A Systemic Evaluation of Energy Digital Transformation Policies for the G20 Group of Countries: A Four-Dimensional Framework and Cross-National Quantitative Analysis

Abstract

The global integration of digital technologies into energy systems constitutes a critical pathway for achieving sustainable and intelligent energy governance. This study evaluates the effectiveness of the energy digital transformation policies across eighteen major economies through a comprehensive four-dimensional framework, which encompasses policy objectives, intensity, instruments, and stakeholder engagement. Through the application of the entropy-weighted TOPSIS method, our comparative analysis identifies a distinct hierarchy in national policy performance. The first tier, including the United Kingdom, the United States, South Korea, Australia, China, and Germany, demonstrates high coherence, enforceable mechanisms, and multi-actor coordination. The second tier, comprising Saudi Arabia, France, Turkey, Russia, Canada, and India, exhibits partial alignment with notable strengths in selected dimensions yet significant gaps in enforceability or stakeholder integration. The third tier, featuring Italy, Brazil, Argentina, Mexico, Japan, and Indonesia, is characterized by fragmented approaches and aspirational goals lacking implementation specificity. Stakeholder inclusiveness emerges as the most influential dimension, accounting for 38.3% of total weighting and substantially accounting for variations in efficacy. Moreover, nonlinear threshold effects are identified, indicating that subcritical performance in any dimension leads to disproportionate declines in overall outcomes. These findings underscore that synergistic policy design, which entails balancing objectives, governance capacity, instruments, and actors, is indispensable for effective energy digitalization.

1. Introduction

The global energy sector is experiencing a profound transformation driven by the dual imperatives of digital innovation and decarbonization mandates [1]. By infusing energy infrastructure with capabilities afforded by artificial intelligence, the Internet of Things, and big data analytics, policymakers and industry stakeholders have achieved significant advancements in operational efficiency, grid stability, and renewable energy integration [2]. This overarching digital transformation thus establishes a critical pathway for developing sustainable energy systems that successfully balance a triad of key objectives: economic viability, environmental responsibility, and security assurance [3].

Substantial financial commitments highlight the growing strategic emphasis on digital and clean energy infrastructure across major economies. Global expenditure on energy is anticipated to exceed USD 3 trillion in 2024, reaching an unprecedented level. Of this total, approximately USD 2 trillion is earmarked for investments in clean energy technologies and related infrastructure [4]. The United States, for instance, has enacted significant legislation, including the Bipartisan Infrastructure Investment and Jobs Act of 2021 and the Inflation Reduction Act of 2022, channeling substantial funding into grid modernization, clean energy demonstrations, and energy efficiency upgrades [5]. Concurrently, the European Union is investing heavily in modernizing its power grid and strengthening its clean technology manufacturing capacity through initiatives like the Net Zero Industry Act [6]. China has also demonstrated notable progress, commissioning vast solar PV capacity and boosting exports in key green industries such as solar cells, lithium batteries, and electric vehicles [7]. Similarly, India has mobilized significant capital through green bonds to finance its renewable energy and low-carbon hydrogen ambitions [8].

In response to these developments, the policy landscape for energy digitalization has evolved significantly. Governments are increasingly formulating comprehensive frameworks to steer this complex transition, reflecting diverse approaches to governance, instrument selection, and stakeholder engagement [9]. National strategies range from market-oriented innovation to state-led coordination and technology-specific focuses, shaped by varying institutional contexts, resource endowments, and political priorities.

Despite this proliferation of policy initiatives, a critical and unresolved question remains: how can the effectiveness of these diverse national energy digital transformation policies be systematically evaluated and compared? Furthermore, what are the key determinants that explain variations in policy efficacy across different governance contexts? This study is guided by the central hypothesis that policy effectiveness is not determined by any single factor but emerges from the synergistic integration of four core dimensions: policy objectives, policy intensity, policy tools, and policy subjects. We further posit that imbalances or critical deficiencies in any one of these dimensions will lead to a disproportionate decline in overall policy performance, a phenomenon characterized as a nonlinear threshold effect. Finally, we hypothesize that among these dimensions, stakeholder inclusiveness plays the most pivotal role in determining policy success.

Despite growing policy activity, significant gaps persist in our understanding of what constitutes effective energy digital transformation governance [10]. The complexity of energy digitization requires a comprehensive analytical approach that integrates multiple disciplines and perspectives. Traditional approaches often fail to fully capture the multidimensional nature of these transformations, which involve technological, economic, social, and political dimensions [11]. Scholarly inquiry has extensively explored the application of public policy frameworks to the energy transition [12]. However, this body of work has primarily examined technological aspects or policy measures within specific sectors, paying scant scholarly attention to the comparative assessment of policies across diverse governance systems [13]. Most studies employ qualitative case methods or descriptive analyses that lack systematic frameworks for measuring policy effectiveness and identifying success factors [14,15]. This gap is particularly notable given the complex, multi-dimensional nature of energy digitalization, which requires coordinated action across policy objectives, implementation mechanisms, instrument selection, and stakeholder engagement.

Theoretical frameworks for policy implementation, notably the approach by Sabatier and Mazmanian from 1980 [16], provide valuable insights for analyzing complex policy domains yet have seldom been applied to energy digital transformation. Similarly, while multi-criteria decision analysis methods like TOPSIS have been used in energy policy assessment, their application to comparative digital transformation policy evaluation remains limited [17]. This study directly addresses these theoretical and methodological gaps by developing and applying a novel four-dimensional analytical framework grounded in policy implementation theory. It subsequently employs the entropy-weighted TOPSIS method to conduct a rigorous, quantitative cross-national comparison of energy digital transformation policies across eighteen major economies.

Our research makes three primary contributions: First, we develop and operationalize a novel framework that integrates policy objectives, intensity, tools, and subjects to assess energy digitalization policies systematically. Second, we provide the first cross-national quantitative evaluation of energy digital transformation policies using entropy-weighted TOPSIS, enabling objective comparison and ranking of policy effectiveness. Third, we identify specific improvement pathways for different governance contexts based on empirical analysis of policy strengths and weaknesses.

2. Literature Review

The global energy industry is presently undergoing a profound transformation driven by the twin forces of digital technological advancement and the transition to a low-carbon economy. As a fundamental pillar of socioeconomic progress and human welfare, energy plays a critical role in addressing the multifaceted issues associated with sustainable development [18]. The sector plays a pivotal role in fostering economic growth and elevating living standards by powering industrial and service activities [19]. Historical energy transitions, documented over the past decade [20]—with notable cases in Europe [21] and the U.S. [22]—reflect evolving strategies to align energy systems with sustainability goals. In recent years, mounting concerns over energy security and climate change have further elevated the energy transition as a critical focus of international policy [23].

The contemporary transition is characterized by a shift from traditional biomass toward modern commercial energy and renewables such as wind and solar power [24]. This transition not only spurs technological innovation but also necessitates advances in energy modeling to capture complex interdependencies among policy, infrastructure, market behavior, environmental impacts, and supply security [25]. A sustainable energy transition extends beyond efficiency gains; it requires the sustainable management of environmental and social costs, risks, and benefits throughout the transition process [26].

The advent of the Fourth Industrial Revolution has propelled a significant fusion of digital technologies with the energy industry, initiating a fundamental shift toward digital modernization [27]. Furthermore, the process of digitalization is redefining operational frameworks and industrial practices, establishing itself as a critical enabler for both economic advancement and the pursuit of sustainability objectives [28,29]. Within the energy and resources sectors, digital innovations have significantly enhanced profitability and operational efficiency [30]. Emerging digital tools, which include artificial intelligence, blockchain, the Internet of Things, robotics, big data analytics, cloud computing, digital twins, and quantum computing are increasingly applied to upgrade and optimize energy infrastructure [31]. In the oil and gas sector, for example, the analysis of historical and real-time well data through advanced analytics has enhanced the efficiency of resource extraction processes [32]. Blockchain facilitates secure peer-to-peer energy trading, enabling innovative business models [33], while cloud computing supports photovoltaic output prediction and grid stability [34]. Moreover, big data analytics improves the forecasting of intermittent energy demand, enhancing supply chain efficiency [35].

Amid these technological advancements, geopolitical and policy dynamics are increasingly influencing national approaches to energy digitalization. The ongoing technological disputes between major economies, such as the United States and China, have introduced complexities in global technology supply chains and collaborative innovation. These tensions affect the diffusion of critical digital technologies, including those essential for smart grids and renewable energy integration, thereby shaping the strategic priorities of individual nations. Similarly, shifts in national policy orientations, such as the U.S. withdrawal from international climate agreements under the Trump administration, have altered the global governance landscape. Such developments underscore the significance of domestic political contexts in determining the pace and direction of energy digital transformation, as countries navigate between international cooperation and sovereign policy interests.

Despite these advancements and repeated calls for broader adoption of digital technologies in the energy sector [36], research examining the specific applications, challenges, and future trajectories of these technologies remains limited. Although some studies have provided comprehensive overviews of their potential to shape sustainable energy systems [37], policy development and evaluation for energy digital transformation still lag. Existing research on energy policies often focuses on specific sectors, such as wind energy [38], or on individual countries’ policy frameworks [39]. While some efforts have been made to systematize energy policy indicators [40] or analyze policy instruments [41], these have not adequately incorporated elements specific to energy digital transformation.

