shown as arrows, with in-phase pointing right and anti-phase pointing left and the Surface Soil Moisture leading by 90◦ pointing straight down. The Cone of Influence (COI) where edge effects might distort the picture is shown as a lighter shadow. In the bottom, the phase lags ΔΦ of SSM-ASC relative to the same times series are shown: (**c**) EXT2 and (**d**) VERT-ASC-DES at WP. The shrinking (P1\*) and swelling (P21\* and P22\*) periods are found using the thick contours of XWT that designate the 5% significant level against red noise (see Materials and Methods section).

**Table 2.** Comparison of Fourier FFT and cross-wavelet XWT analysis of phase angles and time lags of the surface soil moisture (SSM-ASC) relative to the vertical displacement at EXT1, EXT2, West Point (WP), and East Point (EP) for P0, P1, P21, and P22 periods.

**Figure 9.** In the top, the cross wavelet transform (XWT) between SSM-ASC using SMOS ascending track and two times series is shown: (**a**) EXT1 and (**b**) VERT-ASC-DES at East Point (EP). The relative phase relationship is shown as arrows, with in-phase pointing right and anti-phase pointing left and the Surface Soil Moisture leading by 90◦ pointing straight down. The Cone of Influence (COI) where edge effects might distort the picture is shown as a lighter shadow. In the bottom, the phase lags ΔΦ of SSM-ASC relative to the same times series are shown: (**c**) EXT1 and (**d**) VERT-ASC-DES at EP. The shrinking (P1\*) and swelling (P21\* and P22\*) periods are found using the thick contours of XWT that designate the 5% significant level against red noise (see Materials and Methods section).


Meet the editor

Luis Loures is a landscape architect and agronomic engineer who holds a PhD in Planning and a Post-Doc in Agronomy. Since he graduated, he has published several peer reviewed papers at national and international levels and has been a guest researcher and lecturer both at Michigan State University (USA) and at the University of Toronto (Canada), where he developed part of his PhD research. During his academic career he taught

several courses in different universities and countries mainly regarding the fields of landscape architecture, urban and environmental planning, and sustainability. Currently, he is a researcher both at VALORIZA and CinTurs, where he is involved in several financed research projects focusing on different investigation domains such as urban planning, landscape reclamation, urban redevelopment, and the use of urban planning as a tool for achieving sustainable development.

Contents

**Section 1**

Development *by Luis Loures*

**Section 2**

in Turkey

Example

*by Neşe Yılmaz Bakır*

*by Hu Xiaobing and Zhang Yalin*

Areas in the Niger Delta, Nigeria

*by Aroloye O. Numbere*

*and Donald G. Hodges*

*and Fadzilah Majid Cooke*

**Preface III**

Introduction **1**

**Chapter 1 3** Introductory Chapter: Landscape Reclamation as a Key Factor for Sustainable

Landscape Reclamation, Processes, Techniques and Approaches **13**

**Chapter 2 15**

**Chapter 3 37** Making Art Involve in the Paradigm of Ecological Landscape Construction in Ancient Villages—Taking Cai Fan and Longtan Ancient Village in Taihu as an

**Chapter 4 53**

**Chapter 5 71**

**Chapter 6 89**

Project-Based Urban Renewal and Transformation of Urban Landscape

The Impact of Landscape Reclamation on Mangrove Forest and Coastal

Public Perceptions of Values Associated with Wildfire Protection at the

Coastal Landscapes of Peninsular Malaysia: The Changes and Implications

*by Jamilah Mohd Salim, Maizatul Azwa Radzi, Sheriza Mohd Razali*

Wildland-Urban Interface: A Synthesis of National Findings *by Jason Gordon, Adam S. Willcox, A.E. Luloff, James C. Finley*

for Their Resilience and Ecosystem Services


Contents

### **Preface** XI

X Contents

#### Chapter 10 **Health Risk Assessment of Plasticizer in Wastewater Effluents and Receiving Freshwater Systems 191**  Olalekan Fatoki, Olanrewaju Olujimi, James Odendaal and Bettina Genthe


4.5.1. Contraceptive Activity Applications

Different studies [49,50] with extracts from red algae were carried out. These extracts were administrated to female rats in the first seven days of their pregnancies. As result, it was demonstrated that these extracts have post-coital contraceptive protection in the rats without showing any relevant adverse effect.


