Using the adjusted RO with a value of 1.1975 and the average of the population from 1990–2020 (WB 2021), the number of daily tons per country was calculated, the results are shown in Fig. 2.
Figure 2. Wasted urban by country Tn/daily.
The list of the fifteen countries with the highest generation and treatment of urban solid waste (GyTRSU) linearly relates the number of the population with consumerism, and consumerism with purchasing power (Silva and Mello 2020; Takenaka 2020). In this statement there is a tacit contradiction related to the number of people and families with purchasing power, since a large part of the population is in a state of socioeconomic vulnerability (Rosales-Mendoza and Mota 2021).
The claim of Sustainable Development suggests an equitable approach that could solve current difficulties for the present and future generations, hence the importance of an analysis of the indicators used by the World Bank. In this sense, the adjusted metric of the quantity of production of RO would be useful for follow-up and monitoring of actions in the construction of solutions and public policies, reason why it is important to assess the convenience of its use and its interaction with other metrics (indicators).
The assessment of the competence, relevance, and sufficiency of the use of the metric was carried out using information from the World Bank; the product of the proximity analysis of the data 381 final variables of 181 countries processed in the IBM SPSS Statistics software revealed 99.4 % of validity in the re-scaled Euclidean distance used. The data processing did not discard the variable (RO), on the contrary, it includes it within the seven final variables that help in the construction of the two main component factors. Table 2 shows the results with and without the variable.
Table 2. Analysis with and without the RO variable.
Indicators
|
Without RO
|
With RO
|
Parameter
|
Determinant
|
1.944E-5
|
1.830E-7
|
Best close to zero
|
Kaiser-Meyer-Olkin Measure of Sampling Adequacy
|
0.712
|
0.779
|
Greater than 0.5
|
Bartlett´s Test of sphericity
|
Approx. Chi-Square
|
1921.968
|
2743.351
|
Greater amount
|
Distance frequency
|
15
|
21
|
Greater amount
|
Significancy
|
0.000
|
0.000
|
Close to zero
|
Communalities
Extraction method: Principal Component Analysis (PCA)
|
N73(1)
|
0.268
|
0.125
|
Next to one
|
ES403(2)
|
0.951
|
0.973
|
Next to one
|
ES755(3)
|
0.934
|
0.965
|
Next to one
|
ES776(4)
|
0.974
|
0.987
|
Next
|
ES1122(5)
|
0.909
|
0.979
|
Next to one
|
S1414(6)
|
0.972
|
0.978
|
Next to one
|
Eigenvalues
|
Component 1(7)
|
63.076
|
66.096
|
Greater amount
|
Component 2(8)
|
20.390
|
19.196
|
Greater amount
|
Rotation sums of squared loadings
|
Component 1
|
42.049
|
52.980
|
Greater amount
|
Component 2
|
83.466
|
85.292
|
Greater amount
|
Source: Author. (1) Agriculture, forestry, and fishing, value added (current LCU), (2) Exports of goods, services, and primary income (BoP, current US$), (3) Merchandise imports by the reporting economy (current US$), (4) Cell mobile subscriptions, (5) Primary education, pupils, and (6) Urban population, (7) Component of factor 1 (x-axis), and (8) component of factor 2 (y-axis).
In general, the participation of RO favorably affects the development of the results, except for the decrease in the position of variable N73 from 0.268 to 0.125 in the position of communities. The other variables increased their value, as well as an improvement for the rest of the values. The determining index is closer to zero, the KMO index improved, as did the number of times of convergence in Bartlett's rotation, the values of Eigenvalues and rotation sums of squared loadings increased their impact and relevance in the study.
Once validity is confirmed, the results of the applied multivariate analysis BIG DATA of the World Bank Development Indicators (WDI) are compared, shown in Figure 3, in column one: the product of component main factors (PCA) figs. 3a and 3c without the participation of the variable of RO and in column two including the variable RO, figs. 3b and 3d; in row one the analysis is carried out with all the countries figs. 3a and 3b and in line two excluding the participation of China, India and the United States of America figs 3c and 3d.
Figure 3. The World Bank Development Indicators (WDI) / RO.
