Rethinking Climate Models: How to Align Climate Action with the Sustainable Development Goals

Redacción HC
22/07/2024

As the clock ticks toward the 2030 deadline for the Sustainable Development Goals (SDGs), one question looms large: are our climate models sophisticated enough to guide policies that advance both climate action and broader human development?

A recent academic essay published in PLOS Climate by Alexandros Nikas tackles this issue head-on. The author argues that Integrated Assessment Models (IAMs)—the cornerstone tools used to simulate future climate scenarios—are still falling short when it comes to capturing the complexity of sustainable development. These models excel at estimating emissions, temperature rises, and economic costs of climate policies, but they rarely integrate metrics for poverty, health, energy access, or water security.

It’s not just about reducing carbon, the essay insists. We must also know what that reduction will mean for people’s lives.

What’s Missing from Climate Models?

Beyond Carbon: The SDG Dimension

While IAMs have advanced significantly in simulating emissions trajectories, most of them ignore the ripple effects of climate policy across the broader SDG agenda—especially SDG 1 (No Poverty), SDG 3 (Good Health), SDG 6 (Clean Water), and SDG 7 (Clean Energy).

This raises a key policy question:

Can climate-economy models be enhanced to offer integrated projections across climate and development goals?

Nikas reviews six emerging approaches that aim to bridge this gap, advocating for models that are not only climate-accurate but also socially meaningful.

Six Pathways to Improve Climate-Economy Models

1. Mapping SDG Indicators to IAM Variables

Researchers are beginning to link core SDG indicators to internal IAM variables, such as mapping energy poverty to household electricity access or public health to air pollution exposure. This allows for quantitative tracking of co-benefits and trade-offs.

2. Opening the Model Architecture

Efforts like the DIAMOND and PRISMA projects are developing open-source IAMs that allow researchers to add SDG-relevant modules. However, the pace of integration is slow, and many models still fall short of providing guidance for 2030 targets.

3. Understanding SDG Interdependencies

IAMs are being repurposed to simulate synergies and conflicts between goals. For instance, a carbon tax might reduce emissions but also increase energy prices, disproportionately affecting low-income households.

4. Extracting Multiple Indicators from a Single Model

New tools allow for multi-metric outputs from one simulation—such as forecasting changes in income inequality, access to clean water, and dietary diversity—all within the same climate policy scenario.

5. Improving Data Governance and Infrastructure

For IAMs to reflect development goals accurately, they must be fed by interoperable and up-to-date datasets. This means linking global models to national statistical systems and ensuring cross-country comparability.

6. Scaling and Contextualizing Models

IAMs often lack granularity, especially for regional or local scales. Efforts are now underway to localize global models for use in Latin America, Africa, and Southeast Asia—regions where development challenges are acute and interlinked with climate risks.

Implications for Policymakers and Global Institutions

1. Evidence-Based Climate Policy

Governments can use improved IAMs to identify climate policies that also reduce poverty or improve public health, thus achieving multiple SDGs with one policy lever.

Climate policy must be a health, equity, and food policy too, the author writes.

2. Targeted Development Investments

By simulating social impacts, IAMs help international agencies like the UN, World Bank, or IDB better target investments—from rural electrification to clean water infrastructure—aligned with both emissions goals and human needs.

3. Guidance for National Planning

For Latin American countries, enhanced IAMs can support decisions on energy transition pathways that are also equitable and cost-effective. For instance, modeling scenarios that show how solar energy expansion impacts rural employment and nutrition.

Challenges and Next Steps

Despite progress, major limitations persist:

• Gaps in SDG data coverage, especially for equity, mental health, or sanitation
• Lack of regional model calibration
• Slow model evolution compared to the pace of global crises

The essay recommends four key steps for the modeling community:

1. Formalize the mapping of SDG indicators to IAMs
2. Prioritize model openness and modularity
3. Fill indicator gaps (e.g., education, inequality)
4. Pilot these enhanced models in real-world decision-making processes

A Call for Holistic Thinking

The takeaway is clear: climate models that ignore development indicators risk leading us toward solutions that fix the planet but fail people. In contrast, enhanced IAMs can serve as powerful tools for just transitions, guiding us toward futures that are low-carbon and high-wellbeing.

This isn’t a theoretical exercise—it’s a practical imperative. As Alexandros Nikas argues, "We can no longer afford climate models that see only carbon. We must teach them to see people, too."

Topics of interest

Climate