case study green building projects

• Buildings & Facilities
• CommuterChoice
• Energy & Utilities
• Real Estate
• Sustainability
• Transit & Fleet

Departments

• Energy & Facilities
• Events Management
• Global Support Services
• Harvard Faculty Club
• Harvard International Office
• Harvard Real Estate
• Harvard University Housing
• Office for Sustainability
• Transportation

Harvard Energy & Facilities

Leed case studies.

• Green Building Tools & Resources
• Systems, Materials & Products

10 Akron Street - HUH

125 mount auburn - 2nd floor - hls, 15 hawthorn street - huh, 2 grant street - huh, 29 garden street - huh, 40 concord avenue - rad.

• ARB WEld Hill

Batten Hall - Harvard Innovation Lab - HBS

Biolabs lecture hall - fas, blackstone south eb - cs.

• Blackstone South NC - CS
• Byerly Hall - RAD
• Campus Services Center - CS
• Class of 1959 Memorial Chapel - HBS
• Cohen Laboratory - FAS
• DePace Lab, Warren Alpert Building - HMS
• Dean's House - HBS
• EPS Museum Renovation - FAS
• Eddy Rivas Laboratory - FAS
• Engert Lab - FAS
• Esteves Hall Executive Education Residence - HBS
• Extavour Laboratory - FAS
• Fay House - RAD
• Gallatin Hall Renovation - HBS
• Gannett House - HLS
• Girguis Laboratory - FAS
• Gordon Track Lockers and Office Renovation - FAS
• Griswold Hall2 - South Suite and Main Entry Renovation - HLS
• Gutman Library - GSE
• HIM 4th Floor - HMS
• Hamilton Hall Renovation - HBS
• Harvard Center Shanghai - HBS
• Harvard Ed Portal - HPAC
• Harvard Management Company
• Harvard University Employees Credit Union - HUECU
• Hauser Center - HKS
• Hauser Hall Basement Offices - HLS
• Herbaria Building 1st Floor Lab - FAS
• Hip Hop Archive - FAS
• IT Offices - SEAS
• Jacobsen Lab - FAS
• Littauer Fine Arts Library - FAS
• Littauer Library - HLS
• Longfellow Hall Basement - GSE
• Longfellow Hall Renovation
• MA Green High Performance Computing Center
• MCB Renovations - Northwest Labs - FAS
• Mango Lab Renovation - Biolabs - FAS
• Mather/Dunster House Kitchen - FAS
• McCollum Hall - HBS
• McCulloch Hall - HBS
• Meselson Lab - Biolabs - FAS
• Moorcroft Office Renovation - FAS
• Murr Athletic Center Locker Rooms - FAS
• Murthy Lab - Biolabs - FAS
• Museum of Comparative Zoology - FAS
• Ni Lab Renovation - FAS
• North Hall - HLS
• Northwest Labs Biophysics - FAS
• One Western Avenue - HUH
• Peabody Terrace Graduate Commons - HUH
• Pearson Lab, Earth and Planetary Sciences - FAS
• Pound Hall 2nd Floor
• Quad Reorganization - HMS
• Rockefeller Hall - HDS
• Ruth Mulan Chu Chao Center - HBS
• Schlesinger Library - RAD
• Schreiber Lab - FAS
• Science Center - Cabot Library Project - FAS
• Shared Laboratory HEB - FAS
• Sherman Fairchild Laboratory - FAS
• Smith Campus Center Floor 4 - FAS
• Soldiers Field Park Building 4 - HUH
• Somerville Art Storage - HAM
• Spangler Kitchen - HBS
• Stubbs Lab - Gordon McKay Building - FAS
• Tata Hall - HBS
• Teaching Labs - Northwest Labs - FAS
• The Memorial Church - HRE
• Tozzer Library - FAS
• Vanserg/Shannon Child Care Renovation
• Vecitis Labs - SEAS
• Vlassak Lab - SEAS
• Weitz Lab - SEAS
• Wiener Auditorium - HKS
• William James Hall - 10th Floor - FAS
• Women and Public Policy - HKS
• Zero Arrow - HRE
• Zhuang Lab - FAS

As part of Harvard’s effort to share lessons learned from our own experiences, the  Green Building Standards require all capital projects to create a case study using our standard template and post it on this site.

The case studies are organized by rating system (LEED-NC, LEED-CI, etc.) and contain information on everything from design processes to materials and systems incorporated into the facility.

To ensure consistency, please engage Green Building Services at  [email protected] to complete a Project Profile for posting.

Andover Classrooms - HDS

• Environmental Health & Safety
• Green.harvard.edu

• Hispanoamérica
• Work at ArchDaily
• Terms of Use
• Privacy Policy
• Cookie Policy
• Green Building

Green Building: The Latest Architecture and News

Mvrdv reveals construction progress of the terraced lad headquarters in shanghai.

In 2021, MVRDV unveiled the design of a terraced office building created for the agriculture company Lankuaikei. Set within a rapidly developing area of Shanghai , the 11-storey structure is covered by a curved technological roof that follows the stepping structure. The project is conceived as a showcase of the company's vision of food production, with an extensive sustainability agenda encompassing various strategies. These include extensive use of greenery, integration of renewable energy, and the use of low-carbon materials. The construction process is now captured by StudioSZ Photo / Justin Szeremeta, revealing an intermediary state where the bare-bone structure begins to reveal the shape and scale of the building. Structural construction details are also visible at this stage,

• Read more »

MVRDV Announces New Residential Complex for Tencent’s Campus in Shenzhen, China

MVRDV has revealed a large-scale residential complex to take shape as part of a new smart city campus built by technology company Tencent in Qianhai Bay , Shenzhen , China . MVRDV’s intervention, named Tencent P5, is comprised of 11 apartment towers arranged around four courtyards. The project also includes amenities such as an adjacent kindergarten, to offer all the necessary facilities for the company’s employees. Construction began in early 2022 and is scheduled for completion in 2024.

Thatched Roofs: History, Performance and Possibilities in Architecture

At first glance, Dorte Mandrup's design for the Wadden Sea Center seems to mimic the landscape. Its low height, its horizontal lines and, above all, its materiality make it a modern building in perfect harmony with the local nature. But its connection also encompasses the built heritage of the region, more specifically because of its covering with straw, harvested and dried close to the land. This is an extremely traditional and historic building technique, but which is rarely attributed to contemporary buildings. In this article we will rescue some of the history of this natural material, its constructive characteristics and some examples of use.

The Future Beneath Our Feet: Soil-Cement Bricks and the Path to Sustainable Construction

Bricks are part of the collective imagination when thinking about construction. These are elementary, ubiquitous, modular, light, and reliable materials for erecting buildings. However, traditional ceramic block manufacturing relies on burning clay in kilns at high temperatures, often powered by non-renewable fossil fuels such as coal or natural gas. Furthermore, the transportation process significantly increases its environmental footprint, as the materials are heavy and bulky. In light of this, there is a growing interest in alternative construction materials that offer a lower environmental impact and greater sustainability. Soil cement bricks –or Compressed Stabilized Earth Blocks– are a good example of an existing alternative, as they have a smaller environmental footprint due to their use of local raw materials and the elimination of the burning process, while maintaining many of the intrinsic qualities of traditional bricks.

Bill McKibben on COP28, Maintaining Hope, and Walking in the Woods

This article was originally published on Common Edge.

The biennale UN climate conference, COP28 , concluded in Dubai this week with a commitment to the eventual “phasing out” of fossil fuels. It was a classic glass-half-empty/glass-half-full gesture. Yes, as optimists pointed out, it was the first time any reference to moving away from fossil fuels had made it into the text of the final communique. But, like previous COPs, this resolution, too, is nonbinding and was reached over howls of protest from both oil-producing countries and developing countries reliant on existing energy supply chains for future growth. The tortuous nature of the outcome, watered down and officially toothless, left me feeling glum. If we can’t agree on the nature of the problem, it will be exceptionally difficult to fix it.

To offer perspective, I reached out to longtime activist Bill McKibben. A professor at Middlebury College, he has published 20 books; his first,  The End of Nature , appeared in 1989. He was, along with Dr. James Hansen, one of the first to sound the climate alarm. McKibbin is a contributing writer to the  New Yorker , and a founder of Third Act, which organizes people over the age of 60 to work on climate and racial justice. In collaboration with seven Middlebury students, he founded 350.org, the first global grassroots climate campaign.

Are Carbon-Neutral Buildings Expensive?

Decarbonization of the building sector is no longer a choice but a necessity. As nations strive to curb their greenhouse gas emissions by 2050, it is increasingly clear that current building standards do not go far enough to drive tangible change. Achieving climate goals requires economies to advocate measures that drive carbon neutrality while managing associated costs effectively. How would net-zero performance strategies impact building costs?

Architects Must Address the Issue of Toxic Building Materials

This article was originally published on Common Edge .

By the time I was 17 years old, I had moved 11 times. Because of my own experience relocating from one place to another, I’ve spent the better part of the last several decades focused on making sure that everyone has a place to call home, that everyone enjoys the human right to housing. But it was not until my time at Enterprise Community Partners, a nonprofit focused on community development and affordable housing, that I realized the methods and materials we employ to realize that human right matter. 

Embodied Carbon in Real Estate: The Hidden Contributor to Climate Change

The window for solving climate change is narrowing; any solution must include embodied carbon. The Sixth Assessment Report published by the IPCC (Intergovernmental Panel on Climate Change) concludes that the world can emit just 500 gigatonnes more of carbon dioxide , starting in January 2020, if we want a 50 percent chance of staying below 1.5 degrees. In 2021 alone, the world emitted about 36.3 gigatonnes of carbon , the highest amount ever recorded. We’re on track to blow through our carbon budget in the next several years. To quote the IPCC directly: “The choices and actions implemented in this decade will have impacts now and for thousands of years (high confidence).”

Archi-Tectonics' Asian Games Park Rethinks Hangzhou's Ecological Future in China

In 2018, Archi-Tectonics NYC and !Melk were announced as the winners of a competition to develop a masterplan transformation for the Hangzhou Asian Games Park 2022 . Spanning 116 Acres, the now-completed project includes an expansive Eco Park and seven buildings. Although its initial purpose was to serve as a venue for the Hangzhou Asian Games 2022, the team extended its vision far beyond this event, charting a new path for the city’s environmental future.

Zaha Hadid Architects Unveils the Design of the Daxia Tower in China

Zaha Hadid Architects has revealed the design of the Daxia Tower, to be built in the High-Tech Economic and Technological Development Zone of Xi’an, one of China ’s largest inland cities with a population nearing nine million people. The tower will mark the center of Xi’an’s business district and will include offices, retail, and ancillary facilities, all designed with data analytics and behavior modeling to ensure a balanced disposition of spaces.

The Story of the World's Largest Floating Plastic Island (and What to Do With It)

Environmental issues urgency and increasing temperatures on the planet are nothing new. There are many factors contributing to environmental degradation. However, two can be viewed as representative of critical points in the current world system: plastic and waste disposal, better known as garbage.

The environmental crisis cannot be attributed solely to these two examples. They are used here as examples to mobilize issues involving multiple agents, materials, and diverse methods. These issues lead to devastating consequences, increasingly irreversible.

Why the Global South Needs Different Sustainability Benchmarks

As world governments grapple with environmental crises, the construction industry rushes to reevaluate sustainable design and develop new ways of measuring its efficiency. Consequently, green building certification systems (GBCS) started gaining traction in the 20th century to evaluate and promote sustainable construction practices. The Global South faces distinctive challenges in building sustainable cities. Its developing nations demand an exclusive approach to designing an appropriate, economical, and inspiring architecture for their promising futures.

The Bioclimatic Skyscraper: Kenneth Yeang's Eco-Design Strategies

Rising over global cities, the modern skyscraper has long been a symbol of economic growth and environmental decline. For years, they have been reviled by environmentalists for being uncontrolled energy consumers . Malaysian architect Kenneth Yeang acknowledged the skyscraper as a necessity in modern cities and adopted a pragmatic approach to greening the otherwise unsustainable building typology. Yeang’s bioclimatic skyscrapers blend the economics of space with sustainability and improved living standards.

Stefano Boeri Architetti Unveils Vertical Forest Prototype at COP27

Stefano Boeri Architetti presented a new design for the Vertical Forest towers during COP27 in Sharm El-Sheikh, Egypt. The prototype would be in Dubai , the most populous city in the United Arab Emirates (UAE), and the next host of COP28 in 2023. The ambitious project would represent the first Vertical Forest prototype for the MENA (Middle East and North Africa), and it is the latest in an extended list of greenery-covered buildings by Boeri Architetti, including the Bosco Verticale in Milan, the Easyhome Huanggang in China, and a prototype of the First Dutch Vertical Forest .

The Future of Green Buildings with Exterior Insulation Finishing Systems

Construction practices across the world, as well as the types and uses of building materials, have been identified as key factors that impact global warming. Studies have shown that the building sector will play a central role in achieving the UN Climate Change Conference (COP26) CO2 emissions reduction targets for 2030 and net zero CO2 emissions by 2050.

The construction sector's support for the achievement of these targets must focus on sustainable construction, which entails environmentally-friendly structures that consume less energy and have smaller or even net zero carbon footprints.

Green buildings are structures that, in their design, construction or operation, reduce or eliminate negative impacts on our climate and natural environment. They preserve precious natural resources and improve quality of life.

UNStudio Designs Tower in Germany, Focusing on Environmental and Social Sustainability

Incorporating the Environmental, social, and corporate governance objectives, the 45,000 m2 Office Tower in the Europaviertel in Frankfurt aims to be one of Germany's most sustainable office buildings. Designed by UNStudio in partnership with Groß & Partner in collaboration with OKRA landscape architects, the project focuses on environmental and social sustainability as an integral part of Frankfurt's green network. The ecological agenda includes a low-carbon load-bearing structure and recyclable construction materials. The architecture program offers a public urban space to add value to its surroundings to encourage communication and gathering.

Lo-TEK: Reclaiming Indigenous Techniques to Work with Nature

"Indigenous technologies are not lost or forgotten, only hidden by the shadow of progress in the most remote places on Earth". In her book Lo-TEK: design by radical indigenism , Julia Watson proposes to revalue the techniques of construction, production, cultivation and extraction carried out by diverse remote populations who, generation after generation, have managed to keep alive ancestral cultural practices integrated with nature, with a low environmental cost and simple execution. While modern societies tried to conquer nature in the name of progress, these indigenous cultures worked in collaboration with nature, understanding ecosystems and species cycles to articulate their architecture into an integrated and symbiotically interconnected whole.

“Soft Infrastructure” Is Crucial for a Post-Carbon World

On a recent day in Santa Monica, California, visitors sat in the shaded courtyard outside City Hall East waiting for appointments. One of them ate a slice of the orange she’d picked from the tree above her and contemplated the paintings, photographs, and assemblages on the other side of the glass. The exhibit, Lives that Bind , featured local artists’ expressions of erasure and underrepresentation in Santa Monica’s past. It’s part of an effort by the city government to use the new soon-to-be certified Living Building (designed by Frederick Fisher and Partners ) as a catalyst for building a community that is environmentally, socially, and economically self-sustaining.

To read this content please select one of the options below:

Please note you do not have access to teaching notes, a case study on the implementation of green building construction in gauteng province, south africa.

Management of Environmental Quality

ISSN : 1477-7835

Article publication date: 21 February 2020

Issue publication date: 16 April 2020

Green building is a relatively new concept with limited applications in property development in South Africa. The objectives of this study are therefore threefold: identify key green building principles considered by property developers, establish the benefits of implementing the principles and determine the barriers to its applications.

Design/methodology/approach

The study adopted a case study of two Green Star South Africa (SA)-certified buildings in Sandton, Johannesburg. These are Alexander Forbes building, and Ernst & Young Eris Towers. The two certified buildings were purposefully selected because of the insightful information they provide regarding application of green building principles. The main themes investigated in the cases are environmental awareness, green building principles applications, as well as benefits and barriers of green building. A total of six interviewees from the contractors', property developers', environmental/green building consultants' and sustainability consultants' organizations who were involved in the implementation of green building principles in the two cases provided the qualitative data for the study. The qualitative data were supplemented with data relating to the two case studies obtained from the ‘Earth Works for a Sustainable Built Environment’. The interviews were arranged over a period of two months, and each interview took between 20 and 30 minutes. Analysis of the data was done through a phenomenological interpretation of the qualitative opinions expressed by the interviewees.

Key green building principles comprising energy efficiency, water efficiency, resource efficiency, occupants' health and well-being and sustainable site development were implemented in the two cases. The fact that the buildings were rated 4-star enabled inference to be drawn that the implementation of the principles was less than 60 per cent. Energy efficiency of 35 per cent indicated in Case I suggests that the level is consistent with the South African green building standard of 25 per cent to 50 per cent. However, the energy and water efficiency assessment of the building were based on projections rather than on ongoing monitoring and evaluation of the buildings' performance. Moreover, perceived saving in operational cost was identified as dominant driver to green building principles implementation. Conversely, lack of government incentives and absence of reliable benchmarking data regarding performance of green buildings were major barriers to its full implementation.

Practical implications

The findings of this study provide important implications to the developers and government on the application of green building principles. In the first place, the evidence that initial high cost premium could be off settled by long- term saving on operational costs as a result of use of local materials, energy and water savings as well as use of recycled material, as implemented in the two case projects, would improve investment decision in green building by developers. The understanding of the drivers and barriers to implementation of green building principles also has implications for guiding government policies and programmes towards green building.

