List of technological enablers and barriers identified in the academic literature
| Author(s) | Technological enabler/barrier for circular economy | Technological tool(s) |
|---|---|---|
| Ababio and Lu (2023) | Technology and innovation | 3D printing, DT, IoT |
| Adams et al. (2017) | Material recovery technologies | |
| Antwi-Afari et al. (2021) | Improved product takeback | BIM, AI, IoT, RFID |
| Real-time information on inventory of circular materials | BIM, RFID, CT | |
| Argus et al. (2020) | Material and product data | BIM, BCT |
| Track and trace technology | ||
| Cetin et al. (2021) | Enabling technologies for “Closing” resource loops | AM, AI, BIM, BDA, MP, IoT, DT, GIS, MP, BCT |
| Technological enablers for “Slowing” the loop | ||
| Technological enablers for “Narrowing” resource loops | ||
| Technological enablers for “Regenerating” resource loops | ||
| Das et al. (2023) | Technology as an enabler of circular business models | BIM |
| Hart et al. (2019) | R&D and innovation | 3D printing, sensors and controls, IoT |
| Illankoon and Vithanage (2023) | Reducing rework and promoting end-of-life disassembly | BIM, VR, digital twin |
| Lekan et al. (2021) | Improved productivity and efficiency across construction stages | AI, BIM, AR, RFID, IoT, CT, AM sensors |
| Munaro and Tavares (2023) | Absence of technologies and infrastructure | IMS, BIM, MP |
| Development of technologies and tools enhancing circular buildings | ||
| Nascimento et al. (2019) | Use of advanced printing technologies to improve productivity and reliability | 3D printing, AM |
| Oluleye et al. (2023) | Optimised collection of waste | AI |
| Implementing reverse logistics | ||
| Estimating construction and demolition waste generation | ||
| Predicting hazardous building materials | ||
| Estimating technical and economic value of circular materials | ||
| Schut et al. (2015) | Technologies promoting “R” principles | MP |
| Setaki and van Timmeren (2022) | Optimise material use | 3D printing, IoT, BIM, drones, AR, BCT |
| Waste recovery | BIM, AI, drones | |
| Wuni (2023a) | Digital integration | BIM, BCT |
| Supportive technological infrastructure | ||
| Skills, capabilities and technical know-how | ||
| Upskilling, training and capacity building | ||
| Process integration technology | ||
| Wuni (2023b) | Supportive infrastructure and technology | |
| Material recovery technologies | ||
| Information and communication technologies | ||
| Transfer of information and availability of data | ||
| Technologies enabling process integration | ||
| aSoori and Vishwas (2013) | Energy cost savings | LED, CFL, lighting controls (sensors, actuators, dimmers, transformers, BMS, DDC) |
| Support effective thermal insulation | ||
| aHammes et al. (2024) | Zoned lighting depending on individual lighting preference | PIR sensors |
| Automated user-related decision making | ML, controls | |
| Adaptive lighting to individual occupancy patters | PIR sensors |
| Author(s) | Technological enabler/barrier for circular economy | Technological tool(s) |
|---|---|---|
| Technology and innovation | 3D printing, DT, IoT | |
| Material recovery technologies | ||
| Improved product takeback | BIM, AI, IoT, RFID | |
| Real-time information on inventory of circular materials | BIM, RFID, CT | |
| Material and product data | BIM, BCT | |
| Track and trace technology | ||
| Enabling technologies for “ | AM, AI, BIM, BDA, MP, IoT, DT, GIS, MP, BCT | |
| Technological enablers for “ | ||
| Technological enablers for “ | ||
| Technological enablers for “Regenerating” resource loops | ||
| Technology as an enabler of circular business models | BIM | |
| R&D and innovation | 3D printing, sensors and controls, IoT | |
| Reducing rework and promoting end-of-life disassembly | BIM, VR, digital twin | |
| Improved productivity and efficiency across construction stages | AI, BIM, AR, RFID, IoT, CT, AM sensors | |
| Absence of technologies and infrastructure | IMS, BIM, MP | |
| Development of technologies and tools enhancing circular buildings | ||
| Use of advanced printing technologies to improve productivity and reliability | 3D printing, AM | |
| Optimised collection of waste | AI | |
| Implementing reverse logistics | ||
| Estimating construction and demolition waste generation | ||
| Predicting hazardous building materials | ||
| Estimating technical and economic value of circular materials | ||
| Technologies promoting “R” principles | MP | |
| Optimise material use | 3D printing, IoT, BIM, drones, AR, BCT | |
| Waste recovery | BIM, AI, drones | |
| Digital integration | BIM, BCT | |
| Supportive technological infrastructure | ||
| Skills, capabilities and technical know-how | ||
| Upskilling, training and capacity building | ||
| Process integration technology | ||
| Supportive infrastructure and technology | ||
| Material recovery technologies | ||
| Information and communication technologies | ||
| Transfer of information and availability of data | ||
| Technologies enabling process integration | ||
| Energy cost savings | LED, CFL, lighting controls (sensors, actuators, dimmers, transformers, BMS, DDC) | |
| Support effective thermal insulation | ||
| Zoned lighting depending on individual lighting preference | PIR sensors | |
| Automated user-related decision making | ML, controls | |
| Adaptive lighting to individual occupancy patters | PIR sensors |
Note(s): Abbreviations
AI: Artificial intelligence,
AM: Additive manufacturing,
AR/VR: Augmented reality/virtual reality,
BDA: Big data analytics,
BCT: Blockchain technology,
BIM: Building information modelling,
BMS: Building management systems,
CT: Cloud technology,
DT: Digital twins,
GIS: Geographical information systems,
IMS: Information management systems,
IoT: Internet of Things,
MP: Material passports,
RFID: Radio frequency identification,
CFL: Compact fluorescent lamp,
LED: Light-emitting diode,
DDC: Direct digital controllers,
PIR: Passive infrared sensors,
aLiterature related to lighting systems in buildings
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