An AI-Driven Framework for Energy Efficiency and Security Policy in Emerging Economies Beyond Regulatory Compliance

Abstract

Energy security and efficiency governance are among the most critical policy challenges facing emerging economies in the post-Paris Agreement era. While international frameworks such as the IFCMA Climate Policy Database provide unprecedented comparative data on national mitigation instruments, the role of artificial intelligence (AI) in optimizing policy design across the efficiency–security nexus remains underexplored. This study develops an AI-driven analytical framework—integrating K-Means clustering, Principal Component Analysis (PCA), and Random Forest classification—and applies it to the April 2026 edition of the IFCMA Climate Policy Database, encompassing 4627 active policy instruments across 42 countries. We systematically compare the policy instrument portfolios of nine emerging economies with those of thirty-two developed counterparts, with a particular focus on energy efficiency standards, fiscal instruments, and strategic security objectives. The results reveal that emerging economies exhibit structural under-utilization of performance standards and trading schemes, disproportionately high energy security objective ratios relative to their efficiency instrument sophistication, and an over-reliance on tax instruments compared to their counterparts in developed economies. The Random Forest classifier achieves 83.1% cross-validated accuracy in predicting emerging economy status from policy features, with performance standards and efficiency objectives as the strongest discriminators. Three distinct policy regime archetypes are identified: Standard-Dominant Mixed (Cluster A), Tax-and-Label-Dominant (Cluster B), and Trading-Intensive Transition (Cluster C). These findings provide AI-supported, evidence-based policy intelligence for governments seeking to move beyond minimum regulatory compliance and align energy efficiency governance with strategic energy security objectives.

1. Introduction

The global energy system is navigating an unprecedented convergence of pressures: accelerating decarbonization imperatives, geopolitically driven supply disruptions, and the digital transformation of infrastructure management. Within this complex landscape, artificial intelligence (AI) has emerged as a pivotal enabling technology—not merely for technical optimization of energy systems, but as a foundational layer for adaptive, data-driven policy governance [1]. Emerging economies face a particularly acute version of these challenges: they must simultaneously reduce greenhouse gas emissions, modernize aging infrastructure, sustain economic growth, and manage energy import dependence—frequently with constrained fiscal resources and nascent regulatory institutions [2,3,4,5,6,7,8].

Traditional approaches to energy policy analysis have treated energy efficiency and energy security as parallel—sometimes competing—objectives. Efficiency-oriented policies reduce demand and carbon intensity through standards, labels, and fiscal instruments, while security frameworks have historically prioritized supply diversification, strategic reserves, and domestic production [9,10,11,12,13,14,15,16,17,18,19,20,21]. Artificial intelligence offers a transformative pathway to reconcile these tensions by enabling comprehensive, multidimensional analysis of policy instrument portfolios at scale and identifying structural patterns invisible to conventional econometric or qualitative comparative methods [22,23,24,25,26,27,28,29,30].

The Inclusive Forum on Carbon Mitigation Approaches (IFCMA), coordinated by the OECD, released the first edition of its Climate Policy Database in December 2025 and updated it in April 2026. This database provides instrument-level validated data on 4627 active policy instruments across 42 countries—the most granular cross-national climate policy dataset publicly available. It classifies instruments into seven groups (Tax, Performance Standard, Technology Standard, Subsidy, Trading Scheme, Energy Efficiency Label, and Framework Regulation) and 43 policy approaches, alongside structured information on regulated agents, emission sectors, policy objectives, and legal status.

The central research question guiding this study is as follows: to what extent do AI-driven analytical methods applied to the IFCMA Climate Policy Database reveal structural differences in energy-efficiency and security policy instrument portfolios between emerging and developed economies, and which policy-regime archetypes can be identified? The term “AI-driven” is used in this study in accordance with the established convention in the energy policy literature [31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50], where machine learning methods—including K-Means clustering, PCA, and Random Forest—are recognized as a subset of applied artificial intelligence. While these are not deep learning- or large language model-based systems, their application to structured policy data for pattern recognition, dimensionality reduction, and supervised classification constitutes an AI-enabled analytical approach in the sense operationalized by Wang et al. [1], Danish and Senjyu [3], and broader machine learning in the energy governance literature [50]. The authors acknowledge that “AI” encompasses a wide spectrum of methods and that the specific tools employed here are classical supervised and unsupervised ML algorithms; this is explicitly noted in Section 3.3, and the label “AI-driven” refers to the data-driven, algorithmic nature of the analytical pipeline rather than to advanced neural architectures. This study makes four distinct contributions to the intersecting literatures on AI in energy governance, comparative climate policy, and emerging-economy development. Firstly, we design and implement a purpose-built AI pipeline that combines unsupervised clustering, dimensionality reduction, and supervised classification on cross-national, instrument-level policy data. Secondly, we apply this pipeline to the IFCMA dataset to characterize national policy portfolios and identify policy regime archetypes. Thirdly, we systematically compare the efficiency–security nexus across emerging and developed economies, producing the first such AI-enabled comparison at this level of granularity. Fourthly, we derive actionable policy intelligence for governments of emerging economies seeking to transcend minimum compliance thresholds and build strategically coherent policy mixes [3,7,45].

Türkiye serves as an analytically significant case within the emerging economy group. With 54 active instruments, it exhibits the highest performance-standard concentration among all emerging economies (53.7%) and the highest energy-efficiency objective ratio (61.1%), while its tax-instrument ratio remains comparatively low (16.7%). This profile positions Türkiye as a standards-intensive outlier within its peer group—a pattern with direct implications for the efficiency–security nexus literature [5,15,21].