Originality/value

The significance of this study stems from the fact that limited studies, especially in the South African context, have indicated the drivers and barriers to the implementation of green building principles. The case study approach adopted gave a novelty to the study by providing hands-on information from the stakeholders who were known to have played specific roles in the application of green building. The findings indicated that initial high cost premium was not a consideration in developers' choice of green building which justifies the possibility of a costlier product when factors such as environmental sustainability benefit is considered to be ultimate. The study thus suggests further research involving larger cases on energy efficiency, water efficiency and costs of green buildings compared to the conventional type to bring the findings to a broader perspective and assist to benchmark data for green building assessment.

• Construction
• Environment
• Green building
• Sustainability
• Property development

Masia, T. , Kajimo-Shakantu, K. and Opawole, A. (2020), "A case study on the implementation of green building construction in Gauteng province, South Africa", Management of Environmental Quality , Vol. 31 No. 3, pp. 602-623. https://doi.org/10.1108/MEQ-04-2019-0085

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

Related articles

All feedback is valuable.

Please share your general feedback

Report an issue or find answers to frequently asked questions

Contact Customer Support

Featured Buildings

One Bush Street

Certifications

• Energy Star
• Enterprise Green Communities
• LEED Canada
• Living Building Challenge
• AIA/COTE Top Ten
• ED Green Ribbon Schools
• GSA Design Awards
• Cities and Countries
• EPA Regions
• USGBC chapters
• Utility Providers
• Organizational Leaders

Code Green Solutions

• APs and Assessors
• Commissioning Agents
• Consultants
• Facility Managers
• General Contractors
• Project Managers

Organizations

• Image Masking Services
• mediaduplicationsystem
• Go Get Appliances
• Administrative Mba Degrees
• Professional Nursing Degrees
• Devstringx Technologies
• Trademarkely, Trademark Registration Services, Trademark Registration
• Global Higher Education Accreditation Board

The Economic Benefits of Building Commissioning

• Economics & Finance
• Energy & Carbon
• Government & Policy
• Health & Wellbeing
• Market Trends & Updates
• Materials & Resources
• Resilience & Adaptation
• Social Equity
• Sustainable Cities
• Water Conservation

Contributors

• Christopher Wedding
• Dan Winters
• Heather Joy Rosenberg

Case Studies

Leed projects with buildinggreen case studies.

169 Buildings

Gsa leed case studies, 330,661 buildings, nrdc case studies, 363,087 buildings, city of oakland green building case studies.

BuildingGreen Case Studies

Harvard University Green Building Case Studies

Seattle 2030 District Case Studies

Campus Green Builder Case Studies

StopWaste.org Case Studies

Enermodal Case Studies

Institute for Market Transformation (IMT) Case Studies

Center for Active Design Case Studies

Forum on Urban Agriculture Case Studies

NABERS Case Studies

Greensource Case Studies

Green building council australia case studies.

Jetsgroup Case Studies

NBI Deep Energy Savings in Existing Buildings Case Study

NBI Zero-Net Energy Case Studies

Advanced Lighting Guidelines Case Studies

Seattle Energy Benchmarking and Reporting Case Studies

BetterBricks Case Studies

Rocky Mountain Institute Case Studies

Austin Green Roof Case Studies

Loading results...

• Collections
• Advanced Search
• Terms of use
• End User License Agreement
• Privacy Policy
• Cookie Statment

This website collects and uses cookies to ensure you get the best user experience. Please click on "Accept and Close" button to affirm your consent and continue to use our website. For more information, please view our Cookies Statement .

National Green Building Standard Certification Case Studies

While high performance building practices can cost more, many experienced green builders say that an NGBS Certified home costs the same to build as any other home, especially when incentives and rebate programs designed to save home owners money and use less energy are taken into account.

With these case studies, you can take a closer look at single-family, multifamily and remodeled NGBS Certified homes to see what design practices and construction techniques the project teams, as well as any lessons learned.

Multifamily

• Liberty Pass
• Summit Parque

Single Family

• Salmon Creek Net Zero
• Seville 2015 NGBS
• Spring Residence
• Norcross Remodel

Communities

• Bellewood Manor
• Capitol Heights
• Coldwater Reserve
• Lakes of Orange
• Linfield Crossing
• Rivera Greens
• The Village at Burns Harbor
• Warwick Grove
• Wharton's Bluff

If you would like to have your NGBS Certified project added as a case study, please contact us . And for more information on how to certify a project to the ICC/ASHRAE 700-2015 National Green Building Standard, visit homeinnovation.com/green .

NAHB Community

NAHB serves the entire home building, development and remodeling industry.

Explore community

NAHB fights for our members on Capitol Hill, in your state and in the communities where you do business.

About advocacy

Education topics include business management, multifamily, housing and more.

More about education

The NAHB website uses cookies. We do this to provide a personalized experience, improve our content, and monitor the site performance. By clicking Accept you consent to NAHB’s use of cookies in accordance with its Privacy Policy . To learn more and control what cookies are used, go to Manage Cookies .

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Optimization of green building design processes: case studies within the european union.

1. Introduction

2. research goals.

• Identify a specific gap within the current body of knowledge;
• Define a feasible and adequate case study protocol to carry out this research;
• Identify and analyze a satisfactory number of case studies to develop the research;
• Categorize the project management issues affecting the green building design process.
• Evaluate the impact of each project management issues on design processes;
• Investigate the relationship between process integration (Design-Bid-Build vs. Design-Build) and green building design development.

3. Research Method

3.1. overall approach.

• Literature review: To identify the knowledge gap;
• Pilot case study: To define a feasible research protocol;
• Selection of case studies: To identify suitable case studies;
• Case study analysis: To analyze the case studies and to categorize the project management issues affecting the green building design process;
• Cross-case analysis: To identify the impact of each project management issues on Design-Build and Design-Bid-Build processes;
• Conclusions: To highlight the contributions, limitations, and recommendations of the research.

3.2. Pilot Case Study

• Lack integration between the technicians involved;
• Misunderstanding of Commissioning Authority’s tasks and processes;
• Lack of appropriate clauses in bid documentation;
• Systematic cuts to budget due to change orders and delays;
• Misunderstanding of the energy modeling role and processes in the building.

3.3. Selection of other Case Studies and Data Collection

• Newly developed for tertiary-sector activities;
• Budget ranging between 5 and 15 million Euros;
• Footprint ranging between 2000 and 10,000 m 2 ;
• Under certification by LEED or BREEAM;
• Comply with European Union directives;
• Accessibility to the stakeholders involved in the design phase.
• School complex located in Trento (Italy), certified by LEED [ 31 ], with a total budget of approximately 13.2 Million Euros and a total gross footprint of 6000 m 2 . This was the pilot case study;
• Nursing home complex located in Volano (Italy), certified by LEED [ 31 ], with a total budget of approximately 11.0 million Euros and a total gross footprint of 5965 m 2 ;
• Office building located in Barcelona (Spain), certified by LEED [ 31 ], with a total budget of approximately 7.5 million Euros and a total gross square footprint of 3000 m 2 ;
• Office building located in Alicante (Spain), certified by BREEAM [ 32 ], with a total budget of approximately 14.0 million Euros and a total gross square footprint of 5885 m 2 .

3.4. Case Study Analysis

• Working days for the time variance: Considered as the additional working days of delay for the completion of critical and non-critical activities caused by project management issues;
• Euros for the cost variance: Considered as the extra costs paid by the owner and by all stakeholders involved caused by project management issues;
• LEED or BREEAM points for the sustainability variance: Considered as the unsuccessful achievement of the original green building score expected at the beginning of the project due to project management issues.

3.5. Cross-Case Study

4. results and discussion, 4.1. case study analysis.

• Lack of integration between the technicians involved and bad-timing for green building activities: The project design team was formed by veteran technicians accustomed to developing the project following the development process ruled by the Italian legislation, already based on EU regulations. LEED imposed the overlap of project activities through a more integrated process, generating problems between technicians [ 1 , 26 ], as everyone had to participate in each other’s part of the project. This fact, along with other misunderstandings, generated friction between the participants involved, slowing down the whole design process [ 19 , 29 ], and threatening the achievement of the LEED credits.
• Misunderstanding of Commissioning Authority’s activities and process: The Commissioning Authority is, in brief, a consultant hired by the owner, responsible for ensuring that the design and construction of the mechanical components comply with the owner’s requirements and expectations [ 3 ]. The design team leader did not bring in the Commissioning Authority until the very last phases of the design. Thus, the design was not exposed to the analysis of the Commissioning Authority until the end of the design, when all shop drawings, estimates, bid specifications, and related documents had already been approved and closed. Within the European system, project-related documents, estimates, and specifications are developed by designers, not by general contractors. To avoid change orders during the construction phase, the Commissioning Authority should always be hired during the design process. This did not happen in the nursing home project (#2) where, for example, the Commissioning Authority could not insert proper clauses for the activities that had to be performed during the construction stage, causing an estimated extra cost of 30,000 Euros during the construction phase.
• No appropriate clauses in bid documentation: Poor bid clauses refer to sustainability-related issues that were detected in some case studies. Because of the owner’s inexperience or technician’s misunderstanding, not all aspects of sustainability were properly assessed during bid clause formulation [ 1 , 24 ]. For example, in one case, the clauses related to the production of LEED documentation were not considered, which led to an additional cost of 30,000 Euros claimed by the design company (case #2). In case #1, the development of inexact clauses, such as the reference to the wrong standard, led to the redefinition of the whole bid documentation, with an added cost of 5000 Euros for consulting services and bureaucracy.
• Systematic cuts to budget due to change orders and delays: The more delays that affect the design phase, the higher the costs of material, labor, and equipment increase, and, consequently, the shorter the budget becomes. As a result, for projects suffering severe delays at each design step, the team had to apply cuts and re-define the original design, which also affected the green building points of the project [ 23 ].
• Misunderstanding of the energy modeling role and process: As for the Commissioning Authority, technicians did not quite understand the importance and the development process of the energy modeling until the final design phases [ 10 ]. In some cases (#1 and #2), energy modeling was not considered in the initial bid clauses due to the owner’s decision, thus, no one was formally appointed as Energy Modeler when the contract was signed. Mechanical engineers took over the task during the design phase, but they did not have experience in developing energy modeling for LEED. By the end of the final design stage, technicians realized they were not able to do it. An external professional Energy Modeler was contracted by the engineering firm with an additional cost of 10,000 Euros. However, by the time the simulation was ready, the final design had already been approved, along with the project estimate, and the construction bid had already been published. The energy simulation did not match the expected results [ 10 ]. However, no changes could be made as the project had already been approved and bid out. This problem, apart from generating extra costs during the design process, avoided the achievement of several points under the energy-efficiency credit.

4.2. Cross-Case Analysis

4.2.1. first dependent variable: time, 4.2.2. second dependent variable: cost.

• The level of integration within a Design-Bid-Build process affects the cost variance of the design phase from a non-linear perspective;
• For a Design-Bid-Build process, the cost variance results are lower in terms of absolute values for projects implementing a higher level of integration;
• For a Design-Bid-Build process, the cost variance results are higher in terms of percentages for small projects even when implementing a higher level of integration;
• For a Design-Build process, the cost variance resulted to be zero.

4.2.3. Third Dependent Variable: Sustainability

4.2.4. cross-case analysis: general considerations, 5. conclusions.

• Green building activities overlap with regular project management activities, generally with bad timing. This causes misunderstanding between technicians, and it reflects a lack of integration of the design team;
• Change orders and delays cause systematic cuts to budget due and re-definition of the original design, affecting the green building points of the project too;
• There is a lack of appropriate clauses regarding sustainability in bid documentation. This often leads to the redefinition of the documents, adding cost due to consulting services and bureaucracy;
• Key stakeholders are hired very late in the process. The main examples are the Energy Modeler and the Commissioning Authority. Thus, verification that the energy model and the mechanical components comply with the owner’s requirements cannot be done properly during the design phase.

Author Contributions

Acknowledgments, conflicts of interest.

