A multivariable regression tool for embodied carbon footprint prediction in housing habitat

Gardezi, Syed Shujaa Safdar; Shafiq, Nasir; Zawawi, Noor Amila Wan Abdullah; Khamidi, Muhd Faris; Farhan, Syed Ahmad

doi:10.1016/j.habitatint.2015.11.005

Cited by 33 publications

(18 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previous research has dealt with the embodied impacts of a building (or its elements), studying different stages such as A1 to A4 [80], A1 to A5 [66,79,81], A and C [51] and A to C [48,49,82,83]; similar to this study, these works found that the greatest environmental detriment occurs in stage A1 to A3, with values ranging from 85% to 99% for those who evaluated up to A1 to A5 [51,66,[79][80][81] and from 60% to 80% for those who evaluated the complete cycle (A to C) [48,49,82,83]. For the rest of the stages, the results depended on the different criteria established in each study, so there are still discrepancies in the results obtained.…”

Section: A B C a B C A B C A B C A B C Amentioning

confidence: 99%

Environmental Challenges in the Residential Sector: Life Cycle Assessment of Mexican Social Housing

et al. 2019

View full text Add to dashboard Cite

Social Housing (SH) in Mexico has a potentially important role in reducing both the emission of greenhouse gases and the use of non-renewable resources, two of the main challenges facing not only Mexico but the planet as a whole. This work assesses the environmental impact generated by the embodied stages of a typical SH throughout its life cycle (cradle to grave), by means of a Life Cycle Assessment (LCA). Two types of envelope and interior walls and three types of windows are compared. It was found that SH emits 309 kg CO2 eq/m2 and consumes 3911 MJ eq/m2 in the product stages (A1 to A3) and construction process (A4 to A5); the most important stages are those referring to the products, namely, A1 to A3, B4 (replacement) and B2 (maintenance). Additionally, benefits were found in the use of lightweight and thermal materials, such as concrete blocks lightened with pumice or windows made of PVC or wood. Although the use of LCA is incipient in the housing and construction sector in Mexico, this work shows how its application is not only feasible but recommended as it may become a basic tool in the search for sustainability.

show abstract

Section: A B C a B C A B C A B C A B C Amentioning

confidence: 99%

Environmental Challenges in the Residential Sector: Life Cycle Assessment of Mexican Social Housing

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Recent studies have carried out analyses of different types of constructions, such as buildings [44][45][46][47][48] or pavements [49][50][51][52], exclusively considering stages A1-A5 or similar (A1-A4 and A1-A3). The objective of these works is to help researchers conduct environmental assessments in a fast, effective and sustainable way [44], and provide tools that contribute to a better practice of the stages involved in the construction.…”

Section: System Boundaries and Functional Unitmentioning

confidence: 99%

“…In this sense, researchers have studied the carbon footprint of conventional buildings [44] and the built-in energy of industrialized construction buildings that solve the growing demand for housing [46]. Meanwhile, to address the lack of energy data in construction, hybrid LCI models have been developed [47].…”

Section: System Boundaries and Functional Unitmentioning

confidence: 99%

A Cradle to Handover Life Cycle Assessment of External Walls: Choice of Materials and Prognosis of Elements

et al. 2018

View full text Add to dashboard Cite

This research focuses on a comparison of 20 external wall systems that are conventionally used in Spanish residential buildings, from a perspective based on the product and construction process stages of the life cycle assessment. The primary objective is to provide data that allow knowing the environmental behavior of walls built with materials and practices conventionally. This type of analysis will enable promoting the creation of regulations that encourage the use of combinations of materials that generate the most environmentally suitable result, and in turn, contribute to the strengthening of the embodied stages study of buildings and their elements. The results indicate that the greatest impact arises in the product stage (90.9%), followed by the transport stage (8.9%) and the construction process stage (<1%). Strategies (such as the use of large-format pieces and the controlled increase in thickness of the thermal insulation) can contribute to reducing the environmental impact; on the contrary, practices such as the use of small-format pieces and laminated plasterboard can increase the environmental burden. The prediction of the environmental behavior (simulation equation) allows these possible impacts to be studied in a fast and simplified way.

show abstract

“…A plausible method of computer modeling combining genetic algorithms and a chemical reactor network was developed by Christopher [8] to predict alternative fuel gaseous emissions. Multivariable regression analysis was merged with partial life cycle assessment (LCA) and building information modeling (BIM) for the prediction of environmental degradation within the housing sector [9]. However, forecasting results depended on statistical data, which change rapidly over time.…”

Section: Introductionmentioning

confidence: 99%

Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020

Tang

et al. 2016

Sustainability

View full text Add to dashboard Cite

According to the economic and energy consumption statistics in Jiangsu Province, we combined the GM (1, 1) grey model and polynomial regression to forecast carbon emissions. Historical and projected emissions were decomposed using the Logarithmic Mean Divisia Index (LMDI) approach to assess the relative contribution of different factors to emission variability. The results showed that carbon emissions will continue to increase in Jiangsu province during 2015-2020 period and cumulative carbon emissions will increase by 39.5487 million tons within the forecast period. The growth of gross domestic product (GDP) per capita plays the greatest positive role in driving carbon emission growth. Furthermore, the improvement of energy usage efficiency is the primary factor responsible for reducing carbon emissions. Factors of population, industry structure adjustment and the optimization of fuel mix also help to reduce carbon emissions. Based on the LMDI analysis, we provide some advice for policy-makers in Jiangsu and other provinces in China.

show abstract

A multivariable regression tool for embodied carbon footprint prediction in housing habitat

Cited by 33 publications

References 46 publications

Environmental Challenges in the Residential Sector: Life Cycle Assessment of Mexican Social Housing

Environmental Challenges in the Residential Sector: Life Cycle Assessment of Mexican Social Housing

A Cradle to Handover Life Cycle Assessment of External Walls: Choice of Materials and Prognosis of Elements

Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020

Contact Info

Product

Resources

About