Bridging Borders and Brains: ESG Sustainability, Integration, Education and Energy Choices in Developed Economies

Abstract

As ESG sustainability uncertainty intensifies and globalisation deepens, the energy trilemma—security, equity, and sustainability—emerges as the defining calculus of modern energy policy. Therefore, this investigation explores the influence of ESG sustainability uncertainty intensification and globalisation on the energy trilemma, while controlling education, urbanization and economic growth, using data from 2001 to 2022. The energy trilemma offers an all-inclusive gauge for understanding the effect of ESG sustainability uncertainty on energy trilemma. The study employed Lewbel’s Two Stage Least Squares method to examine the connection. The results disclose that ESG sustainability uncertainty is negatively associated with all three trilemma pillars. Globalisation displays a nonlinear influence: its squared terms are negative and statistically significant, implying diminishing marginal benefits at high levels of openness. This paper’s significance lies in evidence that ESG sustainability uncertainty erodes all three pillars of the energy trilemma, while globalization’s benefits taper at high openness—strengthening the mandate for a clean, just, secure, and sustainable transition.

1. Introduction

In recent years, ESG sustainability uncertainty (ESGUI) has emerged as a systemic headwind for the energy sector, extending well beyond firm-level reporting to influence capital allocation, technology adoption, and market design [1,2]. Against this backdrop, we analyse how ESGUI affects the energy trilemma (ET)—the joint pursuit of security, equity, and environmental sustainability. Security denotes reliable supply today and tomorrow with rapid recovery from shocks; equity captures universal, affordable access for productive and household uses; and sustainability reflects mitigation performance, environmental quality, and the pace of decarbonization [3]. By injecting policy and information ambiguity, ESGUI can slow grid and generation investment, raise risk premia, and heighten price swings—undermining progress across ET pillars. Conversely, sustained uncertainty may spur strategic responses: tightening sustainability governance, localising critical supply chains, and scaling renewables and storage—measures that can strengthen energy security while supporting equitable, sustainable outcome.

In pursuing energy security and the wider aims of the ET, globalisation is a critical enabler that should not be overlooked. Our analysis therefore shifts from financial development to the role of GO in shaping trilemma outcomes and in moderating the effects of geopolitical disruptions [4]. Through channels such as trade openness, foreign direct investment, cross-border project finance, and technology diffusion, GO can mobilise capital for energy infrastructure, promote interconnection and market integration, and lower end-user prices [5,6]. These mechanisms reduce vulnerability to supply interruptions, enhance affordability and access, and support decarbonisation—collectively improving performance across the ET’s three pillars.

The literature at the intersection of globalisation (GO), ESG uncertainty (ESGUI), and the energy trilemma (ET) remains limited. This study therefore investigates how ESGUI and GO shape ET outcomes—energy security, energy equity, and environmental sustainability. We address three questions: (1) What role does ESGUI play in navigating the ET’s competing goals? (2) Does GO—through economic, financial, and trade integration—help reconcile trade-offs among security, equity, and sustainability? (3) Does GO moderate the impact of ESGUI on the ET? Using G7 countries (2001–2022), we estimate models with Driscoll–Kraay standard errors, Feasible Generalized Least Squares (FGLS), and Lewbel’s heteroskedasticity-based 2SLS. The results indicate that ESGUI generally depresses energy security and environmental sustainability, with weaker or statistically marginal effects on equity. By contrast, GO is negatively associated with ET components at low levels, but its squared term is positive and significant, revealing a nonlinear (U-shaped) relationship in which deeper integration ultimately improves ET performance. Moreover, GO tends to cushion the adverse effects of ESGUI on the trilemma, moderating uncertainty-driven headwinds for energy system.

This paper advances the literature along two fronts. First, we use the energy trilemma (ET) as the organising lens, assessing security, equity, and sustainability jointly. Prior work typically considers ESG uncertainty (ESGUI) in a piecemeal fashion—linking it to single outcomes such as energy access, consumption, or renewable uptake—or examines energy security in isolation [1,2]. Our ET-centred design offers a more complete view of system performance and distributive justice, consistent with the integrated perspective put forward by the authors of [7]. Whereas environmental performance is often proxied by greenhouse gas emissions alone [8,9,10], the ET’s sustainability pillar also captures mitigation effectiveness, environmental quality (water/air/biodiversity), and the pivot away from fossil fuels [7].

Second, we recast the determinants of the ET around globalisation (GO) and ESG uncertainty, employing multidimensional GO indices (composite and sub-indices) to reflect technology diffusion, capital mobility, and supply chain ties, paired with an ESG uncertainty index that captures sustainability-related information frictions and policy volatility. Testing their direct and interactive effects clarifies how international integration and ESG uncertainty jointly shape trilemma outcomes, addressing a clear gap in earlier research. Empirically, we find that ESG sustainability uncertainty is negatively associated with all three pillars of the energy trilemma—security, equity, and environmental sustainability—consistent with evidence that policy and ESG uncertainty depress long-horizon energy investment and coordination. We also detect a nonlinear effect of globalisation: the squared term of openness is negative and statistically significant, indicating diminishing marginal benefits when integration becomes very high, in line with threshold-type, concave relationships reported for advanced economies. Beyond advancing the literature, these results speak directly to policymakers navigating volatile sustainability signals (ESGUI): strengthening coherence across ET dimensions can cushion systems against ESG-driven information frictions and preserve momentum toward the SDGs—most notably SDG 7 and SDG 13.

The subsequent sections are presented as follows: Section 2 presents literature and theoretical framework, Section 3 presents data and method, Section 4 presents findings and Section 5 present conclusion and policy.

2. Theoretical Framework and Literature Review

2.1. Theoretical Framework

A coherent framework links ESG uncertainty to each pillar of the energy trilemma through investment irreversibility, financing frictions, and innovation incentives. When disclosure rules, taxonomies, or policy support for clean energy are perceived as volatile, the option value of waiting rises, pushing firms to delay or scale down long-lived grid, generation, and storage projects—eroding energy security and raising delivered costs that harm energy equity [11,12]. Higher uncertainty also lifts risk premia, tightening access to capital for infrastructure whose returns depend on stable rules [13]. On the environmental sustainability margin, unpredictable policies weaken directed technical change toward low-carbon technologies and slow diffusion of cleaner capital, dampening emissions gains [14,15]. Overall, ESG uncertainty shifts both the level and composition of energy investment in ways that can simultaneously reduce reliability, affordability, and decarbonization progress.

Globalisation—via trade, production networks, and cross-border finance—conditions these effects through diversification, scale, and knowledge spillovers, but with potential nonlinearities. Greater openness can improve security by diversifying fuel sources and enabling interconnection; compress costs and thus support equity through larger markets; and accelerate sustainability as technology and know-how diffuse across borders [16,17]. Yet at very high integration, exposure to synchronized shocks and rapid price pass-through can dilute equity gains, while volatile capital flows may undermine long-horizon investment—yielding diminishing or even adverse marginal effects [18,19]. Consequently, globalisation moderates the ESG-uncertainty–trilemma nexus: openness can buffer uncertainty’s hit to security via diversification and sustain sustainability via diffusion, but it can also amplify affordability stress through faster transmission of global shocks. The net effect is state-dependent, shaped by the depth of institutions and the credibility of transition policy.