• Marcelino-Sádaba, S.; González-Jaén, L.F.; Pérez-Ezcurdia, A. Using project management as a way to sustainability. From a comprehensive review to a framework definition. J. Clean. Prod. 2015 , 99 , 1–16. [ Google Scholar ] [ CrossRef ]
• Sierra, L.A.; Yepes, V.; Pellicer, E. A review of multi-criteria assessment of the social sustainability of infrastructures. J. Clean. Prod. 2018 , 187 , 496–513. [ Google Scholar ] [ CrossRef ]
• Wu, P.; Low, S.P. Project management and green buildings: Lessons from the rating systems. J. Prof. Issues Eng. Educ. Pract. 2010 , 136 , 64–70. [ Google Scholar ] [ CrossRef ]
• Illankoon, I.C.S.; Tam, V.W.; Le, K.N.; Shen, L. Key credit criteria among international green building rating tools. J. Clean. Prod. 2017 , 164 , 209–220. [ Google Scholar ] [ CrossRef ]
• Lu, Y.; Wu, Z.; Chang, R.; Li, Y. Building Information Modeling (BIM) for green buildings: A critical review and future directions. Autom. Constr. 2017 , 83 , 134–148. [ Google Scholar ] [ CrossRef ]
• Zavadskas, E.; Antucheviciene, J.; Vilutiene, T.; Adeli, H. Sustainable decision-making in civil engineering, construction and building technology. Sustainability 2018 , 10 , 14. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Venkataraman, V.; Cheng, J.C. Critical success and failure factors for managing green building projects. J. Archit. Eng. 2018 , 24 , 04018025. [ Google Scholar ] [ CrossRef ]
• Darko, A.; Chan, A.P.; Huo, X.; Owusu-Manu, D.G. A scientometric analysis and visualization of global green building research. Build. Environ. 2019 , 149 , 501–511. [ Google Scholar ] [ CrossRef ]
• Horman, M.J.; Riley, D.R.; Lapinski, A.R.; Korkmaz, S.; Pulaski, M.H.; Magent, C.S.; Luo, Y.; Harding, N.; Dahl, P.K. Delivering green buildings: Process improvements for sustainable construction. J. Green Build. 2006 , 1 , 123–140. [ Google Scholar ] [ CrossRef ]
• Gerrish, T.; Ruikar, K.; Cook, M.; Johnson, M.; Phillip, M.; Lowry, C. BIM application to building energy performance visualisation and management: Challenges and potential. Energy Build. 2017 , 144 , 218–228. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Robichaud, L.B.; Anantatmula, V.S. Greening project management practices for sustainable construction. J. Manag. Eng. 2011 , 27 , 48–57. [ Google Scholar ] [ CrossRef ]
• Wong, J.K.W.; Zhou, J. Enhancing environmental sustainability over building life cycles through green BIM: A review. Autom. Constr. 2015 , 57 , 156–165. [ Google Scholar ] [ CrossRef ]
• Sertyesilisik, B. Lean and agile construction project management: As a way of reducing environmental footprint of the construction industry. In Optimization and Control Methods in Industrial Engineering and Construction. Intelligent Systems, Control and Automation: Science and Engineering ; Xu, H., Wang, X., Eds.; Springer: Dordrecht, The Netherlands, 2014; Volume 72, pp. 179–196. [ Google Scholar ]
• Lapinski, A.R.; Horman, M.J.; Riley, D.R. Lean processes for sustainable project delivery. J. Constr. Eng. Manag. 2006 , 132 , 1083–1091. [ Google Scholar ] [ CrossRef ]
• Riley, D.R.; Grommes, A.V.; Thatcher, C.E. Teaching sustainability in building design and engineering. J. Green Build. 2007 , 2 , 175–195. [ Google Scholar ] [ CrossRef ]
• Fercoq, A.; Lamouri, S.; Carbone, V. Lean/Green integration focused on waste reduction techniques. J. Clean. Prod. 2016 , 137 , 567–578. [ Google Scholar ] [ CrossRef ]
• Hwang, B.G.; Zhu, L.; Ming, J.T.T. Factors affecting productivity in green building construction projects: The case of Singapore. J. Manag. Eng. 2017 , 33 , 04016052. [ Google Scholar ] [ CrossRef ]
• PMI. A Guide to the Project Management Body of Knowledge (PMBOK Guide) , 6th ed.; Project Management Institute: Newtown Square, PA, USA, 2017. [ Google Scholar ]
• Zhao, X.; Hwang, B.G.; Lee, H.N. Identifying critical leadership styles of project managers for green building projects. Int. J. Constr. Manag. 2016 , 16 , 150–160. [ Google Scholar ] [ CrossRef ]
• Sang, P.; Liu, J.; Zhang, L.; Zheng, L.; Yao, H.; Wang, Y. Effects of project manager competency on green construction performance: The Chinese context. Sustainability 2018 , 10 , 3406. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wu, W.; Issa, R.R. BIM execution planning in green building projects: LEED as a use case. J. Manag. Eng. 2014 , 31 , A4014007. [ Google Scholar ] [ CrossRef ]
• Weerasinghe, A.S.; Ramachandra, T. Economic sustainability of green buildings: A comparative analysis of green vs non-green. Built Environ. Proj. Asset Manag. 2018 , 8 , 528–543. [ Google Scholar ] [ CrossRef ]
• Hwang, B.G.; Zhao, X.; Tan, L.L.G. Green building projects: Schedule performance, influential factors and solutions. Eng. Constr. Archit. Manag. 2015 , 22 , 327–346. [ Google Scholar ] [ CrossRef ]
• Molenaar, K.R.; Sobin, N.; Antillón, E.I. A synthesis of best-value procurement practices for sustainable design-build projects in the public sector. J. Green Build. 2010 , 5 , 148–157. [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Wu, Y.; Shen, L. Embedding “green” in project-based organizations: The way ahead in the construction industry? J. Clean. Prod. 2015 , 107 , 420–427. [ Google Scholar ] [ CrossRef ]
• Azari, R.; Kim, Y.W. Integration evaluation framework for integrated design teams of green buildings: Development and validation. J. Manag. Eng. 2015 , 32 , 04015053. [ Google Scholar ] [ CrossRef ]
• Korkmaz, S.; Messner, J.I.; Riley, D.R.; Magent, C. High-performance green building design process modeling and integrated use of visualization tools. J. Archit. Eng. 2010 , 16 , 37–45. [ Google Scholar ] [ CrossRef ]
• Russell-Smith, S.V.; Lepech, M.D.; Fruchter, R.; Littman, A. Impact of progressive sustainable target value assessment on building design decisions. Build. Environ. 2015 , 85 , 52–60. [ Google Scholar ] [ CrossRef ]
• Knotten, V.; Lædre, O.; Hansen, G.K. Building design management–key success factors. Archit. Eng. Des. Manag. 2017 , 13 , 479–493. [ Google Scholar ] [ CrossRef ]
• Doan, D.T.; Ghaffarianhoseini, A.; Naismith, N.; Zhang, T.; Ghaffarianhoseini, A.; Tookey, J. A critical comparison of green building rating systems. Build. Environ. 2017 , 123 , 243–260. [ Google Scholar ] [ CrossRef ]
• LEED. LEED Reference Guide for Green Building Design and Construction ; United States Green Building Council Institute: Washington, DC, USA, 2009. [ Google Scholar ]
• BREEAM. BREEAM International New Construction ; BRE Global Ltd.: Wtford, UK, 2016. [ Google Scholar ]
• Dahlmann, F.; Veal, G. The role of umbrella agreements in achieving sustainability goals: Energy efficiency at the Empire State building. J. Green Build. 2016 , 11 , 71–94. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Guy, S.; Moore, S.A. Sustainable Architectures: Critical Explorations of Green Building Practice in Europe and North America ; Routledge: New York, NY, USA, 2004. [ Google Scholar ]
• Winch, G. Managing Construction Projects , 2nd ed.; Wiley: Oxford, UK, 2010. [ Google Scholar ]
• Rubio, M.C.; Martinez, G.; Rubio, J.C.; Ordoñez, J. Role of the civil engineer as a coordinator of safety and health matters within the construction sector. J. Prof. Issues Eng. Educ. Pract. 2008 , 134 , 152–157. [ Google Scholar ] [ CrossRef ]
• Pellicer, E.; Yepes, V.; Teixeira, J.C.; Moura, H.; Catalá, J. Construction Management ; Wiley: Oxford, UK, 2014. [ Google Scholar ]
• Pellicer, E.; Victory, R. Implementation of project management principles in Spanish residential developments. Int. J. Strateg. Prop. Manag. 2006 , 10 , 233–248. [ Google Scholar ] [ CrossRef ]
• Pellicer, E.; Sanz, M.A.; Esmaeili, B.; Molenaar, K.R. Exploration of team integration in Spanish multifamily residential building construction. J. Manag. Eng. 2016 , 32 , 05016012. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Miles, M.B.; Huberman, A.M.; Saldaña, J. Qualitative Data Analysis: A Methods Sourcebook ; Sage Pub.: New York, NY, USA, 2013. [ Google Scholar ]
• Yin, R.K. Case Study Research: Design and Methods ; Sage Pub.: Thousand Oaks, CA, USA, 2013. [ Google Scholar ]
• Liker, J. The Toyota Way: 14 Management Principles from the World’s Greatest Manufacturer ; McGraw Hill: New York, NY, USA, 2004. [ Google Scholar ]
• Ko, C.H.; Chung, N.F. Lean design process. J. Constr. Eng. Manag. 2014 , 140 , 04014011. [ Google Scholar ] [ CrossRef ]
• Salgin, B.; Arroyo, P.; Ballard, G. Exploring the relationship between lean design methods and C&D waste reduction: Three case studies of hospital projects in California. Rev. Ing. De Constr. 2016 , 31 , 191–200. [ Google Scholar ]
• Wesz, J.G.B.; Formoso, C.T.; Tzortzopoulos, P. Planning and controlling design in engineered-to-order prefabricated building systems. Eng. Constr. Archit. Manag. 2018 , 25 , 134–152. [ Google Scholar ] [ CrossRef ]
• Woodside, A.G. Case Study Research: Theory, Methods, Practice ; Emerald Group Pub.: Bingley, UK, 2010. [ Google Scholar ]
• Ortiz-González, J.I.; Pellicer, E.; Molenaar, K.R. Determining contingencies in the management of construction projects. Proj. Manag. J. 2019 , 50 , 1–17. [ Google Scholar ]
• Ortiz, J.I.; Pellicer, E.; Molenaar, K.R. Management of time and cost contingencies in construction projects: A contractor perspective. J. Civ. Eng. Manag. 2018 , 24 , 254–264. [ Google Scholar ] [ CrossRef ]
• Senthilkumar, V.; Varghese, K. Case study–based testing of design interface management system. J. Manag. Eng. 2013 , 29 , 279–288. [ Google Scholar ] [ CrossRef ]
• Barratt, M.; Choi, T.Y.; Li, M. Qualitative case studies in operations management: Trends, research outcomes, future research implications. J. Oper. Manag. 2011 , 29 , 329–342. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Firouzi, A.; Yang, W.; Li, C.Q. Prediction of total cost of construction project with dependent cost items. J. Constr. Eng. Manag. 2016 , 142 , 04016072. [ Google Scholar ] [ CrossRef ]
• Antillon, E.I.; Garvin, M.J.; Molenaar, K.R.; Javernick-Will, A. Influence of inter-organizational coordination on lifecycle design decision making: Comparative case study of public–private partnership highway projects. J. Manag. Eng. 2018 , 34 , 05018007. [ Google Scholar ] [ CrossRef ]
• Kang, C.; Choi, B.C. An adaptive crashing policy for stochastic time-cost tradeoff problems. Comput. Oper. Res. 2015 , 63 , 1–6. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Orsi, A.; Guillén-Guillamón, I.; Pellicer, E. Optimization of Green Building Design Processes: Case Studies within the European Union. Sustainability 2020 , 12 , 2276. https://doi.org/10.3390/su12062276

Orsi A, Guillén-Guillamón I, Pellicer E. Optimization of Green Building Design Processes: Case Studies within the European Union. Sustainability . 2020; 12(6):2276. https://doi.org/10.3390/su12062276

Orsi, Alessandro, Ignacio Guillén-Guillamón, and Eugenio Pellicer. 2020. "Optimization of Green Building Design Processes: Case Studies within the European Union" Sustainability 12, no. 6: 2276. https://doi.org/10.3390/su12062276

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 02 September 2024

Integrating machine and deep learning technologies in green buildings for enhanced energy efficiency and environmental sustainability

• Shahid Mahmood 1 ,
• Huaping Sun 1 , 2 ,
• El-Sayed M. El-kenawy 3 , 6 ,
• Asifa Iqbal 4 ,
• Amal H. Alharbi 5 &
• Doaa Sami Khafaga 5  

Scientific Reports volume  14 , Article number:  20331 ( 2024 ) Cite this article

42 Accesses

1 Altmetric

Metrics details

• Energy and society
• Sustainability

A green building (GB) is a design idea that integrates environmentally conscious technology and sustainable procedures throughout the building’s life cycle. However, because different green requirements and performances are integrated into the building design, the GB design procedure typically takes longer than conventional structures. Machine learning (ML) and other advanced artificial intelligence (AI), such as DL techniques, are frequently utilized to assist designers in completing their work more quickly and precisely. Therefore, this study aims to develop a GB design predictive model utilizing ML and DL techniques to optimize resource consumption, improve occupant comfort, and lessen the environmental effect of the built environment of the GB design process. A dataset ASHARE-884 is applied to the suggested models. An Exploratory Data Analysis (EDA) is applied, which involves cleaning, sorting, and converting the category data into numerical values utilizing label encoding. In data preprocessing, the Z-Score normalization technique is applied to normalize the data. After data analysis and preprocessing, preprocessed data is used as input for Machine learning (ML) such as RF, DT, and Extreme GB, and Stacking and Deep Learning (DL) such as GNN, LSTM, and RNN techniques for green building design to enhance environmental sustainability by addressing different criteria of the GB design process. The performance of the proposed models is assessed using different evaluation metrics such as accuracy, precision, recall and F1-score. The experiment results indicate that the proposed GNN and LSTM models function more accurately and efficiently than conventional DL techniques for environmental sustainability in green buildings.

Similar content being viewed by others

Active learning-based machine learning approach for enhancing environmental sustainability in green building energy consumption

Grasshopper platform-assisted design optimization of fujian rural earthen buildings considering low-carbon emissions reduction

Deep learning-based forecasting of electricity consumption

Introduction.

The building and construction industry is recognized for using excessive amounts of natural resources, which has a detrimental impact on the environment 1 . Buildings and construction consume the most energy (36%) and emit the most CO2 (37%) globally 2 , 3 , 4 . The Architecture, Engineering, and Construction (AEC) industry has a significant issue in the form of sustainability, which encompasses resource efficiency. Researchers, specialists, and practitioners in the building and construction sector have endeavored to identify substitute methods for implementing energy conservation throughout the building life cycle. The green building (GB) idea is being implemented as one of the initiatives 5 , 6 .

GB is being endorsed worldwide as an approach to enhancing the building industry’s sustainability 7 . The Green Building Concept pertains to using environmentally friendly and sustainable concepts throughout the building life cycle, starting with the first stages of project development and continuing through to the decommissioning phase. It is frequently considered a method to reduce energy consumption in the building and construction industry 8 , 9 , 10 . At every project step, the contractor, the architects, the engineers, and the client must all support this. The concerns about the economy, well-being, utility, and durability associated with traditional design are lessened by green building practices. More and more people are embracing the notion of green building since it is advantageous from a financial, health, and, most importantly, environmental standpoint 11 .

The GB paradigm reduces the adverse environmental consequences that buildings and human activity within them have by introducing concepts and technology into buildings at every stage of their existence 12 . The environment may be greatly impacted by decisions made at the first stages of building design 13 . However, the design of GB is typically more difficult than conventional structures because of the different design components and building performances that need to be adjusted to accomplish sustainability ideally 14 , 15 . As a result, the GB design process may take longer than expected since it requires a multidisciplinary team project in which team members must elaborate on every GB component of the design 16 .

Technological advancements in construction have enabled digitization, automation, and integration, improving decision making and productivity in (GB) initiatives 17 , 18 , 19 . This has led to global interest and numerous empirical studies on GB’s benefits 20 , 21 . AI, which encompasses intelligent systems capable of learning and problem-solving, further enhances communication and productivity in GB design 17 , 18 . AI includes machine learning (ML) as a subset. It’s a method that gives a system the capacity to grow and learn from its own experiences without programming 22 . It has been thoroughly studied and used throughout the construction process 23 . This method was created during the building design phase to maximize the GB design’s building performance. The growth of digital technology adoption in the building design process has been greatly aided by earlier research on (ML) use in the process carried out in the last few years. The whole design process has been changed by it 24 . For example, in research 25 , an ML model to forecast dependable energy performance in office buildings was developed using the artificial neural network (ANN) approach. This model required 50 times less computing time than the industry standard building performance simulation tools.

However, it has been demonstrated that the Statistical Neural Network and Gaussian Regression techniques used by Rahman and Smith 26 to create an ML model to forecast fuel usage in a commercial building were more accurate. In addition, Geyer and Singaravel 27 created a component-based machine-learning model to forecast the thermal energy performance of office buildings by employing the ANN approach. With an error of less than 3.9%, the forecast may be generated with a significantly smaller computing time. This earlier research shows that the suggested predictive models utilizing machine learning techniques might drastically reduce the time needed for calculation during the design phase, improving the efficiency of engineers and architects creating GB. Though there have been several advancements, further proof is still required to support the use of (ML) in creating a prediction model for GB design. This study attempts to create a design prediction model for GB using the ML and DL classifiers method as one of the techniques to address this gap. This paper is expected to provide references and give insights to building practitioners regarding the utilization of the ML and DL approach to optimize resource consumption, improve occupant comfort, and lessen the environmental effect of the built environment of the GB design process, which can significantly contribute to accelerating technology-based development in the building and construction sector.

Research contribution

The following are this paper’s primary contributions:

We propose a technique for green building design by applying machine and deep learning techniques that can maximize resource use, minimize energy consumption, and reduce the built environment impact to enhance environmental sustainability.

We apply the suggested models to the ASHARE-884 dataset. We perform data preprocessing by applying Exploratory Data Analysis (EDA), which involves cleaning, sorting, and converting the category data into numerical values utilizing label encoding. Also, the Z-Score normalization technique was applied to normalize the data.

The experiment’s findings show that the suggested GNN and LSTM design performs better in terms of accuracy and efficiency in terms of environmental sustainability in green buildings when compared to traditional DL methodologies.

Research organization

This paper is structured as follows: section “ Literature review ” presents the background and relevant works. Section “ Proposed methodology ” introduces the proposed approach to machine learning and the deep learning method for green building design to enhance environmental sustainability. Section “ Results ” assesses the performance of our technique and contrasts it with the baseline methods. Section “ Conclusion ” concludes the paper and provides future direction.

Literature review

This section examines previous studies on environmental sustainability and artificial intelligence (AI) techniques for green buildings to pinpoint research gaps and support the necessity of the suggested strategy.

Environmental sustainability for green buildings

Sustainability is growing in the building and construction sector as a significant driver of social, economic, and environmental benefits with fewer adverse environmental effects. Green and sustainable practices must be established to increase energy efficiency in the building and construction sector. This is especially true when applying the most recent green technologies. The study 28 aims to find the most applicable techniques for employment in green building, assess the advantages of implementing green building, and examine the best practices of green building attributes. The results of this study demonstrated that green buildings can be created with less energy consumption and a lower long-term operating and maintenance cost by utilizing sustainable practices and energy-efficient systems. According to the study’s author 29 , achieving the objectives of resource protection, pollution reduction, and ecological environment improvement requires careful consideration of the environmental benefit analysis of green buildings. This includes efficient energy and resource utilization. The findings demonstrate that the incremental environmental advantages in terms of land saving, energy saving, water saving, material saving, indoor environmental quality, and operation management are increased by 23.15%, 10.37%, 19.30%, 18.25%, and 22.53%, respectively. In contrast, the measured results of the incremental environmental costs of the office building in Taiyuan City are decreased by 13.56%, 11.02%, 25.17%, 14.43%, and 15.25%, respectively. According to those, BIM technology used in the full life cycle cost assessment of green buildings can evaluate, quantify, and direct every stage of building design, construction, and upkeep while also largely satisfying resource and energy conservation requirements.

Green buildings are seen as a vital aspect of attaining sustainability since they incorporate green and natural components that reduce pollution and usage of resources. The goal of 30 research is to define the terms “sustainability” and “green building” as they relate to residential building design since it’s important to comprehend how sustainable design principles can help mitigate negative effects on the environment and society. The case-study methodology is employed. The study focuses on the innovative and sustainable design elements utilized in three case studies of green buildings—one each in China, Indonesia, and Dubai. The results showed that these nations seek to encourage the construction of environmentally friendly structures, especially homes, to achieve maximum social, economic, and environmental sustainability. In 31 , a study was conducted to determine the stages of action and challenges involved in implementing sustainable building practices, as well as to assess the extent of integrating these techniques into professional practice. The study’s suggested purpose was to ensure the best possible outcomes and qualitative and quantitative methodologies were used to examine. The study set a descriptive analysis based on survey analysis for the quantitative approach. The results showed that stakeholders have a respectable degree of awareness and knowledge. The results of this study may close a significant knowledge gap about the benefits of green building practices that exist in the absence of empirical research in developing nations.

The author of article 12 suggested a practical mapping tool that assesses how much a (GB) contributes to the Sustainable Development Goals (SDGs) by applying green building rating tools (GBRTs). It then used the analytic hierarchy technique to examine this contribution quantitatively. The findings demonstrated that GBRTs greatly assist SDGs 3, 7, 11, and 12, with SDG 12 benefiting the most. SDG Target 7.3, on the other hand, is the most notable since it offers the most significant avenue for GBRT to contribute to the SDGs. The study 32 investigates how the information modules suggested by EN 15978 and the three elements of sustainabilityenvironmental, social, and economic are covered by the indicators in GBRS as it moves through the life cycle phases of building development. The 387 sustainability indicators that were part of the eight chosen GBRS were examined and grouped based on three distinct classification criteria: the sustainability dimension, information modules, and stage of the construction process life cycle. Four rounds and meetings of an iterative process of indicator analysis and clustering were conducted by a panel of diverse academic and professional experts in the subject of study, leading to a consensus on the results. According to the analysis’s findings, the environmental dimension is the one that is most valued among the instruments, and to strike a fair balance, more focus needs to be paid to both the social and economic dimensions.