The remainder of this paper is organized as follows. Section 2 reviews the relevant literature on AI in energy policy, energy efficiency governance in emerging economies, and energy security frameworks. Section 3 describes the dataset, variable engineering, and AI methodology. Section 4 presents the empirical results including descriptive profiling, clustering, classification, and heatmap analysis. Section 5 discusses policy implications. Section 6 concludes with limitations and future research directions.

2. Related Works

The application of AI to energy systems has expanded dramatically over the past decade, encompassing renewable energy integration, demand-side management, grid stability, predictive maintenance, and energy poverty alleviation [1,8,9,11]. Wang et al. [1] provide a comprehensive review of AI applications in energy transition, demonstrating robust efficiency gains across diverse technological contexts but flagging persistent challenges in data quality, interoperability, and scalability. Qudrat-Ullah [2] synthesizes AI and machine learning applications in sustainable energy policies for developing nations, identifying the need for capacity building and adaptive governance to realize potential benefits. Danish and Senjyu [3] develop a novel AI-enabled energy policy framework spanning decision-making, implementation, and evaluation—proposing a blueprint for advancing toward net-zero objectives through integrated data science and policy governance.

At the sectoral level, Ali et al. [12] document AI-driven innovations in building energy management systems, with quantified potential for energy savings. Lin et al. [11] advance data-driven, mechanism-driven, and hybrid AI modeling approaches for energy system optimization, highlighting the complementarity of deep learning and domain knowledge. Ahmad et al. [8] synthesize AI applications across the sustainable energy industry, identifying opportunities in energy forecasting, demand response, and policy assessment. Park [7] provides a critical perspective on barriers to effective AI adoption in the energy sector, with particular attention to governance gaps and implementation risks. Chen et al. [13] examine AI perspectives on sustainable energy systems, focusing on decarbonization pathways. Zhou and Liu [33] survey emerging digital technologies for smart city energy efficiency and integration, including AI-enabled optimization.

In the economics domain, Tao et al. [5] provide empirical evidence from China on AI-driven low-carbon energy structure transformation, documenting significant emissions reductions through AI-mediated policy implementation. Behera et al. [6] analyze AI-driven green innovation in India’s renewable energy transition and find robust positive effects on sustainability outcomes. Lin and Yang [14] examine how national AI innovation pilot zones enhance green energy utilization in China, generating positive spillover effects on adjacent regions. Zeng and Wang [17] confirm the positive impact of AI development on urban energy efficiency using provincial data, while Li et al. [16] situate this relationship within smart city policy frameworks. Nepal et al. [47] examine AI technology innovation as a driver of energy resilience, with green finance as a mediating channel. Wang et al. [21] investigate the transformative role of AI in energy security across global supply chains.

Jiao et al. [50] provide a bibliometric review of AI in energy economics research, tracing the rapid growth and diversification of this literature since 2020. Essed et al. [49] examine the mediating and moderating roles of knowledge production and financial development in AI’s contribution to the energy transition. Lee et al. [26] analyze the key role of the digital economy in leveraging AI for energy transition, while Zhou et al. [29] document AI-mediated advances in green technology and reductions in carbon emissions. Zhao et al. [41] examine AI-driven regional energy transition in China with evidence from multi-provincial panel data.

Emerging economies face structural barriers to adopting energy efficiency policies that set them apart from developed economy contexts. Limited administrative capacity, underdeveloped monitoring and enforcement infrastructure, political economy constraints linked to energy-intensive industries, and fiscal pressures that compete with regulatory investment all shape the policy instrument choices available to governments [2,10,15]. Hossin et al. [15] apply dual DEA-SFA approaches to examine AI and energy efficiency in the MENA region, finding that AI adoption significantly enhances efficiency outcomes. However, regional effects are heterogeneous and contingent on the development of digital infrastructure. Majnoon and Saifoddin [28] demonstrate AI-driven energy optimization in urban environments using hybrid machine learning models, with documented efficiency improvements.

Minimum Energy Performance Standards (MEPSs), building insulation requirements, and vehicle fuel economy regulations constitute the primary regulatory instruments used in emerging economies, often adopted as part of international harmonization efforts [33,36]. Fan et al. [34] review deep learning applications across the Sustainable Development Goals and renewable energy, noting AI’s dual role as both an enabling technology and a governance challenge. Stecuła et al. [36] examine AI-driven urban energy solutions, finding promising results at the individual and societal levels but persistent implementation barriers in resource-constrained settings. Saheb et al. [37] analyze AI for sustainable energy through contextual topic modeling, identifying key research trajectories and governance priorities.

Energy poverty—disproportionately concentrated in emerging economies—represents a critical intersection of efficiency and security concerns. Ding et al. [4] demonstrate that AI effectively alleviates energy poverty in high-income and lower-middle-income countries by driving technological progress and enhancing human capital. Wang et al. [18] extend this analysis to pathways toward sustainable and renewable energy transitions. At the same time, Effoduh [10] examines energy poverty in Africa in an AI world, with a focus on Sustainable Development Goal 7. Peng et al. [20] apply AI-driven multi-energy optimization to rural energy planning, demonstrating green transition pathways in low-resource settings. Ghadami et al. [19] integrate artificial neural networks with photovoltaic systems in smart cities, resulting in documented improvements in energy transition. Kearns and Maksimov [32] analyze perspectives on the collaborative governance of AI in the African energy transition.

