Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean
Abstract
:1. Introduction
1.1. Sustainable Project Management
1.2. Sustainability Evaluation Models in Project Management
1.3. Sustainable Development Goals (SDGs) and Their Relation to Project Management
1.4. Sustainability Ranking in Latin America and the Caribbean
1.5. Project Selection and Classification
1.6. Artificial Intelligence and Sustainability
1.7. Machine Learning and Unbalanced Classes
1.8. Research Design
- Why is it necessary to consider the Sustainable Development Goals to assess the sustainability of projects?
- Is it possible to develop the capabilities of a project manager within sustainable development terms?
- How can artificial intelligence break the current paradigm of sustainability through the Sustainable Development Goals?
2. Materials and Methods
2.1. Population and Sample
- n = required sample size;
- N = population size;
- Z1−α/2 = 1.96 (Z-statistic, calculated at 95% confidence level);
- p = q = 0.5 (typical values under worst-case conditions);
- Error () = 0.05.
2.2. Data Collection
2.3. Assessment of the SDG Indicators
2.4. Project Distributions among the Expert Panel
2.5. Data Preparation
2.6. Measurement of Expert Consensus
- X= list of categories (“1—Insignificant (I)”...“5—The Most Significant (TMS)”);
- pi = probability of each X;
- dx = Xmax -Xmin;
- Xi = particular element of X;
- = mean or expected value;
2.7. Categorization of Project Level of Sustainability
2.8. Determination of a Global Sustainability Index for the Project Sample
2.9. Types of Classifiers
2.10. Data Resampling Techniques (SMOTE)
2.11. Choice of the Best Classification Model
- Data preparation and pre-processing;
- Data analysis and exploration;
- Assignment of the characteristic’s matrix and the vector of classes or target;
- Codification of the vector of classes or target in dummy variables;
- Division of the data into training (80%) and testing (20%), with stratification of the output variable, to ensure homogeneity in the representativeness of the data in both groups.
- Training phase:
- Evaluation of the benchmark strategy with DummyClassifier.
- Elaboration of a pipeline containing the SMOTE oversampling technique, the scaling or normalization of the data, and the corresponding classifier.
- Use of the RepeatedStratifiedKFold cross-validation technique to minimize data overfitting.
- Use of the GridSearchCV technique to search for the best parameters.
- Testing phase:
- Model test with records not used during training (without SMOTE).
- Determination of metrics and choice of the model with the best accuracy.
- Printout of results.
2.12. Performance Evaluation Metrics
3. Results
3.1. Differences between Groups of Experts (ANOVA)
3.2. Validation and Reliability of the Measuring Instrument
3.3. Consensus Measures
3.4. Sustainability Level of the Project Sample
3.5. Determination of the Global Sustainability Index for Projects
3.6. Accuracy Metric Threshold Determination
3.7. Base Model Metrics
3.8. Balanced Model Metrics
4. Discussion
5. Conclusions
- There was a gap between sustainability and its application to project management;
- the integration of sustainability should take place throughout the entire project life cycle and not only focus on the outcome; and,
- the sustainability assessment of the project should consider a set of targets and indicators based on TBL.
6. Recommendations
7. Limitations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Materials | TBL Descriptors |
---|---|
Project market | General aspects of the industry. Background study. Demand from potential clients. Entry barriers. |
Project profitability | Social profitability vs. economic profitability. Compensation of monetary deficit. Subsidies, grants, and aids. |
Social investment projects | Promotion of local development. Support for the traditions and rights of indigenous communities. Private vs. social evaluation. |
Technology and environment in the project | Pollution prevention and control. Environmental risk management. Biodiversity preservation. Fight against climate change. Life cycle analysis. Environmental Impact Assessment. Compliance with legal or social regulatory requirements. |
Project risk and uncertainty | Socioeconomic risk mitigation measures. Sensitivity analysis of sustainability projects. |
Dimension | Number of Variables Identified | Indicators |
---|---|---|
Economic | 158 | Survival of the organization Cost management Stakeholder relations Employee welfare |
