Workshop Key Takeaways

Health and Energy Access Discussion

Chair: Bas van Ruijven (IIASA)

The largest co-benefits of mitigation with health and energy access include food/nutrition, transport/mobility, and indoor/outdoor air pollution. In all these areas, behavioral change plays a major role and could be improved in IAMs and mitigation scenarios. A challenge for integrating health into models is exactly related to this, as health dynamics are closely related to individual behavior. Future work needs to relate better to current and past realistic dynamics and good practice examples and modeling (policy) interventions based on this.

It is important to explore behavioral change and how policies can nudge such changes, but it is also important to consider distributional aspects such as impacts on low-income groups. In terms of energy access, the tradeoffs are clear (e.g. providing gas or electricity in areas of low energy access). The same goes for LNG cooling solutions or alternative biogas-based cooking. In Health there are trade offs when subsidies/taxation is regressive. For example, food taxation that harms diets of vulnerable communities if there is a policy when not taking into account individuals with low-incomes. In both cases, carbon pricing revenues could be used for transfers that could benefit poorer households.

Some knowledge wish list items for future exploration emerging from the discussion include: adding near/medium term milestones for SDG targets (this exists for climate mitigation targets but less for others), socioeconomic heterogeneity in response to policies such as food taxation, historical boundaries of change on different dimensions, numbers on elasticity such as price change in a certain good and its effect on that.

Biodiversity/food/water

Chair: Volker Krey (IIASA)

Biodiversity, food, and water are interlinked and have co-benefits, however, when it comes to policy, these three matters do not have a one-size fits all approach. It is highly relevant to evaluate these policies, spatially, temporally for impacts on people (e.g. Income or other socio-economic indicators) whether or not policies are feasible to implement.   

There is a possible need for an external model(s) to inform the IAMs to integrate policy options in terms of feasibility; IAMs solely may not be the best to do so. It would be good to think more broadly about modeling and potentially include links to other tools and analysis, e.g. empirical evidence. Furthermore, it would be important to incorporate policy makers from the very beginning to address relevant questions. 

Further exploration in nature-based pathways could be interesting to look at. Traditionally, IAMs look at global policy but looking at the national/regional level would be important. The issue of planning (city, regional, landscape) needs to be considered and is not regularly covered. Information taken up from more granular models for city-scales need to be incorporated into an IAMs. Local models and processes could represent behavior and with that, scale the knowledge up in a simple way by using parametric approaches (e.g. elasticity). Understanding drivers of change could help inform the process (e.g. drastic changes in food patterns in IAMs, but properly understanding what motivates this is important to take into consideration).

Conclusions

• IAMs are strong on Water/Energy/Land+Social synergies and trade-offs​
• SDG goals are often imposed as targets/model bounds, without specifying the policy measures to achieve those.​
• Many examples of synergies and co-benefits depend on policy coherence and comprehensive policies​
• Two main areas of potential improvement:​
• Improved representation of granular behavioral change ​
• Health/diets/mobility synergies depend strongly on individual choices​
• Improve link to policy modeling, understanding, empirical evidence and policy makers