Synthetic Index Research Articles

Can Machine Learning Explain Alpha Generated by ESG Factors?

Can Machine Learning Explain Alpha Generated by ESG Factors?

Measuring the resilience of rural tourism in Indonesia using the Adjusted Mazziotta-Pareto index

Rural tourism plays a crucial role in rural development in Indonesia by providing employment opportunities, livelihood, infrastructure, cultural preservation, and environmental preservation. However, it is prone to external shocks such as natural disasters, public health events, and volatility in the national and global economy. This study measures the resilience of rural tourism to external shocks caused by the COVID-19 pandemic in 24 rural tourism destinations in Indonesia covering four years from 2019 to 2022. A synthetic composite index of the Adjusted Mazziotta-Pareto index (AMPI) is used to measure rural tourism resilience followed by clustering analysis to determine the typology of the resilience. The AMPI measure is also compared with the conventional Mazziotta-Pareto index (MPI) method. The resilience index is composed of capacity and performance components related to resilience. The results show that in the first year of COVID-19, most tourism villages in Indonesia were severely affected by the pandemic, yet they were able to recover afterward, as indicated by positive differences in the AMPI index before and after COVID-19. Thus, rural tourism villages in Indonesia have a strong capacity and performance to recover from pandemic shock. Lessons learned from this analysis can be applied to policies related to rural tourism resilience in developing countries.

Human and ecotoxicological risk assessment of heavy metals in polymer post treatment sludge from Barekese Drinking Water Treatment Plant, Kumasi

The disposal of polymer post-treatment sludge (PTS) from Barekese Water Treatment Plants (WTPs) as organic fertilizer and aquatic feed is a common practice in Ghana, necessitating a thorough evaluation of its ecological and human health risks. This study aims to assess the suitability of PTS samples for soil amendment and fish feed, scrutinizing potential hazards to consumer health and soil. PTS samples were collected from five distinct lateral sections of three clariflocculator tanks. Potentially toxic metals such as Cd, Zn, Pb, Cu, Ni, and Cr were determined using a flame atomic absorption spectrophotometer. The mean concentration of 7.82 ± 2.43, 0.31 ± 0.021, and 0.78 ± 0.042 mg/kg for Mn, Zn, and Pb respectively. The concentrations of Ni, Cr, and Cd were below their detection limits (BDL) in all PTS samples. Upon detailed exposure assessment, ingestion emerged as the primary exposure route for both adults and children, with non-cancer risks (NCR) determined to be below 1 for both age groups. Additionally, an exploration of potential cancer risks (CR) associated with heavy metal exposure in the PTS samples revealed values below the tolerable intake levels ranging from 10−4 to 10−6 for both adults and children (10−8 and 10−9, respectively). This study also employs various ecological indices, such as Nemerow's synthetic pollution index (PN), single factor pollution index (PI), geo-accumulation index (Igeo), contamination factor (CF), potential ecological risk index (PERI), pollution load index (PLI), polymetallic contaminant index (IPD), and ecological risk index (ERI). These indices consistently highlight a low contamination status and ecological sensitivity. Consequently, the study indicates that the presence of metals in the PTS samples does not pose a significant threat to the surrounding environment and human health. Furthermore, this research underscores the inadequacy of relying solely on regulatory limit values in assessing the health risks of waste materials. Such comprehensive assessments are crucial for safeguarding aquatic and human populations.

Innovation in crop rotations for sustainable integrated crop-livestock systems: The case of a typical semi-arid Mediterranean area

Integrated organic crop-livestock systems represent a sustainability model on which many environmental, economic and social expectations have been placed, especially in some areas with limited availability of natural resources. In recent years, among the fodder crops in rotation on organic livestock farms, conditions have been created for the emergence of soya (Glycine hispida, M.), also due to the coupled aid granted by the Common Agricultural Policy (CAP). A generalised spread of soybean, however, may generate unsustainable production patterns, especially in Mediterranean areas characterised by limited irrigation and weed management issues. With respect to the above, the research assessed the holistic sustainability of using field beans (Vicia faba, L.) in rotation for fodder purposes. To this end, a technical-economic and environmental analysis was carried out with economic balances and Life Cycle Assessment (LCA), through which a number of synthetic efficiency indices were developed. The results demonstrate the validity of the agronomic choice in achieving environmental efficiency, but also the importance of soil management and reducing the livestock load per unit area in order to simultaneously maintain economic and social efficiency levels.

