# Measuring Climate Performance Against National Commitments: A Gap-to-Target Indicator for the Kyoto Protocol, First and Second NDCs

Pengd Wende Richard Nikiema

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Document de Recherche du Laboratoire d'Économie d'Orléans

Working Paper Series, Economic Research Department of the University of Orléans, University Of Tours
and University Clermont-Auvergne, France

DR LEO 2025-24

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# Measuring Climate Performance
Against National Commitments:

## A Gap-to-Target Indicator for the Kyoto
Protocol, First and Second NDCs

NIKIEMA Pengd Wende Richard

MISE EN LIGNE / ONLINE | 22/11/2025

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Laboratoire d'Économie d'Orléans
Collegium DEG
Rue de Blois - BP 26739
45067 ORLÉANS Cedex 2

TÉL | (33) (0)2 38 41 70 37

MAIL | leo@univ-orleans.fr

www.leo-univ-orleans.fr
# Measuring Climate Performance Against
National Commitments:

A Gap-to-Target Indicator for the Kyoto Protocol, First and Second
NDCs

NIKIEMA Pengd Wende Richard*

November 21, 2025

## Abstract

This paper introduces a new indicator to assess countries' climate performance by comparing their effective greenhouse gas (GHG) emissions with the levels implied by their commitments under the Kyoto Protocol and the first and second Nationally Determined Contributions (NDCs). Existing climate indices track emissions or policy ambition but do not provide a harmonised, cross-country measure of the distance to pledged mitigation targets. Building on the literature on distance-to-target metrics, we develop a simple *Gap-to-Target* indicator applicable across successive commitment cycles.

Using data for 135 countries over 1990–2024, we compute annual measures of alignment with national pledges and document systematic patterns across income groups, regions, and commitment periods. Descriptive trends reveal a widening disconnect between rising ambition and realised emissions, especially under the second NDC cycle. Dynamic system-GMM estimations show that economic development, renewable energy deployment, environmental expenditure, fiscal capacity, and climate vulnerability are key determinants of countries’ ability to meet their commitments.

The Gap-to-Target indicator offers policymakers and international financial institutions a transparent tool to track performance, identify structural implementation gaps, and better target climate finance, and it can be extended to other environmental commitments such as biodiversity or land degradation neutrality. The indicator can also be extended to the upcoming 2025 NDCs and future commitment cycles, enhancing its relevance for long-term climate governance. With appropriate sub-national data, it could further be adapted to regional and local authorities, supporting the decentralisation of climate action monitoring.

<footnote>*Laboratoire d'Économie d'Orléans (LEO), Université Clermont Auvergne, France, P-Wende_Richard.NIKIEMA@doctorant.uca.fr</footnote>
# 1 Introduction

Climate change has become one of the defining development and macroeconomic challenges of the twenty-first century, affecting both advanced and developing economies (Stern 2006; Dell, Jones, and Olken 2012; Nordhaus 2015). In response to rising global emissions, the international community has progressively formalised mitigation efforts through successive rounds of commitments, beginning with the Kyoto Protocol and, more recently, the Paris Agreement. Under Kyoto, industrialised countries accepted binding emission-reduction obligations for the first time. Under the Paris Agreement, all Parties are required to submit Nationally Determined Contributions (NDCs), with the expectation that these will be regularly strengthened over time.

The first and second NDC cycles now constitute the backbone of national mitigation planning in the post-Paris era. Monitoring whether countries actually deliver on their pledges is therefore essential: (i) to preserve the credibility and durability of the international climate regime; (ii) to strengthen domestic and international accountability; and (iii) to guide the strategic allocation of scarce and growing climate finance resources. Yet, despite the proliferation of climate-related metrics, there is still no widely accepted global indicator capturing *how far each country stands from the emission target it has committed to*. This gap is surprising given the central role of pledge-based accountability in the Paris Agreement architecture.

Existing climate and environmental performance metrics only partially fulfil this need. Basic indicators such as total greenhouse gas (GHG) emissions, emissions per capita or emissions intensity—widely used in IPCC assessments—are informative about trends but do not indicate whether countries remain aligned with the emission pathways implied by their commitments (IPCC 2022). Composite indices such as the Environmental Performance Index (EPI) (Yale Center for Environmental Law & Policy and Center for International Earth Science Information Network 2024) and the Climate Change Performance Index (CCPI) (Germanwatch, NewClimate Institute, and Climate Action Network 2024) assess environmental quality or mitigation progress but are not tied to the targets that countries have pledged. A related strand of work develops indicators of environmental policy *effort* rather than performance outcomes. The OECD's Environmental Policy Stringency (EPS) index provides a cross-country measure of the strictness of market-based and regulatory instruments aimed at reducing environmental externalities (Botta and Kocak 2014; Botta and Kocak 2020). While valuable for comparing the stringency of environmental policy settings, the EPS remains fundamentally an effort-based metric and does not assess whether countries are on track to meet their quantified emission-reduction commitments. It is also limited in scope—covering around 40 countries and 13 policy instruments, primarily focused on climate and air-pollution mitigation—thus restricting its usefulness for global analyses. The EPS therefore complements, but cannot substitute
for, commitment-based indicators that compare realised emissions with the levels implied
by national pledges. This limitation motivates the development of the Gap-to-Target
indicator introduced in this paper.

Broader sustainability frameworks such as the Ecological Footprint and the Environ-
mental Sustainability Gap (ESGAP) measure resource pressures and ecological thresh-
olds (Global Footprint Network 2023; Usubiaga-Liaño, Comte, Ekins, et al. 2021; Comte
2023), but they are not designed to evaluate performance against specific mitigation com-
mitments. At the global scale, the UNEP Emissions Gap Reports and the Climate Ac-
tion Tracker (CAT) diagnose whether collective action remains consistent with 1.5°C-2°C
pathways (UNEP 2024a; UNEP 2025; Climate Action Tracker 2024). While powerful for
global assessments, these approaches do not provide a harmonised, country-level measure
of the numerical distance between actual emissions and countries' pledged targets.

Some institutions have developed distance-to-target indicators. The European En-
vironment Agency (EEA) constructed metrics assessing deviations from linear emission
pathways aligned with burden-sharing arrangements under the Kyoto Protocol (European
Environment Agency 2009). The OECD's International Programme for Action on Cli-
mate (IPAC) has developed the NDC Delivery Gap—measuring the difference between
2023 emissions and those implied by a linear trajectory toward the 2030 target—and the
2050 Targets Consistency Gap—comparing 2030 targets to trajectories consistent with
long-term neutrality objectives (OECD 2025). These tools offer intuitive readings of
alignment with commitments but are often limited to a single target year, a small set of
benchmark years, or specific world regions.

A central limitation of most scenario-based assessments—common in CAT, UNEP, IPCC and OECD analyses—is their inherent dependence on assumptions about economic growth, technology deployment, energy prices, and the implementation of future policies. Between the base year of projections and the target horizon (e.g., 2030 or 2050), emission pathways may be substantially altered by exogenous shocks such as geopolitical conflicts, global energy crises, extreme weather events, natural disasters or pandemics. These shocks may drive countries away from the projected trajectories independently of their climate ambition or political will. As a result, performance metrics relying primarily on prospective modelling can be highly sensitive to modelling assumptions and the treatment of shocks.

Against this backdrop, this paper introduces a new commitment-based indicator—the
**Gap-to-Target**—designed to measure, for each country and each commitment cycle (Ky-
oto, first NDCs, second NDCs), the deviation between actual emissions and the emission
levels implied by pledged targets. The indicator is constructed at the country level and
can be expressed in absolute terms or as a relative percentage gap. Because it com-
pares *realised* emissions with *pledged* reductions, it remains valid even when unexpected
shocks disrupt expected emission trajectories. It is therefore more robust to modelling
assumptions and scenario uncertainty than indicators built on prospective pathways.

This paper makes two sets of contributions—one to the academic literature on climate policy evaluation, and one to the operational needs of policymakers and international climate finance institutions.

First, the paper advances the academic literature by providing the first harmonised, cross-country indicator that measures countries’ realised alignment with their mitigation commitments across all major global commitment cycles—**Kyoto, NDC1 and NDC2**. Existing metrics either focus on emissions trends, policy effort, or scenario-based assessments, but none offer an annual, backward-looking, pledge-specific measure applicable to both advanced and developing economies. The Gap-to-Target thus fills a critical empirical gap by offering a transparent and scalable tool suitable for descriptive analyses, cross-country comparisons, and econometric estimation.