Looking at column one Fig. 3a it is observed by distance as the countries of China (CHN), India (IND) and the United States of America (USA) stand out, when they are excluded to obtain a better view of a second group Fig. 3c the countries of Japan (JPN), Brazil (BRA), Indonesia (IDN), Ireland (IRN) and Germany (DEU) are already noted, the third group for the rest of the countries. In column two, when comparing the main components factor (x-axis) with the study variable RO (y-axis) in Fig. 3b highlight CHN and IND, countries disappearing USA, and in Fig. 3d remain in the second group IDN, BRA, JPN and Russia (RUS), United Kingdom (GBR) and France (FRA) appear, disappearing from view IRN and DEU. The RO values (y-axis) are from more than one to 80,000 tons accumulated on average per year per country.
China and India differ in their productive structure; however, they share the position of the greatest polluters in the international community (Oliva 2014). In China the electricity sector is considered the main contributor to climate change, air pollution and responsible for 15% of the country's electricity generation, however, this sector contributes less than 1% of total emissions of carbon dioxide (CO2), nitrogen dioxide (NO2) and dry carbon dioxide (SO2) (Wang et al. 2021).
Considering it was projected that China would produce by 2020 more than 30% of the global emissions of Greenhouse Gases (GHG), due to the Neo-Malthusian model of increasing the intensive use of technological devices, modernization of agriculture and the preferential consumption of national products, the value of minus 1% in GHG emissions is notorious (Perdomo 2016). And India, aware of the contribution of emissions emanating from large hydroelectric reservoirs, has generated a series of mitigation measures in sustainable planning (Zhi-Guo et al. 2021).
CHN and IND conditions are not distant from the rest of the countries that make up the G20, nor from the countries that are outside this development classification. An early interpretation of the results leads to think that effectively the generation of waste is directly related to the growth of the world population (Silva and Mello 2020; Takenaka 2020), however, the disappearance of the USA in Fig. 2b and IRN together with DEU and the appearance of other countries in Fig. 2d allows to question that premise.
Such appearance and disappearance could be related to the electronic waste collected to be recycled in developed countries, are simply sent to other developing countries, where the "cost" of treatment is much lower (Natume and Sant-Anna 2011).
The results of the seven main dependent variables that make up the basis of the two main component factors are detailed in Table 3, going forth with the analysis of without and with RO, reading these variables can help to better understand what happens in the world.
These variables in order of importance (results with RO) are: 1) Cell mobile subscriptions (ES776); 2) Urban population (S1414); 3) Ordinary waste index (RO); 4) Imports of merchandise by the declaring economy in US $ currency (ES755); 5) Elementary education, students (ES1122), Exports of goods, services and primary income, balance of goods of people, in US $ currency (ES403); and 6) Agriculture, forestry and fishing, current added value LCU (N73). There is no difference between the socioeconomic variable ES776 and the social variable S1414, both have the same weight of 0.975 in relation to the multivariate component.
Table 3
Code
|
Description
|
Without
|
With
|
1*
|
2**
|
1*
|
2**
|
ES403
|
Exports of goods, services, and primary income (BoP, current US$)
|
0.101
|
0.508
|
0.816
|
-0.513
|
ES755
|
Merchandise imports by the reporting economy (current US$)
|
0.782
|
-0.529
|
0.831
|
-0.497
|
ES776
|
Cell mobile subscriptions
|
0.782
|
-0.568
|
0.975
|
0.050
|
ES1122
|
Primary education, pupils
|
0.800
|
0.519
|
0.610
|
0.678
|
S1414
|
Urban population
|
0.970
|
0.174
|
0.975
|
-0.021
|
N73
|
Agriculture, forestry, and fishing, value added (current LCU)
|
0.101
|
0.508
|
0.263
|
0.705
|
RO
|
Ordinary wasted
|
|
|
0.947
|
0.216
|
* Relationship with the component of factor 1 (x-axis) / ** Relationship with the component of factor 2 (y-axis). Source: Author |
Apparently the first variable of urban population concentrations explains world development (Silva and Mello 2020; Takenaka 2020). However, the second variable with equal weight generates a series of uncertainties regarding from: who, where, why and to how many cellular mobile service subscriptions a person can have, in addition, it does not guarantee that people have purchasing power, due to access to services and requirements to operate on a day-to-day basis, making it a requirement rather than a necessity. Many people live in debt for acquiring a state-of-the-art cellular device; another condition is access to the signal (Aguiar et al. 2014; Rodríguez et al. 2020).
The ES776 variable, as well as the ES403, the ES755 and even the ES1122 are all closely intertwined, the subscription of the mobile service is a variable worthy of study, because it includes the trace of the developmental history of humanity, since the discovery of energy through the development of technology and its intrinsic relationship with ordinary waste.