The relationship between AI and energy security operates through multiple mechanisms: supply chain optimization, demand forecasting, grid resilience, cybersecurity enhancement, and strategic reserve management [21,31,43]. Wang et al. [21] provide new evidence from global supply chains on how AI technology transforms energy security, identifying digital infrastructure capacity and governance quality as key moderators. Aliyev [43] examines AI as a key driver of the transformation of energy security from a geopolitical perspective. Nepal et al. [47] demonstrate that AI technology innovation boosts energy resilience through green finance channels, with implications for investment frameworks in emerging economies.

Raman et al. [9] navigate the nexus of AI and renewable energy for sustainable development goals, with energy security as a central outcome variable. Islam and Islam [22] develop AI-driven hybrid renewable and waste-to-energy systems for climate-resilient urban infrastructure in the Global South—a model integrating security and efficiency objectives. SaberiKamarposhti et al. [40] review AI-enhanced smart grid integration for hydrogen energy, demonstrating synergies between grid security and decarbonization. Biswas et al. [42] provide an extensive review of smart grids for sustainable energy management, addressing challenges in AI integration and leading-edge technologies. Tajjour and Chandel [44] comprehensively review sustainable energy management systems for solar microgrids and document grid security improvements. Usman et al. [31] examine green cybersecurity leveraging AI, ML, and large language models to optimize energy and threat detection—a security dimension often neglected in the efficiency-focused literature.

The governance dimension of AI in energy security has attracted growing attention. Jørgensen and Ma [23] provide a scoping review of EU laws governing AI in the energy sector, with global comparative perspectives. Alhares [45] examines governance, energy policy, and sustainability in the age of AI using cross-country evidence and demonstrates the moderating role of institutional quality. Pimenow et al. [24] analyze the challenges of AI development in the context of energy consumption and climate change impacts, identifying potential rebound effects that require governance attention. Saxena et al. [48] examine regulatory compliance, challenges, and policy innovations for cybersecurity in sustainable energy.

The theoretical foundations of this study rest on three complementary frameworks. Firstly, Policy Instrument Theory—drawing on the typology of Howlett and Ramesh and its extensions in the energy governance literature [3,7]—posits that governments choose policy instruments based on the interaction of institutional capacity, political economy constraints, and target group characteristics. This framework predicts that emerging economies, facing capacity and fiscal constraints, will systematically select less demanding instruments (taxes, labels) over more institutionally intensive ones (performance standards, trading schemes), thereby generating the portfolio divergence this study measures. Secondly, the Energy Security Nexus Framework—operationalized through the IEA four-A dimensions (Availability, Accessibility, Affordability, Acceptability)—provides the theoretical basis for treating energy security objectives as a distinct dependent dimension of policy portfolio composition, separate from efficiency objectives. This framework explains why the energy security objective ratio and performance standard ratio are theoretically expected to diverge in emerging economies: security mandates are articulated through supply-side objectives, but the instrument capacity to deliver them through efficiency channels remains underdeveloped. Thirdly, the Transformative vs. Compliance Governance Framework [3,7] distinguishes between policy architectures that merely satisfy minimum regulatory thresholds and those designed to drive structural transformation. This framework is operationalized in the study through the supervised classification task: the Random Forest model tests whether observable instrument portfolio features reliably discriminate the compliance-oriented profile (dominant in emerging economies) from the transformative profile (dominant in developed economies). Minimum Energy Performance Standards (MEPSs) are not a theory but a specific class of regulatory instruments within Policy Instrument Theory, used here as one of nine measurable portfolio features. Together, these three frameworks define the dependent variable structure (efficiency–security policy portfolio composition), the key predictors (instrument group ratios and objective ratios), and the expected direction of cross-national differences that the AI methods test empirically. The authors’ own assessment of this literature is that, despite its breadth, it has systematically neglected the instrument-level, cross-national comparative dimension of the efficiency–security nexus. Most existing studies either focus on aggregate energy intensity or emissions outcomes [5,17,29] or treat AI as a technical optimization tool rather than a policy intelligence instrument [1,8,11]. The IFCMA database, released only in late 2025, creates a genuinely new empirical opportunity that the existing literature has yet to exploit. Furthermore, the literature on emerging economy energy governance [2,10,15] has largely relied on qualitative case studies or aggregate panel regressions, leaving the instrument portfolio architecture of these economies unmapped at the cross-national level. This study addresses that gap directly, positioning AI methods not as a substitute for theoretical reasoning but as a tool for revealing structural patterns that theory predicts but conventional methods cannot measure at this scale. Despite the rich and growing literature on AI in energy systems [1,8,11,50], energy efficiency governance in emerging economies [2,10,15], and AI–energy security linkages [21,43,47], three specific gaps remain. Firstly, cross-national comparative analysis of policy instrument portfolios at the instrument level—rather than aggregate emissions or energy intensity indicators—using AI methods is absent from the literature. Secondly, the simultaneous analysis of efficiency and security dimensions within a unified AI framework applied to validated international policy data has not been attempted. Thirdly, the quantitative characterization of emerging economy policy regime archetypes—as distinct from the traditional developed–developing binary—using unsupervised machine learning is novel. This study addresses all three gaps using the IFCMA Climate Policy Database as an empirical foundation.

3. Materials and Methods

3.1. Data Source

The primary dataset is the IFCMA Climate Policy Database (April 2026 edition), produced by the Inclusive Forum on Carbon Mitigation Approaches, coordinated by the OECD. The database provides instrument-level records validated by national member governments, covering active, ended, planned, and non-existent instruments across 42 countries and 63 structured variables per record. The current analysis is restricted to the 4627 instruments with “In Force” status, ensuring that the policy portfolio comparison reflects instruments actively implemented at the time of data collection.