**Methodologically, it introduces a unified framework to translate heterogeneous target formats (base-year, BAU, absolute caps) into comparable implied emissions,** enabling robust dynamic analysis of the structural determinants of climate performance, such as economic development, fiscal capacity, governance quality, environmental expenditure and renewable energy deployment.

**Second, the indicator offers substantial operational value for policymakers, international climate funds and development institutions.** By directly comparing realised emissions to pledged reductions, the Gap-to-Target provides a clear and intuitive metric of countries’ progress in delivering their commitments under the Paris Agreement. It therefore supports accountability mechanisms, global stocktake exercises, and evidence-based allocation of climate finance (OECD and UNDP 2025; Black, Parry, et al. 2023; Black, Parry, and Zhunussova 2023).

Because many mitigation actions—such as forest conservation, afforestation, avoided deforestation, land restoration or sustainable agriculture—generate co-benefits for biodiversity, soil protection and ecosystem resilience, **the indicator also serves as a broader tool for monitoring environmental commitments beyond climate mitigation.** In this sense, it can help identify countries that consistently meet, exceed or fall short of their environmental pledges, informing the prioritisation of financial support for nature-based solutions and land restoration programmes.

**Overall, the Gap-to-Target indicator strengthens both the analytical foundations and the policy relevance of climate performance assessment.** It creates a bridge between academic research on climate mitigation outcomes and the practical needs of decision-makers seeking to evaluate commitments, direct financial resources, and accelerate progress toward global climate and environmental objectives.

The remainder of the paper is structured as follows. Section 2 reviews existing climate performance metrics and distance-to-target indicators. Section 3 presents the methodological framework and defines the Gap-to-Target indicator. Section 4 describes the data.
Section 5 presents descriptive results. Section 6 discusses the econometric strategy and empirical findings. Section 7 draws out policy implications. Section 8 concludes.

# 2 Literature Review: Existing Climate Performance Indicators

A broad array of indicators has been developed to evaluate countries’ climate and environmental performance, but these metrics differ substantially in purpose, methodology, and suitability for assessing compliance with national mitigation commitments. This section reviews the main families of indicators and highlights the gaps that motivate the development of the Gap-to-Target measure proposed in this paper.

## 2.1 Basic Emissions Metrics

The simplest and most widely used indicators—total greenhouse gas (GHG) emissions, emissions per capita, and emissions intensity—are routinely employed in IPCC assessments and long-term global modelling exercises (IPCC 2022). While essential for understanding emission trends, these metrics do not provide information on countries’ alignment with the emission levels implied by their official commitments under the Kyoto Protocol or the Paris Agreement.

## 2.2 Composite Environmental and Climate Indices

Several multidimensional indices summarise environmental performance or climate mitigation progress. The *Environmental Performance Index* (EPI) scores countries across a wide set of environmental dimensions, including air quality, biodiversity, and climate policy (Yale Center for Environmental Law & Policy and Center for International Earth Science Information Network 2024). The *Climate Change Performance Index* (CCPI) evaluates mitigation outcomes, renewable energy penetration, energy use, and policy ambition (Germanwatch, NewClimate Institute, and Climate Action Network 2024).

The OECD also provides an indicator of the *Environmental Policy Stringency (EPS)* index, which measures the strictness of environmental regulations and market-based instruments across 40 countries and 13 policy areas (Botta and Kocak 2014; Botta and Kocak 2020). The EPS captures the effort dimension of environmental action by quantifying the intensity of policy instruments aimed at reducing pollution and emissions.

While these indices offer valuable insights into environmental quality, policy effort, or mitigation progress, none of them assess whether countries are on track to meet their internationally pledged emission-reduction targets. They therefore complement—but cannot substitute for—the commitment-based performance measurement
developed in this paper.

## 2.3 Broader Sustainability Indicators

Indicators such as the *Ecological Footprint* and the *Environmental Sustainability Gap* (ES-GAP) framework assess resource pressures or distances from ecological thresholds (Global Footprint Network 2023; Usubiaga-Liaño, Comte, Ekins, et al. 2021; Comte 2023). Although useful for gauging overall sustainability, these tools focus on biophysical standards rather than quantified mitigation commitments. They therefore cannot be used to evaluate whether countries are on track to meet Kyoto or NDC targets.

## 2.4 Global Scenario-Based Assessments

At the global level, the UNEP *Emissions Gap Reports* quantify the difference between projected emissions—assuming full implementation of current NDCs—and the emission trajectories compatible with limiting warming to 1.5°C or well below 2°C (UNEP 2024a; UNEP 2025; UNEP 2024b). Similarly, the Climate Action Tracker (CAT) distinguishes between a *target gap*, which compares announced targets to a 1.5°C pathway, and an *implementation gap*, which measures the difference between these targets and emissions projected under currently implemented policies (Climate Action Tracker 2024). These tools have significantly shaped global climate debates, yet they remain scenario-driven and do not offer a country-specific, backward-looking measure of realised performance against national pledges.

## 2.5 Distance-to-Target Approaches in OECD and EEA Frameworks

Some organisations have applied distance-to-target logic to climate metrics. The European Environment Agency (EEA) developed indicators measuring deviations from linear emission pathways aligned with burden-sharing agreements under the Kyoto Protocol (European Environment Agency 2009). The OECD's *International Programme for Action on Climate* (IPAC) has expanded this approach. Notably:

* the **NDC Delivery Gap** measures the difference between emissions observed in 2023 and those implied by a linear trajectory from 2019 to the 2030 NDC target (OECD 2025);

* the **2050 Targets Consistency Gap** compares 2030 targets to trajectories compatible with long-term net-zero goals.

These indicators are intuitive and policy-relevant, but they are constructed for selected years only and are not designed as time-series datasets suitable for econometric panel
analysis. Their reliance on linear interpolations toward a single future target year limits their use for year-by-year analysis of climate performance. Moreover, IPAC coverage is primarily limited to OECD members and a small set of partner countries, restricting their applicability in global cross-country studies, particularly for low- and middle-income countries where climate performance dynamics are most heterogeneous.

## 2.6 Limitations of Scenario-Based and Distance-to-Target Indicators

A fundamental limitation of scenario-based global assessments (UNEP, CAT, IPCC, OECD modelling exercises) is their dependence on assumptions regarding economic growth, energy prices, technology adoption, and policy effectiveness. Between the base year of projections and the target horizon, countries' actual emissions may deviate substantially from projected trajectories due to exogenous shocks—geopolitical conflicts, crises in energy markets, natural disasters, extreme climate events, or pandemics. These shocks can alter emission dynamics independently of climate ambition or implementation. As a result, scenario-based indicators may misrepresent realised climate performance, especially in countries facing recurrent shocks.

OECD-style distance-to-target indicators provide useful diagnostics but remain limited in temporal resolution and geographic coverage. Most are designed for monitoring dashboards rather than for integration into econometric research, and they generally compare emissions to a trajectory interpolated from a baseline to a target year, rather than comparing realised emission reductions to the reductions implicitly required by the country's commitment.

## 2.7 Motivation for a New Commitment-Based Indicator

The limitations of existing indicators create a clear gap in the literature: there is no harmonised, cross-country indicator measuring whether countries are on track to meet the emission reductions embedded in their Kyoto, first NDC, or second NDC targets. Existing metrics are either scenario-based (UNEP, CAT), multi-dimensional but not target-specific (EPI, CCPI, ESGAP), or target-specific but limited in scope or time (OECD IPAC, EEA Kyoto indicators).

This motivates the development of the **Gap-to-Target** indicator introduced in this paper—an annual, country-level measure of the deviation between realised emissions and the emission levels implied by each country's official targets. Because it is backward-looking and purely commitment-based, the indicator remains valid even when countries face shocks that disrupt projected trajectories. It also lends itself to econometric analysis, enabling a systematic study of the structural determinants of climate commitment compliance and the ambition-implementation gap emphasised in recent work on national
mitigation plans and Paris-compatible pathways (Anderson, Broderick, and Stoddard 2020; OECD and UNDP 2025; Black, Parry, et al. 2023; Black, Parry, and Zhunussova 2023).