Environmental | 248 | Air, water, energy, and soil Waste generation Material consumption Other * |
Social | 270 | Good labor practices Community relations Child labor Human rights Impact of products and services Financing of social actions |
Number | SDGs | Number | SDGs |
---|---|---|---|
1 | No poverty | 10 | Reduced inequalities |
2 | Zero hunger | 11 | Sustainable cities and communities |
3 | Good health and well-being | 12 | Responsible consumption and production |
4 | Quality education | 13 | Climate action |
5 | Gender equality | 14 | Life below water |
6 | Clean water and sanitation | 15 | Life on land |
7 | Affordable and clean energy | 16 | Peace, justice, and strong institutions |
8 | Decent work and economic growth | 17 | Partnerships for said goals |
9 | Industry, innovation, and infrastructure |
ID | Project | Dimensions * | SDGs ** |
---|---|---|---|
1 | Design and construction of infrastructure and public spaces on the right bank of the Chorobamba River in the city of Oxapampa, Perú | Infrastructure management Public and social sector management | Partnerships for the goals Clean water and sanitation Sustainable cities and communities Industry, innovation, and infrastructure Life on land |
2 | Wastewater treatment plant based on an oxidation lagoon for Los Portales housing, Piura, Perú | Infrastructure management Environment | Industry, innovation, and infrastructure Clean water and sanitation Life on land |
3 | Technical trade training center for low-income youth, Chile | Equality and inclusion Economic empowerment Education | Quality education Reduced inequalities Gender equality Decent work and economic growth No poverty |
4 | MSW sorting plant from the Municipality of Yerba Buena, Tucumán, Argentina | Economic empowerment Environment Infrastructure management | Sustainable cities and communities Responsible consumption and production |
5 | Housing project in El Cantón Pedernales–Manabí, Ecuador | Equality and inclusion Economic empowerment | Reduced inequalities Gender equality No poverty |
6 | Urban renewal plan for sidewalks surrounding the San Juan de Dios Hospital, San José, Costa Rica | Infrastructure management Public and social sector management | Partnerships for the goals Sustainable cities and communities Industry, innovation, and infrastructure Life on land |
7 | Environmental management plan for solid waste and organic waste generated by tourism activities around the Combeima River, Ibagué, Colombia | Environment Public and social sector management Economic empowerment | Partnerships for the goals Clean water and sanitation Sustainable cities and communities Life below water |
8 | Playa del Carmen Urban Planning Program, Quintana Roo, Mexico | Infrastructure management Public and social sector management | Partnerships for the goals Sustainable cities and communities Industry, innovation, and infrastructure Life on land |
9 | Accessibility program for people with disabilities in recreational spaces, San Pedro Sula, Honduras | Equality and inclusion Economic empowerment | Reduced inequalities Gender equality Decent work and economic growth |
10 | Training program for coffee producers in the municipality of Mesetas, Meta, Colombia | Equality and inclusion Economic empowerment Education | Quality education Reduced inequalities Gender equality Decent work and economic growth No poverty |
Partners | AI Activities | Related SDGs |
---|---|---|
Food and Agriculture Organization of the United Nations (FAO) | Fishing gear identification Animal disease identification from images Aquaculture mapping Detecting fall armyworm infestations | 1–3 8,9 10–12 |
International Labor Organization (ILO) | From industrial robots to deep learning robots: the impact on jobs and employment The economics of artificial intelligence: Implications for the future of work Skills strategies for future labor markets | 1–5 8–10 16,17 |
International Maritime Organization (IMO) | Maritime Autonomous Surface Ships (MASS) E-navigation Marine Environmental Protection and AI AI for Sustainable Maritime Transport (AI-SMART) | 8,9, 11,13,14, 16,17 |
International Organization for Migration (IOM) | Humanitarian Data Science and Ethics Group IOM—Global Migration Data Analysis Centre (GMDAC) Applying techniques for internal quality control within the Displacement Tracking Matrix (DTM) Global Team | 7,10 17 |
United Nations Program on HIV/AIDS | Health Innovation Exchange & TimBre Project: AIR-TB | 3,4 9 17 |
