Can we project well-being? Towards integral well-being projections in climate models and beyond

The paper addresses whether and how future human well-being can be projected beyond traditional GDP-based approaches. It situates the research within rapid socio-economic development and environmental degradation (notably climate change and biodiversity loss) and critiques GDP’s dominance in projections despite its inability to capture overall well-being, inequality, and sustainability. The study identifies a gap: Beyond-GDP indicators are rarely projected, limiting policy relevance. It proposes projecting the Human Development Index (HDI) to 2100 under the IPCC’s Shared Socioeconomic Pathways (SSPs), exploring drivers of change and the missing feedbacks from climate change to well-being. The research aims to produce long-term HDI trajectories for 161 countries, decompose drivers (health, education, income), assess inequality, and preliminarily integrate environmental feedbacks (air pollution and warming) to move towards integral well-being projections.

A systematic snowballing literature review synthesizes quantified impacts of air pollution and climate change on HDI determinants: health, education, and income. Health pathways include meteorological changes, extreme events, infectious disease dynamics, and air pollution effects, with evidence of increasing mortality and morbidity, including mental health impacts; vulnerability is higher in poorer, warmer regions. Education is affected by heat and extreme events (reducing attendance and performance) and by pollution-related health effects in children; adaptation capacity mediates impacts. Income effects operate via reduced labor productivity and work hours in hotter, humid conditions, crop yield losses, increased health costs, and capital losses from disasters and sea-level rise, with disproportionate impacts in low- and middle-income tropical/subtropical regions. This review guides a qualitative reassessment of HDI projections by comparing 2010 vs. 2100 SSP trends in pollutants (BC, SO2, NO2) and global mean temperature from IAM-SSP baselines.

The study comprises two parts: (1) Quantitative HDI projections using SSP baselines; (2) Qualitative reassessment including environmental feedbacks based on literature. 1) HDI projection and analysis: - Indicators: Life expectancy at birth (health), mean years of schooling (education), and GDP per capita (income). Due to data availability under SSPs, education is proxied by mean years of schooling (15+) and income by GDP per capita (not GNI). - Normalization: Min–max scaling to dimension indices in [0,1] using extended 130-year reference bounds: life expectancy 20–110 years; mean years of schooling 0–17; income uses ln scaling with bounds $ln(100)$ to $ln(402,000)$ (2005 Int$). - HDI calculation: Geometric mean of the three indices. Global HDI is population-weighted across countries. - Modifications vs UNDP: Uses future-extended maxima leading to lower pre-2015 HDI values; substitutes UNDP’s education and income indicators as above to enable long-term projections. - Scenarios: Five SSP baseline scenarios (no additional climate policy) providing population, education, and GDP projections (1970–2100) for 161 countries. - Inequality: Between-country Gini coefficient computed annually for HDI and for each dimension. - Decomposition: Sun (1998) method to attribute HDI changes over three 40-year periods (1970–2010, 2015–2055, 2060–2100) to health, education, and income index changes. 2) Environmental feedback assessment: - Systematic snowballing literature review of quantified impacts of air pollution and climate change on health, education, income. - Qualitative integration using IAM-SSP baseline projections (IIASA SSP Database) for black carbon, SO2, NO2, and global mean temperature. Direction and relative magnitude of adjustments to HDI determinants are inferred by comparing 2010 vs. 2100 levels under each SSP. Data: 161 countries (96% of global population, 93% of global GDP in 2010). Population, life expectancy, education from WIC; GDP per capita from Cuaresma (2017), with OLS extrapolation for missing histories in 25 countries; pollutant and temperature projections from IIASA SSP database.