The extant literature exhibits three major methodological limitations that hinder a holistic understanding of Energy Digital Transformation (EDT) policies. First, many studies confine their focus to singular sectors or specific policy instruments. This approach overlooks the synergistic interplay among multiple policy dimensions, namely objectives, intensity, tools, and stakeholders, which is essential for governing complex socio-technical transitions [38,41]. This fragmented approach fails to capture the systemic nature of EDT, where policy efficacy depends on the alignment of dimensions rather than their isolated performance. Second, prevailing methodological approaches rely heavily on qualitative case studies or descriptive policy reviews [36,37]. While rich in contextual detail, these methods lack the capacity for objective, comparative quantification across nations, limiting the generalizability of findings and impeding the identification of causal relationships between policy design and outcomes. Furthermore, the absence of dynamic, longitudinal frameworks inhibits understanding of how policies evolve and adapt to technological disruptions over time. Third, although foundational theories such as Sabatier and Mazmanian’s policy implementation framework [16] offer robust conceptual lenses for understanding regulatory capacity and stakeholder engagement, their application to EDT remains largely theoretical. Few studies operationalize these constructs into measurable indicators for cross-national empirical validation, leaving a critical gap between policy theory and practice.

These gaps collectively underscore the need for a systemic, quantitatively grounded, and comparatively designed evaluation framework capable of diagnosing policy efficacy across diverse governance contexts. Our study addresses these limitations by proposing a four-dimensional framework rooted in policy implementation theory and employing an entropy-weighted TOPSIS method for objective and multidimensional performance benchmarking. This approach bridges the qualitative-quantitative divide and enables the identification of nonlinear threshold effects and key determinants of policy success, thereby offering a replicable methodology for future comparative policy analysis in energy digital transformation.

3. Research Design

3.1. A Systemic Four-Dimensional Policy Analysis Framework

This study conducts a comparative analysis of energy digitalization policies in major economies. Its objective is to identify the core elements that determine policy outcomes and to develop actionable recommendations for refining governance models. To achieve this, we develop a systemic four-dimensional policy analysis framework, grounded in the classical implementation theory of Sabatier and Mazmanian [16], which enables the construction of an objective, systematic, practical, and scientifically robust evaluation index system for energy policy efficacy. As depicted in Figure 1, the proposed framework structures the examination of policies according to four principal elements, which include Policy Objectives, Policy Intensity, Policy Tools, and Policy Subjects. Each dimension captures a critical aspect of policy design and implementation capacity, collectively offering a holistic view of governance efficacy in energy digitalization.

Policy objectives establish the foundational aims of public intervention, outlining anticipated results and guiding implementation. Policy intensity denotes both the hierarchical level and coordinative capacity of the issuing entity, which substantially affects the policy’s authority as well as its constraining and influential effect on targeted actors. Policy instruments denote the mechanisms employed to achieve stated goals. These instruments span legal, economic, and administrative means, thereby forming a critical linkage between policy intentions and outcomes. Policy subjects encompass the direct recipients of policy measures—individuals, organizations, and societal groups—whose responses and interactions shape the efficacy and execution of policies.

This four-dimensional analytical framework exhibits considerable flexibility and forward-looking relevance in the setting of energy digitalization. It is capable of integrating the progressively complex requirements of intelligent energy transformation while also addressing distinct societal governance needs under the Fourth Industrial Revolution, particularly in terms of governance objectives, policy intensity, instruments, and subjects. As such, the framework offers a solid theoretical basis for examining and assessing policies related to energy digital transformation, yielding valuable perspectives on the formulation and implementation of measures aimed at fostering sustainable and resilient energy systems.

Following the schematic representation in Figure 1, the constituent elements of the proposed four-dimensional analytical framework are elaborated upon. The subsequent sections provide a detailed exposition and theoretical justification for each core dimension—Policy Objectives, Policy Intensity, Policy Tools, and Policy Subjects. This systematic delineation is crucial for operationalizing the abstract framework into a concrete and measurable evaluation index system, thereby establishing a robust foundation for the subsequent empirical analysis and cross-national comparison.

3.1.1. Policy Objectives

Policy objectives are defined as the goals that a nation seeks to achieve through the implementation of policies to address specific issues. Clear and well-defined policy objectives are essential as they reflect the nation’s intentions and provide guidance in social management. They establish a foundational framework and action guidelines for social development while setting behavioral norms for members of society. The clarity of policy objectives enables policymakers and implementers to deeply understand the purpose and significance of the policies, thereby avoiding misinterpretations during the execution process. The clarity of policy objectives serves as a fundamental prerequisite for formulating effective energy digital transformation policies. It directly influences the ultimate accuracy, feasibility, and efficacy of these policies. In this study, the objectives of energy digital transformation policies are refined into four discrete yet synergistic aspects: “Improving energy efficiency and reducing energy consumption”, “Promoting the integration and use of renewable energy, and developing clean energy”, “Enhancing the resilience and security of energy systems” and “Fostering innovation and technological advancement in the energy sector”, which is grounded in a systematic analysis of global policy frameworks and operational requirements. This taxonomy ensures comprehensive coverage of the policy domain while enabling precise operationalization for empirical measurement. Each dimension corresponds to a primary intervention logic: physical system upgrades (Infrastructure), operational efficiency (Grid Intelligence), economic incentives (Market Innovation), and environmental externalities (Sustainability). This structured approach facilitates granular analysis of policy effectiveness across core functional areas of the energy transition, mitigating conceptual ambiguity encountered in binary or tripartite models. These research aims establish a foundational basis for the systematic assessment and cross-national comparison of energy digital transformation policy efficacy.

3.1.2. Policy Intensity

Policy intensity denotes the hierarchical authority and enforcement capacity embedded in policy formulation. We operationalize this dimension through the issuing body’s administrative rank and multi-entity coordination, as both factors critically determine a policy’s jurisdictional reach and implementation efficacy. This approach is theoretically anchored in Sabatier & Mazmanian’s (1980) [16] policy implementation framework, which identifies “hierarchically integrated policy-making bodies” as a core element of regulatory capacity. Higher-tier entities (e.g., national legislatures or cross-ministerial task forces) possess greater constitutional authority to mobilize fiscal resources, mandate cross-sector compliance, and establish accountability mechanisms—attributes that inherently signal the strategic priority accorded to a policy domain.

Empirical validation derives from China’s governance context, where central policies (State Council/Central Committee directives) exhibit 73% higher implementation rates than provincial measures in our pilot analysis (2020–2023 energy digitalization data). This disparity occurs because policies issued at elevated administrative levels: ① Trigger mandatory interagency coordination protocols, ② Command broader oversight (e.g., national audit systems), ③ Allocate larger budgetary commitments, ④ Generate stronger compliance incentives through performance-linked accountability.

Consequently, policy intensity serves as a proxy for institutional commitment and anticipated societal impact, reflecting how governance systems allocate political capital to energy digital transformation.

3.1.3. Policy Tools

Policy tools refer to the specific methods and instruments employed by governing bodies to realize predetermined policy goals. Essentially, they are the means through which policy goals are realized, allowing policy actors to influence and guide the behaviors and developments of policy subjects. In policy analysis, the rational classification of policy tools is a critical step in evaluating policy content and effectiveness. Rothwell and Zegveld (1984) [42] introduced the concept of policy tools, categorizing them into three types: supply-side, environmental, and demand-side tools. They emphasized that during the policy formulation and implementation processes, different means, measures, and methods should be employed from these three levels to influence social phenomena and achieve specific policy objectives. This classification provides a useful framework for understanding and evaluating policy tools.

In this study, energy digital transformation policy tools are categorized into the following types: ① Supply-side policy tools: These focus on delivering the essential inputs and support, such as financial capital, technical expertise, and systemic infrastructure, demanded by the digital transformation of the energy sector. ② Demand-side policy tools: These aim to stimulate and guide public demand for energy digital transformation, such as incentives for adopting renewable energy technologies. ③ Environmental policy tools: These concentrate on creating a favorable external environment for energy digital transformation, such as regulatory frameworks, tax incentives, and standardization efforts. The appropriate selection and application of these tools are crucial for ensuring the successful implementation of energy digital transformation policies.

3.1.4. Policy Subjects

Policy subjects denote the specific groups or entities directly targeted by policy interventions, ranging from individuals and organizations to broader social collectives. As the primary recipients of policy effects, their resulting responses and behavioral changes serve as crucial metrics for evaluating policy efficacy. Within the sphere of energy digital transformation, these subjects demonstrate considerable diversity and complexity, encompassing but not limited to: ① Governments and related departments, ② Energy production and supply companies, ③ Energy service companies, ④ Energy consumers (including industrial, commercial, and residential sectors), ⑤ Research and educational institutions. This classification aids in designing and implementing policies more effectively, ensuring the efficacy and comprehensiveness of energy digital transformation measures.

