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Record W4409836209 · doi:10.63335/j.hp.2025.0006

The Energy Trilemma: An overview of balancing security, sustainability, and affordability

2025· article· en· W4409836209 on OpenAlexaboutno aff
Irfan Khan

Bibliographic record

VenueHabitable Planet · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsTrilemmaSustainabilityEnergy securityEnergy (signal processing)BusinessEnvironmental economicsNatural resource economicsEconomicsMacroeconomicsEngineeringMonetary policyRenewable energy

Abstract

fetched live from OpenAlex

The global energy sector faces a complex challenge in balancing three critical dimensions: energy security, environmental sustainability, and economic affordability—commonly referred to as the energy trilemma. This paper provides a novel review of emerging trends, policies, and technological advancements that address this challenge. It particularly examines the role of top CO₂-emitting countries in navigating the energy trilemma, shedding light on their strategies and potential pathways for achieving a sustainable energy future. Findings demonstrate that these countries have achieved some success in their shift to cleaner energy systems, yet they maintain diverse approaches to policy frameworks, energy systems, and economic programs. Countries deal with specific obstacles because their economic systems combine with their energy reserves and climate agreement responsibilities. China and India remain the world's biggest growing economies, but they must keep their economies expanding while reducing their coal dependence. The United States and Canada hold substantial fossil fuel reserves, yet they must establish a strategy that aligns their home energy security requirements with worldwide climate objectives. Renewable energy development remains vigorous in Germany and Japan, yet their progress is limited by high power costs, affecting their electrical grid stability. Countries with abundant resources, including Russia, Brazil, Indonesia, and Mexico, have not effectively incorporated sustainability into their plans for energy development. The findings underscore the need for integrated policies, increased investments in renewable energy, and international cooperation to achieve a balanced energy transition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.246
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations32
Published2025
Admission routes1
Has abstractyes

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