*4.2. Influence of BMI on Puberty and Growth*

We have confirmed previous observations pointing to accelerated skeletal maturation in children with obesity, resulting in a standardized height over their target height and directly correlated with the severity of obesity [12,13]. Interestingly, the follow up of a significant number of our patients over an extended period of time, some of them encompassing their entire pubertal development and growth spurt, confirms the lack of impairment of obesity-oriented intervention on growth and the attainment of adult height, which was equal to or slightly above that expected according to parental heights [13,14]. Additionally, we observed that the mean pubertal growth spurt in obesity is lower and inversely correlated with the severity of obesity. At the same time the presence of obesity seems to abolish the differences between sexes in the degree of the growth spurt observed in physiological conditions. This could be related to the acceleration in skeletal maturation and the relative childhood overgrowth observed in these patients [12,13]. In contrast, the mean pubertal duration (3.23 years) and time from Tanner stage II to menarche in girls (1.65 years) were similar to that of the general population, despite the inverse correlation observed between obesity severity and duration of puberty. It is clear that several factors such as sex, severity of obesity or the degree of advancement in skeletal maturation determine

the features of growth during childhood, and particularly during puberty. This should be taken into account when evaluating height, bone age and growth pace in children and adolescents with obesity as previously reported [12,23].


**3. Data and Methods**

This study employs data from a Kenyan nationally representative digital credit survey conducted in 2017. The sample constitutes 3130 participants among Kenyan mobile phone users. The data are the property of the Central Bank of Kenya, Kenya National Bureau of Statistics, and Financial Sector Deepening Kenya, and the authors obtained permission to use the data for this study. Since the survey was conducted telephonically, the sample was drawn from mobile phone users in Kenya and was weighted to be representative of mobile phone owners in the country. Given that one can only use digital credit when one has access to a mobile phone, the subsample that reported to have used digital credit can be considered as representative of digital credit borrowers in the country. Out of the total sample (3130), about a third (1040) reported that they are digital credit users, and about 29% (304) of these digital credit users were identified as bettors. The key questions in the instrument that enabled us to assess the likelihood of being financially distressed included whether the participant was ever late in repaying a loan they took from the mobile phone, whether they received an SMS from the lender as a reminder for repayment on an overdue balance, and whether they were ever in a situation when payments were due on multiple loans at the same time and could not make all payments. For the welfare-undermining coping strategies, information on whether participants had to sell assets to pay loans or borrow to repay loans was gathered. Welfare outcomes included going without food or without medicine or medication that was needed.

Descriptive statistics are used in understanding the sample studied and the occurrence of betting and loan repayment behavior. The Chi-square test for association is used to ascertain if there is a relationship between the outcome variables and explanatory variables without controlling for other factors (Chamboko et al. 2017). Univariate logistic regression is used to determine if there is a relationship between the outcome variables and the individual explanatory variables. Further, multivariate logistic regression is used to check for an association between betting and the outcomes variables (proxy measures of financial distress, undesirable coping strategies, and welfare outcomes), controlling for education, age, gender, locality, and income. For the multivariate analysis component, the following specifications are implemented using the binary logistic regressions:

	- *SoldAssetsi* = *β*0 + *β*1*Bettori* + *β*2*Agegroupi* + *β*3*Genderi* + *β*4*Educationi* +*β*5*Localityi* + *β*6 *Incomegroupi* + *εi*

$$\begin{array}{c} \textit{WithoutMeds}\_{i} = \beta\_{0} + \beta\_{1} \textit{Return}\_{i} + \beta\_{2} \textit{Agggroup}\_{i} + \beta\_{3} \textit{Gender}\_{i} + \beta\_{4} \textit{Enduction}\_{i} \\ \quad + \beta\_{5} \textit{Locality}\_{i} + \beta\_{6} \textit{Incomegroup}\_{i} + \ \varepsilon\_{i} \end{array}$$

where the main explanatory variable *bettor* is a binary (Yes/No) derived from a survey question "Have you tried any of the digital betting services?". A more detailed description of the other covariates in the models is provided in Table 1.

**Table 1.** Bivariate relationship between betting and socio-demographic variables.

The outcomes variables are derived from the survey questions and are defined as follows:

*LatePayment*: Have you ever been late in repaying a loan that you took from your phone? *ReceivedSMS*: Received SMS from the lender to encourage repayment on your overdue balance? *MultipleLoans*: Have you ever been in a situation when payments were due on multiple loans at the same time and you could not make all payments?

*SoldAssest*: Sold assets or belongings to pay loan?

*BorrowToPay*: Borrowed to pay loan?

*WithoutFood*: In the last 12 months, how often have (you) or your family gone without enough food to eat?

*WithoutMeds*: In the last 12 months, how often have (you) or your family gone without medicine or medical treatment that was needed?

The welfare variables *WithoutFood* and *WithoutMeds* required respondents to indicate the frequency at which they experienced the situations. Four options were provided, and from these, a binary variable indicating whether or not the person experienced the situation was constructed. All responses given as "often; sometimes or rarely" equal 1, and "never" equal 0.