Systematizes stakeholder mapping, which is essential for three reasons: Firstly, identifying distinct actor categories enables policymakers to tailor instruments to specific roles. For instance, supply-side tools align with research institutions’ innovation capacity, while demand-side tools target behavioral change in end-users. Secondly, clear subject delineation assigns legal responsibilities and rights, reducing regulatory ambiguity. Governments act as enforcers, producers as compliance agents, and service providers as technical intermediaries, ensuring policy obligations are legally attributable. Thirdly, covering all value-chain actors prevents implementation gaps. Omitting service providers would disrupt technology diffusion, while excluding consumers would undermine demand-side flexibility. Thus, this taxonomy operationalizes stakeholder-centric governance, directly enhancing policy efficacy by aligning instruments with actor capabilities and legal roles.

3.2. Research Methods

In order to systematically assess the effectiveness of energy policies, this study employs a composite methodology that integrates the entropy weight method with the TOPSIS technique, applied to the indicator system constructed in Section 3.1. This combined strategy facilitates an objective, data-informed evaluation while enabling comparisons across different national contexts.

The entropy-weighted TOPSIS framework constitutes a multi-criteria decision-making technique that merges the entropy weighting approach with the principles of the TOPSIS model. The entropy method, grounded in information theory, assigns weights objectively by measuring the degree of variation among indicator values. Higher data variability reflects greater informational content within an indicator, which accordingly results in a larger assigned weight. In contrast, the TOPSIS technique operates as a benchmark-based assessment tool. It identifies the optimal and poorest performances across indicators to construct positive and negative ideal reference points. The relative proximity of each evaluated unit to these ideal solutions is then determined, serving as a basis for ranking and evaluating the alternatives.

The entropy-weighted TOPSIS method was adopted for this study due to its demonstrated efficacy in policy evaluation contexts, specifically because it addresses two critical methodological challenges: objective indicator weighting and multidimensional performance benchmarking. First, the entropy method calculates weights based solely on data variability, thereby eliminating subjective bias when aggregating heterogeneous indicators. This objectivity is particularly valuable for cross-national comparisons where expert judgments may introduce cultural or contextual biases. Second, TOPSIS evaluates policy effectiveness through proximity to theoretical ideal solutions [43], which enables systematic ranking of countries against optimized policy configurations.

The applicability of the entropy-weighted TOPSIS method has three characteristics that are consistent with our research design: ① Objective weight derivation ensures replicability, as entropy weights reflect the discriminative power of indicators across policy systems. ② Dimensional integration resolves the problem of comparing disparate policy elements by mapping them to unified positive/negative ideal benchmarks. ③ Theoretical grounding is maintained because the four dimensions (Section 3.1) originate from policy implementation theory [16], while entropy weights empirically validate their diagnostic relevance.

The entropy-weight TOPSIS method is illustrated below.

(a) Basic Definition: Assume there are $M$ evaluation units (policies) and $N$ evaluation indicators (from the four-dimensional framework). The original data matrix is denoted as $\mathit{X}={[x_{ij}]}_{M\times N}$, where $x_{ij}$ represents the raw value of the $j$-th indicator of the $i$-th evaluation unit.

(b) Indicator Normalization: To eliminate dimensional effects, the original matrix is normalized using the extremum method, resulting in a standardized matrix $\mathit{Z}={[z_{ij}]}_{M\times N}$ where

$$\begin{array}{cc}{z}_{ij}={\displaystyle \frac{x_{ij}-\underset{1\le i\le M}{\min }(x_{ij})}{\underset{1\le i\le M}{\max }(x_{ij})-\underset{1\le i\le M}{\min }(x_{ij})}},& i=1,\cdots M;j=1,\cdots N\end{array}$$

(1)

(c–e) Entropy Weight Determination: The entropy weight method [44] is employed to objectively assign weights based on the informational value of each indicator. Specifically:

(c) The proportion p_ij of the j-th indicator for the i-th policy is calculated as:

$$\begin{array}{cc}{p}_{ij}={\displaystyle \frac{z_{ij}}{{\displaystyle {\sum }_{i=1}^M{z}_{ij}}}},& i=1,\cdots M;j=1,\cdots N\end{array}$$

(2)

(d) The entropy $e_j$ of the $j$-th indicator is:

$$\begin{array}{cc}{e}_j=-{\displaystyle \frac{1}{\ln M}}{\displaystyle \sum _{i=1}^M{p}_{ij}\ln p_{ij}},& j=1,\cdots N\end{array}$$

(3)

Note that $p_{ij}\ln p_{ij}=0$ when $p_{ij}=0$.

(e) The weight $v_j$ of each indicator is then derived as:

$$\begin{array}{cc}{v}_j={\displaystyle \frac{1-e_j}{M-{\displaystyle {\sum }_{j=1}^N{e}_j}}},& j=1,\cdots N\end{array}$$

(4)

(f) Weighted Normalized Matrix: Using the entropy-derived weights, the weighted normalized matrix:

$$\mathit{R}={[r_{ij}]}_{M\times N}={[v_j·z_{ij}]}_{M\times N}$$

(5)

(g–i) TOPSIS Evaluation: The TOPSIS method is utilized for evaluation. Specifically:

(g) The positive ideal solution $S_j^{+}$ and the negative ideal-solution $S_j^{-}$ are determined as:

$$\begin{array}{cc}{S}_j^{+}=\underset{1\le i\le M}{\max }{r}_{ij},& j=1,2,\cdots ,N\end{array}$$

(6)

$$\begin{array}{cc}{S}_j^{-}=\underset{1\le i\le M}{\min }{r}_{ij},& j=1,2,\cdots ,N\end{array}$$

(7)

(h) Compute the Euclidean distances of the all evaluation units from the positive and negative ideal-solutions ($D_i^{+}$ and $D_i^{-}$), as shown in Equations (8) and (9), respectively:

$$\begin{array}{cc}{D}_i^{+}=\sqrt{{\displaystyle {\sum }_{j=1}^N{\left(r_{ij}-S_j^{+}\right)}^2}},& i=1,\cdots M\end{array}$$

(8)

$$\begin{array}{cc}{D}_i^{-}=\sqrt{{\displaystyle {\sum }_{j=1}^N{\left(r_{ij}-S_j^{-}\right)}^2}},& i=1,\cdots M\end{array}$$

(9)

(i) Calculate the relative closeness degrees $C_i$ of the evaluation units:

$$\begin{array}{cc}{C}_i={\displaystyle \frac{D_i^{-}}{D_i^{-}+D_i^{+}}},& i=1,\cdots M\end{array}$$

(10)

The higher the relative closeness degree, the better the evaluation unit.

3.3. Selection of Energy Digital Transformation Policy Texts

The evaluation of energy digital transformation policies requires a representative sample that captures diverse national strategies and governance approaches. This study draws its analysis from the policy frameworks of G20 member states, a group that accounts for the majority of global energy consumption and economic activity. This selection ensures the inclusion of advanced economies, emerging markets, and resource-dependent nations, providing a comprehensive perspective on how different countries approach energy digitalization.

Policy selection followed a rigorous multi-stage process to ensure methodological consistency and relevance. Initial identification involved systematic searches of official government portals, legislative databases, and energy ministry websites using standardized search terms related to energy digitalization, smart grid development, and national energy strategy. This preliminary screening yielded numerous policy documents from across the G20 nations.

A four-stage filtration protocol was then applied to identify the most relevant policies. First, documents were screened for jurisdictional authority, requiring them to be issued by national-level government entities rather than subnational authorities. Second, thematic comprehensiveness was assessed by examining whether policies addressed at least three of the four core dimensions of our analytical framework—policy objectives, intensity, tools, and subjects. Third, instrument specificity was evaluated by checking for concrete implementation mechanisms such as funding allocations, regulatory requirements, or clear compliance timelines. Finally, temporal validity was confirmed by verifying that policies remained in effect as of December 2025, regardless of their original publication date.

This process resulted in the selection of eighteen policy texts that represent the diverse approaches to energy digital transformation across the G20 nations. While the study prioritizes recent policies, several foundational documents enacted before 2020 were included due to their continued strategic importance and ongoing implementation status. For instance, Argentina’s Renewable Energy Regulations remain the primary regulatory framework for renewable energy integration despite their 2016 enactment date. Similarly, Saudi Arabia’s National Transformation Program 2030 continues to guide the kingdom’s energy digitalization efforts as its seminal strategic document. Indonesia’s Energy Outlook 2019 retains its status as the official reference for energy infrastructure planning and investment decisions. These policies were included because they represent active, influential frameworks that continue to shape their respective national energy digitalization landscapes, thus providing valuable insights into long-term strategic approaches.

The final policy set encompasses a balanced representation of different governance models and development stages, ensuring that the subsequent analysis captures the full spectrum of contemporary energy digital transformation strategies (see

Table 1. Energy Digital Transformation Policy.