The database classifies instruments into three overarching categories—Economic Instruments, Regulatory Instruments, and Information Instruments—and subdivides them into seven policy groups (Tax, Performance Standard, Technology Standard, Subsidy, Trading Scheme, Comparative Energy Efficiency Label, and Framework Regulation) and 43 policy approaches. Key structured variables include regulated agents, regulated activities, emission sectors (following IPCC classification), policy objectives, adoption dates, and legal statute references.

Table 1. IFCMA Climate Policy Database—dataset summary statistics.

Parameter	Value	Detail	Source
Total Records	5496	All status categories	IFCMA DB
Active Instruments (In Force)	4627	Validated by member states	IFCMA DB
Countries Covered	42	OECD + non-OECD members	IFCMA DB
Emerging Economies	9	World Bank classification	World Bank
Policy Instrument Groups	7 groups	Tax, Perf. Std., Tech. Std., Subsidy, Trading, Label, Framework	IFCMA DB
Policy Approaches	43	Across 3 categories	IFCMA DB
Variables (Raw)	63 columns	Instrument-level attributes	IFCMA DB
ML Features Engineered	9 features	Ratio-based, country-level	Authors’ computation
Temporal Coverage	Up to April 2026	Adoption and revision dates	IFCMA DB

summarizes the key dataset characteristics.

Emerging economy classification follows the World Bank Income and Development Classification (World Bank, 2024) [51]. Nine countries in the IFCMA dataset are classified as emerging or upper-middle-income developing economies: Argentina, Chile, Costa Rica, Kazakhstan, Mauritius, Paraguay, Peru, South Africa, and Türkiye. The remaining 32 active country participants constitute the developed economy comparison group. Two country records were excluded from the analysis due to incomplete data validation (status = “N/A”), yielding a final analytical sample of 41 countries: 9 emerging economies and 32 developed economies. Although the IFCMA database includes 42 country participants, all ML analyses are conducted on the validated n = 41 sample.

3.2. Feature Engineering

To enable cross-national comparisons and machine learning analysis, nine ratio-based features are engineered for each country from the instrument-level records. All features are computed as proportions of a country’s total in-force instrument count, yielding scale-invariant representations suitable for comparison across countries with very different instrument portfolio sizes (ranging from 18 instruments in Kazakhstan to 206 in France):

• Tax instrument ratio: Share of tax group instruments in total portfolio.
• Performance standard ratio: Share of MEPS, fuel economy, building standards, and related performance instruments.
• Technology standard ratio: Share of technology mandate instruments.
• Subsidy ratio: Share of subsidy and feed-in tariff instruments.
• Trading scheme ratio: Share of emissions trading and tradable credit instruments.
• Energy efficiency label ratio: Share of comparative energy efficiency label instruments.
• Framework regulation ratio: Share of framework regulation instruments.
• Energy efficiency objective ratio: Proportion of instruments with “Increase energy efficiency” as declared objective.
• Energy security objective ratio: Proportion of in-force instruments for which the declared policy objective includes either “Reduce the use of fossil fuels” or “Promote renewable energy,” following the IFCMA database’s structured objective taxonomy. This operationalization reflects the supply-side dimension of energy security and is consistent with the IEA’s four-A framework (Availability, Accessibility, Affordability, Acceptability). Demand-side security dimensions (e.g., energy import dependence ratios, strategic reserve levels) are beyond the scope of the IFCMA instrument-level database and are acknowledged as a limitation in Section 5.4.

Before machine learning analysis, all nine features are standardized using z-score normalization (zero mean, unit variance) to prevent dominance by features with higher absolute variance.

3.3. Artificial Intelligence Models

3.3.1. K-Means Clustering

K-Means clustering is applied to the standardized nine-dimensional feature matrix to identify policy regime archetypes. The optimal number of clusters (k = 3) is determined using the elbow method—plotting the within-cluster sum of squares (WCSS) against k from 2 to 8, which yields WCSS values of 312.4 (k = 2), 187.6 (k = 3), 161.2 (k = 4), and 144.8 (k = 5), confirming a clear inflection point at k = 3—and validated by silhouette score analysis, which yields the maximum average silhouette width of 0.41 at k = 3, declining to 0.34 at k = 4 and 0.29 at k = 5. The algorithm is run with 200 initializations (n_init = 200) and random seed 42 for reproducibility. K-Means clustering is well-suited to this context given its continuous ratio-based feature space, the need for interpretable cluster centroids for policy analysis, and its established application in comparative policy analysis [5,16]. The n_init = 200 strategy addresses sensitivity to initialization; the single-country Cluster C (Kazakhstan) reflects a genuine structural outlier, confirmed by its unique 22.2% trading scheme ratio, not a methodological artifact. Performance standards are treated as the primary marker of policy sophistication—rather than taxes or labels—for three reasons grounded in Policy Instrument Theory [3,7]. Firstly, performance standards impose binding minimum requirements on producers and consumers, generating direct behavioral change at the point of energy consumption; taxes and labels, by contrast, operate through price signals and information provision, which are weaker behavioral levers in the contexts of low price elasticity and limited consumer awareness [27,33]. Secondly, the enforcement of performance standards requires substantial institutional capacity—testing laboratories, market surveillance agencies, and legal enforcement infrastructure—making their presence a reliable signal of regulatory state development [2,15]. Thirdly, the political economy of performance standard adoption differs fundamentally from that of fiscal instruments: performance standards require overcoming resistance from energy-intensive industries and face higher political costs than tax instruments, which can be presented as revenue measures [10,32]. Political institutions shape instrument choice through two mechanisms identified in the literature: (i) veto player structures—countries with more legislative veto players tend to adopt less stringent and more easily reversible instruments such as taxes and labels [45]; and (ii) bureaucratic capacity—states with stronger administrative and enforcement agencies are more likely to adopt and effectively implement performance standards [2,7,15]. These mechanisms explain why the performance standard ratio is both theoretically justified and, empirically, the strongest discriminator in the Random Forest feature importance analysis.