#### **4. Results and Discussion**

As shown in Table 1, fifty-five percent of the participants were males, and 51% lived in rural areas. About 28% had primary or no formal education, 46% had secondary education, and 26% had tertiary education. Middle-aged people (26–45 years) dominated the sample (69%), while the remaining 31% was shared almost equally between those under 26 years and those above 45 years of age. In terms of income, more than half (59%) of the sample earned 10,000 shillings or less (1 US \$ ≈ 108 Kenyan shillings), 22% earned 10,001–20,000 shillings, 13% earned 20,001–40,000 shillings, and 7% earns 40,000 shillings or more. Table 1 also shows significant variation of betting by participants' gender, education, age, and income group (Chi-square tests). About 39% of males and 20% of females were bettors. Almost half of those with tertiary education, about a fifth of those with

secondary school, and almost a third of those with primary or no formal education were bettors, a pattern that suggests that betting is common among educated adults. Except for those in the age range of 46–55 years, for all other age categories, more than a fifth of the participants were bettors, and more than a quarter of each income group reported betting. Schmidt (2019, 2020) emphasized that gambling in Kenya is viewed as a legitimate and transparent way of earning a living and is motivated by limited employment and income-earning opportunities, but it is also viewed as a future-income-earning opportunity, especially among the affluent. These are all possible explanations why betting is high among the educated, as well as young and middle-aged adults, and cuts across all income groups.

The bivariate relationships between betting and financial distress, as well as undesirable coping strategies and selected welfare outcomes, are presented in Table 2. The prevalence of having multiple payments due at the same time and not being able to make all payments was 20%, that of receiving an SMS as a reminder for delayed due payment is 53%, and that of being late in repaying digital loans was 48%. In terms of undesirable coping mechanisms, the prevalence of borrowing to pay an existing loan was 16%, while that of selling assets/belongings to be able to repay the loan was 5%. With respect to welfare outcomes, the prevalence of having gone without food at some stage is 29%, while that of going without required medicine is 22%. Chi-square tests were employed to check on the associations of these covariates and outcomes. Bettors (57%) were significantly more likely to receive an SMS for their digital credit repayment from a lender to encourage repayment on overdue balance than non-bettors (51%) (*p* = 0.065). Bettors (54%) were also more likely than non-bettors (45%) to be late in repaying a loan taken through their phones, and this relationship is statistically significant (*p* = 0.007). The results also reveal that bettors (25%) were significantly more likely than non-bettors (17%) to have payments that were due on multiple loans at the same time and to be unable to make all payments (*p* = 0.004).

Regarding betting and coping mechanisms, the results show that bettors (8%) were more likely to sell assets or belongings to pay loans compared to non-bettors (4%), and this relationship is statistically significant (*p* = 0.015). However, bettors (18%) and nonbettors (15%) did not show any differences in terms of borrowing to repay loans (*p* = 0.211). The bivariate analysis between betting and selected welfare outcomes did not show any significance, although the percentages for going without food (30.9% vs. 29.6%) and without needed medicine or medication (23% vs. 21%) were higher for bettors compared to non-bettors.

Table 3 presents the univariate and multivariate association between betting and *digital credit repayment* outcomes. The results of interest are those from the multivariate regressions. After controlling for income, age, gender, location (rural/urban), and level of education, the results show that bettors were almost twice more likely than non-bettors to have payments due on multiple loans at the same time and could not make all payments, and this relationship was significant (odds ratio (OR) = 1.84, *p* = 0.002). Similarly, bettors were almost one and half times more likely than non-betters to receive an SMS from a lender encouraging repayment on the overdue balance, and this association is statistically significant (OR = 1.4, *p* = 0.043). Bettors were also one and a third significantly more likely than non-bettors to be late in repaying a digital loan (OR = 1.33, *p* = 0.072).

As is reported in Table 4, after controlling for income, age, gender, locality (rural/urban), and level of education, being a bettor is significantly associated with selling assets or belongings in order to pay loans. In fact, bettors are more than twice as likely as non-bettors to do so (OR = 2.39, *p* = 0.012).

When "being a bettor" is the explanatory variable for a binary logistic regression model and "going without food" is an outcome, and income, age, gender, locality (rural/urban), and level of education (Table 5) are controlled for, bettors are one and half times more likely than non-bettors to have gone without food at some point in the past 12 months, and this association is significant (OR = 1.56, *p* = 0.017). However, going without medication has no significant association with being a bettor.

*Int. J. Financial Stud.* **2021**, *9*, 10

**Table 3.** Association between betting and digital credit repayment.

**Table 4.** Association between betting and coping mechanism.

Notes: Inc = income group; level of significance: *p* < 0.01 \*\*\*, *p* < 0.05 \*\*, *p* < 0.10 \*.