Synthetic cap rate indices (1991-Covid era)

We introduce a method that combines Euclidean distancing and OLS techniques to project synthetic capitalization rate indices (‘SCXs’) for metropolitan statistical areas in the US. SCXs are projected independently of market prices, asset specific characteristics and geographic location (ex-ante). In contrast to market cap rates, driven by geographic proximity and market comparables, our new method is driven by economic proximity. We find SCXs provide better forward guidance than market cap rates for commercial real estate (‘CRE’) defaults and CRE values before and during the Covid pandemic. Our method establishes CRE benchmark cap rate indices across property types that explicitly connect CRE valuation at the MSA level to macroeconomic indicators through economic proximity.

How Does Specialization in Agricultural Production Affect Soil Health?

The aim of the study was to assess the impact of the specialization of agricultural production on selected parameters of soil health, i.e., soil organic carbon content (SOC), soil acidification, soil nutrient status, i.e., total nitrogen content (NT), available forms of phosphorus, potassium, and magnesium, and microelements content, as well as the content of selected potentially toxic metals (PTMs). For the study, 18 farms located in the Masovian Voivodeship in Central Poland were selected. They were grouped into six types, and each type was represented by three farms. The study included organic farms; farms specializing in: crop, vegetable, poultry, dairy cattle, and pigs production. A total of 144 soil samples were analyzed. The results showed that the specialization of agricultural production and fertilizer management had a significant impact on most of the tested soil health parameters, except SOC and NT content. Despite the high organic fertilizer doses introduced into soils in poultry (170 kg N per hectare as poultry manure) and pig farms (150 kg N per hectare as pig manure), there was no significant influence of these amendments on SOC content. This may indicate low organic carbon sequestration potential in some Polish agricultural soils. Organic farms had the lowest levels of plant nutrients in the tested soil samples, which may limit soil productivity. All the tested soils were strongly acidified, which could restrict both production and regulatory soil functions. Based on the synthetic index of soil fertility (SSFI), vegetable and poultry farms were characterized by very high fertility, while crop, dairy cattle, and pig farms fell into the medium fertility class. Organic farms were in the lowest fertility class. However, the study suggests that the SSFI may not be the best indicator for assessing soil fertility and health; therefore, further research is needed.

Extreme snowfalls and atmospheric circulation patterns in the Cantabrian Mountains (NW Spain)

This research examines the atmospheric patterns associated with damages caused by extreme snowfalls during a 115-year period, using newspapers as the source to document the events and performing then a synthetic damage index. Between 1900 and 2015 there was 180 days with personal and material damages due to snowfalls (without considering associated hazards as snow avalanches or landslides): Apart from the numerous material damages recorded, 60 people were killed and 254 injured. Advections from the north represented a 30% of damaging days (followed by cyclonic and northeastern) accumulating the greatest number of days in the highest deciles of damage, although cyclonic days were the most damaging ones on average. The analysis of data from five weather stations in the area, allowed to relate weather types with temperature and precipitation extremes, showing that northern and cyclonic types led to the lowest temperature and the highest precipitation. Both the number of events and the damage index decreased throughout the study period, with the most serious events (those that caused deaths and injuries) highly concentrated in the first half of the 20th century. This decrease trend is parallel to that observed in the frequency of two of the most damaging advections, northern and northeastern (responsible for a 46.7% of damage), which have gone from representing 7.3% of days before 1930 to 5.8% after 1990. Futhermore, the relationship between the NAO phases and the days of damage has been examined, noting that damages are less frequent and les intense during the NAO's positive phase. Finally, non-atmospheric drivers such as land-use intensity decline in rural areas and Spanish regulations on roof snow loads standards (initiated in the 1960s and refined subsequently, thus mitigating roof collapses and associated casualties), could have partially contributed to the decline in snowfall damage in the area.

