· 11 min read
Executive Summary
Sovereign credit markets are increasingly sensitive to climate-transition execution. Using a panel of 30 emerging and frontier markets between 2010 and 2024, this paper finds that:
• Execution failures are expensive. Threshold breaches are associated with average spread widening of roughly 275 bps, while strong execution tightens spreads by only 20–30 bps on average.
• Three empirically validated thresholds govern non-linear responses:
1. ECM (~61%): below this level of execution, spreads accelerate non-linearly;
2. RRM (~17%): climate-linked revenues must reach this share of potential to be priced as stabilising;
3. LIP (~52%): above this stranded-asset probability, markets begin to price future fiscal deterioration.
• Execution survives serious controls. In a panel fixed-effects model with controls for debt/GDP, real growth, reserves, political risk, commodity prices and the VIX, the execution coefficient remains negative and highly significant.
• The penalty–reward distribution is asymmetric. A Mann–Whitney U test confirms that the penalty and reward distributions differ significantly (p < 0.001), with penalties roughly ten times larger than rewards.
• The model predicts out of sample. Out-of-sample tests (20% hold-out and 10-fold cross-validation) show a mean absolute error of ~12 bps and directional accuracy of 89%.
Policy implications are direct. Governments that (i) keep execution above the ECM, (ii) monetise climate assets beyond the RRM, and (iii) avoid stranded-asset lock-in can materially reduce their sovereign risk premium. Conversely, opaque or inconsistent execution can trigger abrupt repricing even when headline climate ambition remains high.
1. Introduction
This paper investigates whether, and how strongly, sustainability-transition execution affects sovereign borrowing costs. While climate risk and ESG have entered the sovereign discourse, market pricing has often appeared idiosyncratic, case-driven and narrative-heavy. We ask a simple question:
Do sovereign bond markets systematically reprice countries that fail to deliver on their transition commitments, even when macro fundamentals are controlled for?
Using a new 30-country panel (2010–2024), we provide three core results:
1. Execution credibility is a statistically significant determinant of sovereign spreads after controlling for standard macroeconomic and political variables.
2. Spread reactions are non-linear and governed by three thresholds: ECM, RRM and LIP.
3. Penalties for failure are almost an order of magnitude larger than rewards for success.
These findings are relevant for sovereign debt managers, DFIs, rating agencies and climate-finance practitioners who must evaluate how transition execution feeds back into borrowing costs and debt sustainability.
2. Data and variable construction
2.1 Sample
The dataset covers 30 emerging and frontier sovereigns across 2010–2024, yielding 420 country-year observations. The sample includes:
• Climate-exposed EMs: Ghana, Kenya, Gabon, Sri Lanka, Indonesia, Nigeria, Pakistan, Zambia, Vietnam, Philippines, etc.
• Non-climate-exposed or less-exposed controls: Morocco, Serbia, Peru, Uruguay, Azerbaijan, Kazakhstan, among others.
• High-capacity comparators: Chile, Costa Rica, Korea.
2.2 Dependent variables
We use two spread measures:
• EMBI Global Diversified sovereign spreads (basis points over U.S. Treasuries);
• 5-year CDS mid-quotes (basis points).
In the baseline models, the dependent variable is EMBI spreads (bps); CDS results are used for robustness.
2.3 Execution index
The Execution Index (0–100) is a composite measure constructed via principal component analysis (PCA), combining:
• Growth in renewable-energy capacity vs announced targets;
• Forest governance and deforestation trends (incl. MRV robustness);
• Power-sector execution (PPAs honoured vs cancelled/delayed);
• Climate-policy implementation rates vs NDCs/action plans;
• Fiscal execution of climate-related budget lines;
• Transparency (timeliness and completeness of reporting);
• Incidence of policy reversals (e.g. abrupt subsidy/fertiliser bans).
2.4 Threshold variables
From this index, we define:
• ECM: the minimum execution level below which spreads reprice non-linearly;
• RRM: actual realisation of climate-linked revenues (e.g. carbon credits, green tariffs) as a percentage of technically or contractually estimated potential;
• LIP: probability that existing fossil fuel assets become stranded within a 10–15 year horizon, based on NGFS scenarios and Carbon Tracker data.
2.5 Controls
Controls include:
• Debt/GDP (%);
• Real GDP growth (%);
• FX reserves (months of imports);
• Political Stability Index;
• IMF programme dummy;
• Commodity prices (Brent oil, copper, coal indices);
• VIX index (global risk sentiment);
• Rating-distance dummies (distance to next downgrade notch).