Number	Country	Policy Name	PT
T1	United States	National Innovation Pathway of the United States	2023.04
T2	China	China’s Energy Transition	2024.08
T3	Russia	Strategic Directions for the Digital Transformation of the Fuel and Energy Complex in Russia Through 2030	2024.03
T4	Japan	Outline of Japan’s Energy Basic Plan	2021.03
T5	South Korea	Korea Energy Use Rationalization Act	2024.12
T6	France	France Mineral Resources Critical for Low-carbon Energy: Value chains, risks and public policies	2023.07
T7	United Kingdom	UK Digitalising our energy system for net zero: Strategy and Action Plan	2021.07
T8	Germany	Germany Energy Efficiency Act (EnEfG)	2023.11
T9	Italy	Italy Decree-Law No. 144 (PNRR Emergency Measures)	2022.09
T10	Australia	Australia National Electricity Rules, Version 234	2025.08
T11	Brazil	Brazil Energy Development Plan (PDE 2030)	2023.06
T12	Canada	Canada Marine Renewable Energy Regulations	2024.03
T13	Turkey	Turkey Law on Utilization of Renewable Energy Resources for Electricity Generation	2024.05
T14	Mexico	Mexico Electricity Industry Law	2024.04
T15	Argentina	Argentina Renewable Energy Regulations (Decree 531/2016)	2016.03.
T16	Saudi Arabia	Saudi Arabia National Transformation Program 2030 (Energy Digitalization & Renewable Energy)	2016.04
T17	India	India Energy Conservation (Amendment) Act, 2022	2022.12
T18	Indonesia	Indonesia Energy Outlook 2019	2019.09

Note: Sourced from the official websites of various governments.

). Each selected policy contributes to understanding how nations are addressing the complex challenge of integrating digital technologies into their energy systems while pursuing sustainability and security objectives.

3.4. Construction of the Evaluation Index System

Guided by the categorical framework for policy dimensions established in Section 3.1, the current research constructs a comprehensive three-level index system for policy assessment. The first level is the overall evaluation target: the effectiveness of Energy Digital Transformation (EDT) Policies. The second level consists of the four core analysis dimensions: Policy Objectives, Policy Intensity, Policy Tools, and Policy Subjects. The third level operationalizes these dimensions through 14 specific evaluation indicators, which serve as measurable variables for quantitative assessment (see

Table 2. Evaluation Indicator System for Energy Digital Transformation Policies.

Evaluation Target	Analysis Dimension	Evaluation Indicator
Effectiveness of Energy Digital Transformation Policies	Policy objectives	Improve energy efficiency and reduce energy consumption
		Promote the integration and use of renewable energy, and develop clean energy
		Enhance the resilience and security of the energy system
		Promote innovation and technological advancement in the energy sector
	Policy intensity	Validity level
		Consequence
	Policy tools	Supply type
		Environment type
		Demand type
	Policy subjects	The Government and related departments
		Energy production and supply enterprises
		Energy service enterprises
		Energy consumers *
		Research and educational institutions

Note: Derived from the author’s construction based on the four-dimensional policy analysis framework. * The energy consumers include industrial, commercial, and residential sectors.

). This hierarchical structure ensures a logical progression from theoretical constructs to empirical measurement.

The selection of these 14 indicators was not arbitrary but was rigorously derived through theoretical grounding and empirical validation. Each indicator was chosen to capture a critical aspect of policy design and implementation capacity as theorized by foundational public policy literature and observed in contemporary energy governance practice.

Beginning with policy objectives, the four objective indicators were selected to comprehensively cover the core strategic goals of EDT as defined by major international frameworks. The first indicator, “Improving energy efficiency and reducing energy consumption,” is a universal priority quantifiable through energy intensity metrics and is central to the International Energy Agency’s optimization principles and Sustainable Development Goal (SDG) 7. The second objective, “Promoting the integration and use of renewable energy, and developing clean energy,” directly operationalizes Paris Agreement decarbonization imperatives, evident in national strategies like U.S. solar tax credits (§45X) and China’s renewable portfolio standards [37]. The third objective, “Enhancing the resilience and security of the energy system,” incorporates criteria from the World Economic Forum Energy Transition Index, addressing critical cyber-physical vulnerabilities exposed by recent incidents [45]. Finally, the fourth objective, “Promoting innovation and technological advancement in the energy sector,” draws from Rothwell & Zegveld’s (1984) [42] innovation policy theory, focusing on the deployment of applied technologies rather than basic research to avoid implementation gaps [2].

Regarding policy intensity, the two indicators under intensity capture the hierarchical and coordinative capacities of governance structures, a core element of regulatory capacity theory [16]. The “Validity level” indicator operationalizes the concept of legal hierarchy (laws > regulations > guidelines), which directly determines enforceability. Informed by the foundational legal maxim lex superior derogat legi inferiori, such a hierarchical approach is consistently present in comparative analyses of governance structures. The “Consequence” (multi-entity coordination) indicator quantifies the OECD’s whole-of-government approach through interagency density. Policies characterized by inter-agency collaboration, as observed in joint initiatives undertaken by U.S. Departments of Energy, Transportation, and Defense, typically exhibit superior cost-effectiveness and implementation performance. This enhanced outcome stems from pooled mandates and shared resources. This methodology operationalizes the OECD’s whole-of-government approach, quantifying policy effectiveness through the lens of horizontal coordination density and vertical implementation authority.

For policy tools, the tripartite classification (Supply type, Environment type, Demand type) rigorously follows Rothwell & Zegveld’s (1984) [42] instrument typology, which remains the seminal framework for analyzing policy interventions in technological innovation. Supply-type tools measure foundational government inputs (e.g., R&D grants, infrastructure investments) crucial for catalyzing early-stage digital technologies [36]. Environment-type tools evaluate institutional enablers and market-shaping mechanisms, such as regulatory standards and carbon pricing, which create a stable landscape for private investment [41]. Demand-type tools assess market-pull mechanisms, such as public procurement and consumer subsidies, which are critical for scaling deployed technologies and creating sustainable markets [37].

The policy subjects dimension employs Ostrom’s principles of polycentric governance to map stakeholder inclusivity, which is essential for robust policy implementation. The five indicators ensure coverage of all critical actors in the energy value chain. “Government and related departments” quantify state regulatory capacity. “Energy production and supply enterprises” track the obligations of primary operators. “Energy service enterprises” are included as they play a pivotal role as technology diffusion intermediaries, a factor validated by IEA case studies [35]. “Energy consumers” embed energy democracy principles by assessing participatory rights and compensation mechanisms. “Research and educational institutions” measure the engagement of the innovation ecosystem, which is vital for long-term technological advancement and skills development [26].

This framework synthesizes these theoretical domains to ensure analytical coherence. The evaluation system is presented in Table 2.

3.5. Standard for Quantifying Indicators

The essence of policy quantification lies in scoring relevant indicators based on established standards. To deepen the research on Energy digital transformation policies, detailed value assignments were made to the policy texts from the dimensions of policy objectives, policy intensity, policy tools, and policy subjects. Simultaneously, scoring standards were established for each policy based on the characteristics of each indicator and their specific impacts on energy digital transformation policies, resulting in the quantification criteria shown in

Table 3. Judgment criteria for policy objectives.

Evaluation Criterion	Score
Policy characterization is marked by a well-defined goal accompanied by detailed execution procedures.	3
Policy characterization is marked by a well-defined goal yet lacking detailed execution procedures.	2
Policy characterization is marked by an ambiguous goal and an absence of detailed execution procedures.	1

Note: Derived from the author’s refinement of the evaluation criteria defined in Section 3.5.

,

Table 4. Judgment criteria for policy intensity.

Evaluation Indicator	Evaluation Criterion	Score
Validity level	Laws	5
	Administrative Regulations	4
	Departmental Regulations	3
	Normative documents	2
	Policies	1
Consequence	Presence of three or more issuing entities, including key national governmental departments	3
	Existence of two issuing entities, involving national-level governmental departments	2
	Solely issued by a major national governmental department	1

Note: Derived from the author’s refinement of the evaluation criteria defined in Section 3.5.

,

Table 5. Judgment criteria of policy tools.

Evaluation Indicator	Evaluation Criterion	Score
Supply type	It encompasses the provision of essential resources—including funding, talent, infrastructure, technology, and information—supported by comprehensive regulations and detailed measures to accomplish policy aims.	3
	It entails the allocation of essential resources, such as funding, talent, and technology, for policy goal attainment, accompanied by a foundational set of regulations and measures.	2
	It is confined to the basic allocation of indispensable resources, including funds, personnel, and technology, required to meet policy objectives.	1
Environment type	This level encompasses the deployment of specific policy instruments, including target planning, fiscal subsidies, regulatory oversight, standardized protocols, and performance evaluation. These tools are essential for creating an enabling environment to attain policy objectives. The framework is complemented by comprehensive regulatory provisions and detailed implementation guidelines.	3
	This level encompasses the deployment of specific policy instruments, including target planning, fiscal subsidies, regulatory oversight, standardized protocols, and performance evaluation. These tools are essential for creating an enabling environment to attain policy objectives. The framework is complemented by a foundational set of regulatory provisions and implementation guidelines.	2
	This level is limited to the identification of necessary policy instruments, such as target planning and fiscal subsidies, which are required to establish a foundational environment for achieving policy objectives.	1
Demand type	This level encompasses the deployment of specific policy instruments, including service procurement, international collaboration, and policy subsidies. These tools are essential for achieving policy objectives and are supported by comprehensive regulatory provisions and detailed implementation measures.	3
	This level encompasses the deployment of specific policy instruments, including service procurement, international collaboration, and policy subsidies. These tools are essential for achieving policy objectives and are supported by a foundational set of regulatory provisions and implementation measures.	2
	This level is limited to identifying necessary policy instruments, such as service procurement and policy subsidies, which are required to achieve core policy objectives.	1

Note: Derived from the author’s refinement of the evaluation criteria defined in Section 3.5.

and

Table 6. Criteria for policy objects.

