From a complex systems perspective, the cross-cause cost effectiveness model is inadequate, since it fails to fully take into consideration or model the complex interactions and interdependencies between cause areas. Did you know, for example, that combatting inequality (a global development goal) is also a proven way of reducing carbon emissions, i.e. reducing the existential risk of climate change, which in turn would reduce biodiversity loss (an animal welfare goal)?
I invite the RP team to consider two of many similar examples:
[1] The 2019 paper published in Nature Sustainability by Nerini et al., Connecting climate action with other Sustainable Development Goals:
“Abstract
The international community has committed to combating climate change and achieve 17 Sustainable Development Goals (SDGs).
Here we explore (dis)connections in evidence and governance between these commitments. Our structured evidence review suggests that climate change can undermine 16 SDGs, while combatting climate change can reinforce all 17 SDGs but under- mine efforts to achieve 12. Understanding these relationships requires wider and deeper interdisciplinary collaboration. Climate change and sustainable development governance should be better connected to maximize the effectiveness of action in both domains. The emergence around the world of new coordinating institutions and sustainable development planning represent promising progress.”
[2] Carbon emissions, income inequality and economic development
Abebe Hailemariam, Ratbek Dzhumashev, Muhammad Shahbaz
Empirical Economics 59 (3), 1139-1159, 2020
This paper investigates whether changes in income inequality affect carbon dioxide () emissions in OECD countries. We examine the relationship between economic growth and emissions by considering the role of income inequality in carbon emissions function. To do so, we use a new source of data on top income inequality measured by the share of pretax income earned by the richest 10% of the population in OECD countries. We also use Gini coefficients, as the two measures capture different features of income distribution. Using recently innovated panel data estimation techniques, we find that an increase in top income inequality is positively associated with emissions. Further, our findings reveal a nonlinear relationship between economic growth and emissions, consistent with environmental Kuznets curve. We find that an increase in the Gini index of inequality is associated with a decrease in carbon emissions, consistent with the marginal propensity to emit approach. Our results are robust to various alternative specifications. Importantly, from a policy perspective, our findings suggest that policies designed to reduce top income inequality can reduce carbon emissions and improve environmental quality.
In my view, Rethink Priorities should take on board the conclusion of these and similar papers by promoting ‘wider and deeper interdisciplinary collaboration’, and incorporating the results of that collaboration in your models.
Thanks for looking through our work and for your comment, Deborah. We recognise that different parts of our models are often interrelated in practice. In particular, we’re concerned about the problem of correlations between interventions too, as we flag here. This is an important area for further work. That being said, it isn’t clear that the cases you have in mind are problems for our tools. If you think, for instance, that environmental interventions are particularly good because they have additional (quantifiable or non-quantifiable) benefits, you can update the tool inputs (including the cause or project name) to reflect that and increase the estimated impact of that particular cause area. We certainly don’t mean to imply that climate change is an unimportant issue.
From a complex systems perspective, the cross-cause cost effectiveness model is inadequate, since it fails to fully take into consideration or model the complex interactions and interdependencies between cause areas. Did you know, for example, that combatting inequality (a global development goal) is also a proven way of reducing carbon emissions, i.e. reducing the existential risk of climate change, which in turn would reduce biodiversity loss (an animal welfare goal)?
I invite the RP team to consider two of many similar examples:
[1] The 2019 paper published in Nature Sustainability by Nerini et al., Connecting climate action with other Sustainable Development Goals:
“Abstract
The international community has committed to combating climate change and achieve 17 Sustainable Development Goals (SDGs). Here we explore (dis)connections in evidence and governance between these commitments. Our structured evidence review suggests that climate change can undermine 16 SDGs, while combatting climate change can reinforce all 17 SDGs but under- mine efforts to achieve 12. Understanding these relationships requires wider and deeper interdisciplinary collaboration. Climate change and sustainable development governance should be better connected to maximize the effectiveness of action in both domains. The emergence around the world of new coordinating institutions and sustainable development planning represent promising progress.”
[2] Carbon emissions, income inequality and economic development
Abebe Hailemariam, Ratbek Dzhumashev, Muhammad Shahbaz
Empirical Economics 59 (3), 1139-1159, 2020
This paper investigates whether changes in income inequality affect carbon dioxide () emissions in OECD countries. We examine the relationship between economic growth and emissions by considering the role of income inequality in carbon emissions function. To do so, we use a new source of data on top income inequality measured by the share of pretax income earned by the richest 10% of the population in OECD countries. We also use Gini coefficients, as the two measures capture different features of income distribution. Using recently innovated panel data estimation techniques, we find that an increase in top income inequality is positively associated with emissions. Further, our findings reveal a nonlinear relationship between economic growth and emissions, consistent with environmental Kuznets curve. We find that an increase in the Gini index of inequality is associated with a decrease in carbon emissions, consistent with the marginal propensity to emit approach. Our results are robust to various alternative specifications. Importantly, from a policy perspective, our findings suggest that policies designed to reduce top income inequality can reduce carbon emissions and improve environmental quality.
https://link.springer.com/article/10.1007/s00181-019-01664-x
https://www.researchgate.net/profile/Abebe-Hailemariam/publication/331551899_Carbon_Emissions_Income_Inequality_and_Economic_Development/links/5c7fcb91458515831f895d32/Carbon-Emissions-Income-Inequality-and-Economic-Development.pdf
In my view, Rethink Priorities should take on board the conclusion of these and similar papers by promoting ‘wider and deeper interdisciplinary collaboration’, and incorporating the results of that collaboration in your models.
Thanks for looking through our work and for your comment, Deborah. We recognise that different parts of our models are often interrelated in practice. In particular, we’re concerned about the problem of correlations between interventions too, as we flag here. This is an important area for further work. That being said, it isn’t clear that the cases you have in mind are problems for our tools. If you think, for instance, that environmental interventions are particularly good because they have additional (quantifiable or non-quantifiable) benefits, you can update the tool inputs (including the cause or project name) to reflect that and increase the estimated impact of that particular cause area. We certainly don’t mean to imply that climate change is an unimportant issue.