# 3 Methodology

This section presents the methodological framework used to construct the Gap-to-Target indicator. The goal is to harmonise heterogeneous mitigation targets—expressed through different formats, base years and reporting conventions—into a single annual, country-level measure capturing the deviation between country-year realised emissions and the emission levels implied by national pledges under the Kyoto Protocol, the first NDCs and the second NDCs.

## 3.1 Conceptual Framework

Countries formulate mitigation commitments using a variety of formats, including percentage reductions relative to a historical base year, percentage reductions relative to a projected business-as-usual (BAU) scenario, percentage relative to carbon intensity<fnref n="1" />, fixed emission caps, or (in rarer cases) emissions per capita targets. These structural differences complicate cross-country comparisons and prevent the use of raw targets in empirical analysis. The Gap-to-Target indicator relies on a unified conceptual approach: for each country i and for each commitment cycle c, the target is translated into an implied level of emissions for the target year. This implied target value is then compared to the country's realised emissions in a given year t.

## 3.2 Harmonising Different Types of Targets

To operationalise this framework, we distinguish three main types of mitigation commitments, following the structure observed in the Kyoto Protocol, the first NDCs and the second NDCs:

1. **Base-year reduction targets:** Countries commit to reduce emissions by a percentage r relative to a historical base year b. The implied target emissions are:

$$E_{i,c}^{\text{target}} = E_{i,b} \times (1 - r).$$

<footnote n="1">¹Only a few major emerging economies—most notably China, India, Malaysia and, in its first NDC, Chile—formulated their mitigation commitments in terms of emissions intensity relative to GDP rather than absolute reductions. Given their large and growing share of global emissions, we treat these targets as percentage reductions in absolute emissions. This avoids scale effects whereby rapid economic growth mechanically lowers carbon intensity and could misleadingly suggest strong climate performance even when total emissions continue to rise.</footnote>
2. **BAU deviation targets:** Countries commit to reduce emissions by a percentage r relative to projected BAU emissions in the target year. If BAU$_{i,c}$ is the projected emissions under BAU assumptions:

$$E_{i,c}^{\text{target}} = BAU_{i,c} \times (1 - r).$$

3. **Fixed-level targets:** A small number of countries specify direct emission caps for the target year:

$$E_{i,c}^{\text{target}} = \text{FixedLevel}_{i,c}.$$

To ensure strict comparability, all target formats are expressed in terms of total GHG emissions in kilotonnes of CO$_2$-equivalent.

### 3.3 Defining the Gap-to-Target Indicator

Using these harmonised target emissions, we define two versions of the Gap-to-Target indicator: an absolute gap and a relative percentage gap.

**Absolute Gap** The absolute gap measures, in kilotonnes of CO$_2$-equivalent, the difference between realised emissions and the implied target:

$$Gap_{i,c,t}^{\text{abs}} = E_{i,t} - E_{i,c}^{\text{target}}.$$

A positive value indicates that the country is above the pledged emission level (underperformance), while a negative value indicates overachievement.<fnref n="2" />

**Relative Gap (Gap-to-Target Percentage)** The relative measure expresses the deviation as a proportion of the target:

$$Gap_{i,c,t}^{\text{rel}} = \frac{E_{i,t} - E_{i,c}^{\text{target}}}{E_{i,c}^{\text{target}}}.$$

This formulation enables cross-country comparison and normalises differences in scale between small and large emitters. In the econometric applications, we focus on the relative indicator, which offers a clear interpretation in terms of percentage deviation from the pledged emission level and allows comparisons across commitment cycles.

<footnote n="2">²Negative values do not necessarily reflect exceptional mitigation effort. In some cases, apparent overperformance may result from relatively unambitious or weakly binding targets, which are easier to exceed. This issue is less problematic in the context of the Paris Agreement, where NDCs are self-determined and non-binding, but the interpretation of negative gaps should nevertheless consider the underlying ambition of the target.</footnote>
## 3.4 Applicability Across Commitment Cycles

The method is applied consistently across the three major commitment cycles:

* **Kyoto Protocol:** Targets generally concern a fixed base year (1990) with a specified reduction for the 2008–2012 period.

* **First and Second NDCs:** Targets typically refer either to reductions relative to base years that vary across countries or to BAU projections for 2030. Targets expressed in absolute terms are harmonised into a common unit—kilotonnes of CO₂-equivalent (ktCO₂eq)—to ensure comparability across countries and time.

This unified translation allows the computation of a consistent annual gap for each country and cycle, enabling descriptive comparisons and the econometric analysis of compliance dynamics.

## 3.5 Advantages of the Approach

The main advantages of this methodological approach are:

1. **Cross-country comparability:** All target formats are translated into a uniform metric, GHG emissions in ktCO₂eq.

2. **Backward-looking robustness:** The indicator compares realised emissions to pledged reductions, not to modelled scenarios. It therefore remains robust to shocks (conflicts, natural disasters, energy crises) that typically distort projections.

3. **Econometric suitability:** The annual panel structure provides enough temporal variation for dynamic estimation strategies such as system GMM (Arellano and Bond 1991; Blundell and Bond 1998) and other methods.

4. **Evidence-based monitoring of countries' compliance:** By comparing realised emissions directly with the reductions implied by official commitments, the indicator provides a transparent, data-driven tool for assessing countries' compliance with their climate pledges. Its backward-looking nature captures actual performance rather than projected trajectories, making it suitable for accountability exercises, global stocktake processes, and the evaluation of the effectiveness of domestic and international climate policies.

5. **Applicability to successive commitment cycles and other environmental targets:** The same methodological logic applies consistently across the Kyoto Protocol, the first NDCs and the second NDCs, enabling a systematic analysis of how alignment with pledged mitigation efforts evolves over time. Because the approach
relies on a general translation of heterogeneous targets into implied emission levels, it can also be extended to future rounds of NDC submissions. Similar gap-to-target logic could, in principle, be applied to other international environmental commitments, such as national biodiversity targets under the Kunming–Montreal Global Biodiversity Framework or Land Degradation Neutrality (LDN) targets adopted under the UN Convention to Combat Desertification (Convention on Biological Diversity 2024; United Nations Convention to Combat Desertification 2024).

6. Beyond the country level, the Gap-to-Target framework is, by construction, transferable to any level of governance where explicit mitigation targets and emission data are available. National NDCs can be downscaled into regional, municipal, or sectoral targets; when combined with local emission inventories, the same metric can be used to assess subnational alignment with the national trajectory. This opens the door to more decentralised climate governance, where regions and cities are monitored against their own pledges using a harmonised indicator.

7. Similarly, the approach can be applied at the sectoral or microeconomic level. A sector, firm, or even a large emitting facility that adopts a quantified emission-reduction target and reports its realised emissions can compute a Gap-to-Target measure to track its environmental responsibility over time. In this sense, the indicator is not only a tool for assessing national compliance with international commitments, but also a generic metric of climate performance that can support vertical integration of climate governance from the national to the local and corporate levels.

Overall, the methodology provides a transparent, flexible and robust framework for translating heterogeneous national environmental commitments into a harmonised indicator of climate performance. The next section details the data sources used to implement this approach.

# 4 Data

This study combines multiple data sources to construct the Gap-to-Target indicator and to estimate its structural determinants across 135 countries over the period 1990–2022. The dataset integrates information on greenhouse gas emissions, mitigation targets under successive commitment cycles, macroeconomic conditions, institutional quality, environmental variables, climate finance, and exposure to shocks.

## 4.1 Emissions Data

Historical greenhouse gas (GHG) emissions are taken from the EDGAR database, which provides harmonised and internationally comparable time series of CO₂ and non-CO₂
emissions expressed in CO₂-equivalent units. EDGAR offers consistent annual data from 1970 onwards, which makes it suitable for analysing the Kyoto, NDC1, and NDC2 periods.

For BAU-related targets, we use the country-resolved emission and socio-economic pathways constructed by Gütschow et al. (2020)<fnref n="3" />. These scenarios combine Representative Concentration Pathways (RCPs) and Shared Socio-economic Pathways (SSPs). We rely on the RCP8.5-SSP5 combination, which represents a high-emission “business-as-usual” trajectory characterised by strong fossil-fuel dependence and limited environmental policy. This provides a consistent benchmark for harmonising BAU deviation targets across countries.