Evaluation Criterion	Score
The text delineates the entitlements and responsibilities of relevant entities while providing comprehensive guidance and explicit requirements.	3
The text addresses the rights and duties of key stakeholders while offering pertinent criteria and detailed recommendations.	2
The text references the principal actors involved while lacking specific measures and actionable implementation details.	1

Note: Derived from the author’s refinement of the evaluation criteria defined in Section 3.5.

.

For policy objectives, we select 4 evaluation indicators: ① Improve energy efficiency and reduce energy consumption; ② Promote the integration and use of renewable energy, and develop clean energy; ③ Enhance the resilience and security of the energy system; ④ Promote innovation and technological advancement in the energy sector.

The clarity of policy objectives is a crucial indicator for measuring the effectiveness of energy digital transformation policies. Therefore, scores were assigned based on whether the policy texts explicitly reflect the four major policy objectives: “Improve energy efficiency and reduce energy consumption,” “Promote the integration and use of renewable energy, and develop clean energy,” “Enhance the resilience and security of the energy system,” and “Promote innovation and technological advancement in the energy sector” (see Table 3).

The legal effect hierarchy in energy policy evaluation is determined through a systematic classification of normative instruments based on their juridical authority and institutional provenance (see Table 4). Statutory laws enacted by national legislatures are assigned the highest score (5 points) due to their constitutional mandate in governing foundational energy frameworks. These laws are followed by administrative regulations (4 points), which are formulated by the State Council to operationalize legislative intent through sector-specific governance. Ministerial directives (3 points), issued by specialized State Council departments, provide technical implementation protocols, while normative documents (2 points) from subnational agencies facilitate localized regulatory enforcement. At the lowest tier are provisional policy guidelines (1 point), which reflect their non-binding nature and transitional governance functions. By following the lex superior principle that higher laws prevail over lower ones, this layered approach guarantees a harmonized and systematic legal framework across all levels of government.

Policy influence is quantified through a structural analysis of promulgating entities, where multi-source issuance mechanisms signify enhanced governance capacity. Policies co-issued by three or more key national governmental bodies achieve maximum influence (3 points) through inter-ministerial consensus-building and cross-sectoral resource consolidation. In contrast, bipartite departmental collaborations (2 points) demonstrate moderated synergistic potential, while singular promulgation by a major national department (1 point) indicates constrained jurisdictional reach and reduced institutional leverage. This methodology operationalizes the OECD’s whole-of-government approach, quantifying policy effectiveness through the lens of horizontal coordination density and vertical implementation authority.

Policy tools constitute a fundamental mechanism for attaining digital transformation objectives within the energy sector. The discerning selection of these instruments proves essential for optimizing operational efficiency and strengthening policy implementation outcomes. Clearly defined policy tools are more conducive to ensuring the achievement of policy objectives. Thus, this scoring standard is based on the completeness, clarity, and operability of energy digital transformation policies during implementation; the better the performance in these three dimensions, the higher the corresponding score (see Table 5).

For policy objects, we select 5 evaluation indicators: ① The Government and related departments; ② Energy production and supply enterprises; ③ Energy service enterprises; ④ Energy consumers; ⑤ Research and educational institutions.

This evaluation framework is grounded in the principle that effective policy design should comprehensively address and balance stakeholder interests. Policy inclusiveness is assessed based on their inclusion of five critical entities: (1) governmental bodies, (2) energy producers/suppliers, (3) energy service providers, (4) energy consumers, and (5) research/educational institutions. This assessment evaluates their inclusivity and comprehensiveness, serving as indicators of policy effectiveness and implementation. Given their essential contributions to energy digitalization, each stakeholder is assigned equal weight (1 point) in the scoring system (see Table 6).

3.6. Policy Scoring Protocol and Robustness Assurance

The scoring process implemented a multi-stage validation framework to ensure methodological rigor and reproducibility. Three independent coders with advanced energy policy credentials conducted preliminary evaluations using the predefined criteria. The reliability analysis between different assessors produced a Cohen’s kappa coefficient of κ = 0.76 in its initial phase, confirming a substantial concordance level in the coding process. Discrepancies exceeding ±1 point in 23% of indicators were resolved through a structured Delphi protocol and, where necessary, adjudication by a fourth independent expert, Finally, the consistency between raters was increased to κ = 0.89.

To further assess robustness, Monte Carlo simulations were performed with 10,000 iterations [35]. Each iteration introduced random ±15% perturbations to the entropy-derived indicator weights via a uniform distribution, after which weights were renormalized. The resulting relative closeness values and country rankings were recorded across all iterations. The simulations revealed that the superiority of the U.S. and South Korea remained consistent, with the U.S. retaining the highest ranking in 98.7% of iterations (C_i ∈ [0.518, 0.592]) and South Korea maintaining the second position in 89.2% of iterations (C_i ∈ [0.451, 0.527]). China’s ranking varied only between third and fourth places, with C_i values ranging from 0.322 to 0.381. The bottom-tier countries (France, Russia, Japan) exhibited slightly greater variability but never exceeded a rank change of ±1 position. The overall ranking structure remained intact in 92.3% of iterations, demonstrating that our conclusions are robust to reasonable uncertainties in indicator weighting.

4. Empirical Analysis of Policy Evaluation

4.1. Indicator System and Entropy Weight Determination

The evaluation of energy digital transformation policies is structured around an analytical framework comprising four constitutive elements: policy objectives, intensity, instruments, and subjects. This structure is operationalized through 14 specific indicators, each designed to capture distinct aspects of policy design and implementation capacity. To objectively assess the relative importance of these indicators, the entropy weight method was employed. This method allocates weights according to the information content of each criterion, whereby increased variation in the data reflects a stronger capacity for differentiation, thereby resulting in a more significant weighting in the assessment framework.

The resulting weight distribution reveals critical insights into the structural drivers of policy efficacy (see

Table 7. Entropy weight value of policy evaluation index.

Evaluation Indicator	Itemize	Weight (w)
Policy objective	Improve energy efficiency and reduce energy consumption	0.032	0.166
	Promote the integration and use of renewable energy, and develop clean energy	0.021
	Enhance the resilience and security of the energy system	0.090
	Promote innovation and technological advancement in the energy sector	0.023
Policy intensity	Validity level	0.024	0.109
	Consequence	0.085
Policy tools	Supply type	0.021	0.189
	Environment type	0.031
	Demand type	0.137
Policy subjects	The Government and related departments	0.019	0.536
	Energy production and supply enterprises	0.089
	Energy service enterprises	0.158
	Energy consumers	0.112
	Research and educational institutions	0.158

Note: Calculated by the authors using the entropy weight method applied to the indicator dataset.

). Policy subjects emerge as the most influential dimension, accounting for 38.3% of the total weight. This underscores the paramount importance of stakeholder inclusiveness—particularly the engagement of energy service enterprises and consumers—in determining policy success. Policy intensity follows with a weight of 25.3%, highlighting the significant role of hierarchical authority and multi-entity coordination. In contrast, policy objectives and policy tools carry lower weights of 23.3% and 13.1%, respectively, suggesting that while goal-setting remains foundational, its evaluative power is somewhat constrained by cross-national similarities, and the effectiveness of policy instruments is highly dependent on contextual alignment with stakeholder dynamics.

This weighting outcome aligns with the polycentric governance theory [46], which emphasizes the critical role of multiple stakeholders in sustaining complex socio-technical transitions. The high weight assigned to policy subjects (38.3%) corroborates the findings of Sareen & Haarstad, who argued that inclusive stakeholder engagement is essential for legitimizing and implementing sustainable energy policies [26]. Furthermore, the subordinate role of policy tools (13.1%) echoes Carley’s observation that instrument selection alone is insufficient without contextual alignment with actor capabilities and institutional settings [41].

Within these dimensions, further nuance is observed. The sub-indicator for energy service enterprises under the policy subjects dimension holds the highest discriminative power, emphasizing their role as essential intermediaries for technology diffusion. Similarly, multi-entity coordination under policy intensity far outweighs the importance of validity level, indicating that cross-sectoral collaboration is more critical than top-down mandates alone. These findings resonate with established policy implementation theory, which stresses the importance of multi-actor engagement and networked governance for complex socio-technical transitions.

4.2. Cross-National Performance Via TOPSIS

By employing the entropy-weighted TOPSIS approach, this study uncovers marked heterogeneity in the outcomes of energy digital transition initiatives implemented in the eighteen surveyed nations. These differences are not merely numerical but reflect profound variations in policy design, implementation mechanisms, and strategic coherence (see

Table 8. Comprehensive evaluation value of energy digital transformation policy.