2.2. Literature Review

Across diverse samples and methods, the globalization–trilemma literature converges on a broadly favorable association for energy security (ESS) and environmental sustainability (ENSS), with more nuance for energy equity (EEQ). In OECD and EU panels, overall, economic, and political globalization (GO/EGO/PGO) are positively linked to ESS, consistent with portfolio diversification, market depth, and cross-border balancing that mute idiosyncratic supply shocks [20]. Globalization also scales technology diffusion and cost compression, yielding higher renewable penetration [21,22], a result reinforced when financial openness deepens green-capital pools [23]. Evidence from African economies suggests that when economic, social, and political channels move together, sustainability gains extend beyond advanced countries. Two caveats temper this optimistic view: first, financial globalization appears state-dependent, improving or weakening ESS across institutional regimes [24]; second, EEQ shows mixed signs in EU data because openness lowers average costs in normal times but can also amplify pass-through of global price spikes to households [25].

A complementary strand foregrounds policy and ESG-related uncertainty as a conditioning force. Index-based work associates higher ESG sustainability uncertainty (ESGUI) with weaker security—consistent with delayed, irreversible investment and higher financing premia for long-lived assets [2]—and with slower energy transition dynamics in U.S. time-series evidence [26]. Yet results are not uniform: some studies report that ESG/CPU episodes coincide with higher renewables, while others find the opposite [27]. These sign differences likely reflect measurement (ESGUI vs. CPU), outcome definitions (levels vs. volatility; installed capacity vs. generation shares), frequencies (monthly vs. annual), and methods that target different parts of the conditional distribution (e.g., wavelet and cross-quantile tools capturing tail dependence). Even within ESS, discrepancies emerge [2,28], plausibly because “security” metrics vary (import dependence, diversity indices, reliability incidents) and short-run precautionary investment/diversification may transiently raise measured security during uncertainty spikes.

Together, these studies support a state-contingent, potentially nonlinear view. Openness generally enhances ESS and ENSS at low-to-moderate integration, but marginal gains diminish—and can reverse—when financial exposure and shock synchronisation rise [24]. Simultaneously, ESG/policy uncertainty shifts the cost of capital and timing of investment, producing heterogeneous effects that can either stall deployment (via “option to wait”) or spur substitution toward modular, future-proof clean technologies after shocks [1,29]. The upshot for interpretation is clear: results hinge on how globalization is decomposed (GO/EGO/PGO/FGO), which trilemma pillar and metric are used (ESS/EEQ/ENSS), and whether models capture thresholds, dynamics, and distributional effects. A unifying reading is that smart globalization plus credible, time-consistent transition policy is necessary to convert the average gains from openness into robust improvements across all three pillars, even in the presence of episodic ESG and policy uncertainty.

Table 1. Summary of Past Studies.

Author(s)	Period(s)	Nation(s)	Method(s)	Finding(s)
[20]	1980–2018	26 OECD	Panel Regression	GO, EGO & PGO ↑ ESS
[24]	2000–2018	123 countries	Panel Regression	FG ↓↑ ESS
[22]	1999–2018	35 OECD	Panel estimation	GO ↑ renewables
[21]	1970–2015	30 OECD	Panel Regression	EGO ↑ renewables
[30]	2000–2017	36 African	GMM	EGO, SGO, PGO ↑ ENSS
[25]	2000–2019	EU	Panel Regression	GO ↓↑ EEQ
[23]	1990–2022	EU	Panel models	FGO ↑ renewables
[2]	2002–2024	USA + Global	Impulse Response	ESGUI ↓ ESS
[28]	2002–2024	Global	Volatility modelling	ESGUI ↑ ESS
[26]	2000–2021	USA	ARDL/ECM	Policy uncertainty ↓ ET
[29]	2013–2022	Global	Wavelet Tools	CPU ↑ renewables
[31]	1989–2023	Global	RALS	CPU ↓ ESS
[27]	2002–2024	USA	KRQR	ESG ↓ renewables
[1]	2002–2024	USA	WCQR	ESG ↑ renewables
[3]	2000–2021	OECD	Lewbel 2SLS	GPR ↓↑ ET

presents summary of the discussed studies.

2.3. Gap in the Literature

A critical reading of prior studies shows that energy-transition analyses often miss the full breadth of energy system performance and frequently understate the distinct role of ESG-related policy and disclosure uncertainty. To address this gap, we move from fragmented proxies to the World Energy Council’s energy trilemma—integrating security (adequacy and system resilience), equity (access and affordability for households and firms), and environmental sustainability (credible decarbonisation trajectories). This lens clarifies how ESG uncertainty delays irreversible investment, reprices risk, and slows clean-technology diffusion across all three pillars. At the same time, the openness literature commonly proxies globalisation narrowly (e.g., trade ratios), overlooking financial, institutional, and knowledge-spillover channels that shape transition outcomes. We therefore treat globalisation as a multidimensional force that can transmit diversification and learning while also propagating synchronized shocks and price pass-through—thereby moderating the impact of ESG uncertainty on the trilemma. Embedding both ESG uncertainty and multidimensional globalisation in a unified, system-oriented framework provides a more faithful account of how contemporary policy uncertainty and cross-border integration jointly determine progress on security, equity, and sustainability.

3. Data and Method

3.1. Data

Our sample comprises the G7 economies over 2001–2022. Country coverage is driven by the availability of the latest country-specific geopolitical risk. The G7 comprises large, high-income economies that command a disproportionate share of global energy demand, technology deployment, and emissions, making them pivotal testbeds for the energy trilemma—balancing security (reliability and resilience), equity (affordability and access), and environmental sustainability—so their policy choices carry outsized global consequences. We focus on the G7 because they combine (i) policy leadership and agenda-setting power in climate and energy governance (Paris Agreement, CBAM debates, critical-minerals strategies); (ii) deep capital markets and innovation systems that shape the diffusion of clean technologies beyond their borders; (iii) comparably high-quality, long-horizon data that enable consistent econometric identification; (iv) meaningful internal heterogeneity in energy mixes, market structures, and openness, which permits credible inference on nonlinear and state-dependent effects while holding institutional quality at advanced-economy levels. This combination of global leverage, data reliability, and structured variation makes the G7 an analytically and policy-relevant setting for examining how uncertainty and globalisation interact with the three pillars of the energy trilemma. The study period is determined by the availability of the energy-transition (ET) series. Explanatory variables are compiled from the KOF Swiss Economic Institute [32], World Development Indicators [33], World Energy Council [7], and the ESG sustainability uncertainty (ESGUI) database of [34].

The core variable of interest is ET. In line with [7], ET is measured across three dimensions: (i) security—meeting demand now and in the future with robustness to shocks; (ii) equity—ensuring affordable, universal access; (iii) environmental sustainability—advancing a low-impact, climate-aligned energy system. We apply log transformations to the ET metrics to lessen skewness.

Our key explanatory variables are the ESG-Related Uncertainty Index (ESGUI) and globalisation (GO). In our study, “ESG sustainability uncertainty” refers to the ESG-Related Uncertainty Index (ESGUI), which we adopt verbatim. ESGUI is a monthly, country-level index (2002–present) built in three steps from Economist Intelligence Unit (EIU) country reports: (i) construct E, S, and G sub-indices by counting ESG-related keywords in each monthly report, dividing by total words, and normalizing via Min–Max scaling; (ii) construct an uncertainty sub-index from terms such as “uncertain/uncertainty,” following the World Uncertainty Index methodology; (iii) compute ESGUI as the average of the ESG composite and the uncertainty sub-index, scaled to 0–100 (0 = minimum, 100 = maximum) with both global equal-weighted and GDP-weighted aggregates also available. We use the monthly ESGUI developed by [2], which is constructed for 25 countries from 2002 onward in three steps: (i) environmental (E), social (S), and governance (G) sub-indices are derived from ESG-related keywords in the Economist Intelligence Unit’s monthly country reports using NLP, scaled by each report’s word count and normalized via a Min–Max scaler; (ii) an uncertainty sub-index is built analogously using terms such as “uncertain/uncertainty/uncertainties,” consistent with the World Uncertainty Index methodology; and (iii) each country’s ESGUI is the monthly average of the ESG and uncertainty sub-indices.