Enhanced Quick-Cooking Red Beans: An Energy-Efficient Drying Method with Hot Air and Stepwise Microwave Techniques.

This research introduced an energy-efficient drying method combining hot-air drying with stepwise microwave heating for producing quick-cooking red beans. Crucial parameters such as the effective diffusivity coefficient (De), and specific energy consumption (SEC) were examined across varying conditions with the aim of optimizing the drying condition. The results showed that De and SEC varied in a range of 0.53 × 10-9-3.18 × 10-9 m2·s-1 and 16.58-68.06 MJ·(kg·h-1)-1, respectively. The findings from the response surface methodology indicated that optimal drying conditions for cooked red beans are achieved at a hot air temperature of 90 °C, a microwave power of 450 W (corresponding to an initial intensity of 2.25 W·g-1), and a rotational speed of 0.2 Hz. These conditions lead to the maximum effective diffusivity coefficient and the lowest specific energy consumption. Further investigations into step-up (150-300 W to 300-450 W) and step-down (300-450 W to 150-300 W) microwave heating modes were conducted to refine the drying process for enhanced energy efficiency. The synthetic evaluation index revealed that step-down microwave heating strategies of 450 W-to-150 W and 300 W-to-150 W, applied at a temperature of 90 °C and a rotational speed of 0.2 Hz, were notably effective. These methods successfully minimized energy use while preserving the quality attributes of the final product, which were comparable to those of traditionally cooked and freeze-dried red beans. The combined approach of hot-air drying with step-down microwave heating presents a promising, energy-saving technique for producing quick-cooking beans that retain their rehydration qualities and texture.

Non-Working Workers. The Spanish Labour Market under Covid-19

We propose in this paper a way of analyzing the evolution of unemployment that considers, besides conventional unemployment rates, unemployment duration, discouraged workers and workers with suspended jobs. Those variables are combined into a synthetic index of non-employment that can be expressed as the product of two components, incidence and severity. We apply this methodology to analyse the evolution of the Spanish labour market during one year with Covid-19. The data show that the behaviour of the job market has been very asymmetric by regions and types of workers. We find that not only incidence and severity are quite heterogeneous across regions and worker types, but also that the evolution between 2019 and 2020 has been different from that corresponding to the financial crisis, with high unemployment regions and older workers suffering less, in terms of incidence and severity.

Harnessing Digital Twins for Agriculture 5.0: A Comparative Analysis of 3D Point Cloud Tools

Digital twins are essential in Agriculture 5.0, providing an accurate digital representation of agricultural objects and processes, enabling data-driven decision-making, the simulation of future scenarios, and innovation for a more efficient and sustainable agriculture. The main objective of this article is to review and compare the main tools for the development of digital twins for Agriculture 5.0 applications using 3D point cloud models created from photogrammetry techniques. For this purpose, the most commonly used tools for the development of these 3D models are presented. As a methodological approach, a qualitative comparison of the main characteristics of these tools was carried out. Then, based on some images taken in an orange grove, a quality analysis of the 3D point cloud models obtained by each of the analyzed tools was carried out. We also obtained a synthetic quality index in order to have a way to categorize the different pieces of software. Finally, as a conclusion, we compared the performance of the different software tools and the point clouds obtained by considering objective metrics (from the 3D quality assessment) and qualitative metrics in the synthetic quality index. With this index, we found that OpenDroneMap was the best software in terms of quality-cost ratio. Also, the paper introduces the concept of Agriculture 6.0, exploring the integration of advancements from Agriculture 5.0 to envision the potential evolution of agricultural practices and technologies, considering their impact on social and economic aspects.