Recent advances in green building technologies (GBTs) have increased significantly due to environmental, economic, and societal benefits. The primary goal of GBTs is to use resources like water, energy, and other materials sparingly and in a balanced manner. As a result, the environment will be better. GBs improve productivity and health, reduce maintenance and operating costs, and reduce energy use and emissions. The goal of 33 study is to identify important concerns in the field of green building research that pertain to sustainable building practices that are low-impact on the environment, economical, and long-term in nature while also taking future developments into account. To ensure a sustainable future, this article analyzes the current status of green building construction and recommends more research and development. This study also suggested a few potential paths for sustainable development research to stimulate more investigation. The author of 34 study creates a set of valid and reliable social sustainability metrics for evaluating green buildings in China. Indicators of green buildings are required to support practitioners in comprehending social sustainability indicators and to validate different studies. Therefore, the fuzzy Delphi approach is applied to examine these indicators. The findings indicate that the most crucial elements of green building social sustainability are durability and safety. Health, comfort, accessibility, and convenience are further important factors. To attain social sustainability, this set of indicators helps practitioners make decisions and offers appropriate, useful guidance for many stakeholders.

Artificial intelligence techniques for green buildings

In particular, for sustainable projects (i.e., green buildings), a precise expense forecast is essential. In the construction sector, where stakeholders require more knowledge in contract cost estimating, green building construction contracts are still relatively new. Green buildings, in contrast to conventional construction, are made to make use of innovative technology to lessen the negative effects that their operations have on society and the environment. To anticipate the costs of green buildings, the author of the 35 paper proposes machine learning-based techniques such as random forest (RF), deep neural network (DNN), and extreme gradient boosting (XGBOOST). The impact of both hard and soft cost-related attributes is taken into account in the construction of the suggested models. The accuracy of the created algorithms is assessed and compared using evaluation measures. When compared to the DNN’s 0.91 accuracy, XGBOOST’s 0.96 accuracy was the greatest; RF’s 0.87 accuracy came in second.

The Artificial Intelligence-based Energy Management Method (AI-EMM) for green buildings is recommended in 36 article. It can respond intelligently to improve user comfort, safety, and energy efficiency in response to human choices. The AI-EMM model includes subsystems for smart user identification and monitoring of the interior and external surroundings, as well as a universal infrared communication system. Energy usage is improved using Long Short-Term Memory (LSTM) models. The recommended methodology is applied to analyzing energy usage data from green buildings. The proposed method for investigating the interaction between Heating, Ventilation, and Air Conditioning (HVAC) systems should emphasize airside design optimization for the improved interior climate. The results show that environmentally friendly buildings and financial rewards are compatible together. The AI-EMM’s experimental results showed a 94.3% high-performance ratio, a 15.7% lower energy consumption ratio, a 97.4% accuracy ratio, a 95.7% energy management level, and a 97.1% prediction ratio. In research 37 , the author has examined how AI and DL have recently advanced and been applied to promote sustainability in a number of areas, such as attaining the (SDGs), energy efficiency, healthy environments, and energy management for smart buildings. AI has the potential to support 134 of the 169 SDGs targets, making it a valuable instrument for encouraging sustainable practices. However, considering the rapid pace at which these technologies are developing, extensive regulatory control is required to guarantee ethical standards, safety, and transparency. AI and DL have been successfully applied in the renewable energy sector to optimize power grid stability, fault detection, and energy management.

The objective of the 38 study is to create a mathematical model that will investigate the supply and demand balance for external green construction support and the related spending adjustment procedures in a deflationary environment. To determine the key parameters influencing the green building cost prediction process, the most recent datasets from 3578 green projects in Northern America were gathered, pre-processed, analyzed, post-processed, and evaluated using state-of-the-art (ML) techniques. The results indicate that green building costs are expected to decline due to governmental and private expenditures in green development. Moreover, public and private investment has a greater effect on reducing the cost of green construction during deflation than during inflation. Consequently, the proposed approach can be employed by decision-makers to oversee and assess the annual ideal external investment in the development of green buildings.

Proposed methodology

This section explains the whole procedure of the suggested approach. The proposed method comprises multiple steps, such as obtaining datasets, preparing data, and making model predictions. Figure 1 provided a graphic representation of the suggested architecture. The ASHRAE dataset is explored first in the design, and then data preprocessing, such as label encoding and Z-score normalization, is applied. After that, ML and DL classifiers are trained. The ensemble model forecasts energy use, and models are combined for increased accuracy. Metrics are used in evaluation to measure performance, and the most effective model is ultimately selected before the process is accomplished.

Proposed architecture.

Experimental dataset

The dataset was acquired through field investigations of 160 distinct building sites worldwide. It is provided in support of the ASHRAE initiative to create a model of preferred thermal convenience. ASHRAE monitors an accumulation of data from several investigations by various investigators as a component of the RP-884 accessible repository. Numerous climate zones dispersed throughout various regions provide data files 39 .

The dataset was selected because individual turnover depends on a pleasant temperature. Residents of the building might experience discomfort due to an absence of temperature regulation. There are 56 features and 12,595 records in the ASHRAE RP-884 dataset used for this research. The objective of this dataset is to create an adaptive classifier. It comprises over 20,000 consumer convenience scores from 52 polls conducted across 10 climatic regions. The dataset’s primary identifiers are the reading day, age, year, subject, building code, and time of day. A thermal survey that residents submit, including the ASHRAE, the warmth scale (ash), clothing insulation, comfort level, metabolic rate, and high air temperature. Indices that are calculated from existing data, average air temperature, average radiation temperature, operational temperature, an average of three heights airspeed, an average of three heights airspeed, an average of three heights turbulence, air pressure, relative humidity, new standard practical temperature index set, two-node disc index, predicted mean vote, predicted percentage dissatisfied, etc. Perceived control over the thermal environment (PCC), PCED from 1 to 7, and other aspects of private environmental oversight combined with outdoor climate information, the outdoor average of the min/max air temperature, and outdoor maximum humidity percentage.

Dataset preprocessing

Data preprocessing is important since it enhances the model’s effectiveness and produces more precise attributes. In this step, data preprocessing is carried out using Exploratory Data Analysis (EDA), which involves cleaning, sorting, and converting the category data into numerical values utilizing label encoding. The present study utilized a Z-score normalization for cleaning data.

Exploratory data analysis

An essential component of any research endeavor is exploratory data analysis (EDA). Finding patterns and abnormalities in the data that can be utilized to focus the hypothesis testing is the primary objective of exploratory analysis. It also provides resources for data visualization and evaluation, usually through graphical representation, to help in hypothesis generation. After data collection, EDA is completed. Without making any assumptions, the data is effectively evaluated, plotted, and updated to assess the quality of the data and build models 40 . The ASHRAE dataset was subjected to the EDA approach. The dataset consists of one categorical and 55 numeric features, with a normal distribution for most. UW (Uncomfortable Warm), N (Neutral), and UC (Uncomfortable Cold) are the three classes that make up the categorical target attributes. 12,595 records of these classes are found in the dataset; the UW class has 1029, the N class has 10,061, and the UC has 1505 data entries. Figure  2 illustrates the unbalanced quality of the dataset.

Graphical visualization of dataset classes neutral (N), uncomfortable cold (UC), uncomfortable warm (UW).

A measure of imbalance or divergence from normative patterns in an ensemble of data is called skewness. To determine the direction of the outlier, this study measures the data skewness. After determining the kurtosis of the information set and verifying its skewness, if the skewness value is positive, the distribution is asymmetrical. It has an extended tail on the right side. Kurtosis is the total weight of the distribution’s tails expressed proportionately to the distribution’s center. In this work, the kurtosis of the normal distribution is analyzed before the log transformation of skewed data is carried out. The log transformation can be used to approximate normalcy for skewed data. The outcome shows that the normal distribution’s kurtosis is 0.

Encoding categorical variables

Categorical variables challenge certain ML techniques. The classification variable must be transformed into numerical data, which is crucial for the designed algorithms to function as intended. The categorical variables’ coding determines the way various algorithms function. One or more labels in word or numerical form can be present in a feature’s dataset. This makes it simpler for people to evaluate the data, but it is incomprehensible to machines 41 . We utilize an encoding that renders these labels interpretable by machines. There are other encoding techniques, such as one-hot and hash encoding. The label encoding approach is used in this work to encode categorical information.

Label encoder

Numerical label input is made possible in an ML model using label encoding. Label Encoder uses numbers to assign a value to each label, replacing the values of each label in the dataset. When they have divergent priorities, the labels can be employed. This step is crucial in the data preparation process for supervised learning methods 41 . Usually, this technique replaces each value in a category column with a number between 0 and N − 1. In this study, a label encoder assigns a value of 0 to 1 or 2 to each categorical variable.

Data cleaning

The Z-score normalization technique is used in this study to identify and eliminate anomalies while cleaning the data. This study cleans the data after converting the category variable to a numerical value. The data distribution was 0.5 and 0.98 before the cleanup. Figure 3 represents the distribution of features before data cleaning.

Distribution of features before data cleaning.

Z-Score: The degree to which a value resembles the average of a group of values is measured by a Z-score. Standard deviations are used in the average calculation of the Z-score. A data point’s Z-score of zero indicates that it has the same value as the average represented in Eq. ( 1 ). The data distribution is 0.5, 0.98 after the Z-score is applied and the outliers are eliminated. Figure  4 represents the distribution of features after data cleaning.

Distribution of features after data cleaning.

The prediction portion counts the label data as class 0, which has 6919 values; class 1, which has 1014 values; and class 2, which has 651, following the completion of EDA, label encoding, and data cleaning. After preprocessing the data, the modeling phase is carried out. The data is divided into training and testing data, keeping a ratio of 70% training data and 30% testing data to increase accuracy and efficacy for this phase. The machine and deep learning models are trained after splitting.

Model selection

Model selection involves choosing the appropriate hyperparameters, optimizing strategies, and neural network architecture. Configure hyperparameters such as batch size, learning rate, the number of layers, activation parameters, dropout rate, etc. Hyperparameter tuning can significantly impact the model’s performance. Each model architecture is trained on the training set with a distinct set of hyperparameters using the appropriate evaluation criteria. This study utilized the multiple ML and DL models: RF, DT, XGB classifier, Stacking, GNN, LSTM, and RNN for green building sustainable environment.

Random forest

An ensemble learning technique called Random Forest combines several decision trees to produce a model that is more reliable and accurate. It is a popular model for regression and classification applications and is a member of the tree-based model class. Using random feature choice, Random Forest models build several decision trees independently, each trained on bootstrapped dataset samples. The model is less likely to overfit than individual trees because it aggregates predictions from individual trees by majority voting or averaging, which lowers variance and improves generalization performance.

Decision tree

Decision trees are supervised learning algorithms that create regions in the feature area by making decisions based on the supplied characteristic values. At each node, the tree finds which attributes best separate the data by optimizing a chosen criterion, such as data acquisition or Gini impurity. This process iterates backward and forwards until an end condition is met, such as reaching a maximum depth or a minimum number of samples per leaf. Decision trees are widely used in many different sectors because of their ease of understanding and ability to handle numerical and categorical data. However, in large, complex datasets, they may overfit and have difficulty generalizing to new data if the proper regularization procedures are not used.

Extreme gradient boosting

A potent ensemble learning technique built on gradient boosting is called XGBoost (Extreme Gradient Boosting). Gradient descent is used to maximize the weak learners, which are usually decision trees that are constructed one after the other. Rapid processing on structured/tabular data, efficiency, and scalability are well-known attributes of XGBoost. It has built-in functionality for handling missing values and uses regularization techniques to avoid overfitting. Furthermore, XGBoost provides sophisticated functionalities such as cross-validation and early stopping to optimize model performance.

Stacking, also called stacked expansion, is an ensemble learning method that uses a meta-model (logistic regression) to aggregate predictions from several base models (RF, DT). After training fundamental models on the dataset, a meta-model is trained using the predictions of the base models as input characteristics. By determining which to mix the outputs of several models best, stacking attempts to enhance overall forecasting accuracy by utilizing the strengths of each model. It is a well-liked option in machine learning contests and ensemble learning contexts since it frequently performs better than individual models and conventional ensemble techniques.

Graph neural network

Graph Neural Networks (GNNs) were developed to arrange and visualize data in topologies or networks. Graphs are made up of vertices, or nodes, joined by vertices and, in this case, hyperlinks. To acquire information and insights, GNNs are used in data mining 42 . A GNN’s function is to process organized data. In a graph, each point is connected to a feature vector. Node i ’s beginning position is represented by \(g_{i}^{0}\) .

m represents the GNN layer and K ( i ) indicated the node i neighborhood. An aggregation method called Agg collects data from nearby nodes. The edge attribute is \(c_{in}^{m}\) among node i and node n in layer m , and the non-linear activation function is denoted as h . To generate a graph-level participation, data from each node is combined after multiple layers.

b is the final layer, and an aggregation function called readout is used to determine the illustration at the graph scale. A loss function, which is usually a measurement of the discrepancy between the true and anticipated categories, is minimized to train the GNN.

The GNN model output is denoted by Prediction ( f j ). Training uses optimization algorithms and backpropagation to change the model variables θ .

where η represents the learning rate.

Long short-term memory (LSTM)

The enhanced form of the recurrent neural network (RNN) is the long-short-term memory. It is proposed that memory blocks, not normal RNN units, but long-term memory, can address the increasing slope and vanishing issue. The main distinction between LSTM and RNN is that LSTM incorporates the cell state to preserve the long-term states. An LSTM network can retrieve and link previous data to present information 43 . Three distinct gates are used in the architecture of long short-term memory: the input, forget, and output gates. The cell’s new and prior states are designated by n t and n t −1 , respectively, while the current and previous outputs are indicated by o t and o t −1 , respectively. The current input is represented by c t . The following equations provide the rules for the LSTM’s input gate.

In Eq. ( 6 ), the input gate is represented by g i , and the preceding outputs, v t −1 and p t , are passed through the sigmoid layer for deciding which portion of the information needs to be added.

After transferring the old information, p t −1 , and the current information, c t , by tanh layer using the input gate a i , the Eq. ( 7 ) is utilized to obtain the updated information U t . Equation ( 8 ) integrates the information of long-term memory S t −1 into S t and the present state of information C t . The sigmoid output is denoted by D i , while S t is denoted by the tanh output. D i represents the weight metrics, and a i represents the LSTM’s input gate. Using the dot product of the input information J t and the current state of information s t and sigmoid layer, the forget gate of the LSTM then enables the particular transmission of the data.

Equation ( 9 ) is utilized to determine the specific probability of deleting the linked data by the final cell. The weight matrix is represented by D f , the offset is a f , and the sigmoid function is σ .

The inputs in Eqs. ( 10 ) and ( 11 ) establish that the states required for the continuation through the previous and current outputs, o t −1 and p t , respectively, are described by the output gate T t of the LSTM. The decision vector of the condition that transmits new information N t by the tanh layer is multiplied and acquired by the final output F t .

where a o denotes the bias of the LSTM of the output gate and D o is its weighted matrix 44 .

The input, forget, and output gates are the three gates in the model. The first gate, called a forget gate ( h t ), uses a sigmoid function of σ to take the previous output, o t −1 , and the current input, c t , from the prior state, s t −1 . The input gate uses the sigmoid function σ and the tanh layer to take in input information J t after adding the prior data. The information is obtained from the input gate and fed into the output gate, which utilizes the sigmoid function σ to compute all the information and deliver the current state where the output is kept.

Recurrent neural network

An artificial neural network that processes sequential data by preserving a hidden state that records details about earlier inputs is called a recurrent neural network (RNN). Because RNNs feature connections that allow information to remain over time, they are excellent for tasks requiring sequence or time series, in contrast to feedforward neural networks, which analyze each input sequentially.

Recurrent connections, which enable information to move from a single phase to the next, distinguish an RNN. The RNN gets an input i _ t at each time step t , processes it to generate an output o _ t , and changes its hidden state h _ t h, which stores data from earlier time steps. The hidden state at time t is calculated mathematically as an expression of the input i t and the hidden state that came before it, h _ t . An RNN can learn to analyze sequences of varied lengths since an identical set of parameters (weights and biases) are utilized at each time step. Parameter sharing simplifies This design, enabling the RNN to generalize across multiple time steps.

This section examines the suggested model’s effectiveness. Employing the ASHARE-884 dataset, the suggested model applies various DL classifiers. The parameters used to assess the model are f1-score, recall, accuracy, and precision. By comparing the current methods, these standards assess the proposed model’s performance.

Evaluation metrics

This study assesses the framework’s efficacy using extensive evaluation criteria, each offering valuable perspectives on the model’s operation. The first metric, accuracy, is typically used as the standard to evaluate performance. It is computed as the part of accurately recognized samples based on the total sample amount. The procedure is made simpler by Eq. ( 12 ), which emphasizes the measure’s simplicity despite its substantial influence.

The accuracy is the ratio of all positive forecasts the model produces to effectively precise projections; it is a crucial assessment metric utilized in performance evaluation. Equation ( 12 ) proportionally illustrates this value, making the metric notional equation easier to understand.

A model or system’s precision indicates how it forecasts the positive class. It represents the accuracy of the model and the degree of confidence in its ability to produce good predictions. This value is shown proportionately in Eq. ( 13 ), facilitating comprehension of the metric basic equation.

Recall, also called sensitivity, is an evaluation metric that centers on the ratio of every positive instance to the percentage of precise positive predictions. This balanced viewpoint offers a special benefit while estimating, as the computation of Eq. ( 14 ) demonstrates.

The appropriately identified F1 score functions as an equilibrium of memory and precision because it can effectively communicate the essence of a balanced performance. Combining these two metrics yields the F1-score, a popular estimate of model performance that is especially useful for evaluation. This basic estimating procedure is well described by Eq. ( 15 ), which looks complicated but provides much information.