For globalisation, we use KOF’s Economic Globalisation (EG) and its subcomponents—Trade Globalisation (TG) and Financial Globalisation (FG)—each available in de facto (actual flows/activities) and de jure (policies/institutions enabling or restricting flows) forms. De facto trade captures cross-border trade in goods and services, while de jure trade reflects tariffs, trade taxes, restrictions, and FTAs. De facto financial globalisation covers foreign investment flows (e.g., FDI and portfolio), whereas de jure financial globalisation reflects capital-account openness, investment restrictions, and international investment agreements. KOF indices are reported on a 0–100 scale and are available annually with broad country coverage [32].

To mitigate omitted-variable bias, and in keeping with [3], we include: ln(GDP per capita, constant 2015 US$), ln(primary school enrolment, % gross) as an education proxy, industry value added (% of GDP) as our industrialization measure, and the urban-population ratio (% of total) for urbanization.

Table 2. Descriptive Statistics.

Variables Full Name	Variable	Obs	Mean	Std. Dev.	Min	Max
Energy Security	lnESS	154	4.168	0.112	3.853	4.344
Energy Equity	lnEEQ	154	4.564	0.03	4.456	4.601
Environmental Sustainability	lnENSS	154	4.303	0.066	4.14	4.459
ESG Uncertainty	ESGUI	147	30.379	8.701	14.202	63.645
Overall Globalisation	GO	154	81.522	4.972	66.48	88.359
Economic Globalisation	EGO	154	70.754	7.791	46.61	81.91
Financial Globalisation	FGO	154	78.311	8.172	49.117	90.761
Trade Globalisation	TGO	154	62.986	8.372	42.199	80.02
Economic Growth	lnEG	154	10.582	0.176	10.288	11.047
Industry	IND	153	23.153	4.227	16.396	32.514
Education	lnEDU	126	4.624	0.02	4.567	4.670
Urbanization	URB	154	79.349	5.834	67.222	91.867

lists sources and descriptive summaries.

Descriptives indicate the three ET pillars are fairly stable across G7-year observations: lnESS (mean = 4.168; SD = 0.112), lnEEQ (4.564; 0.030), and lnENSS (4.303; 0.066). Sustainability uncertainty (ESGUI) averages 30.38 (SD = 8.70; range = 14.20–63.65), showing much greater dispersion than the ET logs. Globalisation is high but heterogeneous: overall GO averages 81.52 (4.97), with subcomponents EGO = 70.75 (7.79), FGO = 78.31 (8.17), and TGO = 62.99 (8.37). Additional controls show lnEG = 10.582 (0.176), industry value added (IND) averages 23.15% (range = 16.40–32.51), and urbanisation (URB) is high at 79.35% (5.83). Note the slight variation in sample sizes—most variables have 154 observations, ESGUI has 147, and lnEDU (mean = 4.624; SD = 0.020) has 126—indicating some missing data.

Table 3. Correlation Results.

(a): Correlation (lnESS)
Variables	lnESS	ESGUI	GO	EGO	TGO	FGO	lnEG	IND	lnEDU	URB
lnESS	1.000
ESGUI	0.124	1.000
GO	0.429	0.326	1.000
EGO	0.268	0.340	0.913	1.000
TGO	0.126	0.292	0.861	0.924	1.000
FGO	0.398	0.329	0.785	0.882	0.634	1.000
lnEG	0.571	0.096	0.206	0.124	−0.139	0.427	1.000
IND	−0.126	−0.249	−0.421	−0.286	−0.207	−0.336	−0.225	1.000
lnEDU	−0.447	−0.093	0.244	0.382	0.430	0.223	−0.264	−0.051	1.000
URB	0.261	−0.001	−0.028	0.165	−0.079	0.449	0.476	0.084	−0.128	1.000
(b): Correlation (lnEEG)
Variables	lnEEG	ESGUI	GO	EGO	TGO	FGO	lnEG	IND	lnEDU	URB
lnEEG	1.000
ESGUI	0.079	1.000
GO	0.362	0.326	1.000
EGO	0.408	0.340	0.913	1.000
TGO	0.104	0.292	0.861	0.924	1.000
FGO	0.700	0.329	0.785	0.882	0.634	1.000
lnEG	0.611	0.096	0.206	0.124	−0.139	0.427	1.000
IND	−0.079	−0.249	−0.421	−0.286	−0.207	−0.336	−0.225	1.000
lnEDU	−0.067	−0.093	0.244	0.382	0.430	0.223	−0.264	−0.051	1.000
URB	0.538	−0.001	−0.028	0.165	−0.079	0.449	0.476	0.084	−0.128	1.000
(c): Correlation (lnENSS)
Variables	lnENSS	ESGUI	GO	EGO	TGO	FGO	lnEG	IND	lnEDU	URB
lnENSS	1.000
ESGUI	0.203	1.000
GO	0.570	0.326	1.000
EGO	0.559	0.340	0.913	1.000
TGO	0.576	0.292	0.861	0.924	1.000
FGO	0.436	0.329	0.785	0.882	0.634	1.000
lnEG	−0.214	0.096	0.206	0.124	−0.139	0.427	1.000
IND	−0.432	−0.249	−0.421	−0.286	−0.207	−0.336	−0.225	1.000
lnEDU	0.068	−0.093	0.244	0.382	0.430	0.223	−0.264	−0.051	1.000
URB	−0.019	−0.001	−0.028	0.165	−0.079	0.449	0.476	0.084	−0.128	1.000

presents the correlation result.

3.2. Empirical Methods

Using panel data, we analyze the effects of ESGUI and GO on ET, with the estimable model defined in Equation (1).

$$E{T}_{i,t}={\gamma }_i+{\beta }_1{ \mathrm{ESGUI} }_{i,t}+{\beta }_2{GO}_{i,t}+{\delta }_j{X}_{i,t}+{\mu }_t+{\epsilon }_{i,t}$$

(1)

Let $i$ index countries and $t$ years. $E{T}_{i,t}$ is the vector of energy-trilemma outcomes (security, equity, sustainability); $\mathrm{E}\mathrm{S}\mathrm{G}\mathrm{U}{\mathrm{I}}_{\mathrm{i},\mathrm{t}}$ is the ESG uncertainty index; ${GO}_{c,t}$ is the vector of globalization measures (composite, financial, economic, trade); and $X_{i,t}$ is the control set. The model includes country effects γi, coefficients ${\beta }_1 \left(\mathrm{ESGUI}\right) \mathrm{and} {\beta }_2 \left(\mathrm{GO}\right), \mathrm{a} \mathrm{K}\times 1$ control-coefficient vector δ, time fixed effects ${\mathsf{\mu }}_{\mathrm{t}},$ and an error term ${\epsilon }_{i,t}$.