## 4.2 Target Data

Mitigation targets under the Kyoto Protocol, the First NDCs, and the Second NDCs were collected from the UNFCCC repository and the Climate Watch platform. To ensure completeness and cross-country consistency, NDC targets were retrieved using a systematic web-scraping procedure applied to Climate Watch's country-level NDC pages, complemented and cross-validated with metadata provided by the NDC Partnership. Targets include base-year reduction targets, BAU deviation targets, fixed-level targets, and—more marginally—intensity or per-capita targets. All commitments were harmonised into implied emission levels for the corresponding assessment years<fnref n="4" />.

## 4.3 Macroeconomic, Environmental and Shock Variables

For the econometric analysis, economic, demographic, energy-sector, environmental, institutional and shock-related variables are drawn from multiple international datasets. These include the World Development Indicators (WDI), EM-DAT, the OECD, the IMF, the Worldwide Governance Indicators (WGI), the Uppsala Conflict Data Program (UCDP), and the ND-GAIN index. The variables used comprise GDP per capita, control of corruption, electricity access, renewable energy consumption, tax revenue as a share of GDP, environmental public expenditure, climate finance inflows, country vulnerability indices, natural disaster occurrences, and conflict events.

These variables capture the structural, institutional, environmental and shock-related conditions that influence countries' ability to align with their climate commitments.

<footnote n="3">³Climate Action Tracker (CAT) provides an alternative source of emissions projections under current policies. However, its geographical coverage is limited to roughly 40 countries—mainly developed and major emerging economies—plus the EU27. This restricted scope prevents its use for constructing a truly global sample, which is required for the purposes of this study.</footnote>

<footnote n="4">⁴For more details about target variables statistics see figures 7, 8 and 9 in Annex 2</footnote>
# 5 Descriptive Evidence

This section presents descriptive patterns emerging from the Gap-to-Target indicator across commitment cycles (Kyoto, NDC1 and NDC2), income groups, and world regions. The objective is to document how countries' emission trajectories compare to their pledged mitigation targets and to identify early signals of alignment or divergence before moving to the econometric analysis.

## 5.1 Global Patterns of Alignment with Climate Commitments

Figure 1 shows that the global average gap-to-target follows a markedly heterogeneous trajectory across commitment cycles. During the Kyoto Protocol period, the average relative gap declines steadily, reflecting the combination of binding obligations for Annex I Parties, early energy-efficiency gains, and structural changes in several advanced economies.

<figure><img src="image_5.png" /><figcaption>Figure 1: Global Trajectory of Countries’ Compliance with Emission Targets</figcaption></figure>

Source: Authors’ computations based on Climate Watch and NDC Partnership Registry.

After 2015, however, a clear reversal emerges. The introduction of the First and especially the Second NDCs is accompanied by a substantial increase in ambition, but observed emissions do not follow the same downward trajectory. As a result, the global relative gap widens sharply under NDC2. This divergence is consistent with the findings of the UNEP Emissions Gap Reports and Climate Action Tracker assessments, which highlight persistent implementation gaps despite rising ambition (UNEP 2024a; UNEP 2024b; Climate Action Tracker 2024; Black, Parry, et al. 2023; Black, Parry, and Zhunussova 2023). The temporary narrowing of the gap around 2020 reflects the economic contraction induced by the COVID-19 pandemic rather than structural decarbonisation.
## 5.2 Income-Level Heterogeneity

Gap-to-target values differ significantly across income groups (Figure 2). Low-income countries systematically exhibit negative gaps across all cycles. This overachievement, however, primarily reflects low baseline emissions, limited fossil-fuel dependency, and slower economic growth rather than proactive mitigation policies.

<figure><img src="image_6.png" /><figcaption>Figure 2: Relative Gap-to-Target Across Income Groups (Kyoto, NDC1, NDC2)</figcaption></figure>

Source: Authors' computations based on Climate Watch and NDC Partnership Registry.

Upper-middle-income and high-income countries display large and persistent positive gaps, especially under NDC2. This pattern can be explained by their structurally high baseline emissions and continued reliance on fossil fuels, as well as, in several advanced economies, the energy price shocks triggered by the Russia-Ukraine war, which temporarily slowed down coal phase-out and the decarbonisation of power systems (International Energy Agency 2022). Lower-middle-income countries remain close to zero, indicating moderate emissions growth but also relatively modest ambition levels compared with upper-middle-income and high-income countries (Black, Parry, et al. 2023).

These patterns are consistent with the literature on “carbon lock-in” (Seto et al. 2016), which emphasises how existing capital stocks, infrastructures and behavioural norms in high-income and advanced emerging economies constrain the speed at which emissions can be reduced, even when targets are ambitious. They also resonate with findings on hard-to-abate sectors and transport decarbonisation in emerging economies (Briand et al. 2023).

## 5.3 Regional Differences

Substantial regional disparities emerge (Figure 3). Europe and East Asia show relatively small but positive gaps, signalling progress but insufficient alignment with pledged reductions. Latin America performs relatively well during Kyoto and NDC1 but diverges under NDC2, reflecting rising deforestation pressures and increased energy demand
(OECD 2023). The Middle East and North Africa region displays the fastest deterioration from NDC1 to NDC2, driven by fossil-fuel dependence and limited diversification, in line with evidence on delayed energy transitions in hydrocarbon-dependent economies (Ragab 2023). Sub-Saharan Africa and South Asia show large positive gaps under NDC1 and NDC2, driven by rapid population growth, infrastructure expansion, and low access to climate finance (Climate Policy Initiative 2024; OECD and UNDP 2025).

<figure><img src="image_7.png" /><figcaption>Figure 3: Relative Gap-to-Target Across World Regions</figcaption></figure>

Source: Authors' computations based on Climate Watch and NDC Partnership Registry.

## 5.4 Top and Bottom Performers Across Commitment Cycles

Performance across the Kyoto Protocol, the First NDCs, and the Second NDCs exhibits clear structural patterns that reflect both the design of each commitment cycle and the underlying economic and energy profiles of countries.

<figure><img src="image_8.png" /><figcaption>Figure 4: Top and Bottom 10 Performers – Kyoto Protocol (Relative Gap)</figcaption></figure>

Source: Authors' computations based on Climate Watch and NDC Partnership Registry.

Under the Kyoto Protocol, only Annex I countries—advanced economies and economies in transition—had binding reduction commitments. The top performers in-
clude countries such as Lithuania, Belgium, Italy, Ukraine, and the United States, where emissions fell substantially due to structural transformation, energy-efficiency improvements, or post-industrialisation dynamics, as illustrated in Figure 4. In many transitioning European economies, the restructuring of heavy industry led to substantial emission declines during the early 2000s. By contrast, the bottom performers—Hungary, Latvia, Sweden, Denmark, New Zealand, Luxembourg, France, Norway, Iceland, and the United Kingdom—display positive gaps, reflecting persistent challenges in hard-to-abate sectors such as transport, heating, and residential energy use. These cases highlight that even high-income countries with strong institutional capacity may struggle when sectoral constraints are binding.

<figure><img src="image_9.png" /><figcaption>Figure 5: Top and Bottom 10 Performers – First NDC Cycle (Relative Gap)</figcaption></figure>

Source: Authors' computations based on Climate Watch and NDC Partnership Registry.

**Under the First NDC cycle (NDC1)**, the distribution changes dramatically with the universalisation of climate commitments under the Paris Agreement (Figure 5). Top performers are overwhelmingly low-income Sub-Saharan African and fragile states, such as Liberia, Tanzania, Madagascar, Burkina Faso, Chad, Ethiopia, Niger, the Central African Republic, the Democratic Republic of Congo, and Burundi. Their strongly negative gaps primarily reflect structurally low emissions, limited industrialisation, and slow energy-demand growth rather than rapid decarbonisation. The bottom performers include emerging economies and small economies with rapidly rising emissions: Equatorial Guinea, Moldova, India, Malaysia, the Maldives, Trinidad and Tobago, Zambia, Lebanon, Tunisia, and Botswana. Their positive gaps indicate that economic expansion, industrialisation, and rising electricity demand outpaced their pledged reduction trajectories.

