SDI for this https://www.sciencedirect.com/science/article/pii/S0921800919303386#tblfn0010

sdi.py

import math


values = [
    dict(gni_ppp=7480, life_expectancy=79.6, education_index=0.768,country='CUBA', material_footprint=8.04, co2_emissions=3.42),
    dict(gni_ppp=21000, life_expectancy=79.6, education_index=0.768,country='CUBA', material_footprint=8.04, co2_emissions=3.42),
    dict(gni_ppp=14086, life_expectancy=79.6, education_index=0.713,country='COSTA RICA', material_footprint=8.08, co2_emissions=2.66),
    dict(gni_ppp=53741, life_expectancy=79.2, education_index=0.9,country='US', material_footprint=32.36, co2_emissions=18.35),
    dict(gni_ppp=38146, life_expectancy=81.4, education_index=0.911,country='UNITED KINGDOM', material_footprint=22.57, co2_emissions=10.08),
]

mf_boundary = 6.8
co2_boundary = 1.74

for value in values:
    country = value['country']
    gni_ppp = value['gni_ppp']
    life_expectancy = value['life_expectancy']
    education_index = value['education_index']
    material_footprint = value['material_footprint']
    co2_emissions = value['co2_emissions']

    dev_index = ((math.log(gni_ppp) - math.log(100))/(math.log(20000) - math.log(100))*((life_expectancy - 25)/(85 - 25))*education_index)**(1./3.)
    averge_overshoot=math.sqrt((material_footprint/mf_boundary)*(co2_emissions/co2_boundary))
    eco_impact_index = 1 + (math.e**averge_overshoot - math.e**1)/(math.e**4 - math.e**1)

    print(f'[{country}] -  Development Index: {dev_index}. Eco Impact Index: {eco_impact_index}. SDI: {dev_index/eco_impact_index}')