One significant and unique indicator used in the evaluation process is the Confusion Matrix (CM), which is carefully designed to provide precise data regarding the efficacy of the classification model. This essential tool illustrates the model’s efficacy by comparing the anticipated and actual data. True positive (TP), false negative (FN), false positive (FP), and true negative (TN) are the four values displayed by the CM, a unique kind of display. The matrix sections’ labels, which show the actual class designations, correspond to the columns. The correctly recognized samples are arranged along the diagonal, while the incorrectly categorized cases are situated on the diagonal portions. The CM values are an essential tool for assessment that can highlight the advantages and disadvantages of the model. They also provide insightful data that improves the model and produces favorable outcomes.

ML models result analysis

The outcomes of ML models (RF, DT, XGB and Stacking) on the ASHARE-884 dataset are displayed in Table 1 . The models with the best accuracy are the XGB and RF models (0.84), the Stacking model (0.84), and the DT model (0.76). On the other hand, the XGB model has the highest precision (0.83), while the RF model has the lowest (0.82). The RF and XGB versions have the highest recall (0.84), while the DT model has the poorest recall (0.76). The F1-score considers both the precision and recall of a model since it is a harmonic mean of both metrics. The models with the greatest F1-score (0.80) are the XGB and RF models, trailed by the DT (0.77) and the stacking (0.80) models.

Ml model confusion matrix graphical representation is displayed in Fig.  5 . Figure  5 a, the confusion matrix of the RF is shown graphically. The rows of the table indicate the actual classes of the occurrences, and the columns indicate the anticipated classes. The diagonal cells of the matrix show the number of occurrences correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. 1997 instances were projected for class 0, 92 instances for class 1, and 24 cases are accurately anticipated. Figure  5 b, the confusion matrix of the DT is shown graphically. It shows the number of occurrences of correctly identified diagonal cells, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. 1744 instances were projected for class 0, 124 instances for class 1, and 59 cases are accurately anticipated.

Performance visualization of ML model results.

Figure  5 c shows the confusion matrix of the XGB. The diagonal cells of the matrix show the number of occurrences correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. 1992 instances were projected for class 0, 100 instances for class 1, and 34 cases are accurately anticipated. Figure  5 d shows the confusion matrix of the Stacking. It shows the number of occurrences of correctly identified diagonal cells, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. 1997 instances were projected for class 0, 92 instances for class 1, and 18 cases are accurately anticipated.

DL models result analysis on training and testing data

The outcomes of deep learning models (GNN, LSTM, RNN) on the ASHARE-884 dataset are displayed in Table 2 . The models employ various building energy consumption parameters from the dataset to forecast the energy utilization of a building. DL is compared according to their evaluation, which indicates if they were trained on the test or training dataset. The outcomes demonstrate that the training dataset yielded outstanding results from all three models compared to the test dataset. Overall, the GNN model performed exceptionally well, with an accuracy of 0.83 on the test dataset and 0.85 on the training dataset. Accuracy for the LSTM and RNN models was approximately 0.81 on the training dataset and 0.79 on the test dataset. Compared to the LSTM and RNN models, the GNN model performs better on the test and training datasets regarding accuracy. This implies that capturing the relationships between the various features in the data constitutes a task the GNN model does better. Of the three models, the accuracy is higher than the precision and recall. This shows that false positive predictions are more common in the models than false negative ones. Comparing the test dataset to the training dataset, the F1-score of the three models is declining.

Figure  6 shows the performance visualization of the GNN model in terms of accuracy, loss, and roc curve. Figure  6 a demonstrates the training and testing accuracy of the GNN model. The x-axis displays the number of epochs or the number of instances in which the model has evaluated the training data. The percentage of accurate predictions the model generates is displayed on the y-axis, representing accuracy. The training accuracy, or the model’s accuracy using the training data set, is represented by the blue line. The model’s accuracy, represented by the green line, represents the test accuracy. The training accuracy in the figure starts at about 0.80% and rises to about 0.84%. The test accuracy rises to approximately 0.82% from a starting point of about 0.80%. There is a 0.02% applicability gap.

Performance visualization of GNN model results.

Figure  6 b shows the training and testing loss of the model. The training loss starts at about 0.625 and goes down to about 0.450. The test loss begins around 0.600 and gradually drops to about 0.500. Although there is no substantial distinction between the two lines, the training loss is always less than the test loss. This implies that the model is effectively expanding with new data. Over time, there has been a decrease in both training and test loss. From this, the model continues to evolve. The test loss is not that different from the training loss. This implies that there is not a significant overfitting of the model. A visual tool used to assess a classification model’s efficacy is the ROC curve. The y-axis displays the true positive rate (TPR), while the x-axis displays the false positive rate (FPR). In Fig. 6 c, the model performs better; the train ROC curve has an AUC of 0.82, and the test ROC curve has an AUC of 0.71.

The confusion matrix of the GNN model is shown graphically individually for train data and for test data in Fig.  7 . It provides an overview of the operation of a classification algorithm. Because the suggested strategy produces fewer false positive and negative data and more constant, better true positive and negative values, it performs better. The rows of the table indicate the actual classes of the occurrences, and the columns indicate the anticipated classes. The diagonal cells of the matrix in Fig.  7 a show the number of occurrences correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. 7,020 instances were projected for class 0, 497 instances for class 1, and 114 cases are accurately anticipated on training data.

Graphical Visualization of GNN Model Results.

The diagonal cells of the matrix in Fig.  7 b show the number of occurrences correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. Based on test data, 19 cases are successfully predicted, whereas 1982 occurrences for class 0 and 83 instances for class 1 were forecasted.

Figure  8 shows the performance visualization of the LSTM model in terms of accuracy, loss, and roc curve. Figure  8 a demonstrates the training and testing accuracy Curve of the LSTM model. The training accuracy in the figure starts at about 0.81% and rises to about 0.82%. The test accuracy rises to approximately 0.82% from a starting point of about 0.78%.

Performance visualization of LSTM model results.

Figure  8 b shows the training and testing loss of the LSTM model. The training loss starts at about 0.645 and goes down to about 0.44. The test loss begins around 0.62 and gradually drops to about 0.57. Although there is no substantial distinction between the two lines, the training loss is always less than the test loss. This implies that the model is effectively expanding with new data. Over time, there has been a decrease in both training and test loss. From this, the model continues to evolve. The test loss is not that different from the training loss. This implies that there is not a significant overfitting of the model. A visual tool used to assess a classification model’s efficacy is the ROC curve. The y-axis displays the true positive rate (TPR), while the x-axis displays the false positive rate (FPR). In Fig. 8 c, the model performs better; the train ROC curve has an AUC of 0.68, and the test ROC curve has an AUC of 0.65.

The confusion matrix of the GNN model is shown graphically individually for train data and for test data in Fig.  9 . The rows of the table indicate the actual classes of the occurrences, and the columns indicate the anticipated classes. The diagonal cells of the matrix in Fig.  9 a show the number of occurrences that were correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. 7997 instances were projected for class 0, 212 instances for class 1, and 15 cases are accurately anticipated on training data.

Graphical visualization of LSTM model results.

The diagonal cells of the matrix in Fig.  9 b show the number of occurrences that were correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. Based on test data, 2025 cases are successfully predicted for class 0, whereas 30 occurrences for class 1 and 2 instances for class 1 were forecasted.

Figure  10 shows the performance visualization of the GNN model in terms of accuracy, loss, and roc curve. Figure  10 a demonstrates the training and testing accuracy of the RNN model. The training accuracy in the figure starts at about 0.79% and rises to about 0.81%. The test accuracy rises to approximately 0.806% from a starting point of about 0.82%. Figure  10 b shows the training and testing loss of the RNN model. The training loss starts at about 0.65 and goes down to about 0.54. The test loss begins around 0.62 and gradually drops to about 0.57. A visual tool used to assess a classification model’s efficacy is the ROC curve. The y-axis displays the true positive rate (TPR), while the x-axis displays the false positive rate (FPR). In Fig.  10 c, the model performs better; the train ROC curve has an AUC of 0.77, and the test ROC curve has an AUC of 0.66.

Graphical visualization of RNN model results.

The confusion matrix of the GNN model is shown graphically individually for train data and for test data in Fig.  11 . The diagonal cells of the matrix in Fig.  11 a show the number of occurrences correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. 7985 instances were projected for class 0, 212 instances for class 1, and 0 cases are accurately anticipated on training data. The diagonal cells of the matrix in Fig.  11 b show the number of occurrences correctly identified, and the off-diagonal cells show the proportion of instances that were wrongly classified. The anticipated proportions are represented by the class labels 0, 1, and 2. Based on test data, 2027 cases are successfully predicted for class 0, whereas 38 occurrences for class 1 and 0 instances for class 1 were forecasted.

Findings and discussion

This study utilized the ASHARE-884 dataset for green building energy consumption to predict the temperature and environmental sustainability of a smart building. In particular, for green buildings that significantly depend on local climate circumstances, the resolution of the climate statistics was not precise enough to capture localized temperature fluctuations, which are critical for accurately projecting energy usage. The dataset might not encompass enough time to include upward trends or variations in climatic patterns, which are crucial for comprehending energy usage patterns and producing precise forecasts. When combined with climatic data, historical energy consumption data makes it possible for researchers to validate and calibrate forecasting models successfully. Validated models can help make evidence-based choices for green building and operations and increase trust in the reliability of energy consumption projections.

There are advantages and disadvantages to using real-world datasets for DL and ML in the design of green buildings. The complexity and volume of the data, which need a large amount of processing power, as well as problems with data quality and consistency, such as inconsistent or incomplete data, are challenges. Complicating matters further are worries about data security and privacy as well as regulatory compliance. Furthermore, integration and benchmarking are challenging due to the dynamic and heterogeneous nature of the data and the absence of uniformity across many sources. Nonetheless, there are numerous opportunities in the areas of better occupant comfort and health through optimized indoor environments, enhanced predictive modeling for energy efficiency and sustainability, and better design and operation through data-driven insights. Optimization of operations and resource allocation can lead to cost savings, and regulatory adherence can be streamlined with automated compliance and sustainability reporting. Moreover, real-world data may encourage inventiveness, early adopters of ML and DL technology a competitive advantage. Applying machine and deep learning techniques to green buildings utilizing factors such as maximizing resource use, enhancing occupant comfort, and reducing the built environment impact to enhance environmental sustainability.

DL and ML Building energy modeling technologies can benefit from artificial intelligence approaches by increasing forecasting accuracy and calibrating models using actual data. Deep learning algorithms can discover links between building parameters and energy usage, making more realistic simulations that consider intricate interconnections and uncertainties possible. The effectiveness of the suggested model is evaluated using optimal indicators necessary for statistical analysis. Statistical analysis assesses the effectiveness, standardization potential, and usefulness of DL models. The degree to which a deep learning model can identify patterns and correlations in data, as well as the intricacy and refinement of its structure, indicate its complexity. Multiple architectural features determine the DL model’s complexity. A model becomes more complex as its number of parameters increases. Even though sophisticated models can capture complicated relationships in the data, they are increasingly prone to overfitting when improperly regularized. The quantity and kind of features a model uses can affect its complexity. Even while adding more characteristics can make the model more complex, not all characteristics can have a significant impact on the model’s performance. By adding penalty components and regularizing the loss function, various ways lower the complexity of the model. A reduction in the likelihood of overfitting occurs when extremely complex metrics are avoided. The study uses the GNN, LSTM, and RNN models based on DL to tackle green building environmental sustainability. The experiment results indicate that the proposed GNN and LSTM architecture functions more accurately and efficiently than conventional DL techniques for environmental sustainability in green buildings.

Developers often use machine learning and other forms of advanced artificial intelligence, such as deep learning approaches, to help them complete their tasks more quickly and accurately. The goal of this research is to create a predictive model for GB design using ML and DL approaches to maximize resource usage, enhance occupant comfort, and decrease the environmental impact of the built environment throughout the GB design process. The proposed models are applied to a dataset, ASHARE-884. An exploratory data analysis (EDA) and data preprocessing techniques are applied, including cleaning, sorting, converting categorical data into numerical data, and normalizing the data. ML and DL techniques for green building design to enhance environmental sustainability. DL models such as GNN and LSTM perform more accurately and efficiently than all models and outperform conventional DL techniques for environmental sustainability in green buildings. However, since this research is limited regarding the dataset, this study can be extended by adding more feature datasets in further studies.

This study encourages future studies to develop a more robust ML and DL model with improved accuracy performance. Furthermore, other data preprocessing techniques will enhance the performance of models in the future. The study’s future directions will concentrate on addressing the limits of climate data resolution and period to increase the accuracy of energy consumption estimates. Longer periods and higher temporal and spatial resolution of climate information added to the ASHARE-884 dataset will improve its ability to capture long-term climatic trends as well as localized temperature changes. Moreover, integrating meteorological data with large historical energy usage data improves forecasting model calibration and validation considerably. Future research endeavors can leverage the current foundation to enhance the precision, dependability, and adaptability of models for energy consumption prediction and comprehension of the interaction between climate and energy use by exploring these study avenues.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

2021 global status report for buildings and construction | unep-un environment programme. https://www.unep.org/resources/report/2021-global-status-report-buildings-and-construction (Accessed on 02/11/2024).

Peck, D. Buildings. In Handbook of Recycling 235–247 (Elsevier, 2024).

Chapter   Google Scholar  

2021 global status report for buildings and construction | unep-un environment programme. https://www.unep.org/resources/report/2021-global-status-report-buildings-and-construction (Accessed on 02/08/2024).

Nur-E-Alam, M. et al. Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings. Sustain. Energy Technol. Assess. 62 , 103636 (2024).

Google Scholar  

Miraj, P., Berawi, M. A. & Utami, S. R. Economic feasibility of green office building: Combining life cycle cost analysis and cost–benefit evaluation. Build. Res. Inf. 49 , 624–638 (2021).

Article   Google Scholar  

Basher, M. K., Nur-E-Alam, M., Rahman, M. M., Alameh, K. & Hinckley, S. Aesthetically appealing building integrated photovoltaic systems for net-zero energy buildings. Current status, challenges, and future developments—A review. Buildings 13 , 863 (2023).

Basher, M. K., Nur-E-Alam, M., Rahman, M. M., Hinckley, S. & Alameh, K. Design, development, and characterization of highly efficient colored photovoltaic module for sustainable buildings applications. Sustainability 14 , 4278 (2022).

Article   CAS   Google Scholar  

Zuo, J. & Zhao, Z.-Y. Green building research—Current status and future agenda: A review. Renew. Sustain. Energy Rev. 30 , 271–281. https://doi.org/10.1016/j.rser.2013.10.021 (2014).

Venkataraman, V. & Cheng, J. C. Critical success and failure factors for managing green building projects. J. Archit. Eng. 24 , 04018025 (2018).

Vasiliev, M., Nur-E-Alam, M. & Alameh, K. Initial field testing results from building-integrated solar energy harvesting windows installation in Perth, Australia. Appl. Sci. 9 , 4002 (2019).

Sharanya, B. et al. Green and sustainable building practices for museums. AIP Conf. Proc. 2039 , 20010 (2018).

Wen, B. et al. The role and contribution of green buildings on sustainable development goals. Build. Environ. 185 , 107091 (2020).

Basbagill, J., Flager, F., Lepech, M. & Fischer, M. Application of life-cycle assessment to early stage building design for reduced embodied environmental impacts. Build. Environ. 60 , 81–92. https://doi.org/10.1016/j.buildenv.2012.11.009 (2013).

Svalestuen, F., Knotten, V., Lædre, O. & Lohne, J. Planning the building design process according to level of development (2018).

Thomas, J. A., Vasiliev, M., Nur-E-Alam, M. & Alameh, K. Increasing the yield of Lactuca sativa , L. in glass greenhouses through illumination spectral filtering and development of an optical thin film filter. Sustainability 12 , 3740 (2020).

Li, Y., Song, H., Sang, P., Chen, P.-H. & Liu, X. Review of critical success factors (CSFs) for green building projects. Build. Environ. https://doi.org/10.1016/j.buildenv.2019.05.020 (2019).

Article   PubMed   PubMed Central   Google Scholar  

Oesterreich, T. D. & Teuteberg, F. Understanding the implications of digitisation and automation in the context of industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Comput. Ind. 83 , 121–139 (2016).

Berawi, M. A. Managing artificial intelligence technology for added value. Int. J. Technol. 11 , 1–4 (2020).

Hwang, B.-G., Zhu, L. & Ming, J. T. T. Factors affecting productivity in green building construction projects: The case of singapore. J. Manag. Eng. 33 , 04016052 (2017).

Darko, A., Chan, A. P., Owusu-Manu, D.-G. & Ameyaw, E. E. Drivers for implementing green building technologies: An international survey of experts. J. Clean. Prod. 145 , 386–394. https://doi.org/10.1016/j.jclepro.2017.01.043 (2017).

Chan, A. P. C., Darko, A., Olanipekun, A. O. & Ameyaw, E. E. Critical barriers to green building technologies adoption in developing countries: The case of Ghana. J. Clean. Prod. 172 , 1067–1079. https://doi.org/10.1016/j.jclepro.2017.10.235 (2018).

Sahli, H. An introduction to machine learning. TORUS 1—Toward an open resource using Serv. Cloud computing for environmental data 61–74 (2020).

Khean, N., Fabbri, A. & Haeusler, M. H. Learning machine learning as an architect, how to. In Proceedings of the 36th eCAADe Conference , vol. 1, 95–102 (2018).