Before estimation, we evaluate our panel data for key econometric issues—cross-sectional dependence, serial correlation, and heteroscedasticity. Autocorrelation, cross-sectional dependence, and heteroscedasticity tests confirm the presence of all three. In particular, the cross-sectional dependence test rejects the null of independence, prompting the use of cross-sectional Augmented Dickey–Fuller test to assess stationarity. We also use Westerlund panel cointegration test to explore long-run relationships among variables. Given these diagnostics, we estimate our baseline using Driscoll–Kraay standard errors and Feasible Generalized Least Squares (FGLS). The Driscoll–Kraay approach ensures robustness to cross-sectional and temporal dependence, heteroscedasticity, and autocorrelation, while FGLS provides efficient coefficient estimates. In addition, the Lewbel Two-Stage Least Squares (Lewbel 2SLS) estimator is employed to address endogeneity concerns.

To address potential endogeneity concerns, we employ an instrumental variable (IV) approach. Endogeneity may arise from reverse causality between energy transition (ET) components and ESGUI, or from measurement errors in ESGUI. While ESGUI can influence ET, efforts to achieve ET can also heighten ESGUI. Achieving ET requires critical minerals for renewable energy technologies, essential to ensuring energy security, energy equity, and environmental sustainability. However, the geographical concentration of such minerals often fuels international competition, raising ESGUI. The ESGUI measure, derived from news related to adverse ESG events, may also suffer from measurement error, reinforcing endogeneity risks. We therefore apply the Lewbel (2012) 2SLS estimator, which generates internal, heteroscedastic instruments by interacting exogenous covariates with residuals from auxiliary regressions. This approach creates its own instruments from the model’s data by exploiting natural heteroskedasticity—i.e., the fact that the “noise” around variables varies across observations. This variation functions like a built-in randomizer, allowing us to purge endogeneity without relying on external instruments (which are often controversial or unavailable). In practice, this means our identification is grounded in observable distributional features of the data, yielding robust, transparent estimates that complement and strengthen conventional IV strategies.

4. Findings and Discussion

4.1. Cross-Sectional Dependence and Stationarity Results

Table 4. Cross-Sectional Dependence Test Result.

Variable	CD-Test	p-Value
lnESS	26.633	0.000
lnEEQ	7.665	0.000
lnENSS	40.644	0.000
ESGUI	29.038	0.000
GO	33.182	0.000
TGO	26.728	0.000
EGO	17.526	0.000
FGO	26.162	0.000
lnEG	102.76	0.000
IND	49.780	0.000
lnEDU	11.731	0.000
URB	96.360	0.000

presents the results of the Cross-Sectional Dependence test. The null hypothesis (H0) is cross-sectional independence across panel units, against the alternative (H1) of cross-sectional dependence. Given the large CD statistics and p-values of 0.000 (<0.01) for all variables (lnESS, lnEEQ, lnENSS, ESGUI, GO, TGO, EGO, FGO, lnEG, IND, lnEDU, URB), H0 is rejected for every series and H1 is accepted—indicating strong cross-sectional dependence (common shocks/spillovers), so subsequent estimation should use second-generation panel methods that accommodate CSD.

Table 5. CADF Results.

Variables	Levels	First Difference
lnESS	−2.766 **	−12.788 ***
lnEEQ	−2.338 **	−8.512 ***
lnENSS	−1.592 *	−9.541 ***
ESGUI	−1.238	−6.839 ***
GO	−2.287 **	−12.870 ***
TGO	−2.930 **	−7.183 ***
EGO	−1.273	−8.283 ***
FGO	−4.748 ***	−15.938 ***
lnEG	−1.103	−1.896 *
IND	−1.448 *	−7.186 ***
lnEDU	2.105	−3.857 ***
URB	7.928 ***	2.040

Note: * p < 0.10, ** p < 0.05, *** p < 0.01.

presents the result of the CADF test. The Null hypothesis (H₀) states that each series has a unit root (is non-stationary) and alternative (H₁) states that the series is stationarity. The CADF statistics show H₀ is rejected at levels for lnESS, lnEEQ, lnENSS, GO, TGO, FGO, IND, and URB—these are I(0). H₀ is not rejected at levels for ESGUI, EGO, lnEG, and lnEDU, but their first differences are significant, so these are I(1). Overall, the data mix I(0) and I(1) variables, supporting methods that allow heterogeneous integration orders (e.g., Driscoll–Kraay, FGLS and Lewbel IV-2SLS with CSD controls).

4.2. Cointegration Result

We proceed by examining the cointegration among the variables (see

Table 6. Westerlund Cointegration.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
	Energy Security				Energy Equity				Environmental Sustainability
VR	−2.51 ***	−2.95 ***	−2.23 **	−2.17 **	−2.59 ***	−2.66 ***	−2.27 **	−2.14 **	−2.44 ***	−2.48 **	−2.73 ***	−2.93 ***

Note: VR denotes Variance ratio. ** p < 0.05, *** p < 0.01.

). Null hypothesis (H₀): no cointegration among the variables in each specification; alternative (H₁): cointegration exists. The reported Variance Ratio (VR) statistics are significant (mostly at the 1% level) across all 12 models for each pillar—energy security, energy equity, and environmental sustainability (e.g., VR = −2.51 *** to −2.95 ***, with several ** at 5%). Therefore, H₀ is rejected throughout and H₁ is accepted, indicating robust evidence of long-run cointegration across all specifications and pillars.

4.3. Baseline Results

As a first step, we present baseline results based on Driscoll–Kraay standard errors (

Table 7. Effects of ESG Uncertainty and Globalisation on Energy Trilemma (Driscoll–Kraay) results.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
	Energy Security				Energy Equity				Environmental Sustainability
ESGUI	−0.002 ***	−0.002 ***	−0.002 **	−0.003 ***	−0.000	−0.001	0.001	−0.001	−0.001	−0.001	−0.001	−0.001
	(0.001)	(0.001)	(0.007)	(0.001)	(0.008)	(0.004)	(0.005)	(0.000)	(0.001)	(0.006)	(0.003)	(0.004)
GO	0.018 ***				0.003 ***				0.012 ***
	(0.005)				(0.000)				(0.002)
lnEG	0.228 ***	0.291 ***	0.350 ***	0.201 ***	0.069 ***	0.080 ***	0.091 ***	0.064 ***	−0.274 ***	−0.234 ***	−0.190 ***	−0.283 ***
	(0.037)	(0.054)	(0.072)	(0.033)	(0.007)	(0.007)	(0.006)	(0.008)	(0.038)	(0.037)	(0.036)	(0.031)
IND	0.005 *	0.003	0.002	0.006 **	0.000	−0.000	−0.000	0.000 *	−0.007 ***	−0.007 ***	−0.008 ***	−0.006 ***
	(0.003)	(0.002)	(0.002)	(0.002)	(0.000)	(0.000)	(0.000)	(0.000)	(0.001)	(0.001)	(0.001)	(0.001)
lnEDU	−2.789 ***	−3.214 ***	−3.175 ***	−2.943 ***	−0.264 **	−0.349 ***	−0.349 **	−0.289 ***	−1.130 ***	−1.494 ***	−1.621 ***	−1.144 ***
	(0.310)	(0.366)	(0.404)	(0.314)	(0.107)	(0.114)	(0.123)	(0.095)	(0.283)	(0.282)	(0.302)	(0.279)
URB	−0.001	−0.004	−0.001	−0.008 ***	0.003 ***	0.002 ***	0.003 ***	0.002 ***	0.004 ***	0.002	0.004 ***	−0.001
	(0.004)	(0.003)	(0.004)	(0.003)	(0.000)	(0.000)	(0.000)	(0.000)	(0.001)	(0.001)	(0.001)	(0.002)
EGO		0.011 ***				0.002 ***				0.008 ***
		(0.003)				(0.000)				(0.001)
TGO			0.008 ***				0.002 ***				0.007 ***
			(0.002)				(0.000)				(0.001)
FGO				0.013 ***				0.002 ***				0.008 ***
				(0.003)				(0.000)				(0.002)
Constant	13.164 ***	15.468 ***	14.718 ***	15.209 ***	4.509 ***	4.959 ***	4.852 ***	4.863 ***	11.289 ***	13.186 ***	13.289 ***	12.182 ***
	(1.683)	(1.723)	(2.120)	(1.276)	(0.532)	(0.571)	(0.622)	(0.473)	(1.540)	(1.432)	(1.453)	(1.492)
Obs	120	120	120	120	120	120	120	120	120	120	120	120
Time-fixed effect	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
R2	0.709	0.683	0.650	0.712	0.900	0.892	0.880	0.898	0.701	0.708	0.730	0.649