3. Methodology
3.1 Threshold estimation
We estimate structural thresholds using:
• Hansen (2000) fixed-effects threshold regression;
• Bai–Perron (1998, 2003) multiple structural break tests;
• 5,000 bootstrap replications for 95% confidence intervals.
3.2 Baseline panel model
We estimate:
3.3 Difference-in-difference and matched pairs
We complement the panel model with:
• Diff-in-diff around major execution events (e.g. Ghana PPA cancellation cycle, Sri Lanka fertiliser ban, Indonesian coal-phase decisions);
• Matched pairs: Ghana vs Côte d’Ivoire, Kenya vs Uganda, Indonesia vs Philippines, Chile vs Colombia/Peru.
3.4 Out-of-sample validation
We conduct:
• 20% hold-out (last years by country);
• 10-fold cross-validation;
• Metrics: MAE, RMSE, directional accuracy, threshold-classification accuracy.
4. Results
4.1 Threshold estimates
Table 1. Estimated threshold levels and confidence intervals
All thresholds are significant at the 1% level. ECM and RRM are especially stable across subsamples; LIP is tighter in fossil-exposed countries.
4.2 Baseline panel regression
Table 2. Baseline panel regression results (EMBI Spreads, bps)
Note: Dependent variable is sovereign spread (bps). Country and time fixed effects included. Robust standard errors clustered at the country level. ***, ** denote significance at the 1% and 5% levels, respectively.
The execution coefficient remains negative and significant when:
• Adding rating-distance dummies;
• Using CDS spreads instead of EMBI;
• Restricting the sample to 2014–2024 (post-Paris period).
4.3 Penalty–reward asymmetry
We separate observations into:
• Penalty sample: years following ECM/RRM/LIP threshold breaches;
• Reward sample: years with execution ≥ ECM + 10pp and monetisation ≥ RRM.
Table 3. Penalty–reward asymmetry in sovereign spread movements
A Mann–Whitney U test rejects equality of distributions at p < 0.001, confirming a statistically significant asymmetry: penalties are roughly 10× larger than rewards.
4.4 Event-study evidence
Event studies around major execution failures (policy reversals, aborted auctions, disclosure shocks) show:
• Significant abnormal spread widening in 10–20 trading days following the event;
• Jump magnitudes frequently exceeding 100 bps, especially when events reveal hidden liabilities or stranded-asset risks.
4.5 Out-of-sample performance
Out-of-sample tests yield:
• MAE: ~11.8 bps;
• RMSE: ~34% lower vs a no-execution baseline;
• Directional accuracy: 89%;
• Threshold-classification accuracy: 83%.
5. Country case studies
5.1 Gabon — high assets, low monetisation
Gabon’s ecological endowment is exceptional: 187 million verified REDD+ credits, world-class MRV, and extremely low deforestation. But monetisation remains negligible (<5% of potential), keeping RRM below the threshold.
• Execution Score (E): 66
• ECM: Passed
• RRM: Failed
• LIP: Passed
Spread outcome: EMBI widened 380 → 520–550 bps (2018–2022). Markets value climate assets only when they produce cash. Gabon shows the RRM rule.
5.2 Ghana — PPA lock-in as a transition execution failure
Ghana’s crisis stems partly from transition failures: over 40 unsolicited PPAs created a structural oversupply with USD 320–620m/year take-or-pay liabilities.
• Execution Score (E): 54
• ECM: Failed
• RRM: Failed
• LIP: Breached (~60% stranded contractual exposure)
Spread outcome: EMBI surged 700 → 2,300–2,500 bps. Ghana illustrates how an energy-transition execution failure becomes a sovereign-credit shock.
5.3 Kenya — governance drift under ECM pressure
Kenya combines strong renewable fundamentals with governance deterioration and utility arrears.
• Execution Score (E): 63
• ECM: Barely passed
• RRM: Partially passed
• LIP: Passed
Spread outcome: 380 → 480–550 bps (2018–2022). Markets priced proximity to ECM, not structural failure.
5.4 Sri Lanka — transition shock amplifying crisis
Sri Lanka suffered a fiscal and external crisis compounded by transition mistakes — especially the abrupt fertiliser ban.
• Execution Score (E): 47
• ECM: Failed
• RRM: Failed
• LIP: Partially breached
Spread outcome: CDS widened 2,000 → 10,000+ bps. Execution shocks accelerated an already fragile situation.
5.5 Chile — the benchmark for transition execution
Chile is the strongest performer: stable auctions, MRV, and consistent coal phaseout.
• Execution Score (E): 78
• ECM: Passed
• RRM: Passed
• LIP: Passed
Spread outcome: Persistent 15–25 bps greenium; 20–40 bps advantage vs peers. Chile demonstrates reward dynamics.
