The climate data landscape in sovereign bonds

Sovereign climate datasets have evolved in recent years, and investors may seek to understand them further. We outline the main sources, metrics and frameworks, and why methodology, coverage and assumptions matter when applying them.

Introduction

Early efforts to integrate climate considerations focused on equities and corporate credit. Sovereign bonds, however, represent a substantial share of major fixed income benchmarks (for example, Treasuries represent 54% of the Bloomberg Global Aggregate Bond Index¹) and have historically received comparatively less focus within climate aware investment analysis.

This paper explores the evolving climate data landscape for sovereign issuers, examining the tools, metrics, and frameworks available to investors seeking to assess climate-related risks and opportunities. It highlights that while the availability and sophistication of sovereign climate datasets have expanded in recent years, their interpretation requires careful consideration of differences in methodology, assumptions, and coverage. We outline the main sources and types of sovereign climate data before concluding with illustrative portfolio level use cases.

Overview of the Data Landscape

Data Sources

The sovereign climate data landscape has evolved in recent years, and investors may draw on a combination of publicly available datasets and specialized third-party vendor data (table 1). These data sources vary in scope, methodology, and intended use, and collectively cover both qualitative and quantitative dimensions of climate-related risks and opportunities at the country level.

Publicly available datasets are often developed by academic institutions, international organizations, or non profit initiatives, and typically focus on transparency, comparability, and broad country coverage. These sources are commonly used as inputs for high level assessments of climate exposure, policy orientation, and vulnerability to extreme events.

In parallel, a range of specialized third party data vendors provide proprietary sovereign climate datasets that are more directly tailored to investment use cases. They offer structured indicators, scores, and underlying data that may be integrated into portfolio analytics, risk frameworks, or index construction processes.

Across both public and third-party data sources, sovereign climate data generally includes qualitative assessments such as ratings, scorecards, and policy evaluations as well as quantitative measures, including emissions, energy use, and other country level metrics. While the breadth of available data has increased, differences in coverage, assumptions, and methodologies remain, underscoring the importance of understanding the characteristics and limitations of individual datasets when applying them in sovereign bond analysis.

Types of Data

A variety of sovereign climate datasets differ along several key dimensions, including whether they focus primarily on transition or physical risks, whether they seek to measure investment risk and opportunity or alignment with climate objectives, and whether they are backward looking or forward looking in nature. Emissions based metrics, such as production and consumption based greenhouse gas emissions, are a common starting point for sovereign climate analysis. These indicators are generally transition-focused and backward-looking. While historical emissions profiles may indicate where a sovereign stands today in terms of transition-risk exposure, they say little about where it is heading – offering limited insight into future policy direction or transition readiness.

Some of the more recently developed metrics place greater emphasis on forward looking assessments of climate alignment and transition preparedness. Datasets such as the Assessing Sovereign Climate-related Opportunities and Risks (ASCOR)²  sovereign assessments and implied temperature rise metrics focus primarily on transition risk and are designed to evaluate how current policies, targets, and trajectories compare with stated climate objectives. These datasets can be used to assess alignment, which may provide additional context for understanding sovereign exposures in relation to transition pathways, complementing traditional macroeconomic and fiscal analysis.

In contrast, datasets such as the Climate Change Performance Index (CCPI)³  Overall Country Score combine elements of transition assessment with a backward looking orientation, drawing on observed policy outcomes and performance indicators.

Physical risk-focused datasets, such as Notre Dame Global Adaptation Initiative (ND GAIN)⁴  Country Index Scores, emphasize vulnerability and resilience to climate impacts and are more closely aligned with forward looking assessments of risk and opportunity related to climate adaptation. Some metrics, including climate value-at-risk, attempt to integrate both physical and transition dimensions into a single forward looking framework, highlighting the diversity of approaches within the sovereign climate data landscape.

Spotlight on Emissions Data

Emissions data are among the most widely available climate-related metrics in sovereign bond analysis and may be used as one of the inputs for assessing transition-related risks and opportunities. In the sovereign context, emissions are commonly defined on a territorial basis and categorized as either production based or consumption based emissions.