Note: * p < 0.10, ** p < 0.05, *** p < 0.01.

) alongside FGLS estimates (

Table 8. Effects of ESG Uncertainty and Globalisation on Energy Trilemma (FGLS) results.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
	Energy Security				Energy Equity				Environmental Sustainability
ESGUI	−0.001	−0.001	−0.001	−0.002 **	−0.001	−0.001	−0.001	−0.002	−0.001	−0.000	−0.001	−0.002
	(0.001)	(0.001)	(0.005)	(0.006)	(0.005)	(0.005)	(0.009)	(0.004)	(0.003)	(0.008)	(0.005)	(0.001)
GO	0.015 ***				0.003 ***				0.012 ***
	(0.002)				(0.000)				(0.001)
lnEG	0.280 ***	0.334 ***	0.374 ***	0.252 ***	0.070 ***	0.079 ***	0.087 ***	0.066 ***	−0.219 ***	−0.181 ***	−0.151 ***	−0.226 ***
	(0.036)	(0.035)	(0.037)	(0.036)	(0.005)	(0.005)	(0.006)	(0.005)	(0.020)	(0.019)	(0.019)	(0.021)
IND	0.004 ***	0.003 **	0.002	0.005 ***	0.000	−0.000	−0.001 **	0.000	−0.005 ***	−0.006 ***	−0.008 ***	−0.004 ***
	(0.001)	(0.001)	(0.001)	(0.001)	(0.000)	(0.000)	(0.000)	(0.000)	(0.001)	(0.001)	(0.001)	(0.001)
lnEDU	−2.430 ***	−2.778 ***	−2.699 ***	−2.497 ***	−0.269 ***	−0.345 ***	−0.321 ***	−0.306 ***	−0.841 ***	−1.247 ***	−1.364 ***	−0.870 ***
	(0.298)	(0.336)	(0.350)	(0.315)	(0.047)	(0.053)	(0.058)	(0.047)	(0.162)	(0.183)	(0.185)	(0.189)
URB	−0.001	−0.005 ***	−0.003 **	−0.008 ***	0.003 ***	0.002 ***	0.003 ***	0.001 ***	0.004 ***	0.002 ***	0.004 ***	−0.001
	(0.686)	(0.731)	(0.118)	(0.291)	(0.533)	(0.572)	(0.623)	(0.474)	(0.048)	(0.142)	(0.463)	(0.107)
EGO		0.009 ***				0.002 ***				0.008 ***
		(0.001)				(0.000)				(0.001)
TGO			0.007 ***				0.001 ***				0.006 ***
			(0.001)				(0.000)				(0.000)
FGO				0.011 ***				0.002 ***				0.008 ***
				(0.001)				(0.000)				(0.001)
Constant	11.195 ***	13.098 ***	12.394 ***	12.667 ***	4.621 ***	5.038 ***	4.868 ***	5.002 ***	9.359 ***	11.468 ***	11.689 ***	10.256 ***
	(1.515)	(1.665)	(1.695)	(1.606)	(0.238)	(0.264)	(0.278)	(0.243)	(0.804)	(0.909)	(0.897)	(0.956)
Obs	120	120	120	120	120	120	120	120	120	120	120	120
Time-fixed effect	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
R2	0.861	0.901	0.678	0.569	0.726	0.696	0.738	0.784	0.881	0.814	0.562	0.725

** p < 0.05, *** p < 0.01.

). The tables report 12 specifications (four for each pillar of the trilemma) that rotate the definition of globalisation (GO/EGO/TGO/FGO).

Across both estimators, globalisation (GO) is positively associated with all three pillars of the Energy Trilemma (ET), while ESG-related uncertainty (ESGUI) tends to erode—if modestly—performance, most visibly for energy security. In the Driscoll–Kraay (DK) models, ESGUI carries consistently negative and statistically significant coefficients for energy security (≈−0.002 to −0.003), whereas the corresponding FGLS estimates are smaller in magnitude and often insignificant, signalling estimator sensitivity but a common direction.

For energy security (ESS), the DK results show a robust pattern: higher ESG uncertainty is linked to lower security (columns 1–4, −0.002 to −0.003, mostly at ≥1% significance). FGLS mirrors the sign but weakens the precision (−0.001 to −0.002, largely insignificant, with one specification reaching significance), implying that the adverse effect of ESG uncertainty is present but not always precisely estimated once cross-sectional heteroskedasticity/correlation are modelled explicitly.

For energy equity (EEQ), ESGUI effects are generally small and imprecise in both tables (columns 5–8). This suggests that access/affordability outcomes—proxied by the equity dimension—are less contemporaneously sensitive to sustainability-related uncertainty than security, perhaps because equity is anchored by long-lived infrastructure and social policies that buffer short-run information shocks.

The environmental sustainability (ENSS) pillar shows similarly muted ESGUI effects (columns 9–12): coefficients are negative but typically insignificant under both DK and FGLS. Interpreted cautiously, this indicates that annual movements in a news-based ESG uncertainty signal do not systematically map into the pace of greening within the sample, once income, industrial structure, education, urbanisation, and time effects are held constant.

By contrast, the composite globalisation index (GO) displays consistently positive, precisely estimated associations with ET across estimators. Under DK, coefficients are about 0.018 (security), 0.003 (equity), and 0.012 (sustainability); FGLS delivers very similar magnitudes (≈0.015, 0.003, and 0.012, respectively). Given the log specification for ET, these are semi-elasticities: a 10-point rise in GO (roughly two SDs in this sample) aligns with ≈15–18% higher energy security, ≈3% higher equity, and ≈12% higher sustainability, highlighting especially strong links for resilience and decarbonisation.

Decomposing GO clarifies where the lift comes from. Economic globalisation (EGO), trade globalisation (TGO), and financial globalisation (FGO) are all positive and significant across pillars and estimators. For energy security, FGO tends to have the largest coefficient (≈0.011–0.013), followed by EGO (≈0.009–0.011) and TGO (≈0.007–0.008), consistent with deeper financial integration easing capital constraints for reliability and grid hardening. For environmental sustainability, EGO and FGO are similarly strong (≈0.008), with TGO slightly smaller (≈0.006–0.007), suggesting that both financial depth and broader economic integration help scale low-carbon investment and technology diffusion. Equity gains are positive but smaller (about 0.001–0.002), which fits the idea that inclusion improves gradually as integration expands markets and lowers delivered energy costs.