United Nations Environment Program (UNEP) | Water-Related Ecosystems—SDG 6.6.1 UNEP Q & A Chatbot Funding Analysis and Prediction platform UNEP Robotic Process Automation | 6 17 |
World Bank Group | Creating Global Public Goods Developing Knowledge and Policies Piloting Disruptive Technologies in World Bank operations Education-Use AI for Learning through Games Due Diligence—Predicting accounting red flags from external financial reports | 1–3 9–11 13,16 |
Unit of Analysis: | Sustainability of multi-sectorial projects in Latin America and the Caribbean |
Dependent variable: | Level of implementation of Sustainable Development Goals |
Operational definition of the variable | |
Values of the dependent variable: | High, medium, low |
Independent variables: | Sustainable Development Goals |
How do you collect data on the presence or absence of the Sustainable Development Goals in your projects? | |
Unit of observation: | Responses to Likert-scale questionnaires administered to expert panels |
Expert Group | Number of Projects | Mean | Standard Deviation |
---|---|---|---|
1 | 40 | 4.0900 | 0.53340 |
2 | 41 | 4.1244 | 0.54067 |
3 | 40 | 4.0023 | 0.51783 |
4 | 33 | 4.0752 | 0.43769 |
5 | 32 | 3.9850 | 0.47944 |
Interval | Consensus Classification |
---|---|
Very strong consensus | |
Strong consensus | |
Moderate consensus | |
Balance | |
Moderate dissent | |
Strong dissent | |
Very strong dissent |
Classification Algorithms | Features |
---|---|
Dummy Classifier (DMC) | It establishes an average reference metric (accuracy) and its standard deviation, by means of which to compare the rest of the classification algorithms. |
Fuzzy Classifier | A different number of templates can belong partially to one class or to several classes. Class membership is measured by a number in the interval [0,1], where where “A” is the class and “x” is the vector of characteristics or pattern. |
Logistic Regression (LR) | It predicts the probability of an event or class occurring, conditional on a set of “n” independent variables. The model always returns the most probable class. |
K-Nearest Neighbors (KNN) | During the training phase, it searches for the K-nearest neighbors of the point to be classified and subjects them to majority voting—for example, by weighting each neighbor’s vote, according to the inverse square of their distances. An odd number of K will always be used to avoid possible ties. |
Support Vector Machine (SVM) | It has also been reformulated for regression. The objective is to obtain the best “n-1” dimensional hyperplane to optimally separate one class from another, where “n” is the number of coordinate axes or independent variables. It is more efficient than KNN in terms of cost and accuracy. |
AdaBoost (Adaptive Boosting) | It identifies those cases misclassified during training with several weak or base classifiers, giving them a higher weight or importance in successive cycles until the process stops for a certain minimum error value. Lastly, a final robust classifier is constructed as a weighted sum of the previous classifiers. |
Gaussian Process Classifier | They are used for both regression and classification. They are based on the Gaussian probability distribution. As with SVMs, they require the specification of a covariance function (or kernel). The Gaussian process makes predictions with uncertainty and works well with a small data set, as is the case in this study. |
Random Forest | It results from a combination of multiple decision trees created during the training phase. Each decision tree votes for one class, with the final result being the class with the highest number of votes in the entire forest. |
Metric | Description |
---|---|
Overall hit percentage. Not a good indicator for unbalanced data. | |
Individual percentage hit rate per class. Can be used for unbalanced data. | |
Proportion of positive cases correctly identified by the classifier. Determines when false negative costs are high. | |
Proportion of negative cases correctly identified by the classifier. | |
Model quality level. Determines when false positive costs are high. | |
Is used to easily compare measures of precision and sensitivity in a single value. It is very useful for binary classification problems where the study is focused on the positive class, as is the case here. | |
Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) | ROC is a probability curve that represents the fp rate on the abscissa axis and the tp rate on the ordinate axis for different thresholds. It indicates how much the model is able to distinguish between classes. The area under the AUC curve classifies the performance. The closer AUC is to unity, the better the model distinguishes between classes. |