Identification of damages in a concrete beam: a modal analysis based method

Structural Health monitoring (SHM) strategies can play a pivotal role in the perspective of enhancing structures and infrastructures life cycle and maintenance operations. A plethora of sensors and technologies can be employed in this field; in a seismic context, vibrational tests are particularly relevant, being able to give an insight on the dynamic characteristics of the structure itself. In particular, modal parameters can be considered in order to detect damages. A comparison between a certain test time and 0 test time (i.e., undamaged structure) is commonly performed; to this aim numerical models result particularly useful to provide baseline data (often unavailable in pre-existing structures), but they need to be validated before use. Non-contact techniques, like scanning laser Doppler vibrometry, can be exploited to do this. In this paper a numerical model of a scaled concrete beam is realized and validated through LDV data, then it is used to design load tests for progressive damages generation. Modal analysis is conducted after different load trials to evaluate changes of modal parameters in relation to the damage occurred; also, damage-related indices are proposed. The results confirmed the suitability of LDV for dynamic analyses of cement-based structures and this can be particularly useful when big structures (e.g., bridges) have to be monitored in-field. The numerical model was validated with acceptable absolute errors in terms of natural frequencies (between 26 Hz and 131 Hz) and high Modal Assurance Criterion (MAC) values (0.85-0.93). Moreover, the proposed methodology allows to detect damages also in a concise way through synthetic indices (with changes >50% in damaged vs undamaged conditions) and early warnings could be generated according to their values, hence supporting decision-making procedures in the building management scenario.

Response of a pan-European fish index (EFI+) to multiple pressures in rivers across Spain

Rivers are ecosystems highly threatened by human activities and fish are an invaluable tool to measure and communicate environmental degradation and restoration. Fish bioassessment is crucial but notoriously difficult in Mediterranean-climate streams for a number of reasons, including low local species richness, faunas with high spatial turnover and generalist species, and scarcity of reference sites. In this study, we conducted the most comprehensive test of the pan-European fish index (EFI+) in the Iberian Peninsula, analysing its response to multiple anthropogenic pressures. We compiled a database, which we provide online, with 2970 electrofishing samples across Spain, involving 100,732 fish of 69 species. Principal component analyses of many quantitative variables were used to create new synthetic anthropogenic pressure indices. Correlation and multiple linear regression analyses were used to test the relationship between these pressures and the fish index (EFI+) and its four individual metrics scores (i.e., density of species intolerant to oxygen depletion, density of fish ≤150 mm of species intolerant to habitat degradation, richness of species of rheophilic reproduction habitat, and density of species of lithophilic reproduction habitat). We also obtained the same models but including the river basin district to test for spatial or methodological differences. Our results indicate that both the EFI+ index and its individual metrics respond to various anthropogenic pressures. These pressures explained about 36% of the variance of EFI+ values. Notably, downstream and mainstream reaches with higher agricultural or urban land uses, increased hydrologic alteration, and water and habitat quality impairment exhibited lower EFI+ values. Although less variance was explained for the individual metrics than for the fish index, they responded as expected to the different pressures. For instance, the richness of rheophilic species and the number of lithophilic fish decreased with hydrologic alteration, while the number of fish intolerant to oxygen depletion decreased with water quality impairment. Similar correlations were observed when river basin district was included in the model, but with higher explained variation and greater significance of the pressures. While it is possible to develop regional indices with more metrics and a stronger correlation with anthropogenic pressures, EFI+ is the only fish index that has been validated throughout the Spanish peninsular territory. Our results support the use of EFI+ in intercalibration exercises across Spain until better regional indices are developed.

Spatial Modeling of the Relationship Between the Social Map and the Level of Educational Development in the Municipalities of the Luján River Basin, Argentina

This study aimed to determine the degree of association between the social map (Synthetic Economic-Housing Index—ISEH) and the level of educational development (Synthetic Educational Index—ISE) modeled for the municipalities of the Lujan River basin. The indices were constructed by applying multivariate analysis methods based on Geographic Information Systems (GIS), and the Exploratory Analysis of Spatial Data determined their links using a spatial decision support system (SDSS). The result facilitated the contrast of the proposed hypothesis. In this sense, a theoretical statement and a methodological tool are built from the cartography. The analysis highlights the importance of contextual variables and their incidence on educational development indicators since the spatial perspective provides the bases to support spatial decision-making at the municipal management level.