Comparatively, DK and FGLS tell the same story on globalisation—signs, magnitudes, and significance are remarkably robust—while diverging mainly on the strength of ESGUI’s penalty for energy security. DK’s heteroskedasticity- and cross-sectional-correlation-robust inference may better capture common shocks that propagate across countries, inflating the precision of the ESGUI effect; FGLS, which explicitly models the error structure, yields more conservative significance. The stability of the GO and subcomponent estimates across both methods strengthens confidence that integration—especially financial and broad economic channels—systematically supports ET outcomes in this panel.

Goodness-of-fit (R²). DK models fit equity best (R² ≈ 0.900, 0.892, 0.880, 0.898), then security (0.709, 0.683, 0.650, 0.712), then sustainability (0.701, 0.708, 0.730, 0.649). FGLS often lifts fit for security in the first two specs (0.861, 0.901) but drops when replacing GO with sub-indices (0.678, 0.569). For equity, FGLS R² are respectable (0.726, 0.696, 0.738, 0.784), and for sustainability they are high but more variable (0.881, 0.814, 0.562, 0.725). In short: equity equations are the most tightly explained in DK, while FGLS shows strong but specification-sensitive fit, especially for security and sustainability when different globalisation facets are entered.

4.4. Results of Lewbel Two-Stage Least Squares Method

Table 9. Effects of ESG Uncertainty and Globalisation on energy trilemma (Lewbel IV-2SLS results).

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
	Energy Security				Energy Equity				Environmental Sustainability
ESGUI	−0.002 **	−0.002 **	−0.003 **	−0.003 ***	−0.001 ***	−0.002 **	−0.001 **	−0.001 **	−0.002 ***	−0.002 ***	−0.002 ***	−0.002 ***
	(0.001)	(0.001)	(0.001)	(0.001)	(0.000)	(0.000)	(0.000)	(0.000)	(0.001)	(0.001)	(0.000)	(0.001)
GO	0.018 ***				0.004 ***				0.013 ***
	(0.002)				(0.000)				(0.001)
lnEG	0.227 ***	0.291 ***	0.351 ***	0.200 ***	0.067 ***	0.079 ***	0.091 ***	0.062 ***	−0.279 ***	−0.237 ***	−0.190 ***	−0.291 ***
	(0.038)	(0.036)	(0.039)	(0.038)	(0.005)	(0.005)	(0.005)	(0.006)	(0.023)	(0.022)	(0.021)	(0.024)
IND	0.004 ***	0.003 **	0.001	0.006 ***	0.000	−0.000	−0.000 **	0.000	−0.007 ***	−0.008 ***	−0.009 ***	−0.006 ***
	(0.001)	(0.001)	(0.001)	(0.001)	(0.000)	(0.000)	(0.000)	(0.000)	(0.001)	(0.001)	(0.001)	(0.001)
lnEDU	−2.807 ***	−3.209 ***	−3.290 ***	−2.960 ***	−0.285 ***	−0.376 ***	−0.379 ***	−0.315 ***	−1.204 ***	−1.612 ***	−1.695 ***	−1.245 ***
	(0.358)	(0.416)	(0.473)	(0.348)	(0.061)	(0.068)	(0.072)	(0.063)	(0.197)	(0.211)	(0.194)	(0.228)
URB	−0.001	−0.004 ***	−0.001	−0.008 ***	0.003 ***	0.002 ***	0.003 ***	0.002 ***	0.004 ***	0.002 **	0.004 ***	−0.001
	(0.677)	(0.025)	(0.112)	(0.087)	(0.053)	(0.172)	(0.023)	(0.475)	(0.041)	(0.135)	(0.057)	(0.079)
EGO		0.011 ***				0.002 ***				0.008 ***
		(0.001)				(0.000)				(0.001)
TGO			0.009 ***				0.002 ***				0.007 ***
			(0.001)				(0.000)				(0.000)
FGO				0.014 ***				0.003 ***				0.009 ***
				(0.001)				(0.000)				(0.001)
Constant	13.251 ***	15.440 ***	15.260 ***	15.307 ***	4.611 ***	5.094 ***	4.990 ***	5.011 ***	11.654 ***	13.775 ***	13.634 ***	12.749 ***
	(1.783)	(2.043)	(2.261)	(1.761)	(0.301)	(0.337)	(0.349)	(0.325)	(0.987)	(1.052)	(0.953)	(1.163)
Time-fixed effect	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
Obs	120	120	120	120	120	120	120	120	120	120	120	120
R2	0.709	0.683	0.646	0.711	0.896	0.889	0.876	0.894	0.691	0.695	0.725	0.635

Note: ** p < 0.05, *** p < 0.01.

reports Lewbel IV-2SLS estimates that instrument the potentially endogenous regressors using heteroskedasticity-generated instruments. With time fixed effects and the same specification set (12 models: four per pillar), the IV results sharpen the picture: ESG uncertainty (ESGUI) is consistently harmful, while globalisation—aggregate and by facet—remains consistently beneficial to the energy trilemma. Across columns (1)–(4), (5)–(8), and (9)–(12), the ESGUI coefficients are negative (≈−0.001 to −0.003) and statistically significant, whereas GO/EGO/TGO/FGO are positive and highly significant. In short, uncertainty hurts the trilemma while globalization helps it.

For energy security, the IV estimates in columns (1)–(4) indicate that a rise in ESG uncertainty meaningfully reduces the stability and resilience of energy supply (ESGUI ≈ −0.002 ** to −0.003 ***). This pattern fits standard real-options logic: when policy, standards, and disclosure expectations are noisy, firms defer or down-scale irreversible, long-lived energy investments (generation, storage, LNG terminals, interconnectors), elevating supply risk [11,12]. In the G7—where energy systems rely on capital-intensive assets and cross-border fuel and equipment chains—ESG policy whiplash and credibility gaps amplify risk premia, delaying capacity additions and upgrades that would otherwise harden security [16].

Turning to energy equity (affordability and access), columns (5)–(8) show ESG uncertainty again enters negatively and significantly (≈−0.001 to −0.002). In practice, uncertainty raises financing costs for utilities and project developers, especially for low-carbon and network assets with long payback periods. Higher risk premia and a stronger “option to wait” propagate to retail tariffs and connection backlogs, eroding affordability for households and SMEs [13]. G7 markets, with sophisticated but risk-sensitive capital pools, are particularly exposed to this mechanism: small changes in perceived policy durability can reprice billions in capex and shift cost recovery onto consumers.

On environmental sustainability, the IV results in columns (9)–(12) show a clear and precise negative ESGUI effect (−0.002 *** throughout). This aligns with evidence that credible, predictable rules are a prerequisite for directed technical change toward cleaner technologies; uncertainty blunts incentives to invest in green R&D, slows diffusion, and prolongs reliance on higher-emitting capital [14,15]. For the G7, where much of the global low-carbon innovation originates, uncertainty thus has outsized global externalities by dampening innovation pipelines and deployment learning curves.

In contrast, overall globalization (GO) is positively associated with energy security, equity, and sustainability (0.018 ***; 0.004 ***; 0.013 ***). For security, deeper integration diversifies supply sources, enhances cross-border balancing, and broadens access to fuels, equipment, and critical minerals—classic portfolio effects that reduce exposure to idiosyncratic shocks [16]. G7 countries are structurally positioned to benefit as they operate dense interconnection networks, liquid fuel and power markets, and have strong contracting institutions that translate openness into resilience.