Karan, E. & Asadi, S. Intelligent designer: A computational approach to automating design of windows in buildings. Autom. Constr. 102 , 160–169 (2019).

Ascione, F., Bianco, N., De Stasio, C., Mauro, G. M. & Vanoli, G. P. Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach. Energy 118 , 999–1017 (2017).

Rahman, A. & Smith, A. D. Predicting fuel consumption for commercial buildings with machine learning algorithms. Energy Build. 152 , 341–358 (2017).

Geyer, P. & Singaravel, S. Component-based machine learning for performance prediction in building design. Appl. Energy 228 , 1439–1453 (2018).

Article   ADS   Google Scholar  

Sapuan, N. M., Haron, N. F., Kumaran, V. V., Saudi, N. S. & Ridzuan, A. R. Green building best practices in achieving energy and environmental sustainability. Environ. Manag. Sustain. Dev. https://doi.org/10.5296/emsd.v11i4.21052 (2022).

Jiang, L. Environmental benefits of green buildings with BIM technology. Ecol. Chem. Eng. S 30 , 191–199 (2023).

Boshi, A. A. Sustainable design and green building for the design of residential buildings with high environmental value. Tex. J. Eng. Technol. 17 , 7–14 (2023).

Jaradat, H., Alshboul, O. A. M., Obeidat, I. M. & Zoubi, M. K. Green building, carbon emission, and environmental sustainability of construction industry in Jordan: Awareness, actions and barriers. Ain Shams Eng. J. 15 , 102441 (2024).

Braulio-Gonzalo, M., Jorge-Ortiz, A. & Bovea, M. D. How are indicators in green building rating systems addressing sustainability dimensions and life cycle frameworks in residential buildings?. Environ. Impact Assess. Rev. 95 , 106793 (2022).

Meena, C. S. et al. Innovation in green building sector for sustainable future. Energies 15 , 6631 (2022).

Tseng, M.-L., Li, S.-X., Lin, C.-W.R. & Chiu, A. S. Validating green building social sustainability indicators in china using the fuzzy Delphi method. J. Ind. Prod. Eng. 40 , 35–53 (2023).

Alshboul, O., Shehadeh, A., Almasabha, G. & Almuflih, A. S. Extreme gradient boosting-based machine learning approach for green building cost prediction. Sustainability 14 , 6651 (2022).

Xiang, Y., Chen, Y., Xu, J. & Chen, Z. Research on sustainability evaluation of green building engineering based on artificial intelligence and energy consumption. Energy Rep. 8 , 11378–11391 (2022).

Fan, Z., Yan, Z. & Wen, S. Deep learning and artificial intelligence in sustainability: A review of SDGs, renewable energy, and environmental health. Sustainability 15 , 13493 (2023).

Alshboul, O., Shehadeh, A., Almasabha, G., Mamlook, R. E. A. & Almuflih, A. S. Evaluating the impact of external support on green building construction cost: A hybrid mathematical and machine learning prediction approach. Buildings 12 , 1256 (2022).

De Dear, R. & Schiller Brager, G. The adaptive model of thermal comfort and energy conservation in the built environment. Int. J. Biometeorol. 45 , 100–108 (2001).

Article   ADS   PubMed   Google Scholar  

Komorowski, M., Marshall, D. C., Salciccioli, J. D. & Crutain, Y. Exploratory data analysis. Second Analysis Electronic Health Records 185–203 (2016).

Sharma, N., Bhandari, H. V., Yadav, N. S. & Shroff, H. Optimization of ids using filter-based feature selection and machine learning algorithms. Int. J. Innov. Technol. Explor. Eng. 10 , 96–102 (2020).

Zhou, J. et al. Graph neural networks: A review of methods and applications. AI Open 1 , 57–81 (2020).

Chen, G. A gentle tutorial of recurrent neural network with error backpropagation. arXiv preprint arXiv:1610.02583 (2016).

Islam, M. Z., Islam, M. M. & Asraf, A. A combined deep CNN-LSTM network for the detection of novel coronavirus (Covid-19) using x-ray images. Inform. Med. Unlocked 20 , 100412 (2020).

Download references

Acknowledgements

Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R120), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Author information

Authors and affiliations.

School of Finance and Economics, Jiangsu University, Zhenjiang, China

Shahid Mahmood & Huaping Sun

School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China

Huaping Sun

Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt

El-Sayed M. El-kenawy

School of International Studies, Zhengzhou University, Zhengzhou, China

Asifa Iqbal

Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia

Amal H. Alharbi & Doaa Sami Khafaga

MEU Research Unit, Middle East University, Amman, Jordan

You can also search for this author in PubMed   Google Scholar

Contributions

Shahid Mahmood: Writing—original draft, Project administration, Conceptualization. Huaping Sun: Supervision, Investigation. El-Sayed M. El-kenawy: Visualization, Software, Project administration. Asifa Iqbal: Writing—review and editing. Amal H. Alharbi: Software, Data curation. Doaa Sami Khafaga: Visualization, Formal analysis.

Corresponding author

Correspondence to Shahid Mahmood .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Mahmood, S., Sun, H., El-kenawy, ES.M. et al. Integrating machine and deep learning technologies in green buildings for enhanced energy efficiency and environmental sustainability. Sci Rep 14 , 20331 (2024). https://doi.org/10.1038/s41598-024-70519-y

Download citation

Received : 19 February 2024

Accepted : 19 August 2024

Published : 02 September 2024

DOI : https://doi.org/10.1038/s41598-024-70519-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Green building
• Environmental sustainability
• Artificial intelligence
• Machine learning
• Deep learning

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

• Open access
• Published: 09 July 2022

The role of project management in the success of green building projects: Egypt as a case study

• Heba Farouk Abdelkhalik 1 &
• Hisham Hussein Azmy 1  

Journal of Engineering and Applied Science volume  69 , Article number:  61 ( 2022 ) Cite this article

8857 Accesses

5 Citations

Metrics details

Sustainability and project management are two trends that have taken global interest in the last decades due to their significant role in various fields of life. However, these two topics have rarely been addressed in one study or framework. As sustainability and environmental issues are not specifically or systematically considered in most major project management frameworks such as the Project Management Body of Knowledge (PMBOK), Individual Competence Baseline (ICB), International Organization for Standardization (ISO 21500:2012), and so on. Furthermore, sustainability applications in the construction field under the term “green buildings” are facing various types of obstacles that obstruct the pervasion of this type of construction in an adequate and required way. Some of these obstacles have been addressed in recent studies with suggested solutions, but the role of project management in overcoming or even mitigating the risk of these obstacles was almost absent in most of these studies. Therefore, this paper attempts to observe the most important obstacles facing the application of sustainability in the construction field and taking the green construction situation in Egypt as a case study. In addition, this paper aims to investigate the role of project management in green building projects’ success, through project management best practices’ applications to overcome the main reasons that obstruct the green building projects movement. The results showed that there is a lack of management methods that address sustainable construction projects. In addition, there is no clear methodology governing the green building management process. Also, the unspecified responsibilities between stakeholders in green building projects lead to difficulties in managing and implementing green buildings. However, some defined obstructions could be overcome by project management’s best practices and methods.

Introduction

The trend towards sustainability and a better life for future generations is one of the global trends that has received great attention in recent times, especially for those interested in the field of construction. Urban and industrial progress, accompanied by greater consumption of natural resources, reflects negatively on the ability of the planet to renew its resources, and therefore exposes future life to danger. The latest events around the world-like forest fires, floods, and torrents due to global warming, encourage the global interest in sustainability as one of the means to address this phenomenon. And the high cost of oil energy and its negative impact on the environment have prompted the search for alternative sources of energy and developed concepts that aim to reduce dependence on oil energy and rationalize the use of coal and gas for power generation.

A few years ago, concepts such as “eco-friendly building” and “green architecture” emerged within the framework of sustainable development that goes beyond the narrow economic outlook for rapid profit and the aspiration to conserve natural resources and allow them to be exploited for longer periods to serve future generations. The main feature that distinguishes green buildings or ecological buildings from the remaining buildings is that they do not disturb the ecological balance and they aim to produce structures that will benefit both nature and human beings [ 1 ].

Green building applications are facing a lot of challenges on more than one level. In this paper, green building applications at the project level were considered. The lack of green buildings in developing countries and the gap between the percentage of the registered projects and the certified projects from green building rating systems in countries like Egypt indicates that there are some obstacles facing this kind of project in all project phases. Some previous studies addressed these obstacles, and some of them provided suggested solutions at governmental and professional levels. But a few of them attempt to find a practical solution from project management and the project manager’s point of view.

This paper aims to investigate the obstacles that face green building applications in developing countries due to the size of the challenges that face these projects there and takes Egypt as a case study. Furthermore, the study observed challenges facing project managers or green building administrators in this project through a questionnaire and online interviews with them. Finally, the study attempted to find solutions through project management best practices to overcome the main reasons that impede the green building movement in developing countries like Egypt.

Literature review

Sustainability and green architecture.

Sustainability and a better life for current and future generations have captured global attention in recent decades. In 1972, the term “sustainability” was developed for the first time at the world environmental conference in Stockholm with the Club of Rome through the discussions within the framework of “eco-development” [ 2 ]. In 1987, the World Commission on Environment and Development (WCED), published a report entitled “Our common future. In this report, “sustainable development” was defined as development that meets the needs of the present without compromising the needs of future generations [ 3 ]. A broader concept of SD is based on the integration of three dimensions: economic, environmental, and social [ 2 ], constituting the sustainability known as the Triple-Bottom Line. In 1997, John Elkington, in his book “Cannibals with Forks,” coined the term “triple bottom line” (3BL), which refers to economic prosperity, environmental quality, and social justice. Also, knowing the three pillars of sustainability, or triple P, as follows:

Profit—The first bottom line is the traditional measure of financial performance—How responsible has the company been in terms of assuring its competitive prosperity?

People—The second bottom line is the measure of a company’s social account—How socially responsible has the organization been in terms of its impact on the quality of life of the individuals it affects?

Planet—The third bottom line is the measure of the company’s environmental account—How environmentally responsible has it been in terms of its impact on natural ecosystems? [ 4 ].

Sustainable development insights have been applied in several fields in our life, but the application of SD in the construction field creates a type of building under the term “Green Building”. According to the World Green Building Council, a “green’ building is a building that, in its design, construction, or operation, reduces or eliminates negative impacts, and can create positive impacts, on our climate and natural environment. Green buildings preserve precious natural resources and improve our quality of life” [ 5 ].

The pervasion of the concept of sustainability and green architecture in the world has been accompanied by the so-called “Rating Systems” programs, which act as arbitrators on whether a building is a “green building” or not, and how green it is. Furthermore, there is an active role played by these programs in the marketing of the green architecture concept around the world by working on the spirit of competition in the design, construction, and operation of buildings. In addition, building owners sought to obtain certificates from these global evaluation programs to prove that their buildings are subject to the principles of green architecture and compete at the highest level in the evaluation.

The most famous and widely used rating system is the American system (Leadership in Energy and Environmental Design (LEED)), which was introduced in 1998 by the US Green Building Council (USGBC). In addition (Building Research Establishment’s Environmental Assessment Method (BREEAM)) system in the UK is the world’s first green building assessment system.

• Project management

With the increasing complexity of projects in general and construction projects in particular, the need for a holistic system to manage all the project’s resources, stakeholders, documents, finance, requirements, and solve all issues that come up with the project’s progress. According to the American Project Management Institute (PMI), project management is the application of knowledge, skills, tools, and techniques to project activities to meet the project requirements. Furthermore, project management enables organizations to execute projects effectively and efficiently through the appropriate application and integration of the project management processes identified for the project [ 6 ].

In the 1960s, Dr. Martin Barnes introduced the iron triangle (also called the triple constraint), which refers to the idea of being on time, within budget, and according to specifications. The triple constraints were the indicators of the project’s success for decades until sustainable projects started to pop up, and other constraints have been raised as environmental and community dimensions [ 4 ]. The triple P took attention to a different project’s success dimensions that were not realized before, along with the iron triangle, or triple constraint cost, time, and quality.

Integration between sustainability and project management

Project management and sustainability are two topics rarely integrated into one study or a framework, although project management could be a means of positive influence on the integration of sustainability dimensions into projects [ 7 ]. Recently, a few studies realized the role of project management in sustainability and green building’s success; however, the existing studies are still insufficient [ 8 ]. In addition, most major project management frameworks, such as PMBok, ICB, ISO21500:2012, and Prince2, did not take sustainability and environmental issues into consideration [ 9 ]. Furthermore, it is noticed that most previous studies care about studying sustainable management and environmental management, but few of them address project management and its great role in sustainable and green architecture.

According to Wu and Low [ 10 ], the credits related to project management in some of the rating systems (LEED2.2, Green Globes, BCA Green Mark 3.0), take around 20% of the credits in these rating systems. Furthermore, green buildings must be viewed as a comprehensive solution that integrates sustainable principles throughout the project life cycle, from project planning to design, construction, and operation, rather than simply as a collection of green materials, technologies, and other environmentally friendly innovations [ 10 ].

Green buildings are often developed according to rating system guidelines, which provide guidance on measurements and can provide recognition and verification of the level of compliance [ 11 ]. Rating systems are designed to evaluate the performance of an entire building or a specific section of a building from planning, to design, construction, and operations phases. This requires a specific management system to manage all procedures and processes of the rating system, the registration and documentation of credits, the interactions between the various stakeholders in the project, the responsibilities of everyone on the project team, resources, cost, and time management.

It is worth mentioning that the management systems of these projects must have a specific nature that is adaptable to the project requirements and sustainable goals. Recently, there are few attempts to develop frameworks or methodologies for sustainable projects. However, until now, most of those attempts have not yet materialized from being studies and have not been applied to green building projects in a significant way. For example, Marcelino-Sádaba et al. [ 8 ] developed a framework in their study published in 2015 to help project managers deal with sustainability projects based on four dimensions: products, processes, organizations, and managers [ 8 ].

Globally, there is the methodology of (Project Integrating Sustainable Methods (PRISM)) which was introduced in 2013 by the international organization of green project management (GPM). PRISM is a structured methodology for sustainable—“Green Project Management”, which is based on a series of standards and incorporates their use in the standard ISO 21500:2012 “Guidance on Project Management” [ 12 ]. But this methodology is not yet experienced in a significant way with a lot of applications as well as the studies that address this methodology are very rare. In addition, the methodology is totally not remedied at the local level according to the applied questionnaire in this study.

Another perspective or level that addresses the integration between sustainability and project management is the organizational level. Sustainable project management is an integral part of the sustainable management of organizations. Where organizations interested in sustainable development, determine clear sustainable goals and issue sustainability reports in which they define their vision and future plans towards sustainability. In order to achieve organizations’ sustainable goals, organizations define internal practices and projects either in the form of individual projects, programs, or portfolio aims to achieve the defined sustainability goals. Naturally, not all sustainable projects are implemented due to the sustainable organizations’ strategies, as there are a lot of sustainable projects that have been implemented due to marketing considerations or to go with the new trend, especially in the construction field. However, the projects that are achieved based on clear organizations’ goals and visions from a sustainability perspective are most likely to have a good chance for continuity and improvement.

Although there are numerous studies on energy management and environmental conservation via ISO 50001 and ISO 14001, a holistic method for the management of sustainability in the context of an organization is still lacking [ 13 ]. Moreover, there is a gap between organizations’ perception of the importance of sustainable management and its actual use in practice [ 14 ]. However, there have been some attempts recently in some studies to integrate the management methods with sustainable principles with the aim of introducing organizational sustainable management. Mustapha et al. [ 13 ] proposed the development of an integrated green management framework called the Sustainable Green Management System (SGMS). A systematic, integrated, and efficient approach for collecting, monitoring, analyzing, and managing information and resources. SGMS leads to sustainable organizations, saves resources, removes significant redundancies, promotes cleaner production, and enhances the profitability and efficiency of an organization [ 13 ].

Another important aspect in addressing the integration between project management and sustainability or green buildings is the contribution of the project managers to the success of sustainable projects. According to Hwang and Ng [ 15 ], many studies have been concerned with the efficiency of project managers to ensure the success of the project. A few of them have been concerned with the project managers’ execution of green architecture projects and the challenges they face in such quality of projects. Therefore, in their study, they identified the most important challenges facing project managers in green architecture buildings. Among them, the long period required for planning and designing green buildings; the unavailability of subcontractors, professionals, green materials, and equipment; high-cost and risk; and the lack of experience and knowledge. Hwang and Ng [ 15 ] also identified critical knowledge areas and skills that are essential to respond to the challenges. The most important knowledge areas were schedule management and planning, stakeholder management, communication management, cost management, and human resources management. In addition, the most important skills that are required to mitigate the challenges were analytical, decision-making, team working, delegation, and problem-solving skills [ 15 ].

Also, Martens and Carvalho [ 14 ] pointed out that project managers can improve their results in projects when looking at four factors, which are sustainable innovation business model, stakeholders’ management, economics, competitive advantage, environmental policies, and resource saving [ 14 ].

This qualitative exploratory research aims to define the role of project management in the success of green building applications and how it helps in overcoming the obstacles facing these kinds of buildings. For this purpose, a systematic literature review was conducted for a better understanding of the green buildings’ obstacles and challenges facing these buildings in developing countries like Egypt as a case study for some reasons as follows:

Egypt is one of the countries that suffers from a lack of energy sources, environmental pollution, the pervasion of some diseases due to this pollution, and economic problems. As the movement of sustainability and green building principles contribute significantly to solving these problems, it becomes necessary to study the reasons that prevent the pervasion of sustainability and green buildings in Egypt, find solutions, and overcome these obstacles.

