Energy efficiency evaluation of construction projects using data envelopment analysis and Tobit regression

Energy efficiency (EE) in the construction sector is crucial for sustainable development, particularly in emerging economies like Pakistan, where the industry accounts for a large share of energy consumption and environmental degradation. Despite its economic significance, Pakistan's construction sector suffers from inefficiencies in energy use, with limited comprehensive assessments to guide improvements. This research introduces a novel, integrated approach combining Data Envelopment Analysis (DEA) and Tobit regression to evaluate and enhance EE in construction projects. DEA was applied to data from 120 construction firms, revealing an average technical efficiency (TE) of 84.4%, pure technical efficiency (PTE) of 93.2%, and scale efficiency (SE) of 90.4%, highlighting notable inefficiencies. The Tobit regression analysis identifies key factors influencing EE, including contractor training, access to loans, experience, and project site distance. This dual-method framework not only measures EE but also provides actionable insights to address inefficiencies, offering practical implications for policymakers and industry stakeholders. The findings emphasize the need for targeted interventions, such as government-supported financing and contractor training programs, to promote energy-efficient practices in Pakistan's construction sector. This approach provides a replicable model for other developing economies seeking sustainable construction practices.

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Publication Details

Journal
Scientific Reports
Published
2025-04-03
DOI
https://doi.org/10.1038/s41598-025-90671-3
Citations
8
Primary Topic
Efficiency Analysis Using DEA
Type
article
Field-Weighted Citation Impact
16.85
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article

Energy efficiency evaluation of construction projects using data envelopment analysis and Tobit regression

Mukesh Pushkarna, Naveed Ahmad, Junhai Ma, Bashar Tarawneh et al.
8 citations
Scientific Reports
Efficiency Analysis Using DEA
16.85
article

Energy efficiency evaluation of construction projects using data envelopment analysis and Tobit regression

Mukesh Pushkarna, Naveed Ahmad, Junhai Ma, Bashar Tarawneh, Muzaffar Iqbal, Wajid Khan, Zulqarnain Mushtaq, Євген Зайцев, Muhammad Zain Yousaf
article en
8 citations

Abstract

Energy efficiency (EE) in the construction sector is crucial for sustainable development, particularly in emerging economies like Pakistan, where the industry accounts for a large share of energy consumption and environmental degradation. Despite its economic significance, Pakistan's construction sector suffers from inefficiencies in energy use, with limited comprehensive assessments to guide improvements. This research introduces a novel, integrated approach combining Data Envelopment Analysis (DEA) and Tobit regression to evaluate and enhance EE in construction projects. DEA was applied to data from 120 construction firms, revealing an average technical efficiency (TE) of 84.4%, pure technical efficiency (PTE) of 93.2%, and scale efficiency (SE) of 90.4%, highlighting notable inefficiencies. The Tobit regression analysis identifies key factors influencing EE, including contractor training, access to loans, experience, and project site distance. This dual-method framework not only measures EE but also provides actionable insights to address inefficiencies, offering practical implications for policymakers and industry stakeholders. The findings emphasize the need for targeted interventions, such as government-supported financing and contractor training programs, to promote energy-efficient practices in Pakistan's construction sector. This approach provides a replicable model for other developing economies seeking sustainable construction practices.

Scientific ReportsVol. 15(1)
Al-Ahliyya Amman University (JO), University of Jordan (JO), Tianjin University (CN), Dalian Maritime University (CN), Jiangxi University of Finance and Economics (CN), Virtual University of Pakistan (PK)
Affordable and clean energy
Openalex Percentile: Top 1%
Efficiency Analysis Using DEA
16.85
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