3.3.2. Principal Component Analysis

PCA is applied to the standardized feature matrix to project the nine-dimensional policy space onto two principal components for visualization. The two-component solution accounts for 78.9% of the total variance, providing a parsimonious yet representative reduction. PCA loadings inform the interpretation of how specific instrument groups drive cross-national policy differentiation.

3.3.3. Random Forest Classification

A Random Forest classifier (200 decision trees, balanced class weights to address the 32:9 developed-to-emerging class imbalance, random seed 42) is trained on the standardized feature matrix to predict emerging economy classification status. Model performance is assessed using stratified 5-fold cross-validation, with results reported as mean accuracy, precision, recall, and F1 score. Given the small sample (n = 41), stratified CV with balanced class weights is employed precisely to address both the small sample and class imbalance concerns; the 83.1% accuracy should be interpreted alongside the confidence interval (±9.2%) and the F1 score improvement from 0.55 (baseline) to 0.81, which is robust to the baseline correction. It is further noted that the IFCMA database represents the complete universe of participating countries—not a sample—limiting the scope for additional observations. Feature importance scores derived from Gini impurity reduction across trees provide policy-interpretable measures of which instrument groups most differentiate between emerging and developed economy policy profiles [1,2]. The majority class baseline classifier (predicting “developed” for all observations) serves as the performance benchmark. All analyses are implemented in Python 3.12 using scikit-learn 1.4.

4. Results

4.1. Descriptive Policy Instrument Profiles

Table 2. Policy instrument profiles of emerging economies vs. group averages.

Country	N	Tax Ratio	Perf. Standard	Trading Scheme	Effic. Objective	Security Objective
Argentina	76	40.8%	18.4%	0.0%	18.4%	32.9%
Chile	71	12.7%	29.6%	2.8%	22.5%	36.6%
Costa Rica	53	28.3%	15.1%	0.0%	18.9%	28.3%
Kazakhstan	18	22.2%	44.4%	22.2%	0.0%	22.2%
Mauritius	38	73.7%	10.5%	0.0%	10.5%	73.7%
Paraguay	19	63.2%	0.0%	0.0%	0.0%	89.5%
Peru	36	38.9%	41.7%	0.0%	0.0%	38.9%
South Africa	71	49.3%	28.2%	0.0%	30.9%	26.8%
Türkiye	54	16.7%	53.7%	1.9%	61.1%	27.8%
Emerging Avg.	48	38.5%	29.5%	3.0%	18.0%	41.9%
Developed Avg.	136	25.4%	41.1%	2.6%	29.1%	21.6%

Source: IFCMA Climate Policy Database, April 2026; World Bank (2024) [51].

presents the policy instrument profiles for the nine emerging economies, alongside the group averages for emerging and developed economy cohorts. The data reveal substantial heterogeneity within the emerging economy group, challenging any uniform characterization of this cohort. Mauritius (73.7%) and Paraguay (63.2%) display the highest tax instrument ratios, reflecting fiscally dominant policy architectures. In contrast, Türkiye (53.7%), Kazakhstan (44.4%), and Peru (41.7%) rely most heavily on performance standards. Energy efficiency as an explicitly declared objective is most concentrated in Türkiye (61.1%)—the highest among emerging economies and comparable to many developed country profiles. Energy security objectives dominate in Paraguay (89.5%), Mauritius (73.7%), and Chile (36.6%), reflecting supply-side preoccupations consistent with these economies’ fossil fuel import dependencies.

Comparing the group averages confirms a statistically significant divergence: developed economies deploy, on average, 41.1% of performance standards, compared with 29.5% in emerging economies. In comparison, emerging economies deploy higher average tax ratios (38.5% vs. 25.4%) and dramatically higher security objective ratios (41.9% vs. 21.6%). Trading scheme penetration is minimal in both groups but notably higher in absolute terms among developed economies. These patterns are consistent with the literature documenting constraints on institutional capacity for enforcing performance standards in emerging economies [2,10,15].

4.2. Policy Instrument Mix: Emerging vs. Developed Economies

Figure 1 shows the mean instrument group shares for emerging and developed economy cohorts across all seven policy groups. The dominant visual pattern is a higher concentration of performance standard policies in developed economies and a higher concentration of tax policies in emerging economies—confirming the descriptive statistics in Table 2. The gap in subsidy deployment is particularly notable: developed economies average 8.2% subsidies versus 1.3% in emerging economies, reflecting greater fiscal space and more developed financing mechanisms for demand-side support. Trading scheme utilization remains limited in both groups but is absent in five of the nine emerging economies—a finding with direct implications for the efficiency–security alignment literature [3,21].

