Climate change has widespread and profound impacts on the well-being of people around the world, from rising temperatures and extreme weather events to water scarcity and rising sea levels. Research identified more than 400 the different pathways through which societies experience climate stress. Despite the severity of these consequences, the dynamics of the impacts of climate change on well-being are largely excluded from integrated environment-society-economy (ESE) models that are crucial for policymaking.

The problem of exogenous development

Many influential predictive tools, notably the process-based integrated assessment models (PB-IAMs) used in the Intergovernmental Panel on Climate Change (IPCC) reports, do not or rarely include climate impacts on well-being. These models often assume demographic and economic developments as exogenous (determined from outside) through so-called shared socioeconomic pathways (SSPs).

Models that lack feedback loops linking climate change impacts to socio-economic consequences lead to problematic and illogical results. For example, a fossil fuel-based development scenario (SSP 5) that predicts 5 degrees of warming by 2100 was found to lead to the same upward trajectory in the Human Development Index (HDI) as a sustainable development scenario (SSP 1) that predicts only 3 degrees of warming. The assumption that an additional 2 degrees of warming would not affect human development is absurd.

Also, cost-benefit-oriented models (CB-IAMs) used to calculate the societal cost of carbon (SCC) focus mainly on economic welfare (GDP) and have been criticized for simplistic economic damage functions and the use of high discount rates, which can lead to underestimation of future damages.

Quantified paths and missing links

An analysis of the literature and 18 ESE models showed that while there are more quantified pathways of influence in the literature than are currently included in ESE models, three areas are relatively well represented in both spheres: temperature-related mortality, food security and GDP. Ten models include temperature-related mortality (e.g. E4A, WILIAM), and GDP is included in fourteen models. Impacts on crop yields related to temperature or water availability are included in ten models.

Most other impacts quantified in the literature are poorly or not at all present in ESE models. Promising avenues for model development include:

1. Health: Although some models include temperature-related mortality, they are underrepresented infectious diseases (e.g. malaria and dengue) and respiratory, cardiovascular, and neurological outcomes.
2. Mental health: Although quantitative studies confirm the impacts of increased temperatures and extreme events on psychological stress, depression, anxiety, and suicide, none of the models reviewed include this area.
3. Work and productivity: Occupational health and work productivity are affected by heat and are quantified (e.g. estimated lost working hours), but only a few models (WILIAM, ENVISAGE, NGFS) include this impact.
4. Security and governance: Increasing temperatures or extreme precipitation have been shown to increase the risk of conflict and violence (by 4 % for interpersonal violence and 14 % for intergroup conflict), but no model has included the impacts on conflict.
5. Inequality and poverty: Poor communities are more vulnerable and exposed to environmental shocks. Climate change has likely contributed to increasing inequality between countries. Impacts on poverty through impacts on agriculture and health are quantified but are only limitedly included in ESE models (e.g. iSDG, E4A).

A challenge for the future

Relationships that are well quantified in the literature but underrepresented in models—particularly infectious diseases, respiratory and cardiovascular disease outcomes, occupational health, air quality, and biodiversity loss—represent promising avenues for the development of ESE models.

For useful policy evaluations, it is essential that models incorporate feedbacks between climate change and socio-economic development. It is also important to take into account unevenly distributed adaptive capacity, especially in low- and middle-income countries, and non-linear dynamics or turning points (tipping points) that could cause historical relationships to no longer apply to future developments.

Integrating these complex influences is key for models to provide more accurate and policy-relevant guidance, thus avoiding suboptimal or incorrect recommendations. JRi

Study published in The Lancet Planetary Health magazine