Spatial heterogeneity in the urban household living conditions: A-GIS-based spatial analysis

ABSTRACT The unplanned urban expansion and extreme population pressure have caused several bottlenecks for city dwellers, which have been reflected in the form of inter-urban spatial inequality in living conditions at the household level. The present study integrates GIS with a composite synthetic index to measure the spatial variation of urban household conditions across 146 urban centers in Eastern India (West Bengal). The study critically scrutinizes the influence of urban housing conditions, amenities level, and asset possession on the well-being of urban households. Based on these three inter-connected dimensions, a composite index for urban household living condition index was computed based on the optimum combinational composite index model (OCCIM). Besides, the study provides insight into the geographic distribution of urban household living conditions considering their spatial patterns, hot spots, and clusters identification. The spatial heterogeneity of the selected dimensions is shown on the maps produced using the GIS. The spatial variation was measured using Moran’s I at the global level and Getis-Ord Gi * to identify influential locations through clusters and hot spots detection of urban housing condition, amenities level, assets possession, and composite living condition scenario. Additionally, the constructed UHdCLI model’s consistency is assessed using the r-squared value of regression analysis (R2 = 0.815). The latest findings provide a realistic view of the spatial inter-urban variations that exist at the level of particular households concerning several aspects of their standard of life.

Statistical index for the diagnosis of sarcopenia in physically active older women: A cross-sectional study

The detection and classification of sarcopenia involves the analysis of many variables (50 to 60), which increases the time and costs required to diagnose and manage this condition. The objective of the study was to develop a synthetic statistical index to diagnose and classify sarcopenia in physically active older women. With this in mind, we conducted a cross-sectional study in 100 physically active women (64.88 ±4.4 years) in whom body composition measurements, muscle strength, and gait tests were performed. One thousand random selections of both training and test sets (80% and 20%, respectively) were made, logistic regression was fitted, and the regularization procedure (Elastic net regression) was performed. Results showed that the skeletal appendicular mass index (kg/m2) and slow gait speed (m/sec) were the variables that contributed the most to the diagnosis of sarcopenia. In conclusion, appendicular lean mass, gait speed, and explosive strength sufficiently describe the state of muscle and functional deterioration (sarcopenia) in physically active older women.

Does the cohort approach say more about convergence in divorce trends? The case of post-socialist Czechia and Slovakia after dissolution in 1993

Abstract: This study investigates whether convergence or divergence in divorce trends occurred in two countries with common history and legislative framework measured both by transversal and cohort methods and tries to predict some short-term trends as well. Slovakia and Czechia are countries with a common history and divorce legislation, in which the post-1989 social transformation began at the same time as the start of independent development. It is therefore possible to compare retrospectively the transformation of marital dissolution over three decades in the context of social transformation as well as the development in separate state units. Despite the attractiveness of research so focused, there are surprisingly few comprehensive comparative studies of marital dissolution in such countries. Several transversal and longitudinal indicators have been calculated to compare both countries in the last three decades. A synthetic index of differentiation has been applied then. The Arima model for prediction has been applied and qualitative assumptions on divorce factors are discussed, too. Seemingly clear differences and developmental patterns in the intensity of divorce measured in a transversal way are not as evident in the case of longitudinal analysis of marriage cohorts. The reality of the transformation of family dissolution is simply more complex; the factor of the changing timing of the process – the duration of a marriage – also comes into play and cohort analysis is essential in discovering the changing divorce patterns.