Although the significant recognition of green building projects’ importance in Egypt, a very limited number of certified green building projects have been observed, principally in the national rating system GPRS.

All previous studies addressing the green building project crisis in Egypt totally neglected the role of the most important factor in project management, which led to wondering how these projects are managed in Egypt and how the cases and numbers of green building projects could be improved by a successful project management system.

Following the SLR, an online questionnaire and interviews with project managers and sustainability consultants were conducted to determine how green project buildings are managed in Egypt, a more specific ranking for the most affective challenges that obstruct green buildings in Egypt from the challenges identified previously in previous studies, and finally to determine how the green building situation in Egypt could be improved.

The questionnaire consists of 18 questions with two types of questions, open questions, and multiple-choice questions aiming to benefit from the experience of project managers and to define obstacles they faced in managing green building projects in Egypt, the main aims need to be elicited from the questionnaire as follows:

1. The main project phases that the project managers participated in and their main role in the project.

2. Are the project managers following specific management methods/methodologies to address green buildings’ unique natural and requirements?

3. Most management methodologies that have been followed in these projects and what are the most useful software programs have been used.

4. Who decides the management methods in the project and are the project stakeholders participating in choosing the way in which the project has been managed.

5. The main obstacles that project managers face when managing green buildings in Egypt.

6. The main factors which caused discrepancies between the estimated project cost/time and the final project cost/time achieved.

7. The project managers’ point of view on how management systems could be developed to be convenient for green building projects.

The target group for the study is project managers and green building consultant who have worked in certified/registered green building projects in Egypt. The method which has been used to collect data is an online survey and personal interviews by using voluntary response sampling. The total number of responses are 10 responses varies among project managers and green building consultants.

For more clarification, the research method was summarized in Fig. 1 .

Research method. Ref: Researchers

Results and discussion

Green architecture insights have appeared in Egyptian buildings since the early eras in building design considerations such as taking advantage of building location, designing buildings to overcome external environmental conditions without harming the environment, benefiting from daylight, optimizing resource use, and other environmental design concepts now adopted by green architecture. But with the passage of the ages and industrial progress, these concepts faded away and the natural solutions were replaced with artificial solutions in buildings, which led to environmental harm and natural resource exploitation.

Green architecture as a term was introduced in Egypt in the 1990s at the first symposium of “Bioclimatic Architecture”, which was held in 1996 [ 16 ]. After launching the LEED system (the most popular rating system in the world) in 1998, small steps have appeared toward this trend in this period until the first green building approval in 2010 under the LEED rating system. Following this, a few investors and developers in Egypt were interested in registering their buildings in the LEED program as a kind of marketability to keep up with the new trend.

Green building project challenges in Egypt

Sustainable construction projects known as green building projects face some obstacles and challenges with their implementation in reality. Particularly in developing countries due to certain factors that will be discussed later. In Egypt, as a case study, there are limited numbers of green buildings in the modern era which are certified by third-party or green rating systems, whether by LEED or the Green Pyramid Rating System (GPRS) the national green building rating system in Egypt). The number of buildings registered in LEED until 2021 reached 63, with only 22 certified [ 17 ]. As well as there is only one building that has gained the LEED platinum certification in Egypt. On the other hand, the application of the (GPRS) has been neglected at the level of the public and private sectors since its launch in 2011 by the Egyptian green building council. Unfortunately, there are only 5 buildings that were certified under this system [ 18 ].

Comparing the number of certified green buildings in Egypt with other countries in light of the rapid movement globally toward sustainability and green buildings, found that Egypt’s movement toward green architecture is very slow and needs more encouragement from the government and construction developers, as well as more studies of the factors leading to such delays and exploring solutions to promote strongly the application of green architecture principles.

During the last decade, local studies in Egypt focused on studying the application of green buildings, but few really addressed the main reasons for preventing the pervasion of green buildings in Egypt and the main problems that face these buildings. In this section, the reasons behind the green building crisis in Egypt will be discussed from the most important previous studies. Some studies focused on general reasons and determining the problems facing green building in Egypt are listed below:

- The absence of governmental incentives toward green building.

- High initial cost for the green building compared with the traditional type.

- Lack of design team specialists who are aware of environmental control strategies and building simulation programs to choose the optimum choices for the building’s environmental performance.

- Unavailability of the required technology for some credits.

- Lack of contractors’ awareness.

- Unavailability of recycling companies for construction materials.

- Unavailability of data about the life cycle cost of the available materials.

- Unavailability of low-emitting materials in the Egyptian market [ 19 ].

- Lack of a database related to green building materials [ 20 ].

-The unified Building Law No.119 that was released in 2008 and its executive appendix, which was released by the Ministerial decree No. 144 in 2009, were not formulated having green concepts as a governing parameter [ 21 ].

On the other hand, there are studies that point to some reasons behind the inapplicability of the Green Pyramid Rating System (GPRS) in the Egyptian environment. For instance, the lack of knowledge or awareness by architects towards certain elements, principles, or even criteria when it comes to the GPRS. Also, the failure to adapt to the local context to cultural issues, resources, priorities, practices, and economic challenges.

According to Attia and Dabaieh [ 22 ], GPRS requires compliance with Egyptian and American codes at the same time, which has led to inconsistencies in some cases and requires a lot of effort. Furthermore, there are missing guidelines and documentation methods in some credits, for example, indoor air quality and material credits. In addition, GPRS ignores the local Egyptian built environment, for example, local building techniques, vernacular architecture, heat island effect, informal housing, natural ventilation and ceiling fans, solar water heating, Cairo air pollution, occupational behavior, health, Egyptian society, and economic aspects [ 22 ].

Furthermore, some studies highlighted the lack of a database related to (GPRS) certified materials that can be used as a benchmark for assessment and as a guide for the user. In spite of that, there are currently over 120 international green labeling programs for building materials worldwide [ 19 ]. As well as the lack of comprehensiveness in achieving the remains of social, cultural, and economic sustainability goals [ 23 ].

All the mentioned studies did not recognize role of project management, along with project manager competency, and how a poor management system could affect the successful implementation of green building projects anywhere, particularly with regard to overcoming the extracted obstacles, and helping in implementing successful green building projects as the previous studies emphasized as mentioned in the literature review.

From the systematic literature review, the research reached an important hypothesis, which is that green building situation in Egypt could be improved and go faster in steady steps by developing and improving the project management methods used in implementing the green building projects. Therefore, to experiment research hypothesis, it is needed to know how green building projects are managed in Egypt and study the management methods used in these projects.

The role of project management in green building project success

This section of the study aims to investigate how green-building projects are managed in Egypt. Moreover, discover if the way of managing these buildings affects project success in achieving the sustainability goals and whether it is among the factors leading to the obstruction of the construction of green buildings in Egypt. Furthermore, the study investigated how to overcome the obstacles identified in the research problem by project management. Accordingly, an online questionnaire and interviews were conducted with Egyptian project managers and green building administrators (with experience of 3 to 20 years in green buildings) who worked in green buildings in Egypt, whether registered or certified buildings, under LEED or GPRS. The results came as follows:

There is confusion between the roles of project managers and green building consultants in most cases, while the responsibilities of each of them are also unspecified.

In some cases, the project manager is not involved in the green building certification process and all responsibilities related to the green building process, and rating system certification is the green building consultant’s responsibility.

Involved personnel presented themselves as project manager and green building consultant at the same time, in spite of the fact that their responsibilities did not cover all aspects of project management. This means that there are some neglected management areas in the projects due to multiple responsibilities.

The project managers/green building consultant most involved in green building projects is at the construction stage, followed by the design development, then the schematic design and bid stage, then the conceptual design, and finally in the pre-concept design stage as shown in Fig.  2 .

The main role of the project manager in the green building project is sustainable management and then selection of rating system credits and verification of rating system achievement of prerequisites and credits. However, there are some management areas that do not get proper attention as other important issues such as time, quality, and risk management. Moreover, there is a neglected area such as stakeholder management, as shown in Fig.  3 .

Seventy percent of the results showed that there is no specific management methodology to be followed in managing green building projects, and the most used management methodology is Agile due to its ability to control project output and then Waterfall, Prince 2, Critical Path, and PM Book framework as shown in Fig.  4 .

The most commonly used software programs in managing green projects in Egypt are Revit, then Excel, Autodesk Green Builds, Primavera, and finally Microsoft Project and Green Wizard as shown in Fig.  5 .

The responsibility of choosing the methodologies and software programs used in green building projects falls on the project manager, then the green building consultant, and finally the project management office, as shown in Fig.  6 .

The main obstacles that project managers face when managing green buildings in Egypt by the order are as follows and shown in Fig.  7 :

The lack of awareness of contractors.

The absence of government incentives.

The lack of professional expertise.

The lack of recycling companies.

The lack of data on the lifecycle cost of available materials.

The lack of green resources and their data.

The lack of technology required for some credits.

57% of the results showed that there were no discrepancies between the estimated project cost and the final project cost achieved, while the other 43% who admitted that the discrepancy existed, 67% of them classified the discrepancy as minor, and 33% as intermediate, as shown in Fig.  8 .

The project phases in which project managers participate in green building projects. Ref: Researchers

The area of knowledge in which project managers participate in green building projects. Ref: Researchers

Project management methodologies most commonly used in green building projects. Ref: Researchers

The most used software programs in managing green building projects in Egypt. Ref: Researchers

Responsibility for defining project management methodologies and software. Ref: Researchers

The Obstacles that are facing project managers in green building projects in Egypt. Ref: Researchers

Discrepancies between the estimated project cost and final project cost achieved. Ref: Researchers

The reasons behind this discrepancy are the unrealistic estimation, the change in material costs, and those green building requirements that were overlooked in the early design phase.

Seventy-eight percent of the results showed that there are discrepancies between the estimated project duration and the final project duration achieved in green building projects, and these discrepancies are estimated as 50% intermediate and 25% major and 25% minor as shown in Fig.  9 . The reasons for this are that the process is not usually smooth, there are many project stops, the client changed the design, and the estimates are unrealistic.

Discrepancies between the estimated project duration and final project duration achieved. Ref: Researchers

Conclusions

In general, as mentioned in the literature review, there is a lack of project management methods/methodologies that address sustainable construction projects around the world. In addition, it is concluded from the study that there are many defects in the way that green building projects are managed in Egypt, which could be one more obstacle in addition to the set of obstacles extracted from the previous studies, which led to delays in the green building movement. As it is concluded from the questionnaire results, open questions, and the interview with the project managers as follows:

There is no clear methodology governing the green building management process in Egypt. All efforts in that field rely on the vision and experience of the project manager with the assistance of current general management methodologies such as Agile, PM BOOK, and Waterfall. As well as, these methodologies are not used efficiently to overcome the major obstacles and solve the problems that these projects are exposed to in Egypt.

The roles of project managers and green building consultants are unclear. Sometimes the project manager and the green consultant are the same person in charge of the managerial work as well as the technical consultant and certification process, which is a huge task, especially in large-scale projects. In other cases, the project manager is completely isolated from the sustainable management or the green certification process, which also leads to poor project bonding, and does not activate the principles of the integrative process.

Cases in which the project manager and the green building consultant are the same person, showed complete ignorance of some management knowledge areas like stakeholder management and weak risk management. As well as, the concept of a green project manager is missing, the person who has the project management knowledge, including management methodologies, methods, tools, and techniques, and has leadership skills to lead the entire project team and organize all project processes in an integrative manner holistically in the context of sustainability.

Late commissioning of a green consultant in the project or deciding to follow building green principles after the start-up design phase may result in repeat work, increase budget, schedule delays, and failure to obtain green building certification.

Stakeholder and risk management knowledge areas are the most neglected, although studies emphasize the importance of these areas in green building projects’ success.

Recommendations

 Green architecture needs to develop more simply applied management methods, methodologies, tools, and techniques in order to overcome some of the obstacles facing green buildings around the world, especially in Egypt, to encourage the sustainability movement.

There should be a distinction between the roles and responsibilities of project managers and green building consultants. The main factor in the success of the project is that everyone knows their role in the project and what their duties are.

The green building consultant is the person who leads the building certification process and must have knowledge of the technical data involved in green building construction, be supportive of the team on technical matters, and coordinate all project disciplines. Meanwhile, the green project manager is the person who deals with the management aspect of the project in the context of sustainability. In addition, he must be familiar with the principles of sustainability and green buildings, the requirements of the rating system, and the process involved in the system of certified green buildings.

The participation of a green project manager in the project from the pre-design stage is mandatory for organizing all project operations, maintaining project sequence, putting the project on track, recording and solving problems, making decisions, and others. Any delay in involving the green project manager or even the green consultant from the pre-design stage of the project affects the success of the project.

It is very important to incorporate green building requirements into the design from a very early stage. This doubles the ease of fulfilling these requirements and increases cost-efficiency in addition to saving time.

Overcoming issues of lack of knowledge of the team, contractors, suppliers, and operators through scheduled training during the project life cycle. This training should be continuous, repetitive, and defined in pre-design in a separate plan developed by the project managers.

Documentation of green building projects is a very important issue, especially for the certification process, so the documentation plan should be defined at the initiation stage and developed throughout the project life cycle.

Stakeholder management is an effective management area that needs to be noticed and given more attention by project managers in the field of green building.

Most of the obstacles that contradict sustainable construction in Egypt could be overcome by project management. The most important obstacles identified in the literature review and ranked by the project manager in the questionnaire are as follows, with some suggestions from the project management point of view:

Lack of awareness of contractors and professional expertise, which could be mitigated in the current projects and future projects by organizing scheduled training that is performed throughout the project life cycle. In addition, recording the lessons learned and sharing them within the organization and outside the organization, if possible, is an active action toward increasing awareness and professionalism in this field.

Through stakeholder management, project managers could participate with the authorized government agencies in the early project discussions to be involved in the project and recognize the benefits that the project will introduce to the surrounding environment and the community, which could lead to an increase in the authorized agency interest and recognition toward sustainable construction and green buildings and could lead to increase government incentives for the project.

National database systems for all available green building materials with lifecycle assessment data, recycling companies, sustainability, responsible manufacturers, and all required green building resources are needed to facilitate the green certification process and overcome the lack of information and verified green resources. The database systems should have frequent updates periodically to include all the new resources and companies.

Project managers could avoid or mitigate the problem of the lack of professionals or technology required for some credits by involving professionals or specialized agencies from abroad in the project, which required efficient human resources management and strong communication plans to acquire and manage the project virtual team effectively.

Finally, research on green building project management should be encouraged, especially at the local level, due to its important role in the success of the project, overcoming the obstacles that may face this type of construction, and the ability to organize the process and coordinate between several of its elements.

Availability of data and materials

All data generated or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Building Research Establishment’s Environmental Assessment Method

Green Pyramid Rating System

Individual competence baseline

International Organization for Standardization

Leadership in Energy and Environmental Design

Project Management Body of Knowledge

Sustainable development

Systematic literature review

US Green Building Council

Arslan, H.D. (2019). Ecological design approaches in mosque architecture. Int J Scientific Engineering Res. 10(12):1374–1377.

Arslan, H.D. Arslan, M.H. (2017). New trends on green buildings: investigation of the feasibility of using plastic members in RC buildings with SWs, 2nd International Conference on Green Energy Technology, IOP Conf. Series: Earth and Environmental Science 83 (2017). https://doi.org/10.1088/1755-1315/83/1/012022 .

World Comission on Environment and Development (WCED) (1987) Our common future. Oxford University Press, Oxford, UK

Google Scholar  

Green Project Management Institute (2019) The GPM P5™ standard for sustainability in project management, version 2.0. GPM Global, USA

World green building council (2022). https://www.worldgbc.org/what-green-building . Accessed 17 Feb 2022.

Project Management Institute (2017) A guide to the project management body of knowledge. Project Management Institute, Pennsylvania, PA

Bocchini, P., Frangopol, D.M., Ummenhofer, T., Zinke, T. (2014) Resilience and sustainability of civil infrastructure: toward a unified approach. J Infrastructure Systems, 20(2):1–16.

Marcelino-Sádaba, S., González-Jaen, L.F., Pérez-Ezcurdia, A. (2015) Using project management as a way to sustainability. From a comprehensive review to a framework definition, Journal of cleaner production, 99:1–16.

Brones, F., De Carvalho, M.M., De Senzi Zancul, E. (2014) Ecodesign in project management: a missing link for the integration of sustainability in product development? J Cleaner Prod. Volume 80.

Wu, P., Low, S.P. (2010) Project management and green buildings: lessons from the rating systems, Journal of professional issues in engineering education and practice, Volume 136. https://doi.org/10.106/(ASCE)EI.1943-5541.0000006 .

RSMeans (2011) Green building: project planning and cost estimating. John Wiley & Sons, Hoboken, NJ

Green Project Management (2013) Projects integrating sustainable methods: the GPM® reference guide to sustainability in project management, version 1. GPM Global, USA

Mustapha, M.A., Manan ,Z.A., Wan Alwi ,S.R .(2017) Sustainable green management system (SGMS) – an integrated approach towards organisational sustainability. J Cleaner Production, 146:158–172.

Martens, M.L., Carvalho, M.M. (2016) The challenge of introducing sustainability into project management function: multiple-case studies, J Cleaner Production, Volume 117.