5.6 Indonesia — forward pricing of coal lock-in
Indonesia maintains macro stability but faces future risk from a 35 GW coal fleet and long-dated
PPAs.
• Execution Score (E): 58
• ECM: Borderline
• RRM: Partial
• LIP: Breached (~55–60%)
Spread outcome: 30–50 bps wider than comparators. Markets are already pricing future stranded-asset exposure — classic forward-pricing behaviour.
6. Robustness checks
We test robustness through:
• Leave-one-out country tests (coefficients remain in –2.1 to –3.0 range, p < 0.01);
• Alternative execution metrics (CPIA climate rows only; IEA transition scoring);
• Winsorisation at 1% and 5%;
• CDS vs EMBI spreads;
• Pre- vs post-COVID subsamples.
In all cases, the execution coefficient remains negative, economically meaningful and statistically
significant.
7. Interpretation
The empirical results suggest three channels through which execution credibility affects spreads:
1. Default-probability channel: Lower execution raises perceived medium-term default risk, even if short-term fiscal numbers look stable.
2. Rating-cliff channel: When execution deteriorates near ratings thresholds, downgrades and outlook changes magnify spread reactions (80–150 bps).
3. Contagion channel: In climate-exposed regions, visible execution failures (e.g. one country’s default) raise spreads in peers with similar exposure and weak execution.
The penalty–reward asymmetry reflects how information is processed: positive execution is incorporated slowly and cautiously; negative execution causes abrupt upward revision of default probabilities.
8. Policy implications
Key implications for sovereigns, DFIs and rating agencies:
• Execution is a credit variable. Maintaining execution above ECM is akin to preserving an implicit credit enhancement.
• Monetisation matters as much as conservation. Climate assets that are not monetised beyond the RRM threshold do not reduce spreads.
• Avoid lock-in. High stranded-asset probabilities push countries towards the LIP, after which spreads widen regardless of near-term policy promises.
• Transparency is cheap insurance. Regular, credible disclosure slows adjustment speeds and reduces the probability of discontinuous jumps.
9. Conclusion
Sustainability execution is no longer a soft, reputational topic. It is a quantitatively significant, causally relevant determinant of sovereign borrowing costs. Thresholds in execution, revenue realisation and stranded-asset risk govern non-linear adjustments in spreads. The penalty for execution failure is roughly ten times the reward for success.
For governments, this transforms transition delivery from an ESG narrative into a hard macro-financial variable. For markets, it provides a framework to discipline promises and price performance.
Figures
Figure 1. Threshold effects on sovereign spreads
Description: Sovereign spread (bps) on the y-axis; execution index on the x-axis. Piecewise regression line with kink points at ECM, RRM and LIP. Slopes steepen sharply below ECM and beyond LIP.
Note: Vertical dashed lines mark estimated threshold points (ECM, RRM, LIP); slopes reflect regime-specific marginal effects from the panel threshold regression. Figure 1 illustrates the non-linear mapping from execution quality to sovereign spreads implied by the estimated thresholds: small rewards in the high-execution regime, and sharply rising penalties once execution falls below the ECM and stranded-asset risks approach the LIP.
Figure 2. Event study around execution failure events
Description: Time axis in trading days (t–60 to t+60) around key execution failures. Cumulative abnormal spread plotted, showing sharp widening after t=0.
Figure 2 shows the average cumulative abnormal spread around major execution failures. While some deterioration is visible in the run-up to the event, the bulk of the repricing occurs within 10–20 trading days after the shock, underscoring the importance of execution and transparency for managing adjustment speed.
Figure 3. Estimated Coefficients and Confidence Intervals
Description: Coefficient plot (bar or dot-whisker) showing β for execution and key controls with 95% confidence intervals. Zero line marked; execution coefficient clearly negative and significant.
Figure 3 presents the estimated coefficients and 95% confidence intervals from the baseline panel regression. The execution coefficient is negative and precisely estimated, remaining significant even after controlling for debt, growth, reserves and political risk. This visualises the key result: transition execution is priced much like a core macro-fiscal variable.
Source for all figures: Author’s calculations using EMBI/CDS data (JP Morgan, Bloomberg) and transition indicators (IMF, World Bank, IEA, NGFS, Carbon Tracker), 2010–2024
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Sources & references
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ESG, ratings and spreads (corporate & sovereign)
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Data sources & EM market benchmarks
• J.P. Morgan (various years). J.P. Morgan Emerging Market Bond Index (EMBI) methodology and data.
• Bloomberg (various years). Sovereign CDS and bond spread data (EM Asia, Africa and LatAm curves).
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