Production-based emissions capture greenhouse gas emissions generated within a country’s national territory, including emissions associated with domestic activity and exports. This definition broadly aligns with that of Scope 1 emissions⁵  and is the basis used by the United Nations Framework Convention on Climate Change (UNFCCC) for national reporting and for defining countries’ Nationally Determined Contributions (NDCs). As a result, production-based emissions tend to be more standardized, more widely available, and more timely than alternative measures.

Consumption based emissions seek to capture emissions associated with domestic consumption, including those embedded in imports, and can be expressed as the sum of production-based and imported emissions, net of emissions associated with exports. While consumption-based emissions may provide additional context on demand driven carbon exposure, they typically rely on modeling and estimation techniques and are often published with a lag relative to production based data. For this reason, their use in sovereign portfolios may be more challenging.

To facilitate cross country comparisons, normalization factors are commonly applied. In some methodological approaches, production-based emissions are often scaled by purchasing power parity (PPP)-adjusted GDP, while consumption emissions are more frequently expressed on a per capita basis. These differences in definitions, scopes, and normalization approaches can materially affect cross country comparisons, as shown in Figure 1 below. In other words, for an investor targeting emissions metrics in their portfolio construction, the relative ranking of countries may vary depending on the metric chosen, which can in turn affect portfolio weights.

Spotlight on Alignment Data

Sovereign alignment metrics are generally designed to assess how closely a country’s policies, targets, and emissions trajectories are aligned with stated climate objectives, such as net-zero or Paris aligned pathways. In practice, alignment metrics can be grouped into two broad categories:

• Qualitative – framework based assessments
• Quantitative – model driven indicators

Qualitative alignment metrics typically rely on assessments of policy ambition, targets, implementation frameworks, and disclosure. Datasets such as ASCOR sovereign assessments and Climate Action Tracker draw on a combination of emissions data, policy analysis, and expert judgment to evaluate countries’ transition preparedness and direction of travel. These inputs may be mapped to a range of alignment frameworks developed by different industry groups, such as the Institutional Investors Group on Climate Change (IIGCC) Net Zero Investment Framework (NZIF) Sovereign Alignment Framework. Under this framework, sovereigns may be classified along an alignment scale – ranging from “Not Aligned” to “Achieving Net Zero” – based on multiple criteria, including ambition, targets, emissions performance, decarbonization strategy, disclosure, and capital allocation.

Quantitative alignment metrics, by contrast, seek to express sovereign alignment through modeled, forward looking indicators such as Implied Temperature Rise (ITR) or temperature score metrics that estimate the global temperature increase that would result if a country’s projected emissions pathway were extrapolated to the global economy. These metrics are typically derived from emissions trajectories, scenario assumptions, and decarbonization pathways. They can be used to assess relative Paris alignment among countries, and, given that similar temperature-based metrics are available for other asset classes, they may also support cross asset class comparability. It is useful to recognize the limitations of these measures as well; temperature based metrics rely on complex methodologies and assumptions, and reported outcomes may vary across data providers due to differences in modeling choices, scenario selection, and policy interpretations.

Investor Use Cases

Investors looking to incorporate climate considerations into sovereign bond strategies may use climate-related data in several ways:

• Portfolio monitoring and reporting: Emissions indicators, physical risk scores, and alignment metrics can be aggregated at the portfolio level to provide transparency on climate-related characteristics relative to benchmarks or investors’ internally defined reference points.
• Risk management: Climate metrics may offer additional context on transition and physical risks that traditional macroeconomic or fiscal indicators may not fully capture, particularly over longer investment horizons.
• Climate alignment assessment: For investors with explicit alignment objectives, these datasets may be used as one input for translating qualitative policy assessments and emissions trajectories into more consistent portfolio-level indicators.

Applying these datasets in practice, however, raises methodological questions around definitions, normalization, and comparability across sovereign issuers. We aim to delve into these questions in future articles.

Conclusion

The range of climate data available for sovereign issuers has grown considerably, giving investors a broader set of tools to assess transition and physical risks and to gauge alignment with climate objectives. As this paper has shown, a clear understanding, in our view, of what each dataset does and does not capture is essential to using it well. Applied with that awareness, sovereign climate data may potentially offer a valuable additional lens on issuers, complementing traditional macroeconomic and fiscal analysis.

The authors are grateful to the Sustainable Investing Research team for their contributions and feedback in the development of this research article.