Item | Scaling Average if the Element Has Been Suppressed | Scale Variance if the Element Has Been Suppressed | Total Correlation of Corrected Elements | Cronbach’s Alpha if the Item Has Been Deleted |
---|---|---|---|---|
1 | 65.47 | 57.937 | 0.297 | 0.865 |
2 | 65.06 | 59.342 | 0.223 | 0.867 |
3 | 65.30 | 57.487 | 0.344 | 0.863 |
4 | 65.53 | 54.996 | 0.593 | 0.854 |
5 | 65.58 | 56.277 | 0.388 | 0.862 |
6 | 65.22 | 57.802 | 0.356 | 0.863 |
7 | 65.70 | 54.266 | 0.600 | 0.853 |
8 | 65.53 | 54.521 | 0.580 | 0.854 |
9 | 65.94 | 54.461 | 0.560 | 0.855 |
10 | 65.96 | 53.247 | 0.604 | 0.852 |
11 | 65.90 | 53.292 | 0.609 | 0.852 |
12 | 65.96 | 52.863 | 0.612 | 0.852 |
13 | 65.64 | 55.172 | 0.525 | 0.856 |
14 | 65.85 | 54.312 | 0.426 | 0.862 |
15 | 65.34 | 57.178 | 0.385 | 0.862 |
16 | 65.94 | 53.494 | 0.540 | 0.855 |
17 | 66.04 | 53.485 | 0.573 | 0.854 |
Dimension | SDGs | Consensus Mean |
---|---|---|
Environmental | 6, 7, 11–15 | 71.56 |
Social | 1–5, 7, 8, 10–12, 16, 17 | 71.78 |
Economic | 7–9, 11, 12 | 71.52 |
Mean | 71.66 | |
SD | 3.70 |
Class | Grouping Range | Frequency | % |
---|---|---|---|
Low | Values ≤ 64 | 40 | 21.5 |
Medium | Values 64–76 | 112 | 60.2 |
High | Values ≥ 76 | 34 | 18.3 |
Classifier | Class | tp | tn | fp | fn | Accuracy | Overall Accuracy | Precision | Recall | F1 Score | ROC/AUC |
---|---|---|---|---|---|---|---|---|---|---|---|
LR | High | 5 | 29 | 2 | 2 | 0.89 | 0.84 | 0.71 | 0.71 | 0.71 | 0.98 |
Low | 6 | 30 | 0 | 2 | 0.95 | 1.00 | 0.75 | 0.86 | 1.00 | ||
Medium | 21 | 11 | 4 | 2 | 0.84 | 0.84 | 0.91 | 0.87 | 0.97 | ||
SVM | High | 6 | 30 | 1 | 1 | 0.95 | 0.89 | 0.86 | 0.86 | 0.86 | 0.98 |
Low | 6 | 30 | 0 | 2 | 0.95 | 1.00 | 0.75 | 0.86 | 1.00 | ||
Medium | 22 | 12 | 3 | 1 | 0.89 | 0.88 | 0.96 | 0.92 | 0.97 | ||
RF | High | 5 | 30 | 1 | 2 | 0.92 | 0.79 | 0.83 | 0.71 | 0.77 | 0.98 |
Low | 3 | 30 | 0 | 5 | 0.87 | 1.00 | 0.38 | 0.55 | 1.00 | ||
Medium | 22 | 8 | 7 | 1 | 0.79 | 0.76 | 0.96 | 0.85 | 0.94 | ||
KNN | High | 6 | 29 | 2 | 1 | 0.92 | 0.84 | 0.75 | 0.86 | 0.80 | 0.99 |
Low | 5 | 30 | 0 | 3 | 0.92 | 1.00 | 0.62 | 0.77 | 0.88 | ||
Medium | 21 | 11 | 4 | 2 | 0.84 | 0.84 | 0.91 | 0.87 | 0.90 |
Classifier | Class | tp | tn | fp | fn | Accuracy | Overall Accuracy | Precision | Recall | F1 Score | ROC/AUC |
---|---|---|---|---|---|---|---|---|---|---|---|
LR | High | 7 | 29 | 2 | 0 | 0.95 | 0.89 | 0.78 | 1.00 | 0.88 | 0.98 |
Low | 6 | 30 | 0 | 2 | 0.95 | 1.00 | 0.75 | 0.86 | 1.00 | ||
Medium | 21 | 13 | 2 | 2 | 0.89 | 0.91 | 0.91 | 0.91 | 0.97 | ||
SVM | High | 7 | 29 | 2 | 0 | 0.95 | 0.92 | 0.78 | 1.00 | 0.88 | 0.99 |
Low | 7 | 30 | 0 | 1 | 0.97 | 1.00 | 0.88 | 0.93 | 1.00 | ||
Medium | 21 | 14 | 1 | 2 | 0.92 | 0.95 | 0.91 | 0.93 | 0.98 | ||
GAUSS | High | 5 | 30 | 1 | 2 | 0.92 | 0.87 | 0.83 | 0.71 | 0.77 | 0.98 |
Low | 6 | 30 | 0 | 2 | 0.95 | 1.00 | 0.75 | 0.86 | 1.00 | ||
Medium | 22 | 11 | 4 | 1 | 0.87 | 0.85 | 0.96 | 0.90 | 0.97 | ||
KNN | High | 7 | 29 | 2 | 0 | 0.95 | 0.87 | 0.78 | 1.00 | 0.88 | 0.98 |
Low | 6 | 29 | 1 | 2 | 0.92 | 0.86 | 0.75 | 0.80 | 0.99 | ||
Medium | 20 | 13 | 2 | 3 | 0.87 | 0.91 | 0.87 | 0.89 | 0.95 |
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García Villena, E.; Pascual Barrera, A.; Álvarez, R.M.; Dzul López, L.A.; Tutusaus Pifarré, K.; Vidal Mazón, J.L.; Miró Vera, Y.A.; Brie, S.; López Flores, M.A. Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean. Appl. Sci. 2022, 12, 11188. https://doi.org/10.3390/app122111188
García Villena E, Pascual Barrera A, Álvarez RM, Dzul López LA, Tutusaus Pifarré K, Vidal Mazón JL, Miró Vera YA, Brie S, López Flores MA. Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean. Applied Sciences. 2022; 12(21):11188. https://doi.org/10.3390/app122111188
Chicago/Turabian StyleGarcía Villena, Eduardo, Alina Pascual Barrera, Roberto Marcelo Álvarez, Luís Alonso Dzul López, Kilian Tutusaus Pifarré, Juan Luís Vidal Mazón, Yini Airet Miró Vera, Santiago Brie, and Miguel A. López Flores. 2022. "Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean" Applied Sciences 12, no. 21: 11188. https://doi.org/10.3390/app122111188
APA StyleGarcía Villena, E., Pascual Barrera, A., Álvarez, R. M., Dzul López, L. A., Tutusaus Pifarré, K., Vidal Mazón, J. L., Miró Vera, Y. A., Brie, S., & López Flores, M. A. (2022). Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean. Applied Sciences, 12(21), 11188. https://doi.org/10.3390/app122111188