Research · Human Wellbeing & Ecological Sustainability · Nordic Countries · 2000–2022

Wellbeing Economy: Beyond-GDP Data Analysis in Nordics 2000–2022

Main findings from my quantitative research: how three “beyond-GDP” measures of human wellbeing (Human Development Index, Social Progress Index, SDG Index) relate to the Ecological Footprint and Biocapacity across the five Nordic countries, 2000–2022. Built on the National Footprint & Biocapacity Accounts I compiled. The wider mapping of each Nordic country’s national wellbeing framework against the OECD Wellbeing dimensions and the UN SDG framework is in the full write-up (linked at the bottom). An interactive summary. By Marina Ermina, MA Environment & Natural Resources, University of Iceland.

Period:

Wellbeing indicator:

The analysis — wellbeing indicators vs the ecological account

This is a quantitative comparison. For the five Nordic countries across 2000–2022, it sets three “beyond-GDP” measures of human wellbeing — the Human Development Index (HDI), the Social Progress Index (SPI) and the SDG Index (SDGI) — against the Ecological Footprint and Biocapacity from the National Footprint & Biocapacity Accounts I compiled. It asks, in turn: how each country scores on wellbeing and on its ecological account; whether the footprint decouples from rising wellbeing over time; how strongly each index correlates with the footprint (Pearson r); and — looking past the flattering headline scores — which individual SPI and SDGI indicators quietly underperform.

Indicator snapshot —

Each value shown against the world benchmark. The idea: judge progress not by one number (like GDP) but by a set of wellbeing indicators alongside the ecological account.

The Nordic paradox — high wellbeing, high footprint

Selected wellbeing indicator (switch HDI / SPI / SDGI above) vs Ecological Footprint per capita. The dashed line is Earth’s biocapacity per person — a “one-planet” fair share; every Nordic country sits far to its right.

Indicator:

Ecological balance

Footprint (consumption) vs Biocapacity (regeneration), per capita — selected period. Red = ecological deficit.

Signs of decoupling? Wellbeing up, footprint down

Trend across the three periods for : the selected wellbeing indicator rising while Ecological Footprint per capita falls. Each series has its own axis (left / right) so they read independently.

Indicator:

Wellbeing vs footprint — correlation

Pearson correlation with Ecological Footprint, by country.

Inside the indices — where the headline scores hide problems indicator-level

Overall SPI (~90) and SDG Index (~78–86) make all five Nordics look uniformly excellent. Disaggregating to the indicator level tells a different story: a consistent set of weak spots — housing affordability, health, diet, and the environmental / global-responsibility goals — that a single number hides entirely.

Index:

How to read:Weakest performance (<36)Low–moderate (37–55)Moderate–good (56–69)High performance (70+)

Behind the numbers — data & SQL methods

These are excerpts from my actual analysis SQL — multi-CTE statistical queries joining three MySQL databases: the National Footprint & Biocapacity Accounts I produced, plus the HDI and SDG-Index tables. The full analysis lives in CLUSTER.sql and All_research_ideas.sql.

5Nordic countries (of ~180 in DB)

2000–20223 periods

HDI · SPI · SDGI · EFindicators

3MySQL databases joined

MySQL · Python · Rtoolchain

1 · Period means — the query behind this dashboard

-- MEAN
SELECT
  ef.country,
   CASE
        WHEN ef.year BETWEEN 2000 AND 2007 THEN '2000-2007'
        WHEN ef.year BETWEEN 2008 AND 2015 THEN '2008-2015'
        WHEN ef.year BETWEEN 2016 AND 2022 THEN '2016-2022'
    END AS period,
	ROUND(AVG(ef.total),2) AS mean_efc,
    ROUND(AVG(hdi.hdi),2) AS mean_hdi,
	-- ROUND(sp.SPI_score,2) as mean_spi,
	ROUND(AVG(sd.SDG_index_score),2) AS mean_sdgi,
    ROUND(AVG(ef.Crop_Land),2) AS mean_crop_land,
    ROUND(AVG(ef.Grazing_Land), 2) AS mean_grazing_land,
    ROUND(AVG(ef.Forest_Land), 2) AS mean_forest_land,
    ROUND(AVG(ef.Fishing_Ground), 2) AS mean_fishing_ground,
    ROUND(AVG(ef.Built_up_Land), 2) AS mean_built_up_land,
    ROUND(AVG(ef.Carbon), 2) AS mean_carbon
   