Notes: Inc = income group; level of significance: *p* < 0.01 \*\*\*, *p* < 0.05 \*\*, *p* < 0.10 \*.

The risks associated with gambling in general are well-known, and Effertz et al. (2018) posit that the discussion about the gambling risks is as old as gambling itself. Research on online gambling is relatively recent, however, and Papineau et al. (2018) put the time frame of research focusing on online gambling and public health concerns as having started about twenty years ago, with the advancement in technology facilitating online gambling. In the current study, digital financial services, facilitated by accessibility of advanced technology-savvy gadgets such as smartphones and tablets, allow for ease of access to gaming and online applications for gambling. Effertz et al. (2018) argue that gaming and online applications for gambling are faster, more attractive, and less costly, yet they are more addictive when compared to traditional gambling opportunities. Zhang et al. (2018) find that mobile phones, especially smartphones, are the most commonly used platforms for online gambling among Asian individuals. Black et al. (2017) and Papineau et al. (2018) report gambling problems to have more adverse effects among online gamblers compared to offline gamblers. The current study reports such negative effects of digital betting on credit repayment, coping mechanisms, and welfare outcomes in a digitized developing economy.

The self-reported financial distress measures show that bettors have a higher likelihood of becoming financially distressed when compared to non-bettors. Mihaylova et al. (2013) and Håkansson and Widinghoff (2020) also report that online gambling is associated with problem gambling, overspending, and over-indebtedness. Online gambling as a behavioral addiction (Mallorquí-Bagué et al. 2017) is in our study found to be associated with negative outcomes. Participants in our study embraced financial innovations that are accessible via mobile phones and tablets, thus making finances accessible through digital means and at the same time have the opportunity to gamble. This puts them in a position to easily engage in online or digital gambling.

The current study also indicates that betting is associated with undesirable coping mechanisms as shown by the tendency to use assets or other belongings to repay loans among bettors. These undesirable coping mechanisms are exemplary of what Black et al. (2017) and Papineau et al. (2018) consider as extra burden impacts of online gambling on the lives of gamblers. Together, the association between betting and being financially distressed and engaging in undesirable coping mechanisms, as well as the risk of going without food among bettors, suggest an impaired quality of life among bettors. A study by Papineau et al. (2018) shows that online gambling impacts gamblers' work, relationships, mental and physical health, finances, and quality of life. Although financial innovations such as digital financial services improve peoples' lives, for the betting segment of the population, the negative effects of betting pose a considerable threat given that it enables problem gambling, Black et al. (2013) and other earlier researchers argue to be a public health problem that is costly to the society.

#### **5. Conclusions**

Digital financial services and, more importantly, mobile money, have become an important financial innovation to advance financial inclusion in developing and emerging economies. A growing body of literature also reports a positive and significant impact of digital financial services on household welfare outcomes. Nevertheless, to the growing betting segment of the Kenyan population, digital financial services have brought great convenience to betting by allowing easy access to digital credit that can be used for betting. Using survey data from Kenya, this study shows that digital betting is associated with undesirable outcomes on credit repayment, coping mechanisms, and the welfare of bettors. When controlling for socio-economic and demographic factors, bettors were shown to be more likely than non-bettors to be financially distressed, engage in welfare undermining coping strategies, and have inferior welfare outcomes. These findings suggest the need for educating the public about the possible effects of betting and gambling in general.

This study has some limitations. First, it only shows associations between betting and identified outcomes, and it does not infer causal relationships. As such, studies that can isolate the effects of betting on these outcomes using careful identification strategies are needed. The second limitation in the data is that there is no specific survey question that captures the amount of the wagers. For example, a 100 shillings wager every day, though higher in frequency, is less significant than a 2000 shillings wager three times a week. In addition, no information was gathered with respect to an increase in the amount of a wager over time. These data limitations can be addressed by developing a specific questionnaire that gathers detailed information with regard to gambling and welfare outcomes in Kenya.

**Author Contributions:** R.C. contributed to this article in terms of conceptualization, formal analysis, funding acquisation, methodology, visualization and reviewing and editing of the manuscript. S.G.'s contribution is on conceptualization, funding acquisition, methodology, writing up original draft and reviewing and editing the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This project benefits from the research funds from the Institute for Intelligent Systems at the University of Johannesburg and the University of the Free State. The authors are very grateful.

**Acknowledgments:** The authors acknowledge the insightful comments from the unknown reviewers as well as the Central Bank of Kenya, Kenya National Bureau of Statistics & Financial Sector Deepening Kenya for allowing access to the data.

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

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