AI for UN Sustainable Development Goals Tracking

Module: Sustainable Tech | Difficulty: Premium

SDG Progress Index

where is indicator value, is target, and is weight.

Multi-Dimensional Poverty Index

where is headcount ratio and is intensity of deprivation.

Comparison

Python Implementation

import numpy as np

class SDGTracker:
    def __init__(self, n_goals=17, n_indicators=232):
        self.n_goals = n_goals
        self.n_indicators = n_indicators

    def progress_index(self, current, baseline, target, weights=None):
        if weights is None:
            weights = np.ones(len(current)) / len(current)
        progress = (current - baseline) / (target - baseline + 1e-10)
        return np.sum(weights * np.clip(progress, 0, 1))

    def predict_achievement(self, historical_data, target_year=2030):
        n_years = len(historical_data)
        years = np.arange(n_years)
        predictions = {}
        for indicator, values in historical_data.items():
            if len(values) > 1:
                slope = np.polyfit(years, values, 1)[0]
                current = values[-1]
                remaining_years = target_year - (2024 - n_years + 1)
                predicted = current + slope * remaining_years
                predictions[indicator] = predicted
        return predictions

    def multidimensional_impact(self, indicators, dimensions):
        impact_scores = {}
        for dim, ind_list in dimensions.items():
            dim_values = [indicators.get(ind, 0) for ind in ind_list]
            impact_scores[dim] = np.mean(dim_values)
        overall = np.mean(list(impact_scores.values()))
        return {'dimensional': impact_scores, 'overall': overall}

    def spillover_index(self, domestic_progress, external_effects):
        domestic = np.mean(list(domestic_progress.values()))
        external = np.mean(list(external_effects.values()))
        return 0.7 * domestic + 0.3 * external

Research Insight: Machine learning can predict SDG achievement probability for 232 indicators with 85% accuracy, enabling proactive policy interventions.