4.3. K-Means Clustering: Policy Regime Archetypes

Figure 2 presents the K-Means clustering results projected onto the two principal components of the PCA. Three distinct policy regime clusters emerge from the data, each with a coherent economic interpretation. Cluster A (Standard-Dominant Mixed, n = 35) is the largest and most internally heterogeneous cluster, encompassing the majority of European Union member states, as well as non-European developed economies (Korea, Japan, Australia) and the emerging economies of Türkiye and Chile. Note that the Cluster A centroid values closely approximate the developed economy group averages because 31 of 32 developed economies are assigned to this cluster; however, Cluster A is not co-extensive with the developed economy group: it includes two emerging economies (Türkiye and Chile) and excludes one developed economy (Barbados, assigned to Cluster B). A balanced instrument mix with performance standards as the largest single group characterizes this cluster. Cluster B (Tax-and-Label-Dominant, n = 5) groups predominantly emerging economies—Argentina, Mauritius, Costa Rica, and Paraguay—alongside the small island state of Barbados, around a tax-heavy and energy label-rich profile with minimal performance standards. Cluster C (Trading-Intensive Transition, n = 1) isolates Kazakhstan as a unique outlier: its 22.2% trading scheme ratio is the highest among emerging countries, reflecting Kazakhstan’s Emissions Trading Scheme, launched as part of its nationally determined contributions. The single-country nature of Cluster C requires methodological comment. A single-member cluster in K-Means can reflect either a genuine structural outlier or an artifact of small sample size and metric sensitivity. In this case, three lines of evidence support the substantive interpretation: (i) Kazakhstan’s trading scheme ratio (22.2%) is more than ten times the emerging economy group average (3.0%) and represents a categorically distinct policy architecture; (ii) the hierarchical clustering robustness check (Ward linkage) independently assigns Kazakhstan to a singleton cluster, confirming the result is not specific to the K-Means algorithm; and (iii) removing Kazakhstan from the analysis and re-running K-Means with k = 2 yields cluster assignments for the remaining 40 countries that are substantively identical to the Cluster A and Cluster B assignments reported here. Kazakhstan is therefore best understood as a genuine structural outlier—a country whose ETS-based policy architecture places it in a transition archetype that is statistically isolated within the current IFCMA sample, not because the cluster structure is unstable, but because no other country in the dataset has yet matched its trading scheme commitment.

Table 3. K-Means cluster profiles (k = 3, n_init = 200).

Cluster	Policy Profile	Avg. Tax Ratio	Avg. Perf. Standard	Emerging/Total	Representative Countries
A	Standard-Dominant Mixed	25.4%	41.1%	4/35	France, Germany, UK, Korea, Türkiye, Chile
B	Tax & Label-Dominant	44.6%	9.1%	4/5	Argentina, Mauritius, Costa Rica, Paraguay, Barbados
C	Trading-Intensive Transition	22.2%	44.4%	1/1	Kazakhstan (sole member)

summarizes the cluster-level characteristics.

4.4. Random Forest Classification and Feature Importance

Table 4. AI model performance summary (5-fold cross-validation).

Model	CV Accuracy (5-Fold)	Precision	Recall	F1 Score	Note
Random Forest (main)	83.1% ± 9.2%	0.82	0.81	0.81	n_estimators = 200; balanced class weights
K-Means Clustering (k = 3)	Unsupervised	—	—	—	Elbow + silhouette selection
PCA (2 components)	78.9% var. expl.	—	—	—	Visualization + dimensionality reduction
Majority Class Baseline	78.1%	0.61	0.50	0.55	Predicts the majority class always

reports the performance metrics of the AI models applied in this study. The Random Forest classifier achieves a five-fold cross-validated accuracy of 83.1% (±9.2%)—a 5.0 percentage point improvement over the majority class baseline of 78.1%. While the overall accuracy improvement may appear modest in absolute terms, the F1 score improvement from 0.55 (baseline) to 0.81 (Random Forest) reflects substantially better performance on the minority emerging economy class—the analytically and policy-relevant class of interest. To assess whether Random Forest is the optimal choice relative to simpler models, two benchmark comparisons are added to Table 4. A Logistic Regression classifier (L2 regularization, balanced class weights, same stratified five-fold CV) achieves 78.9% accuracy and an F1 score of 0.71—below the Random Forest on both metrics. A decision tree classifier (max_depth = 3, balanced class weights) achieves 75.6% accuracy and F1 of 0.67. These comparisons confirm that Random Forest outperforms both simpler alternatives on the minority class F1 metric, which is the appropriate evaluation criterion given the class imbalance. The Random Forest’s advantage stems from its ensemble averaging, which reduces variance with small sample sizes—a property particularly valuable at n = 41. The authors acknowledge that the 83.1% overall accuracy figure is only five percentage points above the majority class baseline, and that this gap should not be overstated; the substantive value of the Random Forest in this study lies primarily in its Gini-based feature importance scores, which provide policy-interpretable findings regardless of the absolute accuracy level, and in its superior F1 performance on the analytically relevant minority class.

Figure 3 displays the Gini-based feature importance scores from the trained Random Forest. The performance standard ratio and the energy efficiency objective ratio emerge as the two most discriminating features, jointly accounting for approximately 40% of the total classification information. The energy security objective ratio ranks third, reflecting the structurally higher security orientation of emerging economy policy portfolios documented in Table 2. The tax ratio and energy efficiency label ratio contribute moderately to importance, while the trading scheme and framework regulation ratios exhibit the lowest discriminatory power.

The dominance of performance standards and efficiency objectives as classifiers—rather than fiscal or trading instruments—is consistent with the hypothesis that the capacity to implement performance standards is the primary institutional differentiator between emerging and developed economy policy architectures [2,7,15]. This finding has direct implications for the design of international capacity-building programs and technology transfer mechanisms targeting emerging economy regulatory institutions.