**Under the Second NDC cycle (NDC2)**, disparities widen further (Figure 6). Top performers—including Senegal, Uganda, Guyana, Tanzania, Niger, Mali, Burundi, the Central African Republic, the DRC, and Chad—remain concentrated in low-income countries with low energy consumption and limited industrial growth. Their negative gaps are again structurally driven rather than indicative of accelerated mitigation. Bottom performers include countries where emissions have increased sharply relative to NDC2
<figure><img src="image_10.png" /><figcaption>Figure 6: Top and Bottom 10 Performers – Second NDC Cycle (Relative Gap)</figcaption></figure>

Source: Authors' computations based on Climate Watch and NDC Partnership Registry (2024).

targets: Equatorial Guinea, Djibouti, the United Kingdom, India, Georgia, Malaysia, Turkey, Denmark, Malta, and Zambia. In some cases, gaps exceed 30–70%, reflecting either rapidly increasing emissions (e.g. India, Malaysia, Turkey) or ambitious NDC2 targets that were not matched by implementation.

Overall, patterns across commitment cycles reveal that the ambition-implementation gap is less a function of target-setting and more a reflection of countries’ structural constraints, development levels, and vulnerability to external shocks. While ambition generally rises across NDC cycles, alignment depends critically on economic structure, access to finance, exposure to shocks, and the capacity to implement mitigation policies. These descriptive patterns motivate the need for an econometric analysis to identify the structural determinants of alignment with climate commitments.

# 6 Econometric Evidence on the Drivers of NDC Compliance

This section investigates the economic, institutional, environmental and shock-related drivers of countries’ alignment with their climate commitments. Using the Gap-to-Target indicator constructed for the First and Second NDC cycles, we estimate dynamic panel regressions based on the System GMM estimator (Arellano and Bond 1991; Blundell and Bond 1998), which is well suited to address endogeneity, time persistence, and unobserved heterogeneity.

The econometric specification includes a lagged dependent variable to capture the dynamic adjustment of countries’ climate performance. Potential endogeneity in economic, energy, and institutional variables is addressed through internal instruments within the System GMM framework. The model incorporates country and time fixed effects and relies on the two-step estimator with robust standard errors. Since the Paris Agreement
replaced Kyoto protocol as the central international framework for mitigation commit-
ments, the econometric analysis focuses exclusively on the First and Second NDC cycles.

The GMM estimations provide a consistent set of results on the structural, institu-
tional, financial, and environmental determinants of countries’ alignment with their NDC
mitigation targets. Columns (1) and (3) report results for the full sample, while Columns
(2) and (4) restrict the analysis to developing countries—the main recipients of interna-
tional climate finance.

**The gap-to-target exhibits persistence over time, confirming that misalignment is a dynamic rather than a purely static problem.** In all specifications, the lagged dependent variable is positive and highly significant: past deviations from the pledged trajectory tend to carry over to subsequent years. This is true both for the First NDCs (Columns 1–2) and for the Second NDCs (Columns 3–4), and for the global sample as well as for developing countries. The magnitude of the coefficient is slightly lower in the developing-country regressions, which suggests that external support and more volatile macroeconomic conditions may reduce the inertia of the gap-to-target, but they do not eliminate it. These results confirm that once countries are off track, returning to their pledged pathway requires sustained and deliberate policy effort rather than one-off interventions.

**Economic development is associated with larger gaps once structural and institutional controls are included, especially in developing countries.** The coefficient on GDP per capita is positive and highly significant in three out of four models, including both developing-country specifications. This means that, conditional on other factors, richer countries tend to display larger gaps between actual emissions and their NDC trajectories. A plausible interpretation is that higher-income economies—both advanced and emerging—have set more ambitious targets, but their structural dependence on energy- and carbon-intensive consumption patterns makes it harder to deliver the required reductions. In the developing-country subsample, the effect is particularly strong under NDC2, which is consistent with the idea that more advanced emerging economies have substantially increased their ambition but still face binding implementation constraints, especially in heavy industry, transport and urban infrastructure (Seto et al. 2016; Briand et al. 2023; UNFCCC 2024; Peterson and Asselt 2025).

**Electricity access highlights the importance of the underlying power mix: electrification can either close or widen the gap.** In the global sample, access to electricity is either insignificant (NDC1) or positively associated with the gap (NDC2), indicating that in many countries additional electrification has been met by fossil fuel-based generation, increasing emissions relative to pledged trajectories. By contrast, in developing countries (Columns 2 and 4), the coefficient is strongly negative and significant, suggesting that expanding access is associated with better alignment. This likely reflects a combina-
tion of low initial consumption levels and the deployment of more efficient or cleaner tech-
nologies (e.g. mini-grids, solar home systems) in rural electrification programmes. Taken
together, these results confirm that electrification is not inherently “good” or “bad” for
NDC performance: what matters is the carbon intensity of the power system into which
new demand is integrated.

Natural resource dependence is a structural risk factor for NDC implemen-
tation, especially under the Second NDCs. Natural resource rents are positively
and significantly associated with the gap-to-target in the NDC1 global sample, indicat-
ing that resource-dependent economies perform worse relative to their pledges. In the
developing-country specifications, the coefficient is negative for NDC1 but turns strongly
negative and significant for NDC2, suggesting that resource-rich developing countries are
particularly exposed to the challenge of decoupling fiscal revenues and growth from fossil
fuels and other emission-intensive exports. This pattern is consistent with the emerging
literature on the “carbon curse”, which shows that resource-rich economies tend to have
higher emissions and slower decarbonisation due to locked-in carbon-intensive infrastruc-
tures, weak governance, and political resistance to phasing out fossil rents (Khan et al.
2022; Shao et al. 2024; Okere et al. 2025).

At the same time, the global race for critical minerals needed for the energy tran-
sition (lithium, cobalt, nickel, rare earths, etc.) risks creating a “new resource curse”
in mineral-rich developing countries. Recent work points to growing transition risks,
stranded assets and governance challenges in oil- and mineral-rich economies as climate
policies in importing countries tighten (Hoffart et al. 2024; World Bank 2023). Analyses of
critical minerals warn that, without strong institutions and environmental safeguards, the
scramble for transition minerals can generate new macroeconomic vulnerabilities, social
conflict and environmental degradation, rather than easing the decarbonisation constraint
(Aragão et al. 2025; IRENA 2023; Watts et al. 2023). In this context, our results sug-
gest that natural resource dependence—whether based on fossil fuels or, increasingly, on
critical minerals—can undermine the capacity of countries to align their emission trajec-
tories with their NDC pledges, unless it is accompanied by deliberate policies to diversify
economies and manage resource revenues in a climate-consistent way.

**Foreign direct investment is systematically associated with smaller gaps, point-
ing to a potential channel for technology transfer and low-carbon investment.**

Across all four specifications, FDI inflows are strongly negative and highly significant: higher FDI is correlated with better NDC performance. A natural interpretation is that foreign investment brings cleaner technologies, more efficient capital, and additional financing capacity, thereby facilitating the implementation of mitigation measures. This result is consistent with the “pollution halo” literature, which shows that multinational firms tend to employ cleaner production techniques and higher environmental standards
than domestic firms, even in countries with weaker regulation (Eskeland and Harrison 2003; Cole and Elliott 2005). In our context, this translates into lower realised emissions for a given level of output, and thus a smaller gap between actual emissions and pledged reductions. The effect is particularly pronounced in the developing-country NDC1 regression, where FDI may also proxy for deeper integration into global value chains subject to environmental norms and investor scrutiny, reinforcing the alignment between foreign investment and progress toward nationally determined climate targets.

**Renewable energy deployment and environmental expenditure emerge as key levers for aligning emissions with NDC trajectories.** In all models where it is significant, the coefficient on renewable energy use is negative, with especially large effects under NDC2 for the global sample. This confirms that increasing the share of renewables in the energy mix directly contributes to closing the ambition-implementation gap. This finding aligns with empirical evidence showing that renewable energy expansion is one of the most effective drivers of sustained emission reductions, particularly when combined with supportive policy frameworks (Sadorsky 2009; Papaioannou, Tzavara, and Skordas 2019).

Environmental public expenditure exhibits an even stronger pattern: its coefficient is consistently large, negative and highly significant across all specifications. Countries allocating a greater share of their public budgets to environmental protection, climate programmes, and green infrastructure are systematically closer to their pledged trajectories under both NDC cycles and in both samples. This result is consistent with the literature emphasising that public environmental spending enhances regulatory enforcement, finances clean infrastructure, and catalyses private climate investment (Aghion et al. 2016; OECD and UNDP 2025).