Hwang, B.G., Ng, W.J. (2013) Project management knowledge and skills for green construction: overcoming challenges, International journal of project management. 3(2):272–284.

Wazeri Y (2003) Eco-friendly architectural design – towards green architecture. Madboli Library, Egypt

US green building council (2021). https://www.usgbc.org/projects . Accessed 13 Aug 2021.

Egyptian Green building council (2021). http://egypt-gbc.org/ratings.html . Accessed 13 Aug 2021.

Khalifa S, Abdelkader M, Eissa M, Hamdy A.M. (2018) Obstacles of application of green pyramid rating system (GPRS) on local projects in Egypt. Paper presented at the international conference on sustainability, green buildings, environmental engineering & renewable energy (SGER 2018), Kuala Lumpur, Malaysia

Azouz, M. (2018) The future of green building materials in Egypt: a framework for action. Resourceedings 1. https://doi.org/10.21625/resourceedings.v1i1.180

Ayyad K.M., Gabr M (2012) Greening building codes in Egypt. Paper presented at the sustainable futures: architecture and urbanism in the global south. Uganda Martyrs University, Kampala, Uganda, pp 15–30

Attia S (2013) The usability of green building rating systems in hot arid climates: a case study in Siwa, Egypt. Paper presented at the 4th biennial subtropical cities conference. Florida Atlantic University, Fort Lauderdale, FL, pp 17–19

Ammar, M.G. (2012) Evaluation of the green Egyptian pyramid. Alexandria Engineering J. Volume 51.

Download references

Acknowledgements

The authors would like to thank everyone who participated in answering the questionnaire.

Not applicable.

Author information

Authors and affiliations.

Department of Architecture, Faculty of Engineering, Cairo University, Giza, Egypt

Heba Farouk Abdelkhalik & Hisham Hussein Azmy

You can also search for this author in PubMed   Google Scholar

Contributions

All the authors have read and approved the manuscript. HF collected the study data and did the analysis. HH reviewed the study data and made the appropriate modifications, as well as both authors cooperated in the questionnaire structure.

Corresponding author

Correspondence to Heba Farouk Abdelkhalik .

Ethics declarations

Ethics approval and consent to participate.

The researchers of the current work used an online questionnaire on google form, which has been published on LinkedIn to induce experts to share their experience. https://docs.google.com/forms/d/e/1FAIpQLSeS0RLlFBmL7bebgFDobiwvNDtLdDtAYTq880MoLhXysLKWQA/viewform?usp=pp_url

The authors do believe that there is not a need for informed consent from experts as all the participants of the questionnaire gave their consent by answering the questionnaire on the google form link.

Consent for publication

The manuscript does not contain any individual person’s data in any form.

Competing interests

The authors declare that they have no competing interests.

Supplementary Information

Additional file 1..

Questionnaire.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Abdelkhalik, H.F., Azmy, H.H. The role of project management in the success of green building projects: Egypt as a case study. J. Eng. Appl. Sci. 69 , 61 (2022). https://doi.org/10.1186/s44147-022-00112-5

Download citation

Received : 23 November 2021

Accepted : 14 June 2022

Published : 09 July 2022

DOI : https://doi.org/10.1186/s44147-022-00112-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Sustainability
• Green building
• Rating systems
• Green pyramids

10 Most Inspirational Green Buildings in India

Green Buildings in India – The world is moving towards greenways in all aspects of life. Man has recognized the urgency of the need to preserve the nature around him. In architecture, green and sustainable building construction is becoming popular by the day. And then raises the question, what exactly is a green building? A green building is that which tries to eliminate the negative impact it has on the environment and attempts to leave a positive impact on the same. Green buildings in India help in sustaining the depleting natural resources, simultaneously increasing the quality of life.

Here are 10 of the most inspiring greenest building in India.

1. Rajiv Gandhi International Airport, Hyderabad | Greenest Building in India

The Rajiv Gandhi airport, commissioned in 2008, is one of the leading examples of highly environmentally sensitive infrastructure design and management in India. Spread over an area of 2223 hectares, the airport’s capacity stands at 12 million passengers and 0.3 million tons of cargo per annum. It is run in a public-private partnership by GMR Hyderabad International Airport Ltd (GHIAL). The GMR airport is the 1st airport in Asia and 2nd globally to have won a LEED silver rating certification. The environmental aspects taken into consideration are greenhouse gas emission management, carbon footprint reduction, material and energy intensity, green supply chain, clean energy use, waste and water consumption reduction, and management.

2. Suzlon One Earth Campus , Pune

The Suzlon one earth campus situated in Pune boasts of being one of the greenest corporate campuses in the world. 7% of the energy consumed by the campus is produced by on-site hybrid wind turbines, solar panels, and photovoltaic cells. Rest 93% is brought in from offsite wind turbines. The building as a whole has 154.83KW renewable energy incorporated. Designing of each of the components, from HVAC to the solar photovoltaic roof of the atrium, is done with special care administered for maximum utilization of green energy. Another interesting feature is the landscaping that has employed Xeriscaping with very efficient water management systems. Reflective pools form the main feature of the landscape design, which along with adding to the natural beauty of the campus, creates a cool microclimate in the surrounding structures.

3. Infinity Benchmark, Kolkata | Greenest Building in India

Located in Salt Lake City in Kolkata, the infinity benchmark is a 20 storied structure with a total floor area of 560,000 square feet. The company is Kolkata’s first LEED platinum-rated structure. From the conceptualization, the design stood for the exploitation of optimum resources, innovative sources for green energy, and protection of the environment around.

Green features of the building include- rooftop terrace garden , insulation layers in walls and ceilings that help in reducing heat consumption, zero discharge rainwater harvesting system, smart indoor air quality monitoring system that senses the CO2 and occupancy levels, effective glazing for natural lighting, low carbon footprint in construction, utilization of recyclable construction materials and even introduction of electric cars for commutation of the employees, with charging points provided.

4. The ITC Green Centre, Gurgaon

The ITC green center was the first corporate building in India to be certified LEED platinum in 2004, and it was the biggest platinum-rated green building with a floor area of 170,000 square feet. Green material like Fly-ash based concrete and Glass with 19% recycled content was used for building the majority of the façade. 10% of the total materials used were either recycled or obtained from other demolished sites. The rest of the construction was carried out by vernacular and low VOC materials .  The design of the ‘V’ shaped structure itself was cleverly carried out, to reduce the heat gain within the building. Stormwater harvesting and zero discharge water facilities are also provided.

5. CII-Sohrabji Godrej Green Business Centre, Hyderabad

CII-Sohrabji Godrej Green Business Centre (GBC), built in the cyber city of Hyderabad was set up with the idea of educating society about the need of adopting sustainable and green development. The center was the first building in India and the first outside the United States to acquire a LEED Platinum certification . The structure is located centrally in the flattened part of the site. Thick vegetation was developed surrounding it, to reduce heat gain and pollution. The interventions done for energy efficiency include- two air-cooling towers that cool air by up to 8 degrees, a terrace garden in about 55% of the roof, solar cells on the roof producing nearly 20% of the required energy. All materials were certified by the green building council, and 96% of construction waste was recycled.

6. Infosys Limited, Mysore | Greenest Buildings in India

Infosys Software Development Block 5 in Mysore, Karnataka, was certified LEED Platinum in the year 2012. Designed by architect Hafeez Contractor , this was the third Infosys building to achieve the feat, and it took up the total Platinum-certified building area at Infosys to 780,000 square feet.

Light shelves are installed on all the windows to ensure the utilization of daylight into the depths of the building. The lighting design within the building is so efficient that it is in fact 35% more efficient than the ASHRAE regulations . The 100% energy need of the building is facilitated by green power. The walls and roofs are well insulated and high-performance equipment and advanced automation are employed. All of these have resulted in a 40% saving of energy over the ASHRAE norms.

7. T-ZED Homes, Bengaluru

T-ZED, completed in 2009 is India’s first IGBC Platinum-rated residential apartment complex. The project was carried out by Biodiversity Conservation India Ltd (BCIL), Bangalore. Completed in 2009, the whole construction was done without the use of concrete blocks, bricks, vitrified tiles, chemical paints, or ceramics in its construction, and a very optimum amount of reinforced steel and composite cement was used. In the complex that spreads in 5 acres, there are 80 apartments and 15 individual houses.

A 44 interconnected rainwater percolation wells system, that is connected to a 400,000-liter underground water tank, purification system of water using reverse osmosis, utilization of greywater for irrigation and in toilets, a biogas digester for generating power from biodegradable wastes, the list of innovative sustainable interventions in the complex is remarkable. In the individual units, lights controlled by mobile phones, ‘water conscience meters’ etc are some modern innovations.

8. Raintree Hotel, Chennai

Raintree was the fifth hotel in India and the first in south India to get an Ecotel certification in 2006. Conservation of energy and water, solid waste management, employee environmental education and community involvement, and a steadfast commitment to the environment are the main focus areas of the hotel. The five-star hotel, constructed by Ceebros Property Development Limited , employed rubberwood, bamboo, medium density fiber, and Portland Pozzolana cement that contains 15 to 20% of fly ash in construction. The sewage treatment plant recycles water and uses it for air-conditioning, while the heat produced by air conditioners is used for heating water.

9. AnsalEsencia, Sector 67, Gurgaon  | Greenest Building in India

The Esencia Township, undertaken by Ansal API, was completed as the first project for Green Rating by GRIHA under the Integrated Habitat Assessment Norms. Often referred to as the greenest community in the NCR (National Capital Region), Esencia had employed several innovative technologies to make sure it reduced the impact it had on the environment.

The green interventions include –renewable solar energy used for street lights and hot water systems, sensor-controlled lighting, master switches in each apartment, trees with large foliages and green zones in landscaping, pedestrian and bicycle tracks, and an organized waste management system. The trees chosen for landscaping were carefully picked for a larger crown, faster growth, and reduction of air pollution.

10.  Patni Knowledge Center, Noida

This IT-BPO center in Noida, that’s spread over an area of 5 acres and seating of 3,500 people, is the second-largest Platinum-rated building in the world, and the largest Platinum-rated building outside the United States. The Knowledge Center has achieved this by practicing climatic responsive architecture . Over 50% of the site is a green area, 75% receives natural daylight, zero discharge, and 100% recycling of sewage is carried out, drip water irrigation and solar water heating are provided. Eco-friendly and recyclable materials were used along with low VOC emitting materials.  Air quality and CO2 level sensors are also employed.

The Indian market for green buildings is estimated to double in the near future as more citizens are getting aware of the negative consequences humans have caused to earth due to extensive use of minerals, manmade products, pesticides, etc. though it is not a very common concept in India, as of now, it is said to be beneficial for the growth.

Melva Joseph is a young, passionate architecture graduate from TKM College of Engineering, Kerala. Being extremely curious and adaptable made her an extensive reader, avid traveller and a good conversationalist. She holds close the belief that the existing gap between architecture and the common man should be bridged.

15 Famous Temples of South India

15 Places to Visit in Corsica For The Travelling Architect

Related posts.

Resilient Housing Solutions for Affordable and Sustainable Living

10 Projects by Architects that showcase traditional practices (abroad)

Trends in Sustainable Architecture

Sustainability and Universal Design: Accessibility of Green Buildings

Cities as a Forest

Gender Gaps in Urban Planning

• Architectural Community
• Architectural Facts
• RTF Architectural Reviews
• Architectural styles
• City and Architecture
• Fun & Architecture
• History of Architecture
• Design Studio Portfolios
• Designing for typologies
• RTF Design Inspiration
• Architecture News
• Career Advice
• Case Studies
• Construction & Materials
• Covid and Architecture
• Interior Design
• Know Your Architects
• Landscape Architecture
• Materials & Construction
• Product Design
• RTF Fresh Perspectives
• Sustainable Architecture
• Top Architects
• Travel and Architecture
• Rethinking The Future Awards 2022
• RTF Awards 2021 | Results
• GADA 2021 | Results
• RTF Awards 2020 | Results
• ACD Awards 2020 | Results
• GADA 2019 | Results
• ACD Awards 2018 | Results
• GADA 2018 | Results
• RTF Awards 2017 | Results
• RTF Sustainability Awards 2017 | Results
• RTF Sustainability Awards 2016 | Results
• RTF Sustainability Awards 2015 | Results
• RTF Awards 2014 | Results
• RTF Architectural Visualization Competition 2020 – Results
• Architectural Photography Competition 2020 – Results
• Designer’s Days of Quarantine Contest – Results
• Urban Sketching Competition May 2020 – Results
• RTF Essay Writing Competition April 2020 – Results
• Architectural Photography Competition 2019 – Finalists
• The Ultimate Thesis Guide
• Introduction to Landscape Architecture
• Perfect Guide to Architecting Your Career
• How to Design Architecture Portfolio
• How to Design Streets
• Introduction to Urban Design
• Introduction to Product Design
• Complete Guide to Dissertation Writing
• Introduction to Skyscraper Design
• Educational
• Hospitality
• Institutional
• Office Buildings
• Public Building
• Residential
• Sports & Recreation
• Temporary Structure
• Commercial Interior Design
• Corporate Interior Design
• Healthcare Interior Design
• Hospitality Interior Design
• Residential Interior Design
• Sustainability
• Transportation
• Urban Design
• Host your Course with RTF
• Architectural Writing Training Programme | WFH
• Editorial Internship | In-office
• Graphic Design Internship
• Research Internship | WFH
• Research Internship | New Delhi
• RTF | About RTF
• Submit Your Story

Technical skills building gets the green light in Salford

Proposals for a new flagship building for the Greater Manchester Institute of Technology (GMIoT) in Salford have been given the go-ahead. The new learning centre will focus on higher-level, real world technical education and training across the construction, engineering, computing, creative media, business and health sciences sectors.

The £14.3m building is due to start construction in Autumn this year at the heart of the University of Salford’s Peel Park Campus, just 1.5 miles from city centre of Manchester. Part of the University’s Campus Connectivity Plan, the building will be low carbon and feature a state-of-the-art green roof with photovoltaic panels to generate renewable energy, alongside a green living wall supported by rainwater harvesting.

Inside you’ll find cutting-edge spaces to support learning, including flexible digital labs, a prototyping workshop and ICT studios, all within close proximity to leafy, green Peel Park. The modern two-storey 1840 m² building has been designed by jmarchitects, with Tilbury Douglas named as the construction partner for the project.

Led by the University of Salford, with Wigan & Leigh College as the lead Further Education (FE) partner, the GMIoT brings together a number of colleges and employers in the city region. The new building will be a base for teaching the University’s GMIoT students and for students and staff from partner institutions to visit for collaborative work together and with industry.

The learning centre will deliver training and employment opportunities that are co-created with employers and specifically designed for the careers of the future, as well as responding to current workforce needs. For students seeking a more hands-on approach, a quicker route into the workforce, or an innovative alternative to the traditional three-year degree, the GMIoT offers the opportunity to make an impact in Salford and across the Manchester Technical City Region.

Jo Purves, Pro Vice-Chancellor Academic Development at the University of Salford, said: “I’m excited to see plans for our innovative GMIoT building come to life. Collaboration is in our DNA as a university and over the past few years we’ve been working alongside our further education and industry partners to envision a new home for our technical skills courses. We are also committed to ensuring the University’s expertise in research and innovation links directly to how we equip learners with skills for future employment in Greater Manchester.

“The GMIoT is a response to industry’s constantly evolving demand for skills, with courses on offer intended to fast track learners into jobs in growth sectors for the region. I’m proud to have the hub of this ambitious project based in Salford.”

The GMIoT is a reaction to Mayor of Greater Manchester Andy Burnham’s vision of an integrated technical education, skills and work city-region. It also supports the Greater Manchester Local Skills Improvement Plan (GM LSIP), which aims to make the technical and vocational education system more responsive to local skills needs and ultimately local economic needs.

Claire Foreman, Director of the GMIoT, said: "Watching our new hub take shape is truly inspiring. At the GMIoT, we’re passionate about nurturing local talent and fostering a vibrant community of learners. Our courses, shaped by industry experts, offer students not just knowledge but practical skills and real-world experience.

“With access to cutting-edge facilities and opportunities to connect with industry leaders, our students are stepping into the future equipped and empowered. Together, we’re shaping a brighter tomorrow for Greater Manchester."

The building is part of the University of Salford’s Campus Connectivity Plan, which is bringing together industry, education and innovation through their ever-evolving developments on campus.

David Shoreman, Architect at jmarchitects, said: “jmarchitects, together with the assembled design team, are delighted to commence into the next phase which will deliver the construction phase of the new GMIoT. The striking pattern of the shingle design reflects the idea of pixelation and how that is drawn together to form pattern or image through various digital platforms and technologies.”

Martin Horne, Regional Director for Tilbury Douglas in the North West, added: “Tilbury Douglas is proud to be the construction partner for the new GMIoT building. This project represents a major step forward for technical education in the region, and we are excited to deliver the cutting-edge, sustainable facility that aligns with the university’s vision for innovation.”

Find out more about the GMIoT here:  Greater Manchester Institute of Technology (gmiot.ac.uk)

*Please note images are conceptual designs and computer-generated impressions.

For all press office enquiries please email  [email protected] .

Related news

‘for salford, from salford’: new report reveals social and economic contribution made by the university of salford.

Following a national report commissioned by Universities UK showing the full scope of the...

All aboard the night bus!

Our Peel Park Campus was the host location for the launch of a very special service for...

Mentoring programme success: A case study of effective student mentoring

As a Philanthropy, Alumni and Supporter Engagement team, we take immense pride in the...