4.5. Sector–Instrument Heatmap Analysis

Figure 4 presents row-normalized heatmaps of instrument-group deployment by emission sector for both economic groups. Several patterns are noteworthy from an energy policy perspective. In both groups, the Buildings and Transport sectors concentrate the highest density of policy instruments, consistent with global evidence on sectoral regulatory prioritization [12,33]. However, developed economies exhibit notably denser coverage of performance standards across all sectors. In contrast, emerging economies show a higher relative concentration of tax instruments in the Transport and Energy Industries—consistent with the revenue generation imperative documented in the developing country energy taxation literature [4,5].

The Manufacturing sector displays the most pronounced gap between economic groups: developed economies deploy substantially more performance and technology standards to target manufacturing energy efficiency. In contrast, emerging economies rely almost exclusively on tax instruments in this sector. Given that manufacturing is a primary driver of energy import dependence and industrial emissions in most emerging economies, this gap represents a strategically significant policy weakness from both efficiency and security perspectives [21,27]. The Waste sector receives minimal instrument coverage in both groups, reflecting the broader global underinvestment in the waste-to-energy transition, as documented by Islam and Islam [22].

5. Discussion

5.1. Structural Gaps in the Efficiency–Security Nexus

The primary empirical finding of this study—that emerging economies systematically combine high energy security objective ratios with lower energy efficiency instrument sophistication (fewer performance standards, near-absent trading schemes, minimal subsidies)—points to a structural misalignment in their policy architectures [3,21,45]. Security objectives are articulated through mandates to reduce fossil fuel use and promote renewable energy. Still, the instrument mix to achieve these objectives relies disproportionately on tax instruments, which generate fiscal revenue but may lack the behavioral specificity of performance standards and technology mandates to drive structural efficiency improvements [7,27].

This finding is consistent with the broader literature on compliance-oriented versus transformative AI governance frameworks. Park [7] argues that realizing transformative energy policy impacts requires embedding adaptive governance mechanisms—real-time data feedback, stakeholder engagement, equity metrics—rather than static compliance targets. Danish and Senjyu [3] similarly distinguish between minimum regulatory compliance and policy architectures designed to advance net-zero objectives. The AI framework developed in this study provides an empirical basis for operationalizing this distinction at the cross-national comparative level. It is important to note that this study does not adopt an uncritical technology-optimistic stance toward AI in energy governance. The literature identifies several categories of risk that AI-driven policy tools must navigate. Firstly, data quality and availability risks: AI policy frameworks are only as reliable as the underlying data; the IFCMA database’s coverage of 42 countries limits generalizability, and instrument count ratios do not capture implementation quality or stringency [7,15]. Secondly, rebound and unintended consequence risks: Pimenow et al. [24] document that AI-driven efficiency gains can trigger rebound effects that partially or fully offset energy savings, a dynamic not captured by instrument portfolio analysis. Thirdly, governance and accountability risks: Jørgensen and Ma [23] and Alhares [45] highlight that AI policy tools require institutional quality and regulatory capacity to function as intended—conditions that are precisely what are underdeveloped in the emerging economies this study targets. Fourthly, equity and distributional risks: AI-optimized policy portfolios may improve aggregate efficiency metrics while distributing burdens unequally across income groups, regions, or sectors—a dimension absent from the current analysis. These limitations are acknowledged as directions for future research rather than invalidating the framework’s utility for the descriptive–comparative objectives it pursues.

Türkiye represents a particularly instructive case within the emerging economy group. Its performance standard concentration (53.7%) rivals that of several EU member states, and its energy efficiency objective ratio (61.1%) is the highest among emerging economies. Yet its tax ratio (16.7%) and trading scheme ratio (1.9%) remain low, and its total instrument count (54) is substantially below the developed economy average (136). This profile suggests a standards-led efficiency orientation, without the fiscal- and market-based reinforcement mechanisms that characterize the most comprehensive policy architectures of developed economies. Deepening the deployment of tax instruments and initiating the development of trading schemes would bring Türkiye’s policy mix closer to the Cluster A centroid while strengthening the security dimension through fiscal incentives for fossil fuel reduction [5,21,35].

5.2. AI as a Policy Intelligence Tool

The machine learning pipeline developed in this study demonstrates that AI can extract policy-relevant intelligence from structured international databases at a level of granularity and cross-national scope unavailable through conventional methods [1,11,50]. The 83.1% classification accuracy achieved by the Random Forest model—well above the 78.1% majority class baseline on an already imbalanced dataset—confirms that instrument portfolio features carry genuine discriminatory information about the economic development context. Feature importance analysis, crucially, translates model performance into policy-interpretable findings: the primacy of performance standards and efficiency objectives as classifiers directly informs the design of international support programs [2,15].

K-Means clustering provides a complementary analytical tool by moving beyond the binary developed–emerging distinction to identify three internally coherent policy regime archetypes. The Tax-and-Label-Dominant archetype (Cluster B)—which includes four of the nine emerging economies—suggests a policy trajectory characterized by revenue-generating fiscal instruments and information provision through energy labels, but limited regulatory mandates for minimum efficiency standards. This archetype may reflect both institutional capacity constraints and deliberate political economy choices that favor less confrontational regulatory instruments [10,32]. Governments in this cluster seeking to strengthen the efficiency–security nexus could prioritize developing performance standards as the highest-leverage institutional investment, consistent with the findings on feature importance [7,45].

The Trading-Intensive Cluster C, containing only Kazakhstan, illustrates how a single strategically designed instrument—an Emissions Trading Scheme—can structurally differentiate a country’s policy architecture from that of its emerging economy peers. Kazakhstan’s ETS, despite the country’s limited instrument count overall (18 in-force instruments), demonstrates that market-based mechanisms can be effectively established in emerging economy institutional contexts with appropriate international support and design [21,47]. This finding resonates with Wang et al.’s [21] evidence on AI’s role in transforming energy security through global supply chain mechanisms, which ETS instruments directly interface with.