Taken together, these findings demonstrate that tangible policy effort—through renewable energy deployment, targeted public spending, and sustained investment—plays a decisive role in translating ambitious commitments into real emission reductions, beyond the ambition embedded in the targets themselves.

**Better control of corruption is consistently associated with smaller gaps, underlining the importance of governance quality for effective climate action.**

The governance indicator based on control of corruption (WGI) is negative and highly significant in all columns: improvements in corruption control are strongly correlated with better NDC performance. This result echoes a substantial body of evidence showing that corruption distorts public expenditure, weakens regulatory enforcement, and reduces the credibility of environmental commitments. Empirical studies demonstrate that corruption hampers environmental policy implementation by lowering compliance incentives and enabling rent-seeking behaviour in pollution-intensive sectors (Fredriksson and Svensson 2003). In the climate domain, recent analyses show that corruption significantly reduces
the effectiveness of climate finance and slows down the deployment of mitigation policies, particularly in developing countries where administrative capacities are constrained (Dania et al. 2020). Taken together, these findings suggest that strong governance and corruption control enhance the ability of countries to translate their mitigation pledges into concrete emission reductions by improving regulatory enforcement, strengthening institutional accountability, and ensuring that climate and environmental resources are allocated efficiently.

**The impact of natural disasters differs sharply between the global sample and developing countries, reflecting both immediate emission effects and longer-run reconstruction dynamics.** In the global specifications (Columns 1 and 3), the number of natural disasters is negatively and significantly associated with the gap-to-target, indicating that disaster years tend to coincide with smaller deviations from pledged emission trajectories. This pattern is consistent with the well-established macroeconomic finding that major disasters depress economic activity in the short run through the destruction of capital, the disruption of production networks, and reductions in energy demand (Dell, Jones, and Olken 2012). Recent empirical work also documents immediate declines in CO₂ emissions following disasters, especially in industrial and transport-intensive sectors (Dou et al. 2022). In this sense, the apparent improvement in climate performance during disaster years does not reflect deliberate mitigation but rather the contraction of economic activity induced by the shock.

In the developing-country sample (Columns 2 and 4), however, the coefficient becomes positive and significant, suggesting that natural disasters are associated with larger gaps relative to NDC trajectories. This reversal highlights the dominant role of medium-run reconstruction dynamics in poorer and more vulnerable economies. Reconstruction typically requires carbon-intensive materials (cement, steel, fuels), emergency logistics, and rapid deployment of backup generation systems, all of which tend to increase emissions relative to pre-disaster levels. Moreover, natural disasters frequently divert fiscal resources away from mitigation and toward emergency response, while also weakening administrative capacity and delaying ongoing climate programmes.

These results align with the broader literature on “focusing events” (Birkland 1997), which suggests that disasters can, under certain institutional conditions, trigger policy reform, heighten public attention to climate risks, or accelerate environmental regulation. However, evidence remains mixed: while some extreme events have catalysed climate policy adjustments (Rowan 2023), others have failed to produce durable policy change, particularly where institutional capacity is weak or political priorities shift toward short-term reconstruction needs (Nohrstedt and Parker 2024).

Taken together, our findings support this more nuanced interpretation. In high-capacity or diversified economies, the short-run emission decline dominates, making disasters appear to “improve” climate performance. In developing countries, where recon-
struction is prolonged and resource constraints more binding, disasters act less as fo-
cusing events and more as structural shocks that widen the implementation gap. This
underscores the need to strengthen resilience, fiscal buffers, and institutional capacity in
vulnerable countries to prevent climate shocks from undermining long-term mitigation
trajectories.

**Conflict events are associated with lower emissions and smaller gaps, but this reflects destructive rather than constructive dynamics.** The number of conflict events carries large negative coefficients in three of the four specifications and is insignificant only in the NDC2 developing-country model. This implies that countries experiencing more conflict tend to have smaller gaps, but the mechanism is clearly undesirable: armed conflict depresses economic activity, disrupts infrastructure, and can destroy high-emitting industrial and transport assets, thereby lowering emissions relative to the pledge. This result is consistent with empirical evidence showing that civil wars and armed conflicts typically reduce GDP, industrial output and trade volumes, often for prolonged periods (Collier, Hoeffler, and Sóderbom 2003; Cerra and Saxena 2008). Recent work also documents that conflict can lead to temporary reductions in energy consumption and CO2 emissions by disrupting fuel supply chains, transport networks and productive capacity, even as it generates severe human and social costs (Messer, Cohen, and D'Acosta 2004; Schultz et al. 2019).

As with natural disasters, such conflict-related “overperformance” should not be in-
terpreted as successful climate mitigation. Rather, it reflects forced emission reductions
driven by economic collapse, displacement and destruction of capital in fragile and conflict-
affected states. Moreover, conflict tends to weaken institutions, erode administrative ca-
pacity, and divert public resources toward military and security expenditures, thereby
undermining the design and implementation of long-term climate policies. In the medium
to long run, reconstruction efforts, unplanned urbanisation driven by displacement, and
the breakdown of environmental governance can even lock countries into more carbon-
intensive development paths once violence subsides. From a policy perspective, the as-
sociation between conflict and smaller gaps underscores the importance of integrating
peacebuilding, state-building and climate strategies, rather than viewing reduced emis-
sions in conflict settings as a sign of genuine progress toward NDC objectives.

**Financial capacity and international climate finance play a differentiated but crucial role across commitment cycles.** Fiscal capacity, measured by the tax-to-GDP ratio, is not significant under NDC1 but becomes positive and highly significant in the NDC2 global regression. One interpretation is that countries with higher fiscal capacity—often advanced and emerging economies—have adopted much more ambitious second-generation NDCs, which they have not yet fully implemented, resulting in larger observed gaps despite stronger revenue bases. In the developing-country sample, the
coefficient remains statistically insignificant, suggesting that variations in domestic fiscal capacity are less important than external support in explaining NDC performance.

By contrast, climate finance inflows—only available in Columns 2 and 4—are strongly negative and highly significant in both NDC cycles. Higher levels of international climate finance are systematically associated with smaller gaps, and the magnitude of the effect increases under NDC2. This provides robust econometric support to the argument that concessional climate finance is a key enabler of NDC implementation in developing countries, particularly as ambition rises and investment needs grow (OECD and UNDP 2025; Black, Parry, et al. 2023; Black, Parry, and Zhunussova 2023). At COP30 in Belém, negotiators elevated climate finance to a headline issue: one session described finance as “the lifeline of climate action” for vulnerable countries. Several developing-country coalitions demanded a tripling of annual adaptation funding to USD 120 billion, highlighting that the current flows remain widely inadequate relative to the scale of the challenge (UNEP 2024b; OECD and UNDP 2025). Meanwhile, host Brazil launched the “Baku to Belém Roadmap” aiming to mobilise up to USD 1.3 trillion per year by 2035 for climate finance, underscoring the long-term financing ambition but also signalling the large gap that remains to be closed. These political developments at COP30 align perfectly with our econometric findings: more predictable, higher-volume, concessional and well-targeted climate finance can help reduce the ambition-implementation gap. However, the summit also exposed key bottlenecks – including access procedures, debt burdens for recipient countries, and transparency of finance flows – which must be addressed if the finance is to become not just pledged but truly effective. For policymakers and international institutions, this means not only increasing volumes of finance, but also improving the quality of flows (grants rather than loans, sector-specific alignment with energy, transport, buildings and industry), streamlining access and ensuring finance responds to the rising ambition embedded in NDC2 commitments.

**Overall, the econometric evidence confirms that closing the ambition implementation gap requires a combination of structural transformation, governance improvements, and sustained financial and policy effort.** Renewable energy deployment, environmental expenditure, good governance, and climate finance all contribute to reducing the gap-to-target, while high income levels, natural resource dependence, disasters and conflict tend to widen it, especially where institutional and fiscal capacities are limited. These results underscore the importance of viewing NDC implementation not merely as a question of setting ambitious targets, but as a broader development and governance challenge that demands coordinated action across policy domains.
# 7 Policy Implications

Strengthening economic and institutional capacity is essential for turning ambition into implementation. Although higher GDP per capita is associated with larger gaps once ambition levels are controlled for, this reflects the fact that richer and emerging economies tend to set more ambitious targets while facing deeper structural lock-ins. Strengthening productive capacities, modernising industrial and urban systems, and improving public-sector planning, monitoring and regulatory capabilities can significantly enhance countries' ability to deliver emission reductions. These reforms are particularly important for rapidly urbanising and industrialising economies, where infrastructure choices shape long-term emission trajectories (Seto et al. 2016; Müller et al. 2013; UNFCCC 2024).