- Global HDI trajectories: All SSPs project improvements from near-low toward higher levels by 2100, but with divergence: • SSP1 (Sustainability) and SSP5 (Fossil-fueled development): reach very high global well-being (HDI_global,2100 > 0.80), with SSP5 highest at ~0.84. • SSP2 (Middle of the road): reaches high well-being (HDI_global,2100 ≈ 0.74). • SSP3 (Regional rivalry) and SSP4 (Inequality): slow growth and late-century stagnation; only medium well-being on average by 2100 (HDI_global,2100 < 0.6). - Drivers of change (decomposition): In SSP1 and SSP5, education drives early gains (2015–2055), while health dominates later (2060–2100). In SSP3 and SSP4, limited progress in education (especially 2060–2100) explains stagnation; across all SSPs, health’s contribution grows over time. - Inequality: Between-country HDI inequality has declined historically. Future inequality continues to decline under SSP1, SSP2, SSP5 (with similar accelerated reductions), driven by improvements across health, education, and income. Under SSP3/SSP4, inequality reductions are modest; health and income inequality may reverse trend, reflecting slower progress in poorer, populous countries and slowing convergence among rich countries. By 2100 under SSP3/SSP4, education inequality remains high (mean years of schooling spanning ~2 to 16 years across countries). - Country outcomes: All 161 countries improve by 2100, but magnitudes differ. Under SSP1/SSP5, most currently low-HDI countries (many in Africa, parts of Asia and South America) reach high HDI; large developing economies (e.g., China, Iran, Russia) reach very high HDI. Under SSP3/SSP4, roughly a quarter of the global population and most African countries remain at low HDI; about a third at medium HDI; only a few advanced economies reach very high HDI. - Environmental feedbacks (indicative): IAM baseline runs show by 2100 global mean temperature rises ~3.0°C (SSP1) to ~5.1°C (SSP5) above pre-industrial; black carbon emissions range from ~2.0 Mt/yr (SSP1) to ~5.8 Mt/yr (SSP3). Literature indicates substantial health, education, and income damages from higher temperatures, extreme events, and air pollution, with heterogeneous regional impacts. SSP5 presents countervailing feedbacks (improved SO2 air quality via strong pollution controls vs. worst warming). Accounting for feedbacks likely raises HDI in SSP1 relative to initial projections and lowers HDI in SSP3; SSP5 effects are ambiguous due to opposing forces.

The analysis reveals a paradox: despite divergent narratives, SSP1 (sustainability) and SSP5 (fossil-fueled development) yield similarly high HDI trajectories when environmental feedbacks are omitted, indicating that current SSP/IAM frameworks inadequately capture climate–well-being feedbacks. The study argues for integrating explicit feedbacks from climate change and air pollution into well-being projections. Health emerges as a pivotal channel linking climate, the economy, and education, and temperature is a cross-cutting indicator of warming and extreme event risks affecting all HDI dimensions. Incorporating these mechanisms would produce more realistic, policy-relevant well-being trajectories, differentiate pathways, and better reflect distributional outcomes. The findings emphasize the need for interdisciplinary collaboration among well-being researchers, climate scientists, and modelers to embed comprehensive feedback loops into climate-economic models and scenario frameworks.

The paper delivers one of the first global HDI projections to 2100 for 161 countries using SSP baselines, decomposes drivers of change, and evaluates inequality dynamics. It highlights substantial variation in outcomes across SSPs, identifies education as an early driver and health as increasingly important later, and shows that omitting climate feedbacks can yield counterintuitive results (e.g., similar HDIs for SSP1 and SSP5). A preliminary qualitative reassessment using pollution and temperature trends indicates that integrating environmental feedbacks would likely improve SSP1 outcomes and worsen SSP3 outcomes, with ambiguous net effects for SSP5 due to opposing forces. The authors call for next-generation models and scenarios that explicitly link climate, health, education, and income; expand Beyond-GDP coverage; and operate at finer spatial scales to inform policy. Future research should incorporate comprehensive feedbacks, climate justice, mental health, and additional sustainability metrics (e.g., Inclusive/Comprehensive Wealth, Planetary Boundaries), and explore policy pathways (e.g., work patterns) affecting well-being.

- Indicator scope: HDI captures health, education, and income but omits broader well-being aspects (e.g., mental health, social cohesion, conflict) and many sustainability dimensions. - Feedback omission in baseline: Core projections neglect explicit climate–well-being feedbacks, potentially overstating HDI in high-warming pathways and understating damages in vulnerable regions. - Distributional granularity: Between-country inequality assessed; within-country disparities and climate justice considerations are not captured. - Impact channels: Mental health effects, conflict risks, and heterogeneous vulnerabilities are acknowledged but not modeled; many damages may be underestimated by current economics-centered IAMs. - Data and indicator substitutions: Use of mean years of schooling (15+) and GDP per capita instead of UNDP’s education dual-indicators and GNI may affect comparability; extended normalization bounds lower historical HDI levels. - Qualitative feedback integration: Environmental feedback assessment is directional and relative, not a quantified re-estimation of HDI; mechanisms across pollutants and temperature are not directly comparable.