The Impact of Political Instability on School Performance: The Case of African Countries in The Franc Zone

The aim of this paper is to analyse the impact of political instability on school performance in the African countries of the Franc zone. Based on Houssaye’s (1988) pedagogical triangle, we develop a theoretical model in which political instability has a negative impact on knowledge production factors and, in turn, on school performance. These factors include teachers, teaching infrastructure and equipment, and teaching time. Empirical analysis shows that an increase of one percentage point in the synthetic index of internal conflict (reflecting an increase in political instability) leads to a significant drop of 0.07 percentage points in the primary completion rate. The same applies to the pupil-teacher ratio, where an increase (relatively fewer teachers) leads to a reduction in the completion rate. In addition, public spending, official development assistance, and real GDP per capita each have a positive and significant effect on the primary completion rate. A number of recommendations are put forward with a view to limiting the risk of internal conflict.

Особенности моделирования динамики социально-экономических показателей по сильно зашумлённым данным

Features of socio-economic indicators which must be taken into account at econometrical modeling of their dynamics were established on the empirical basis of the VСIOM sociological center. They are: 1) high measurement inacuracy of socio-economic indices of sociological nature, which almost an order more than the sampling error; 2) restriction on the use of traditional methods of parametric statistics due to non-compliance with the condition of normal distribution of informative indicators. Recommendations are given on econometric modeling of the dynamics of highly noisy data using regression analysis methods: 1) transition from building "accurate" regression dynamics models to "useful models", when "leaps" of informative indicators and/or changes in the direction of their trend are considered not as statistically established judgments, but as "trends"; 2) transition from analysis of synthetic indices dynamics to analysis of their components dynamics; 3) parallel analysis of indices dynamics based on respondents' answers to the related questions; 4) use of graphical analysis methods together with the analytic ones that contribute to interpretation of statistical conclusions results.

Measuring Economic Resilience of Tourism Villages: A Spatiotemporal Analysis of Pre and Post-Covid-19 Pandemic

Tourist village plays an important role in rural development in Indonesia. Nevertheless, tourist village is also prone to external shocks such as national and global economic volatilities and recent public health events of the Covid-19 pandemic. This study attempts to analyze a temporal variations of tourist village economic resilience from pandemic shock in 24 tourist village destinations covering the period of 2019-2022 in Indonesia. A synthetic composite index of the Adjusted Mazziotta-Pareto Index (AMPI) was used to measure resilience, followed by clustering analysis to determine the typology of the resilience. The resilience index was composed of capacity and performance dimension related to resilience. The results show that most villages were severely affected in the first year of Covid-19, yet they recovered afterward, as indicated by positive differences in the AMPI index before and after Covid-19. This result shows that tourist villages in Indonesia have a tendency of strong capacity and performance to recover from the pandemic shock. The economic components of the capacity and performance were able to readjust after the pandemic indicating that these components are relatively adaptable to the shocks. The indicator that has the most significant influence on the typology of resilience in the performance dimension is the number of visitors. Meanwhile, the Development Village Index (DVI) indicator is the most significant influence on the capacity dimension.

Aggregate Trend in the Financial Performance of Socially Responsible Investment: Case of Listed Moroccan Companies

"The development of socially responsible investment (SRI) in the mid-1990s opened up a vast area of research in portfolio construction. Indeed, investors are breaking with traditional financial theory by integrating extra-financial elements into their portfolio management strategies. In this sense, the emergence of this new type of investment has triggered a craze in the scientific community about the performance of SRI, which has led to mixed results. One of the possible explanations for this heterogeneity of results is that the methodology employed by the different studies has an inevitable influence on its result, or that the financial performance of SRI can be influenced by the measure of financial performance employed (risk or profitability variable). For this reason, the analysis of our data is conducted using a principal component analysis of financial performance, which permits the construction of a synthetic index that includes most of the variables used to measure financial performance in the empirical literature. The objective here is to capture a general trend in the impact of SRI on this composite index of financial performance. The results of the multivariate test on the composite index show that non-SRI firms have a negative and statistically significant impact on the financial performance index. Similarly, the effect of investments made by Engaged companies has a negative, but not statistically significant impact on financial performance."