Accelerating renewable energy deployment remains one of the most reliable pathways to closing the ambition-implementation gap. Expanding renewable capacity, modernising grids and storage systems, removing barriers to clean energy investment, and phasing out fossil-fuel subsidies are essential steps for aligning emissions with NDC trajectories. Progress on these fronts also requires coherent international coordination, particularly with fossil-fuel-producing countries, as discussions at COP30 emphasise the need to manage the political economy of subsidy phase-outs and fossil-fuel transition policies. The strong negative association between renewable energy use and the gap-to-target across commitment cycles underscores that alignment depends critically on countries' ability to shift away from carbon-intensive energy systems. In this context, the influence of fossil-fuel interests—both state and corporate—may slow or weaken transition efforts, potentially jeopardising collective progress toward the 1.5 °C objective if not adequately addressed through transparent, rules-based policy processes.

Sustained environmental public expenditure is a powerful driver of compliance with climate commitments. Environmental spending consistently exhibits large and significant effects, highlighting the importance of predictable, multi-year investment in environmental programmes, regulatory enforcement, and low-carbon infrastructure. Ensuring stable climate budgets—supported where possible by programmatic and budget-support instruments from development partners—helps maintain implementation momentum even when fiscal pressures arise.

Mobilising domestic fiscal resources is increasingly necessary as mitigation costs rise under NDC2. Stronger tax systems, improved revenue administration, and the integration of climate priorities into medium-term fiscal frameworks enhance countries' capacity to finance mitigation. Fiscal space also strengthens the credibility of national climate strategies and helps countries leverage concessional and private climate finance more effectively.
**Scaling up international climate finance is indispensable to support the rising ambition of the Paris Agreement.** The strong negative coefficients on climate-finance inflows in our estimations confirm that external financial support plays a crucial role in helping countries remain on track with their climate pledges, especially in developing economies.

**Addressing climate vulnerability and fragility is central to enabling sustained mitigation progress.** Countries with high vulnerability scores face systematically larger gaps, and natural disasters and conflicts significantly shape performance. Investing in resilience, adaptation, disaster-risk reduction and climate-security interventions is therefore not only vital for protecting people and infrastructure—it also underpins the feasibility of meeting mitigation commitments. In fragile and conflict-affected settings, mitigation progress must be understood through the lens of constrained institutional capacity, where shocks can easily derail implementation.

**Building shock-responsive climate policies is necessary to prevent disasters and conflicts from undermining mitigation trajectories.** Mitigation plans must integrate contingency mechanisms—such as climate budget buffers, resilient energy and transport systems, and accessible emergency financing—to maintain progress even when large shocks occur. Doing so helps avoid reconstruction paths that lock countries into high-emission systems and ensures that temporary disruptions do not translate into long-term divergence from NDC targets.

**Overall, the findings highlight that renewable energy deployment, sustained environmental investment, strong governance institutions, and scaled-up climate finance are the cornerstones of accelerated NDC alignment.** Achieving the objectives of the First and Second NDC cycles—and preparing for more ambitious future rounds—will require a coordinated effort between domestic reforms, resilient financing systems and enhanced international support.

# 8 Conclusion

This paper introduces a new, transparent and policy-relevant indicator to assess countries' climate performance: the Gap-to-Target. By comparing actual greenhouse gas emissions with the levels implied by national commitments under the Kyoto Protocol, the first NDCs and the second NDCs, the indicator provides a unified metric to track alignment across successive generations of international pledges.

**Three central findings emerge.** First, descriptive evidence reveals a widening disconnect between rising ambition and actual emission trajectories, particularly under the second NDC cycle. While countries have increased their mitigation commitments, most remain off track to meet their own targets, consistent with the ambition-implementation
gap highlighted in global assessments (UNEP 2024a; UNEP 2024b; Black, Parry, et al. 2023; Black, Parry, and Zhunussova 2023). Second, the distribution of performance is highly uneven across income groups and world regions, with advanced and fast-growing economies facing the largest gaps and low-income countries often overachieving for structural rather than policy-driven reasons. Third, dynamic system-GMM estimations show that a small set of structural factors strongly shape countries' ability to deliver their pledges: economic development, renewable energy deployment, environmental expenditure, fiscal capacity, and climate vulnerability.

**Two environmental variables stand out as the most powerful and consistent predictors of progress: renewable energy expansion and environmental public expenditure.** These findings underscore the central role of sustained investments in the energy transition and environmental protection to narrow the gap between ambition and implementation.

**The Gap-to-Target indicator also provides operational value for public policy and international climate finance.** It can help identify structural bottlenecks, target mitigation support to where it is most needed, and strengthen accountability under the Paris Agreement. Its simple and replicable structure makes it suitable for national monitoring systems, global stocktakes, and international climate assessments.

**Ultimately, closing the ambition-implementation gap requires a coordinated strategy that combines domestic reforms, increased resilience, and expanded international support.** As countries prepare future NDC rounds, the Gap-to-Target framework offers a practical tool to guide implementation, monitor progress, and inform the global transition toward a low-carbon and climate-resilient future.

Finally, as a planned avenue for future work, we intend to extend the indicator to the new 2025 NDC submissions and to subsequent commitment cycles. Depending on data availability and methodological refinements, the indicator could also be adapted for sub-national entities, enabling a decentralised approach to climate governance and improving the monitoring of climate action at the regional and local levels.

# Annex 1: Methodological Details

This annex provides details on the econometric strategy.

To identify the drivers of countries climate performance, we use a dynamic specification based on the GMM estimator: for country i in year t can be written as:

$$Gap_{i,t}^{rel} = \alpha Gap_{i,t-1}^{rel} + \beta' X_{i,t-1} + \mu_i + \lambda_t + \varepsilon_{i,t},$$

where $Gap_{i,t}^{rel}$ is the relative Gap-to-Target under NDC1 or NDC2, $X_{i,t-1}$ is a vector of lagged explanatory variables (GDP per capita, access to electricity, renewable energy use,
environmental expenditure, tax revenue, natural resource rents, governance indicators, climate vulnerability indices, climate finance inflows, and shock variables such as disasters and conflict events), $\mu_i$ is an unobserved country fixed effect, $\lambda_t$ a time effect, and $\varepsilon_{i,t}$ the idiosyncratic error term.

The presence of the lagged dependent variable and potentially endogenous regressors motivates the use of the System GMM estimator proposed by Arellano and Bond (1991) and Blundell and Bond (1998). We treat the lagged dependent variable and selected policy variables (e.g. GDP per capita, environmental expenditure, FDI, governance) as endogenous or predetermined and instrument them with suitable lags in levels and differences. Strictly exogenous controls (such as time dummies) enter the model without instrumentation.