FROM 
    `footprint_24_ult6_final2`.`Summary_Results` ef
JOIN `6599_marina`.`HDI_data_all_ult` hdi 
    ON ef.country = hdi.country AND ef.year = hdi.year
JOIN 6599_marina.SDG_index sd
    ON ef.country = sd.country AND ef.year = sd.year
-- JOIN `6599_marina`.`SPI` sp
   -- ON ef.country = sp.country and ef.year = sp.year      
WHERE 
	ef.record = 'EFConsPerCap'
    AND ef.year between 2000 and 2022
GROUP BY 
    ef.country, period
ORDER BY 
    ef.country, period;

2 · Pearson correlation (Footprint vs HDI) with interpretation

-- INCLUDE. Pearson Correlation for 2000-2022 with interpretation
WITH base_data AS (
    SELECT 
        ef.country,
        ef.year,
        ef.total AS efc,
        hdi.hdi AS hdi
    FROM `footprint_24_ult6_final2`.`Summary_Results` AS ef
    JOIN `6599_marina`.`HDI_data_all_ult` hdi 
        ON ef.country = hdi.country AND ef.year = hdi.year
        WHERE ef.record = "EFConsPerCap"
      AND ef.year BETWEEN 2000 AND 2022
),
means AS (
    SELECT
        country,
        AVG(efc) AS mean_efc,
        AVG(hdi) AS mean_hdi
    FROM base_data
    GROUP BY country
),
stats AS (
    SELECT
        b.country,
        COUNT(*) AS n,
        AVG((b.efc - m.mean_efc) * (b.hdi - m.mean_hdi)) AS covariance,
        SQRT(AVG(POWER(b.efc - m.mean_efc, 2))) AS stddev_efc,
        SQRT(AVG(POWER(b.hdi - m.mean_hdi, 2))) AS stddev_hdi
    FROM base_data b
    JOIN means m ON b.country = m.country
    GROUP BY b.country
)
SELECT 
    country,
    ROUND(covariance, 6) AS covariance,
    ROUND(stddev_efc, 6) AS stddev_efc,
    ROUND(stddev_hdi, 6) AS stddev_hdi,
    ROUND(covariance / (stddev_efc * stddev_hdi), 4) AS pearson_r,
    CASE
    WHEN ABS(ROUND(covariance / (stddev_efc * stddev_hdi), 4)) < 0.2 THEN 'Neutral'
    WHEN ROUND(covariance / (stddev_efc * stddev_hdi), 4) < -0.8 THEN 'Very strong negative correlation'
    WHEN ROUND(covariance / (stddev_efc * stddev_hdi), 4) < -0.6 THEN 'Strong negative correlation'
    WHEN ROUND(covariance / (stddev_efc * stddev_hdi), 4) < -0.4 THEN 'Moderate negative correlation'
    WHEN ROUND(covariance / (stddev_efc * stddev_hdi), 4) < 0 THEN 'Mild negative correlation'
    WHEN ROUND(covariance / (stddev_efc * stddev_hdi), 4) < 0.4 THEN 'Mild positive correlation'
    WHEN ROUND(covariance / (stddev_efc * stddev_hdi), 4) < 0.6 THEN 'Moderate positive correlation'
    WHEN ROUND(covariance / (stddev_efc * stddev_hdi), 4) < 0.8 THEN 'Strong positive correlation'
    ELSE 'Very positive correlation'
    END AS interpretation
FROM stats
ORDER BY country;