Decomposing globalization clarifies channels. Economic globalization (EGO) is strongly positive across the trilemma (0.011 ***; 0.002 ***; 0.008 ***), consistent with the idea that cross-border flows of goods, services, and value chains scale markets for clean tech, lower unit costs via learning-by-doing, and accelerate replacement of inefficient capital [35]. In the G7, this means cheaper access to turbines, PV modules, batteries, and digital grid components—improving affordability and enabling faster decarbonization without sacrificing reliability.

Trade globalization (TGO) also shows positive and significant links (0.009 ***; 0.002 ***; 0.007 ***). Trade expands the feasible set of energy mixes by enabling imports during domestic shortfalls and exports when local supply is abundant; it also speeds technology diffusion through embodied know-how in traded intermediate goods [17]. For the G7, robust trade in fuels, equipment, and balancing services supports smoother integration of variable renewables and lowers system costs, jointly advancing security and equity while cutting emissions intensity.

Financial globalization (FGO) registers the largest security coefficient among the components (0.014 ***; with 0.003 *** for equity and 0.009 *** for sustainability). International capital deepens funding pools for large-scale, capital-intensive projects (offshore wind, grids, storage), spreads risk, and reduces the cost of capital when policy frameworks are credible [13]. The G7’s sophisticated financial sectors, green bond markets, and disclosure regimes (e.g., TCFD-style reporting) help channel global savings into domestic and cross-border low-carbon infrastructure, reinforcing all three trilemma goals.

Comparing with Table 7—Driscoll–Kraay and Table 8—FGLS confirms the pattern but highlights why IV matters. In 7, ESGUI is strongly negative for energy security but often imprecise for equity and sustainability; in 7b, most ESGUI estimates are small and statistically weak. By contrast, Table 9’s Lewbel IV-2SLS yields uniformly negative and significant ESGUI effects across all pillars, and consistently positive, significant effects of GO/EGO/TGO/FGO. This is exactly what we expect if (a) measurement error in ESG uncertainty or (b) reverse causality (e.g., deteriorating trilemma outcomes fuelling sustainability-related uncertainty) biases conventional estimators toward zero; Lewbel’s heteroskedasticity-based instruments help recover exogenous variation, sharpening inference [36]. The similarity in signs—and the IV’s tighter precision—suggest Table 8’s results are broadly consistent with Table 7 and Table 8 but arguably more credible causally.

4.5. Examining the Nonlinear Effect of Globalisation on the Energy Trilemma

Since the relationship between globalisation and environmental and energy indicators can exhibit nonlinear connection, the current investigation included the squared terms of the compositive globalisation and trade globalization (TGO), economic globalisation (EGO) and financial globalisation (FGO) variables in our estimations. The results are reported in

Table 10. Nonlinear effect of globalisation on the energy trilemma (Lewbel IV-2SLS results).

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
	Energy Security				Energy Equity				Environmental Sustainability
ESGUI	−0.001	−0.001	−0.003 ***	−0.002 *	−0.000	−0.002 *	−0.001 **	−0.003	−0.001 **	−0.001 **	−0.002 ***	0.000
	(0.001)	(0.001)	(0.001)	(0.001)	(0.000)	(0.007)	(0.000)	(0.006)	(0.001)	(0.001)	(0.000)	(0.001)
GO	0.561 ***				0.087 ***				0.145 ***
	(0.079)				(0.024)				(0.052)
GO2	−0.003 ***				−0.001 ***				−0.001 **
	(0.000)				(0.000)				(0.000)
lnEG	0.175 ***	0.325 ***	0.529 ***	0.185 ***	0.060 ***	0.084 ***	0.106 ***	0.058 ***	−0.291 ***	−0.220 ***	−0.167 ***	−0.311 ***
	(0.035)	(0.036)	(0.050)	(0.036)	(0.006)	(0.005)	(0.008)	(0.005)	(0.024)	(0.018)	(0.029)	(0.018)
IND	0.005 ***	0.002 *	0.004 ***	0.004 ***	0.000	−0.000	−0.000	−0.000	−0.007 ***	−0.008 ***	−0.008 ***	−0.009 ***
	(0.001)	(0.001)	(0.001)	(0.001)	(0.000)	(0.000)	(0.000)	(0.000)	(0.001)	(0.001)	(0.001)	(0.001)
lnEDU	−1.818 ***	−2.785 ***	−3.123 ***	−2.801 ***	−0.131 *	−0.316 ***	−0.358 ***	−0.255 ***	−0.949 ***	−1.376 ***	−1.675 ***	−0.899 ***
	(0.349)	(0.402)	(0.416)	(0.339)	(0.071)	(0.066)	(0.071)	(0.058)	(0.215)	(0.188)	(0.197)	(0.181)
URB	0.004 ***	−0.004 ***	−0.003 **	−0.008 ***	0.004 ***	0.002 ***	0.003 ***	0.002 ***	0.005 ***	0.001 **	0.003 ***	−0.001
	(0.001)	(0.001)	(0.001)	(0.001)	(0.000)	(0.000)	(0.000)	(0.000)	(0.001)	(0.001)	(0.001)	(0.001)
EGO		0.203 ***				0.028 ***				0.107 ***
		(0.037)				(0.006)				(0.021)
EGO2		−0.001 ***				−0.000 ***				−0.001 ***
		(0.000)				(0.000)				(0.000)
TGO			0.153 ***				0.014 ***				0.026 *
			(0.026)				(0.004)				(0.013)
TGO2			−0.001 ***				−0.000 ***				−0.000
			(0.000)				(0.000)				(0.000)
FGO				0.076 **				0.022 ***				0.118 ***
				(0.030)				(0.004)				(0.017)
FGO2				−0.000 **				−0.000 ***				−0.001 ***
				(0.000)				(0.000)				(0.000)
Constant	−13.334 ***	6.260 **	8.052 ***	12.309 ***	0.528	3.842 ***	4.348 ***	4.016 ***	5.096 *	8.982 ***	12.712 ***	7.107 ***
	(4.207)	(2.594)	(2.434)	(2.050)	(1.220)	(0.434)	(0.421)	(0.359)	(2.647)	(1.178)	(1.159)	(1.076)
Time-fixed	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
Obs	120	120	120	120	120	120	120	120	120	120	120	120
R2	0.786	0.747	0.715	0.725	0.926	0.909	0.884	0.918	0.712	0.758	0.728	0.779

Note: * p < 0.10, ** p < 0.05, *** p < 0.01.

. With the linear terms generally retaining their positive coefficients, the squared terms of FGO, EGO and EGO all negative and statistically significant. Accordingly, the hypothesized nonlinear effect is supported; higher levels of globalisation—FGO, EGO, and TGO—are associated with improvements in energy security, equity, and environmental sustainability at low to moderate degrees of integration, while the negative and statistically significant squared terms indicate diminishing marginal returns (and possible reversal) at very high integration.

The Lewbel IV–2SLS estimates in Table 10 indicate a robust nonlinear (concave) relationship between globalisation and the three pillars of the energy trilemma in the G7. Across the overall globalisation index (GO) and its subcomponents—economic (EGO), trade (TGO), and financial (FGO)—the linear terms are positive while the squared terms are negative, implying diminishing marginal returns to openness at higher levels of integration. Using the reported coefficients, the implied turning points for GO occur around ≈94 for energy security, ≈44 for energy equity, and ≈73 for environmental sustainability $(\mathrm{computed} \mathrm{as} -{\beta }_1/\left(2{\beta }_2\right)),$ consistent with the G7’s location near the global openness frontier [19].