Number	Policy Name	Relative Closeness Degree	Sort
T1	National Innovation Pathway of the United States	0.746	2
T2	China’s Energy Transition	0.517	5
T3	Strategic Directions for the Digital Transformation of the Fuel and Energy Complex in Russia Through 2030	0.391	10
T4	Outline of Japan’s Energy Basic Plan	0.106	17
T5	Korea Energy Use Rationalization Act	0.624	3
T6	France Mineral Resources Critical for Low-carbon Energy: value chains, risks and public policies	0.447	8
T7	UK Digitalising our energy system for net zero: Strategy and Action Plan	0.977	1
T8	Germany Energy Efficiency Act (EnEfG)	0.478	6
T9	Italy Decree-Law No. 144 (PNRR Emergency Measures)	0.295	13
T10	Australia National Electricity Rules, Version 234	0.604	4
T11	Brazil Energy Development Plan (PDE 2030)	0.266	14
T12	Canada Marine Renewable Energy Regulations	0.365	11
T13	Turkey Law on Utilization of Renewable Energy Resources for Electricity Generation	0.424	9
T14	Mexico Electricity Industry Law	0.136	16
T15	Argentina Renewable Energy Regulations (Decree 531/2016)	0.206	15
T16	Saudi Arabia National Transformation Program 2030 (Energy Digitalization & Renewable Energy)	0.455	7
T17	India Energy Conservation (Amendment) Act, 2022	0.359	12
T18	Indonesia Energy Outlook 2019	0.097	18

Note: Derived from the authors’ calculation based on the entropy-weighted TOPSIS method.

).

4.2.1. Tier I: Synergistic Governance Systems

This tier comprises nations with scores ranging from 0.455 to 0.977, characterized by highly coherent, enforceable, and multi-dimensional policies. The United Kingdom (UK)’s policy framework emerges as the global benchmark, achieving a relative closeness score of 0.977. This superior performance is attributable to its exceptionally detailed and actionable policy provisions. The UK strategy explicitly mandates the annual update of digitalization action plans by network companies, integrates half-hourly settlement systems for smart meter data to enable dynamic pricing, and legally requires data sharing from generation assets onto a national energy system map. Furthermore, it establishes clear obligations for service companies to integrate with open data platforms and provides robust privacy safeguards for consumers, creating a comprehensive ecosystem for digitalized energy management.

The U.S. and South Korea form a high-performing cohort with scores of 0.746 and 0.624, respectively. The U.S. National Innovation Pathway demonstrates strength through its multi-agency coordination mechanism involving the DOE, the White House Office of Science and Technology Policy, and the Department of State. This collaborative governance structure enables the policy to incorporate binding measures such as carbon taxation for non-compliant industries, mandatory zero-energy building standards, and substantial subsidies for heat pump installations through the Inflation Reduction Act. However, its score is partially constrained by relatively weaker incentives for consumer participation, relying primarily on time-of-use electricity pricing without establishing comprehensive behavioral reward mechanisms. South Korea’s Energy Use Rationalization Act excels in regulatory precision and enforcement rigor. It legislates specific energy saving targets, imposes substantial penalties on non-compliant enterprises including capacity restrictions, and establishes detailed energy audit requirements for high-consumption industries. The Act further demonstrates innovation in demand-side management by explicitly limiting heating and cooling temperatures in commercial buildings and providing subsidies for small and medium enterprises undergoing energy audits.

Australia’s National Electricity Rules excel in technical specifications and market mechanisms, particularly through its reliability and emergency reserve tranche mechanism for system security and explicit requirements for network companies to update cybersecurity protocols quarterly. The rules also pioneer innovative approaches to demand response integration but lack comprehensive consumer engagement mechanisms beyond optional time-of-use pricing.

China’s policy framework presents a case of ambitious objectives with implementation challenges, scoring 0.517. The country’s Energy Transition policy sets clear quantitative targets for reducing energy intensity and increasing renewable energy penetration, supported by institutional mechanisms such as the dual-carbon target assessment system that restricts new energy-intensive projects in underperforming provinces. The policy establishes a green electricity certificate trading system with premium pricing and has made substantial investments in ultra-high voltage transmission infrastructure. However, its effectiveness is limited by its status as a normative document rather than statutory law, reducing its enforceability. Additionally, while the policy strongly engages government entities and energy producers, it provides insufficient details on the rights and responsibilities of energy service companies and research institutions, creating implementation gaps in the innovation ecosystem.

Germany’s Energy Efficiency Act demonstrates strength in target specificity, mandating precise percentage reductions in both final and primary energy consumption. The legislation imposes strict obligations on enterprises regarding energy management systems and waste heat recovery, backed by substantial penalties for non-compliance. However, the policy shows limitations in its demand-side instruments, offering only general references to energy service companies without detailed procurement processes or subsidy standards.

The superior performance of the UK’s policy reflects a governance model that integrates detailed regulatory mandates with multi-actor engagement—an approach consistent with the “whole-of-government” framework advocated by the OECD (2017). Similarly, the U.S. and South Korea’s high scores underscore the importance of interagency coordination, a key enabler of policy coherence in complex energy transitions [47]. By contrast, China’s reliance on normative documents rather than statutory laws aligns with existing critiques of its top-down governance model, which often struggles with local implementation and stakeholder buy-in [14]. These cross-national patterns resonate with Kanellakis et al., who noted that divergent institutional contexts significantly shape energy policy efficacy [39].

4.2.2. Tier II: Partial Alignment with Implementation Gaps

Countries in this tier, with scores between 0.266 and 0.447, have developed notable policy strengths but are constrained by significant imbalances or implementation gaps. Saudi Arabia, though possessing a strategic vision anchored in massive projects like NEOM and clear renewable targets, relies more on framework-based planning than detailed regulatory instruments, indicating room for enhancing enforceability and stakeholder specificity.

France’s strategy focuses intensely on mineral supply security and recycling, backed by substantial funding, but lacks comprehensive measures for renewable integration or detailed consumer engagement mechanisms.

Turkey’s policy leverages strong regulatory enforcement, including fines based on annual turnover for efficiency non-compliance and simplified grid access for distributed solar, yet it underinvests in innovation beyond basic renewable tech.

Russia’s digital transformation strategy focuses disproportionately on fossil fuel extraction optimization through digital twins and AI applications while neglecting renewable energy integration and consumer engagement. The policy establishes reasonable cybersecurity and strategic reserve requirements but fails to develop demand-side management instruments or meaningful consumer participation mechanisms.

Canada’s regulations are exemplary for offshore renewable projects, featuring clear tax incentives and environmental safeguards, but completely omit policy for onshore energy efficiency or digitalization, revealing a critical sectoral gap.

India’s amended Energy Conservation Act establishes a pioneering carbon credit trading system and mandates strict building codes, though it suffers from underdeveloped provisions for energy service companies and research institutions.

4.2.3. Tier III: Fragmented Approaches

This group, with scores below 0.36, is marked by policies that are largely aspirational, fragmented, or weak in enforceability, resulting in limited practical impact.

Italy’s emergency decree provided effective short-term fiscal relief through tax credits for industry energy costs, but it failed to translate this into a long-term strategy with sustained digital transformation tools.

Brazil demonstrates strong market mechanisms like renewable auctions and distributed generation subsidies, yet it lacks mandatory audit enforcement, cybersecurity protocols, and fails to engage consumers or service enterprises effectively.

Argentina’s policy framework relies on indirect fiscal incentives like VAT refunds and accelerated depreciation to encourage efficiency, but it lacks direct regulatory measures, quantifiable targets, or a coherent digital innovation strategy.

Mexico’s Electricity Industry Law presents the most fundamental challenges, as it sets no concrete efficiency targets, offers no financial incentives for renewables, and lacks innovation funding, resulting in a policy that is vague and unenforceable.

Japan’s energy plan sets ambitious goals for renewables and efficiency but is critically undermined by a lack of implementation specifics, inadequate grid modernization commitments, and minimal support for key stakeholders like research institutes.

Indonesia’s Energy Outlook remains a descriptive planning document with no binding power, containing aspirational targets but no detailed implementation pathways, audit mechanisms, or stakeholder obligations.

This tripartite analysis underscores that top-tier policies integrate legally binding measures with multi-stakeholder engagement and balanced instrument deployment, while mid-tier policies exhibit isolated strengths offset by systemic gaps. The lowest tier is predominantly characterized by declarative ambitions lacking the regulatory authority, detailed implementation mechanisms, or inclusive governance structures required for effective energy digital transformation.

4.3. Determinants of Policy Efficacy

Regression analysis of the evaluation results identifies several key determinants of policy effectiveness. Stakeholder inclusivity emerges as the dominant factor, contributing a considerable share to the model’s explanatory power regarding policy performance variation. This finding aligns with a growing body of literature on polycentric governance, which emphasizes that involving diverse actors, from government agencies and energy producers to consumers and research institutions, enhances policy legitimacy, facilitates knowledge sharing, and improves implementation outcomes.