3 · Sustainability clusters — HDI / SDGI / footprint-to-biocapacity

-- INCLUDE. separte cluster_labels based on Country_biocapacit for period 2000 - 2022
SELECT 
    n.country,
    n.year,
    n.hdi,
    n.sdg_index_score,
	n.ecological_footprint,
	n.country_biocapacity,
    n.ef_to_biocap_country,
    CONCAT( 
        CASE 
            WHEN n.hdi < 0.550 THEN 'Low HDI'
            WHEN n.hdi >= 0.550 AND n.hdi < 0.700 THEN 'Medium HDI'
            WHEN n.hdi >= 0.700 AND n.hdi < 0.800 THEN 'High HDI'
            WHEN n.hdi >= 0.800 THEN 'Very High HDI'
        END
    ) AS cluster_label_HDI,
CONCAT( 
        CASE 
            WHEN n.sdg_index_score >= 70 THEN 'High perfomance SDG'
            WHEN n.sdg_index_score >= 50 AND n.sdg_index_score < 70 THEN 'Medium performance SDG'
            ELSE 'Low performance SDG'
        END
    ) AS cluster_label_SDGI,
CONCAT( 
        CASE 
            WHEN n.ef_to_biocap_country > 1 THEN 'Unsustainable'
            ELSE 'Sustainable'
        END
    ) AS cluster_label_EFC

FROM (
    SELECT 
        h.country,
        h.year,
        h.hdi,
        s.sdg_index_score,
        ef.Total AS ecological_footprint,
        country_bio.Total AS country_biocapacity,
        (ef.Total / country_bio.Total) AS ef_to_biocap_country
    FROM `6599_marina`.`HDI_data_all_ult` h
    JOIN `6599_marina`.`SDG_index` s 
        ON h.country = s.country AND h.year = s.year
    JOIN `footprint_24_ult6_final2`.`Summary_Results` ef 
        ON h.country = ef.country AND h.year = ef.year
    JOIN `footprint_24_ult6_final2`.`Summary_Results` country_bio 
        ON ef.year = country_bio.year AND ef.country=country_bio.country
     WHERE 
          ef.record = 'EFConsPerCap'
      AND h.year BETWEEN 2000 and 2022 
      AND s.year BETWEEN 2000 and 2022
      AND ef.year BETWEEN 2000 and 2022
      AND country_bio.record = 'BiocapPerCap'
      AND country_bio.year BETWEEN 2000 and 2022
) n 
ORDER BY n.country, n.year;