Going deeper and to obtain a representation of the countries before these variables, the analysis of characterization/hierarchical classification (CHD) of main components applied to the independent variables (countries) was used. The use of this technique was helpful because by grouping the participants it facilitates interpretation from the cluster and infers in the total study population (Niño 2020).
In the first Fig. 4a it shows the result of the analysis without RO, the product of four main clusters, the first formed by Colombia (COL), Japan (JPN), Republic of Korea (KOR), Vietnam (VNM), Indonesia (IDN) and Iran is added to the second, Uzbeskistan (UZB) and Iraq (IRQ) are added to the third, and the fourth cluster includes the rest of the countries. In Fig. 4b includes RO, the four main clusters are made up of Paraguay (PRY), Myanmar (MMR), COL, KOR, UZB, VNM, IDN, the second cluster adds Lao (LO), Guinea (GIN), Cambodia (KHM), Uganda (UGA) and Tanzania (TZA), the third adds Iran (IRN), and the fourth the rest of the countries. Figures 4c and 4d show the position of the main component variables without and with RO respectively.
Figure 4. Characterization Hierarchical of data (CHD) / PCA position without and with RO.
The countries that maintain their presence in the main group final product of the analysis correspond to Colombia, Republic of Korea and Vietnam. In Colombia, efforts have been made to reach remote territories and rural areas with technology as part of the fulfillment of the SDGs through programs such as Digital Social Inclusion and WIFI Zones (Cervera-Quintero 2021).
In Korea, efficiency in the use of renewable energy is the basis for GHG reduction, strengthened by the implementation of public policy based on the use and protection of natural resources (Sosa 2020); and in Vietnam the trade war between the USA and CHN has caused large technology firms to increase their manufacturing operations in that country (Reyes-López 2020); another aspect that may favor development also as a result of the trade war is the diversion of CHN's global supply chain, despite global supply chain disruption, the post-pandemic Vietnamese economy may accelerate if countries such as the USA, JPN and the European Union divert the CHN supply chain and place it in VNM.
Thus, it makes sense to reduce the main component factors to the seven variables. Subscription to cellular technology services represents a wealth of information that allows us to explain the evolution of world and country development.
The results of the analysis of textual data of the world development indicators compared with scientific articles with gold classification in the SCOPUS database and access through the CAPES Newspaper platform are displayed in Fig. 5, in column one indicators World Bank Development Committee (IDWB) and in column two 2074 Scientific Articles (AC) from 1996 to 2021.
Row one (Fig. 5a and 5b) reveals the number of related indicators according to the classification given in the study and the number of scientific articles collected in the SCOPUS database in the period 1996 to 2021; row two (Fig. 5c and 5d) shows the neural network of each of the items subject to analysis by column, and row three reveals the product in the word cloud (Fig. 5e and Fig. 5f).
Figure 5. Comparative analysis WDI (1990–2020) versus AC SCOPUS (1996–2021)
The supremacy of indicators related to socioeconomic aspects in regard to the other indicators (Fig. 5a) confirms the results of the quantitative analysis, of the seven main components, of which five are identified as ES, which is proportionally valid. The scientific work represented in the graph (Fig. 5b) even though, in the last year the number of articles decreased from 176 to 172, reveal the commitment and growing concern of the scientific community in addressing the issue of world Sustainable Development and of the countries.
Neural networks (Fig. 5c and 5d) and word clouds (Fig. 5e and 5f) emphasize population, trade, development, education, control characterizing the reality of the Anthropocene, the imposition of humanity on nature (Milton et al., 2021; Palmer et al., 2022). Searching in the results for some word that is related or characterized with the subscription to the cellular service, almost invisible in Fig. 5d the word technology appears.
This word also contains the evolution of world development and countries; its link with waste also makes sense, for example, the production of the cellular devices has undergone several radical changes over time, both in cost, models, shapes, figures, capacity, durability, etc. Its acquisition reveals an adjustment of the service to the population without neglecting the ability to pay.
The result of the comparison of the neural network and word cloud between the world development indicators (WDI) and the scientific contributions, shows an agreement in the socioeconomic approach, with specific observations with the growth of the population, financial transactions, education, and even placing the female gender in the focus of its rights, in such a way that the related indicators are apparently biased in their favor. In scientific production, environmental issues appear that begin to shape public policy and interrelationships of countries, as the way to guarantee a healthy environment for the present generation as well as for future ones, facilitated by the development of communication technology.