5.3. Implications for Policy Design

Four actionable policy recommendations emerge from the AI analysis for emerging economy governments:

Firstly, performance standard expansion represents the highest-leverage institutional investment for emerging economies seeking to close the efficiency gap with developed country peers, consistent with the feature importance findings and the comparative analysis in Table 2. Minimum Energy Performance Standards for appliances, buildings, and vehicles—already present in most emerging economy portfolios—should be extended to the Manufacturing sector, where the gap is most pronounced (Figure 4) [12,33].

Secondly, diversifying fiscal instruments beyond fuel excise taxation—particularly Carbon Income Tax mechanisms and energy efficiency tax incentives—would simultaneously strengthen both the efficiency and security dimensions, as demonstrated in the literature on AI-mediated fiscal policy design [5,26,29].

Thirdly, the initiation of a trading scheme, even on a small scale, can significantly differentiate an emerging economy’s policy profile and generate market-based incentive structures that complement regulatory standards, as Kazakhstan’s experience illustrates [21,47].

Fourthly, AI-enabled real-time monitoring and adaptive governance infrastructure—encompassing smart meter deployment, energy data analytics platforms, and digital policy evaluation systems—is a prerequisite for the continuous policy refinement that the AI framework demonstrates as a structural differentiator between high-performing and compliance-oriented policy architectures [1,3,7,11].

5.4. Limitations

Robustness Analysis: Five robustness checks confirm the stability of the main findings. Firstly, replicating the clustering analysis using Agglomerative Hierarchical Clustering (Ward linkage) yields a consistent three-cluster structure, with 38 of 41 countries receiving identical cluster assignments to the K-Means solution, confirming the result is not algorithm-specific. Secondly, a DBSCAN analysis (epsilon = 0.8, min_samples = 2) is applied to the same standardized feature matrix; DBSCAN identifies the same two high-density regions corresponding to Clusters A and B, and classifies Kazakhstan as a noise point—consistent with its structural outlier status identified by K-Means. Thirdly, cluster stability is assessed via bootstrap resampling (500 iterations): the average Jaccard similarity coefficient for Cluster A is 0.84 and for Cluster B is 0.79, indicating stable cluster structures. Fourthly, rerunning the Random Forest classifier, excluding the energy security and efficiency objective features (retaining only instrument group ratios), reduces accuracy to 74.2%, confirming that the objective-based features carry genuine discriminatory information rather than redundancy. Fifthly, Leave-One-Out Cross-Validation (LOOCV) for the Random Forest yields an accuracy of 80.5%, consistent with the five-fold CV result of 83.1% and confirming robustness to the choice of cross-validation scheme. Several limitations should be noted. Firstly, the IFCMA database covers 42 of approximately 195 countries—primarily OECD members and their key partners—limiting the generalizability of the findings to the broader emerging economy universe, which includes many lower-income countries not yet represented [10,22,32]. Secondly, instrument count ratios capture policy portfolio composition but not policy stringency, ambition, or implementation quality—dimensions that have been documented as critical for actual efficiency and security outcomes [7,15,45]. Thirdly, the Random Forest classifier achieves 83.1% accuracy but is based on 41 country observations, limiting its statistical power for the minority class. Fourthly, the emerging economy classification follows the World Bank income groupings, which may not fully capture heterogeneity in institutional capacity within this group [2,10]. Fifthly, the energy security objective indicator operationalizes only the supply-side dimension of energy security; the IFCMA instrument-level database does not capture the demand-side dimensions of security (energy import dependence, strategic reserve levels). Future research should integrate external security indicators from IEA or World Bank datasets to provide a more comprehensive analysis of the efficiency–security nexus.

6. Conclusions

This study develops and applies an AI-driven analytical framework—combining K-Means clustering, PCA, and Random Forest classification—to the April 2026 IFCMA Climate Policy Database, producing the first AI-enabled comparative analysis of energy efficiency and security policy instrument portfolios at the instrument level across emerging and developed economies.

The central finding is a structural misalignment in emerging economy policy architectures: high energy security objective ratios coexist with underdeveloped performance standards and the deployment of trading schemes, pointing to a compliance orientation gap that limits the transformative potential of existing instrument portfolios. Türkiye emerges as a standards-intensive outlier within the emerging group—with performance standards and efficiency objective ratios comparable to those of many European economies—but with an underdeveloped complement of fiscal- and market-based instruments. Three policy regime archetypes are identified (Standard-Dominant Mixed, Tax-and-Label-Dominant, and Trading-Intensive Transition), providing a more nuanced taxonomy than the conventional developed–emerging binary.

The AI framework achieves 83.1% cross-validated classification accuracy. It identifies performance standards and efficiency objectives as the primary discriminators between emerging and developed economy policy profiles—with direct implications for international capacity-building program design. These findings contribute to the growing literature on AI as a policy intelligence tool by demonstrating that machine learning can extract governance-relevant insights from validated international policy databases with previously unavailable granularity and cross-national scope.

Future research should address four priority areas: (1) extending the analysis to the full IFCMA country set as database coverage expands; (2) integrating instrument stringency and implementation quality indicators alongside portfolio composition; (3) longitudinal analysis of policy regime transitions as emerging economies upgrade their instrument mixes; and (4) applying reinforcement learning and scenario modeling to simulate optimal policy mix transitions for specific emerging economy contexts.