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ENERGY EFFICIENCY IN RESIDENTIAL BUILDINGS: ANALYSIS OF NATIONAL ANDINTERNATIONAL EXPERIENCE

2025· article· W7116853813 on OpenAlexaboutno aff
Н.С. Сторчай, О.М. Назаренко, Є.М. Малий, Є.Є. Дудавський

Bibliographic record

VenueMunicipal economy of cities · 2025
Typearticle
Language
FieldEngineering
TopicConstruction Management and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsEfficient energy useZero-energy buildingDirectiveRoofRenewable energyBuilding scienceCertificationASHRAE 90.1Passive solar building designPassive house

Abstract

fetched live from OpenAlex

The article provides a comprehensive analytical review of contemporary approaches to energy-efficient residential building design, comparing practices in Ukraine with those established in Western countries. The study examines the regulatory and legal framework that governs the design, construction, and certification of buildings according to energy efficiency criteria, highlighting the evolution of standards and their impact on the construction industry. A detailed comparison is conducted between Ukrainian building codes (DBNs) and state standards (DSTUs) and their European, American, and Canadian counterparts, including the Energy Performance of Buildings Directive (EPBD), ISO 52000 series, ASHRAE 90.1, and the National Energy Code of Canada for Buildings (NECB). This comparative analysis reveals significant disparities in methodology, calculation procedures, and minimum energy performance requirements. The research demonstrates that Western standards incorporate more comprehensive approaches to lifecycle assessment, renewable energy integration, and occupant behavior modeling, while Ukrainian regulations are gradually transitioning toward these advanced methodologies. Based on the practical example of the Lofthouse cottage in the Netherlands, the article illustrates a technological model of an energy-efficient building that exemplifies Nearly Zero Energy Building (NZEB) principles. The study explores critical design considerations including optimal building orientation for passive solar gain, advanced structural solutions for foundations that minimize thermal bridging, high-performance wall assemblies with enhanced insulation values, innovative roof systems that integrate renewable energy generation, high-efficiency windows with low U-values, and sophisticated mechanical ventilation systems with heat recovery capabilities. The article systematically identifies the principal differences between Ukrainian and Western design models, focusing on aspects such as energy modeling requirements, thermal performance standards, air tightness specifications, and renewable energy obligations. These differences represent both challenges and opportunities for the Ukrainian construction sector as it seeks to align with European Union energy efficiency directives. Based on this analysis, the research formulates practical recommendations for harmonizing Ukraine's regulatory framework with international best practices and accelerating the implementation of NZEB technologies in Ukrainian residential construction. The recommendations address policy development, professional training, technology transfer mechanisms, and financial incentive structures. The results demonstrate that the adoption of modern energy-efficient technologies and design principles can reduce energy consumption in Ukrainian residential buildings by 40-60% compared to conventional construction methods. Furthermore, economic analysis indicates that despite higher initial capital costs, these investments achieve financial viability with payback periods of 10-12 years, considering current energy prices and available subsidy programs. These findings underscore the significant potential for improving Ukraine's building stock performance while reducing environmental impact and enhancing energy security.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.256
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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