For energy security, the positive GO coefficient (0.561 ***) with a negative GO² (−0.003 ***) suggests that incremental integration initially diversifies supply portfolios, strengthens cross-border balancing, and deepens markets, thereby reducing outage and price-spike risk [16]. As openness approaches the turning point (≈94), marginal gains taper, consistent with higher exposure to correlated external shocks, chokepoints in global logistics, and potential import dependence that partly offsets diversification benefits. This concavity is mirrored in EGO, TGO, and FGO, indicating that the underlying channels—production networks, trade logistics, and cross-border capital—each improve security up to a point before delivering smaller incremental benefits at very high integration [17].

For energy equity (affordability and access), the positive GO term (0.087 ***) and negative GO² (−0.001 ***) yield a lower turning point (≈44), implying that early-to-mid stages of openness reduce delivered energy costs via market scale, competition, and technology cost compression; beyond that, global price pass-through becomes more salient, attenuating equity gains. This pattern aligns with trade-and-environment models where scale and technique effects dominate at moderate openness, while price transmission and volatility exposure rise at advanced openness [35,37]. In liberalised G7 retail and wholesale markets, such pass-through is typically strong, which rationalises the relatively low equity turning point.

For environmental sustainability, the GO estimates (0.145 ***; GO² −0.001 **) indicate substantial green gains up to ≈73 on the GO scale, consistent with technology diffusion, embodied knowledge flows, and learning-by-doing that accelerate the uptake of low-carbon capital [17]. Beyond that threshold, rebound dynamics, offshoring of emissions-intensive stages, and the rising prominence of embodied emissions across global value chains jointly produce diminishing returns, curbing additional environmental gains. In the absence of complementary policies—such as carbon pricing with leakage safeguards, embodied-carbon standards, and green procurement—these channels temper, and can even reverse, the earlier sustainability improvements [35]. These results align with directed technical change, which holds that innovation follows incentives and policy certainty. Predictable, credible rules—stable carbon pricing with leakage safeguards, technology-neutral performance standards, taxonomy-aligned finance, green procurement, and transparent embodied-carbon disclosure—shift relative prices and risk-adjusted returns toward clean technologies, crowding in private R&D and deployment rather than inducing “wait-and-see” behavior. In short, openness becomes a durable engine of low-carbon investment only when anchored by consistent, forward-looking policies that direct search, learning, and capital deepening toward cleaner trajectories [14].

The subindex decomposition clarifies mechanisms. EGO exhibits pronounced concavity across pillars (EGO > 0; EGO² < 0), indicating that market enlargement and integration initially compress costs and speed diffusion, but frontier economies require coordination and standards rather than additional openness to extract further gains. TGO is consistently positive; curvature is material for security and equity, whereas for sustainability the squared term is statistically weak, suggesting trade integration continues to facilitate decarbonisation in the G7 by keeping clean hardware and intermediate inputs inexpensive and available. FGO shows strong concavity: moderate financial openness expands green funding pools and lowers the cost of capital, but at very high levels short-termism, volatility, and “too-much-finance” effects may erode the marginal benefits for long-horizon infrastructure [13,18].

The G7-specific concavity is economically intuitive. These economies already possess dense interconnectors, liquid commodity and power markets, and sophisticated green-finance channels. Consequently, the first-order benefits from additional openness are smaller, while exposure to globally correlated shocks (geopolitical disruptions, pandemics, critical-mineral bottlenecks) becomes relatively more important. This frontier-saturation mechanism naturally produces inverted-U patterns in the data: openness is beneficial on average, but its marginal impact declines as systems approach best-practice integration [16,19].

5. Conclusions and Policy Recommendations

5.1. Conclusions

This paper pioneer investigation into the effect of ESG sustainability uncertainty on the energy trilemma—capturing energy security, equity, and environmental sustainability—and assesses how globalisation moderates this relationship. The study’s relevance lies in the imperative to deliver a clean and just transition without compromising reliability or affordability. Controlling for education, urbanization, economic growth, and industry, and using multiple econometric approaches on G7 data for 2001–2022, we find that ESG sustainability uncertainty is negatively associated with the three pillars of the energy trilemma, namely security, equity, and environmental sustainability, which aligns with evidence that policy and ESG uncertainty depress long-horizon energy investment and coordination efforts [1,28]. We also detect a nonlinear effect of globalisation, as the squared term of openness is negative and statistically significant, indicating diminishing marginal benefits when integration becomes very high, consistent with concave or threshold-type relationships reported for advanced [38,39]. The positive findings of economic growth and urbanization also affirmed the results of references [40,41,42].

5.2. Policy Recommendations

Anchored in the G7 context, the evidence indicates that ESG-related sustainability uncertainty systematically weakens all three pillars of the energy trilemma—security, equity, and environmental sustainability—while globalisation exerts a nonlinear, concave influence. At low to moderate levels of integration, globalisation is associated with supportive effects on trilemma outcomes; at very high levels, marginal benefits diminish and may become adverse. These patterns imply that uncertainty reduction and calibrated openness are complementary policy levers rather than substitutes.

Accordingly, policy should prioritise credible, time-consistent transition pathways—durable carbon-pricing frameworks, stable taxonomies and disclosure requirements, and sustained public support for clean infrastructure—to reduce uncertainty premia and enable investment that enhances reliability, affordability, and decarbonisation. “Smart openness” is warranted: leverage trade and cross-border finance to scale clean technologies and diversify supply, while hedging upper-tail risks through diversified critical-mineral and fuel sources, stress-tested interconnection and storage, strategic reserves, and coordinated technical standards. To preserve equity as exposure to external price shocks rises with integration, adopt targeted, time-limited affordability measures linked to efficiency upgrades, accelerate grid and flexibility investments that dampen volatility, and protect vulnerable consumers during global disruptions. Finally, align green financial development with SDG 7 and SDG 13 by expanding credible labelled debt, encouraging financing of long-lived low-carbon assets, and embedding transparent transition-planning expectations for financial institutions, so that deep capital markets translate into measurable improvements across all three trilemma goals.

5.3. Limitation of Study and Future Direction

This study is constrained by data availability and scope, covering a limited set of G7 countries and years with harmonized series; consequently, external validity beyond the G7 is tentative. Measurement error may persist in the ESG sustainability uncertainty proxy and globalisation indices, and the quadratic specification may only approximate more complex nonlinearities; estimated turning points could therefore be scale dependent. Although Lewbel IV–2SLS mitigates endogeneity, residual bias is possible if heteroskedasticity-based instruments are weak or correlated with omitted shocks, and dynamic adjustment (e.g., lagged trilemma responses) is only partially captured by time effects. Future research should (i) expand temporal and cross-country coverage, including non-G7 economies; (ii) test alternative proxies (e.g., climate policy uncertainty, granular KOF subcomponents) and nonparametric or spline-based nonlinearities; (iii) incorporate heterogeneous treatment effects via country-specific geopolitical risk typologies and regime/shock break tests; (iv) employ distributional and dynamic estimators (quantile/QARDL, panel local projections, wavelet methods) and machine learning IV for robustness; and (v) integrate subnational and firm-level finance/technology data to identify transmission channels from openness and ESG uncertainty to investments, tariffs, and emissions—ultimately enabling policy stress testing of “smart globalisation” and credibility-enhancing transition frameworks.