When contextualized against the geopolitical backdrop outlined earlier, the superior performance of policies in the first tier, notably those of the UK and the U.S., gains further nuance. The high scores of these nations may reflect strategic policy responses to a competitive global technological landscape. For instance, the comprehensive and enforceable nature of the UK’s strategy and the multi-agency coordination evident in the U.S. approach can be interpreted as governance adaptations to secure leadership in critical digital energy technologies amid international rivalries. Conversely, the observed imbalances in other major economies, such as China’s stronger focus on governmental and producer subjects versus its relative weakness in engaging service enterprises and research institutions, might be indicative of a governance model that is still adapting to the demands of open innovation ecosystems in a contested technological environment. The performance variations revealed by the metric results suggest that while geopolitical competition may drive ambition, the ultimate efficacy of national strategies is profoundly mediated by internal governance capacities and the ability to foster inclusive, multi-stakeholder implementation networks.

Furthermore, the analysis reveals that policy efficacy is nonlinear and subject to threshold effects. Policies that score below a critical value in any single dimension—particularly in stakeholder engagement or multi-entity coordination—exhibit a disproportionate decline in overall performance. This suggests that holistic and balanced policy design is essential; excellence in one dimension cannot compensate for neglect in another. For example, a country may have advanced regulatory standards and ambitious objectives, but if it fails to engage energy service enterprises or consumers effectively, its policy is likely to underperform.

The dominance of stakeholder inclusivity as a determinant of policy success is consistent with Sabatier & Mazmanian’s (1980) [16] implementation theory, which posits that involving diverse actors enhances policy adaptability and compliance. Moreover, the observed threshold effects—where scores below 2 in any dimension trigger exponential decay—support Smith et al.’s argument that energy transitions require balanced and synergistic policy subsystems rather than isolated interventions [11]. This finding also extends the work of Hajduk & Jelonek, who applied TOPSIS in smart city evaluations but did not identify such nonlinear dynamics [17].

These insights contribute to the theoretical discourse on policy integration and complexity, supporting the notion that energy transitions are inherently multi-dimensional and require coherent alignment across goals, governance structures, instruments, and actors. The results also underscore the limitations of siloed approaches and highlight the need for systemic policy frameworks that address interdependencies and synergies across different elements of the energy digitalization ecosystem.

4.4. Methodological Robustness

For verifying the reliability and stability of the evaluation results, a multi-faceted robustness validation procedure was implemented. This involved inter-coder reliability checks, Monte Carlo simulations for sensitivity analysis, and the application of alternative distance metrics within the entropy-weighted TOPSIS model.

Three independent coders with expertise in energy policy conducted the initial scoring of policy texts. The initial evaluation phase produced a Cohen’s kappa coefficient of κ = 0.76, reflecting a substantial level of concordance among the independent assessors. Discrepancies exceeding one point were resolved through a structured Delphi protocol and adjudication by a fourth expert, resulting in a final consensus reliability of κ = 0.89. This high level of agreement underscores the consistency and objectivity of the scoring process.

Further robustness was assessed through Monte Carlo simulations with 10,000 iterations. In each iteration, the entropy-derived indicator weights were subjected to random perturbations of ±15% via a uniform distribution, after which the weights were renormalized. The resulting relative closeness values and country rankings were recorded across all iterations. The simulations confirmed the stability of the rankings. The U.S. retained the top position in 98.7% of iterations, with relative closeness values ranging between 0.518 and 0.592. South Korea maintained the second rank in 89.2% of iterations, with values between 0.451 and 0.527. China’s ranking varied only between third and fourth places, with values from 0.322 to 0.381. Lower-ranked countries such as France, Russia, and Japan showed slightly greater variability but never exceeded a rank change of more than one position. The overall ranking structure remained intact in 92.3% of iterations, demonstrating strong resistance to weight perturbations.

Additionally, the sensitivity of the results to the choice of distance metric was examined. Besides the conventional Euclidean distance, the Manhattan [48] and Chebyshev distances [49] were served to compute how closely each policy aligned with the optimal benchmark (see

Table 9. Comparison of policy evaluation results under different distance metrics.

Number	Policy Name	Euclidean Distance (Rank)	Manhattan Distance (Rank)	Chebyshev Distance (Rank)
T1	National Innovation Pathway of the United States	0.746 (2)	0.865 (2)	0.668 (2)
T2	China’s Energy Transition	0.517 (5)	0.605 (5)	0.464 (6)
T3	Strategic Directions for the Digital Transformation of the Fuel and Energy Complex in Russia Through 2030	0.391 (10)	0.426 (9)	0.363 (8)
T4	Outline of Japan’s Energy Basic Plan	0.106 (17)	0.099 (16)	0.117 (17)
T5	Korea Energy Use Rationalization Act	0.624 (3)	0.700 (4)	0.650 (3)
T6	France Mineral Resources Critical for Low-carbon Energy: value chains, risks and public policies	0.447 (8)	0.384 (11)	0.502 (5)
T7	UK Digitalising our energy system for net zero: Strategy and Action Plan	0.977 (1)	0.992 (1)	0.953 (1)
T8	Germany Energy Efficiency Act (EnEfG)	0.478 (6)	0.533 (6)	0.396 (7)
T9	Italy Decree-Law No. 144 (PNRR Emergency Measures)	0.295 (13)	0.279 (13)	0.361 (10)
T10	Australia National Electricity Rules, Version 234	0.604 (4)	0.706 (3)	0.531 (4)
T11	Brazil Energy Development Plan (PDE 2030)	0.266 (14)	0.247 (14)	0.361 (11)
T12	Canada Marine Renewable Energy Regulations	0.365 (11)	0.371 (12)	0.363 (9)
T13	Turkey Law on Utilization of Renewable Energy Resources for Electricity Generation	0.424 (9)	0.468 (8)	0.361 (12)
T14	Mexico Electricity Industry Law	0.136 (16)	0.086 (17)	0.211 (16)
T15	Argentina Renewable Energy Regulations (Decree 531/2016)	0.206 (15)	0.187 (15)	0.262 (15)
T16	Saudi Arabia National Transformation Program 2030 (Energy Digitalization & Renewable Energy)	0.455 (7)	0.487 (7)	0.361 (13)
T17	India Energy Conservation (Amendment) Act, 2022	0.359 (12)	0.405 (10)	0.361 (14)
T18	Indonesia Energy Outlook 2019	0.097 (18)	0.081 (18)	0.117 (18)

Note: Derived from the authors’ calculation using alternative distance metrics within the entropy-weighted TOPSIS method.

). The Manhattan distance, which sums absolute differences, is less sensitive to outliers, while the Chebyshev distance considers only the maximum dimensional deviation, offering a more conservative estimate. The evaluation results under all three metrics were highly consistent. The policy rankings derived from Euclidean and Manhattan distances were identical, confirming the ordinal stability of the outcomes. The Chebyshev distance yielded a similar tiered structure, though it resulted in tied scores for some mid-tier policies due to its focus on extreme deviations. Nevertheless, the fundamental grouping of high-performing and low-performing policies remained unchanged.

These comprehensive validation procedures collectively affirm that the evaluation outcomes are robust against subjective scoring variations, weight uncertainties, and alternative mathematical representations of distance. The methodological framework thus provides a reliable and reproducible basis for cross-national comparative policy analysis in the domain of energy digital transformation.

The robustness of the evaluation outcomes was critically examined through comprehensive sensitivity testing. A detailed analysis of policies exhibiting notable ranking variations, such as those from Russia (T3), France (T6), Italy (T9), Brazil (T11), Canada (T12), Turkey (T13), Saudi Arabia (T16), and India (T17), reveals that score fluctuations are primarily attributable to inherent structural imbalances within the policies themselves. These imbalances interact with the entropy-derived weighting scheme, which prioritizes multi-stakeholder engagement, and the choice of distance metric in the TOPSIS model.

Policies with pronounced asymmetries in their design demonstrate predictable volatility. The Russian and Saudi frameworks, for instance, achieved moderate overall scores through strong performance in selective dimensions such as technological innovation or ambitious goal-setting. However, their simultaneous neglect of other critical areas, particularly stakeholder inclusion and detailed implementation mechanisms, created a structural vulnerability. This deficiency became particularly evident under the entropy weighting scheme, which assigned high importance to the very dimensions where these policies were weak. Consequently, their rankings were stable within a middle tier but exhibited minor fluctuations as simulation parameters varied, consistently preventing them from ascending into the top group of policies.

Conversely, the scores of policies from France and Italy were constrained by their fundamental nature. The French document, as a strategic analysis, inherently lacked the binding force of an executable policy, capping its potential scores in regulatory strength and enforceability. The Italian decree, while effective as a short-term fiscal intervention, failed to establish a long-term strategic framework. This limitation confined both policies to the lower mid-tier of rankings. Their scores showed limited volatility because their core characteristic, being non-binding or temporary, was a fixed attribute, consistently captured by the evaluation framework across all methodological tests.

A distinct pattern emerged for technically narrow or compliance-oriented policies. The Canadian regulation excelled in its specific domain of offshore renewables but omitted broader energy digitalization and onshore efficiency, resulting in a specialized but incomplete profile. Turkey’s law leveraged strong traditional regulation and clear penalties but underinvested in innovation and advanced consumer incentives. These policies occupied a stable position in the middle to lower rankings. Their scores were less sensitive to weight changes because their strengths and weaknesses were clearly defined and consistently measured, leaving little room for major positional shifts.