4 · Footprint components vs HDI — per-land-type Pearson r

-- EFC component correlation against HDI
 WITH base_data AS (
    SELECT
        ef.country,
        ef.year,
        ef.Carbon,
        ef.Crop_Land,
        ef.Grazing_Land,
        ef.Forest_Land,
        ef.Fishing_Ground,
        ef.Built_up_Land,
        ef.Total,
        hdi.hdi,
        hdi.hdicode
    FROM `footprint_24_ult6_final2`.`Summary_Results` ef
    JOIN `6599_marina`.`HDI_data_all_ult` hdi 
        ON ef.country = hdi.country AND ef.year = hdi.year
WHERE ef.record = 'EFConsPerCap'
      AND ef.year BETWEEN 2000 AND 2022
),
means AS (
    SELECT
        country,
        AVG(Carbon) AS mean_carbon,
        AVG(Crop_Land) AS mean_crop,
        AVG(Grazing_Land) AS mean_grazing,
        AVG(Forest_Land) AS mean_forest,
        AVG(Fishing_Ground) AS mean_fishing,
        AVG(Built_up_Land) AS mean_built,
        AVG(Total) AS mean_total,
        AVG(hdi) AS mean_hdi
    FROM base_data
    GROUP BY country
),
stats AS (
    SELECT
        b.country,
        b.hdi,
        b.hdicode,
        -- Carbon
        AVG((b.Carbon - m.mean_carbon) * (b.hdi - m.mean_hdi)) AS cov_carbon,
        SQRT(AVG(POWER(b.Carbon - m.mean_carbon, 2))) AS std_carbon,
        -- Crop
        AVG((b.Crop_Land - m.mean_crop) * (b.hdi - m.mean_hdi)) AS cov_crop,
        SQRT(AVG(POWER(b.Crop_Land - m.mean_crop, 2))) AS std_crop,
        -- Grazing
        AVG((b.Grazing_Land - m.mean_grazing) * (b.hdi - m.mean_hdi)) AS cov_grazing,
        SQRT(AVG(POWER(b.Grazing_Land - m.mean_grazing, 2))) AS std_grazing,
        -- Forest
        AVG((b.Forest_Land - m.mean_forest) * (b.hdi - m.mean_hdi)) AS cov_forest,
        SQRT(AVG(POWER(b.Forest_Land - m.mean_forest, 2))) AS std_forest,
        -- Fishing
        AVG((b.Fishing_Ground - m.mean_fishing) * (b.hdi - m.mean_hdi)) AS cov_fishing,
        SQRT(AVG(POWER(b.Fishing_Ground - m.mean_fishing, 2))) AS std_fishing,
        -- Built-up
        AVG((b.Built_up_Land - m.mean_built) * (b.hdi - m.mean_hdi)) AS cov_built,
        SQRT(AVG(POWER(b.Built_up_Land - m.mean_built, 2))) AS std_built,
        -- Total
        AVG((b.Total - m.mean_total) * (b.hdi - m.mean_hdi)) AS cov_total,
        SQRT(AVG(POWER(b.Total - m.mean_total, 2))) AS std_total,
        -- HDI std
        SQRT(AVG(POWER(b.hdi - m.mean_hdi, 2))) AS std_hdi
    FROM base_data b
    JOIN means m ON b.country = m.country
    GROUP BY b.country
)
SELECT 
    country,
    ROUND(cov_carbon / (std_carbon * std_hdi), 4) AS r_carbon,
    ROUND(cov_crop / (std_crop * std_hdi), 4) AS r_crop,
    ROUND(cov_grazing / (std_grazing * std_hdi), 4) AS r_grazing,
    ROUND(cov_forest / (std_forest * std_hdi), 4) AS r_forest,
    ROUND(cov_fishing / (std_fishing * std_hdi), 4) AS r_fishing,
    ROUND(cov_built / (std_built * std_hdi), 4) AS r_built,
    ROUND(cov_total / (std_total * std_hdi), 4) AS r_total,
    hdi,
    hdicode
FROM stats
ORDER BY country;

These are my original queries, verbatim from All_research ideas_INTERNSHIP.sql and CLUSTER.sql — the Pearson r values, cluster labels and period means used across this dashboard all come from them.

What the data shows

Averages flatter the Nordics. A GDP-style single score would call them a success. Setting three wellbeing indices (HDI, Social Progress, SDG Index) against the Ecological Footprint shows the fuller picture: world-leading wellbeing, a real decoupling signal (footprint falling as wellbeing rises), but persistent ecological overshoot — extreme in Iceland, whose footprint runs many times its biocapacity.

But drilling deeper flips the picture. The detailed indicators expose clear failures in specific places — land and water (SDG 14 & 15), consumption and the impacts embodied in imports (SDG 12 — from e-waste to imported deforestation), and housing. A single headline score hides every one of them — which is exactly why the disaggregated, beyond-GDP view of the data matters.

Further reading: the full analysis — including the mapping of each Nordic country’s national wellbeing framework onto the OECD Wellbeing dimensions and the UN SDGs — is in the thesis: Exploring Indicators of Human Wellbeing and Ecological Sustainability in the Nordic Countries →

Data source: Ermina, M. (2025), Exploring Indicators of Human Wellbeing and Ecological Sustainability in the Nordic Countries, MA thesis, University of Iceland — Tables 16, 18 & 22. Underlying indicators: Ecological Footprint & Biocapacity (Global Footprint Network / NFBA); Human Development Index (UNDP); Social Progress Index (Social Progress Imperative); SDG Index (SDSN). Earth biocapacity reference ≈ global BC per capita from the same accounts.
Method & tools: indicators assembled in MySQL, analysed in R, and reproduced in Python (nordic_analysis.py), then visualised here as an interactive summary. Full indicator tables are in the thesis appendix.