We rely on the two-step System GMM estimator with finite-sample corrections to the covariance matrix. Instrument proliferation is limited by collapsing the instrument matrix and restricting the lag depth. Model specification is evaluated using standard diagnostic tests: (i) Arellano-Bond tests for first- and second-order serial correlation in the differenced residuals; and (ii) Hansen tests of overidentifying restrictions. The reported diagnostics in the main text suggest no evidence of second-order serial correlation and acceptable Hansen $p$-values, supporting the validity of the chosen instrument set.
# 9 Econometric Results

<table><caption>Table 1: Dynamic System GMM Estimates for the Gap-to-Target (Relative Gap)</caption><thead><tr><th></th><th colspan="2">First NDC (NDC1)</th><th colspan="2">Second NDC (NDC2)</th></tr><tr><th></th><th>(1)</th><th>(1+CF)</th><th>(3)</th><th>(3+CF)</th></tr></thead><tbody><tr><td colspan="5"><strong>Dynamic adjustment</strong></td></tr><tr><td>Lagged Gap NDC1</td><td>0.0834***<br>(0.000565)</td><td>0.0607***<br>(0.00126)</td><td></td><td></td></tr><tr><td>Lagged Gap NDC2</td><td></td><td></td><td>0.0525***<br>(0.000802)</td><td>0.0430***<br>(0.00142)</td></tr><tr><td colspan="5"><strong>Economic and structural variables</strong></td></tr><tr><td>GDP per capita (log)</td><td>0.673***<br>(0.238)</td><td>8.279***<br>(0.724)</td><td>0.350<br>(0.385)</td><td>11.24***<br>(1.312)</td></tr><tr><td>Access to electricity</td><td>-0.00819<br>(0.00781)</td><td>-0.177***<br>(0.0211)</td><td>0.0739***<br>(0.0163)</td><td>-0.241***<br>(0.0413)</td></tr><tr><td>Natural resource rents</td><td>0.0458***<br>(0.00559)</td><td>-0.0408*<br>(0.0238)</td><td>0.00000<br>(0.00768)</td><td>-0.149***<br>(0.0315)</td></tr><tr><td>Foreign direct investment (log)</td><td>-1.368***<br>(0.0324)</td><td>-2.761***<br>(0.149)</td><td>-2.370***<br>(0.0388)</td><td>-1.729***<br>(0.104)</td></tr><tr><td colspan="5"><strong>Energy and environmental indicators</strong></td></tr><tr><td>Renewable energy (log)</td><td>-0.826***<br>(0.0758)</td><td>-0.0773<br>(0.282)</td><td>-1.640***<br>(0.110)</td><td>-0.121<br>(0.238)</td></tr><tr><td>Environmental expenditure (log)</td><td>-16.68***<br>(0.179)</td><td>-7.167***<br>(0.654)</td><td>-21.25***<br>(0.289)</td><td>-7.979***<br>(0.774)</td></tr><tr><td colspan="5"><strong>Governance and institutions</strong></td></tr><tr><td>Control of corruption (WGI)</td><td>-2.589***<br>(0.248)</td><td>-7.366***<br>(0.681)</td><td>-8.348***<br>(0.431)</td><td>-8.847***<br>(0.797)</td></tr><tr><td colspan="5"><strong>Shock variables</strong></td></tr><tr><td>Natural disasters (count)</td><td>-0.470***<br>(0.0246)</td><td>0.504***<br>(0.0601)</td><td>-0.234***<br>(0.0227)</td><td>0.551***<br>(0.0544)</td></tr><tr><td>Conflict events (count)</td><td>-3.892***<br>(0.214)</td><td>-2.797***<br>(0.439)</td><td>-2.935***<br>(0.296)</td><td>-0.834<br>(0.519)</td></tr><tr><td colspan="5"><strong>Financial capacity</strong></td></tr><tr><td>Fiscal capacity (tax/GDP)</td><td>-0.00920<br>(0.0168)</td><td>-0.00460<br>(0.0521)</td><td>0.550***<br>(0.0234)</td><td>0.0648<br>(0.0523)</td></tr><tr><td>Climate finance inflows (log)</td><td></td><td>-0.646***<br>(0.0557)</td><td></td><td>-0.931***<br>(0.0763)</td></tr><tr><td>Constant</td><td>5.492***<br>(1.901)</td><td>-43.44***<br>(5.122)</td><td>-2.087<br>(2.566)</td><td>-61.47***<br>(8.482)</td></tr><tr><td>Observations</td><td>2682</td><td>1516</td><td>2682</td><td>1516</td></tr><tr><td>AR(1) p-value</td><td>0.235</td><td>0.231</td><td>0.267</td><td>0.270</td></tr><tr><td>AR(2) p-value</td><td>0.376</td><td>0.368</td><td>0.296</td><td>0.297</td></tr><tr><td>Hansen p-value</td><td>28 1.000</td><td>1.000</td><td>0.999</td><td>1.000</td></tr></tbody></table>

Standard errors in parentheses.
<table><caption>Table 2: Summary Statistics</caption><thead><tr><th>Variable</th><th>Obs</th><th>Mean</th><th>Std. Dev.</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Gap-to-target (Kyoto, %)</td><td>972</td><td>0.8784</td><td>1.8853</td><td>-0.9464</td><td>11.8828</td></tr><tr><td>Gap-to-target (NDC2, %)</td><td>3780</td><td>1.2837</td><td>10.7821</td><td>-0.9984</td><td>235.4621</td></tr><tr><td>Gap-to-target (NDC1, %)</td><td>3780</td><td>0.7473</td><td>8.5166</td><td>-0.9992</td><td>191.1254</td></tr><tr><td>GDP per capita (log)</td><td>3780</td><td>8.5593</td><td>1.4752</td><td>5.4620</td><td>11.6294</td></tr><tr><td>Access to electricity (%)</td><td>3780</td><td>75.4838</td><td>34.5163</td><td>0</td><td>100</td></tr><tr><td>Climate vulnerability index</td><td>3780</td><td>4.2892</td><td>14.3808</td><td>0.2510</td><td>80.3903</td></tr><tr><td>Renewable energy use (log)</td><td>3780</td><td>3.0753</td><td>1.1423</td><td>0</td><td>4.5981</td></tr><tr><td>Environmental expenditure (log)</td><td>3780</td><td>0.1837</td><td>0.0965</td><td>-0.0768</td><td>1.1714</td></tr><tr><td>Fiscal capacity (tax revenue/GDP)</td><td>3780</td><td>17.1547</td><td>4.9895</td><td>0.7797</td><td>39.9862</td></tr><tr><td>Control of corruption</td><td>3232</td><td>0.0238</td><td>1.0314</td><td>-1.6900</td><td>2.4591</td></tr><tr><td>Natural resource rents (% GDP)</td><td>3780</td><td>5.8624</td><td>8.5491</td><td>0</td><td>88.5924</td></tr><tr><td>Number Natural disasters</td><td>3780</td><td>2.1770</td><td>4.0867</td><td>0</td><td>43</td></tr><tr><td>Number Conflict events</td><td>3780</td><td>0.3526</td><td>0.6590</td><td>0</td><td>3</td></tr><tr><td>Foreign direct investment (log)</td><td>3780</td><td>1.4032</td><td>0.9000</td><td>-3.2358</td><td>6.1164</td></tr></tbody></table>

## Annex 2: Additional Figures

<figure><img src="image_11.png" /><figcaption>Figure 7: Distribution of Emission Reduction Targets: Kyoto, First NDC, and Second NDC</figcaption></figure>

Source: Authors' computations based on Climate Watch and NDC Partnership Registry.
<figure><img src="image_12.png" /><figcaption>Figure 8: Comparison of Average Emission Reduction Targets Across NDC Cycles</figcaption></figure>

Source: Authors' computations based on Climate Watch and NDC Partnership Registry.

<figure><img src="image_13.png" /><figcaption>Figure 9: Distribution of GHG Emission Target Types in First and Second NDCs</figcaption></figure>

Source: Authors' computations based on Climate Watch and NDC Partnership Registry.
<caption>Table 3: List of Country</caption>

ARG, AUT, BGR, BHS, BIH, BLR, BRB, BRN, BTN, BWA, CAN, CHE, CHL, COM, CPV, CRI, CYP, CZE, DEU, DNK, DOM, EST, FIN, FJI, GAB, GEO, GMB, GNQ, GRC, GRD, GUY, HRV, HUN, IRL, ISL, ITA, JAM, JPN, KAZ, KHM, KOR, LBR, LCA, LKA, LSO, LTU, LUX, LVA, MDA, MDG, MDV, MKD, MLT, MNG, MUS, NOR, NZL, OMN, PRT, ROU, SGP, SLB, SRB, STP, SVK, SVN, SWE, TTO, UZB, VCT, VNM, ZMB, ZWE

ARM, AUS, AZE, BDI, BEL, BEN, BFA, BGD, BRA, CAF, CHN, CIV, CMR, COD, COG, COL, DJI, DZA, ERI, ESP, ETH, FRA, GBR, GHA, GIN, GTM, HND, HTI, IDN, IND, ISR, JOR, KEN, KGZ, LBN, MAR, MEX, MLI, MRT, MYS, NER, NGA, NLD, PAK, PER, PHL, POL, PRY, RUS, SEN, TCD, TGO, THA, TJK, TUN, TUR, TZA, UGA, UKR, USA, VEN, ZAF
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