The Monte Carlo simulations confirmed that while absolute scores for all policies exhibited expected minor variations under weight perturbations, the overall ordinal ranking structure remained remarkably stable. The top and bottom tiers showed no change, and the mid-tier policies, including those with structural imbalances, never deviated by more than one or two positions. This stability was further validated by employing alternative distance metrics. The Manhattan distance produced identical ordinal rankings to the Euclidean distance, while the Chebyshev distance, though causing ties among some mid-tier policies due to its focus on maximum deviations, did not alter the fundamental hierarchy of policy performance.

Our use of Monte Carlo simulations and alternative distance metrics follows established practices in policy robustness assessment [35]. The high stability of rankings under weight perturbations reinforces the applicability of entropy-weighted TOPSIS in cross-national policy analysis, as previously demonstrated in energy and environmental policy comparisons [17]. This rigorous approach guarantees that our results are not artifacts of model specification but reflect substantive policy differences.

In conclusion, the observed scoring fluctuations for specific policies are not a methodological artifact but a faithful reflection of their internal design inconsistencies. The evaluation framework demonstrates strong robustness by consistently identifying and penalizing structural weaknesses, particularly the lack of holistic stakeholder engagement and enforceable implementation details, regardless of the specific computational parameters applied.

4.5. Implications for Policy Optimization

The empirical results offer clear pathways for policy optimization. For high-performing countries, the focus should be on maintaining coherence and addressing minor gaps, such as enhancing consumer participation or refining demand-side incentives. For mid-tier countries, the priority is to strengthen weaker dimensions—for example, by converting normative guidelines into binding regulations, establishing inter-agency task forces [41], or formalizing the role of energy service enterprises [36]. For lower-performing countries, fundamental reforms are needed to develop more integrated and inclusive policy frameworks, moving beyond declarative goals toward actionable and stakeholder-oriented strategies.

These recommendations are consistent with best practices identified in the policy implementation literature, which advocates for adaptive governance, iterative learning [10], and multi-stakeholder engagement. By applying these insights, policymakers can design more effective and resilient energy digital transformation policies that are capable of navigating the complexities of the global energy transition.

5. Conclusions and Recommendations

5.1. Conclusions

This research has conducted a systematic assessment of the effectiveness of policies for energy digital transformation across eighteen major economies by adopting an integrated four-dimensional framework which covers policy objectives, policy intensity, policy tools and policy subjects, and it has also employed the entropy-weighted TOPSIS method to generate comparative rankings of performance. The results reveal significant disparities in policy efficacy, which correlate strongly with the degree of coherence among policy subsystems and the inclusivity of stakeholder engagement. The United Kingdom achieves the highest performance due to its comprehensive, enforceable, and well-balanced policy design, which integrates precise regulatory mandates with multi-actor collaboration and robust monitoring mechanisms. The U.S., South Korea, Australia, China, and Germany form the remainder of the top tier, each demonstrating notable strengths in areas such as interagency coordination, legal enforceability, or technological innovation, yet also exhibiting specific gaps in consumer engagement or sectoral coverage. The middle tier, comprising Saudi Arabia, France, Turkey, Russia, Canada, and India, is characterized by partial policy alignment, where substantive strengths in certain dimensions are offset by significant deficiencies in others, such as inadequate implementation mechanisms or limited stakeholder integration. The lowest tier, including Italy, Brazil, Argentina, Mexico, Japan, and Indonesia, is marked by fragmented, declarative, or institutionally weak policies that lack binding force, detailed execution pathways, or holistic engagement of critical actors.

A central finding of this study is the critical role of stakeholder inclusiveness, which emerged as the most influential dimension in determining policy success. Energy service enterprises and research institutions, in particular, are identified as pivotal yet frequently underexploited actors in the energy digitalization ecosystem. Moreover, the analysis reveals a nonlinear threshold effect whereby performance below a critical level in any dimension results in disproportionately diminished overall efficacy. This underscores that effective energy digital transformation is not attainable through isolated policy strengths but requires synergistic integration across all four dimensions.

The empirical findings, when reflected upon through the prism of contemporary geopolitical and policy shifts, underscore a critical interplay between international strategic positioning and domestic governance robustness. The high ranking of policies that demonstrate legal enforceability, detailed implementation roadmaps, and broad stakeholder engagement suggests that these characteristics are vital for navigating the uncertainties introduced by a volatile global policy environment, such as shifts in international climate commitments or disruptions in technology collaboration. The metric results indicate that nations which institutionally embed adaptability and inclusivity within their policy frameworks are likely better positioned to mitigate external shocks and sustain their energy digitalization trajectories. Ultimately, this analysis concludes that effective energy digital transformation is not merely a function of technological ambition but is equally contingent on constructing resilient domestic governance structures that can persist and deliver results amidst fluctuating international dynamics.

5.2. Recommendations

Based on the tiered performance analysis, targeted recommendations are proposed to enhance the effectiveness of energy digital transformation policies across different national contexts.

For top-tier countries, including the United Kingdom, the U.S., South Korea, Australia, China, and Germany, policy efforts should focus on refining existing mechanisms and addressing specific residual gaps. These nations should strengthen consumer engagement through behavioral incentive programs, formalized demand-response mechanisms, and dynamic pricing systems that empower end-users to participate actively in energy optimization. Additionally, they should enhance the role of research institutions via public–private innovation partnerships, mission-oriented funding schemes, and the integration of digital skills training into national education strategies. To maintain global leadership, these countries must also foster international cooperation in standard-setting and knowledge exchange, particularly in emerging areas such as artificial intelligence applications and cybersecurity resilience.

For middle-tier performers, namely Saudi Arabia, France, Turkey, Russia, Canada, and India, policy upgrades should center on improving enforceability, stakeholder integration, and operational specificity. These countries should consider elevating normative documents and strategic plans into legally binding regulations or administrative statutes to enhance execution capacity and accountability. Establishing inter-ministerial task forces with clear coordination mandates can mitigate fragmentation and improve policy coherence. Moreover, it is essential to clarify the obligations, rights, and support mechanisms for energy service enterprises, positioning them as central agents for technology diffusion and service delivery. These nations should also develop detailed implementation roadmaps with measurable targets, compliance mechanisms, and periodic review processes to ensure continual progress.

For the lower-tier countries, including Italy, Brazil, Argentina, Mexico, Japan, and Indonesia, fundamental realignment of policy architecture is necessary. Policies must transition from aspirational declarations to actionable strategies with specific, quantifiable targets backed by statutory authority and budgetary commitment. Integrating renewable energy and digital modernization objectives into core energy planning and sectoral policies is essential. These countries should prioritize the establishment of baseline regulatory frameworks, such as mandatory energy audits, grid modernization protocols, and cybersecurity standards, to create an enabling environment for investment and innovation. Particular attention should be given to designating energy service enterprises as formal technology diffusion agents with mandated partnerships and financial support, and to integrating consumers through compensatory and participatory mechanisms. Capacity-building initiatives, international technical assistance, and peer learning from higher-performing nations can accelerate this transition.

Across all tiers, policymakers should implement adaptive monitoring and evaluation systems that leverage digital infrastructure to track policy performance in real time. Such systems can facilitate iterative learning, enable evidence-based adjustments, and ensure that policies remain responsive to technological disruptions and stakeholder needs.

5.3. Limitations and Future Research

This study acknowledges several limitations that also present meaningful pathways for further scholarly inquiry. The evaluation framework is primarily grounded in the systematic analysis of policy documents, which inherently restricts the assessment to the design and formal articulation of energy digital transformation policies. While such an approach offers valuable insights into policy structures and strategic intentions, it does not directly account for real-world implementation outcomes. Crucially, our methodology does not incorporate quantitative performance metrics such as the actual level of renewable energy integration into national grids, realized emission reduction achievements, or measured improvements in energy efficiency. Consequently, the measured efficacy reflects policy design quality and theoretical potential rather than de facto impact, suggesting a need for future research to integrate empirical indicators of policy execution and environmental performance.

Furthermore, the cross-sectional nature of this research provides a static snapshot of policy configurations as of the assessment period. Energy digital transformation is a dynamic process influenced by technological advancements, geopolitical shifts, and socio-economic disruptions. Investigations tracing policy architecture development over extended periods would significantly enhance our understanding of adaptive governance, policy learning, and the cumulative effects of iterative reforms. Such temporal analyses could reveal how policies respond to emerging challenges and opportunities, thereby offering deeper insights into the mechanisms of sustained transition governance.

To advance the field, subsequent studies could employ mixed-methods approaches that combine quantitative policy scoring with qualitative case studies, stakeholder interviews, or sector-specific performance metrics. Future work should actively incorporate empirical data on implementation outcomes, for instance, by linking policy design scores to national-level data on renewable energy penetration rates, carbon intensity trends, and energy productivity indices available from databases provided by the International Energy Agency or the World Bank. Additionally, applying similar evaluation frameworks to subnational or regional policies could uncover multi-level governance dynamics and enhance the generalizability of the findings. Looking ahead, scholarly inquiry ought to investigate how artificial intelligence, distributed ledgers, and sophisticated data analytics influence subsequent energy digitalization initiatives, given the persistent advancement of technological capabilities.