Artificial Intelligence in Net-Zero Carbon Emissions for Sustainable Building Projects: A Systematic Literature and Science Mapping Review
Abstract
:1. Introduction
- Analyze the annual publication trends of published articles and select peer-reviewed journals on AI in NZCEs for sustainable building projects.
- Apply a science mapping approach to analyze influential keywords and document analyses of AI in NZCEs for sustainable building projects.
- Identify and discuss mainstream research topics related to AI in NZCEs for sustainable building projects.
- Develop a framework for depicting research gaps and future research directions on AI in NZCEs for sustainable building projects.
2. Research Methods
2.1. Search Strategy
2.2. Selection Criteria
2.3. Science Mapping Analysis
2.4. Qualitative Discussion
3. Results
3.1. Annual Publication Trend
3.2. Selection of Relevant Peer-Reviewed Journals
3.3. Co-Occurrence Analysis of Keywords
- Building eco-friendly, efficient, and energy-efficient structures can significantly reduce the problems associated with excessive carbon emissions. It has been shown that quantifying and analyzing the carbon footprint of public buildings over their life cycle can reduce negative environmental impacts [73]. Tushar et al. [74] applied sensitivity analysis to reduce the carbon footprint, thus improving energy efficiency. Developing implicit databases is also a good way to reduce carbon emissions and can be combined with machine and deep learning algorithms to combat climate change and resource scarcity [75]. It has also been reported that embodied carbon can be used throughout the life cycle of a building to improve the safety and environmental impact of a building project [76,77,78,79]. Additionally, the heating and cooling aspects of buildings consume more energy; therefore, the development of intelligent control systems is necessary. To reduce emissions, scalability should be the focus [69].
- The use of AI to minimize carbon emissions in construction projects is the second cluster of research. AI can be used to create smart energy networks and reduce energy costs [80]. By applying AI techniques, building energy and carbon footprints can be used to predict energy consumption and CO2 emissions [81,82,83]. Deep learning and ML are branches of AI techniques that are widely used as data analytics techniques for reducing NZCEs for sustainable building projects. For example, ANN has been used to quantify environmental costs in residential buildings and optimize commercial building design [84,85]. To achieve this goal, Palladino [86] studied the use of ANN in specific energy strategies in the Umbria Region. It has been reported that the application of ML can reduce the power consumption of buildings and help optimize building performance in the design and development of smart buildings [87,88].
- A multi-objective optimization technique is proposed to reduce residential construction carbon emissions, accomplishing the dual goals of economic development and environmental conservation, and conforming to the sustainable development principle [89]. Multi-objective optimization combined with AI technology, can contribute to the development of sustainable buildings in terms of building material selection, retrofitting energy systems, and decision-making in building construction [90]. For example, the combination of an ANN with a multi-objective genetic algorithm can optimize the design of residential buildings [91,92]. Clustering techniques are integrated with multi-objective optimization to identify urban structures based on their energy performance. This strategy can be replicated in other cities to increase energy efficiency and execute carbon-cutting initiatives [70]. Multiple goals can help sustainable buildings achieve NZCEs.
- Improving energy consumption efficiency and strengthening building energy management are critical for mitigating the greenhouse effect and global warming trend [93]. Reduced carbon emissions, green buildings, and sustainable development have emerged as major concerns worldwide [2,94]. On the one hand, renewable energy-driven building systems based on solar and wind resources can reduce environmental effects and costs [95,96]. Building carbon emissions must be minimized to achieve energy sustainability [97]. However, focusing on building carbon emissions throughout their life cycle, including the design, transportation, construction, and operation stages, and quantifying them as environmental and carbon costs, can contribute to the long-term development of the construction industry [98]. In summary, reducing energy consumption can contribute to economic benefits and achieve sustainable development [77,99].
- In the face of serious problems posed by climate change, efficient ways to minimize carbon emissions in the construction sector are receiving considerable attention. China is attempting to assess the feasibility of NZCEs, provide a path to reduce emissions, adjust and optimize the industrial structure, and achieve the policy goals of green development and carbon neutrality [1,100]. The prediction of carbon emission intensity in different countries can help policymakers devise environmental policies to address the adverse environmental effects of climate change [101,102]. Enhancing building management systems and promoting smart buildings will also help reduce the energy footprint and continuously optimize building performance [88]. Carbon capture and storage technologies currently play an essential role in lowering carbon dioxide emissions; however, they face problems such as high costs and regulatory issues, and related technologies still need to be developed [103].
- Consider a structural design scheme for upgrading a building based on the decision support system (DSS). Carbon capture and storage technologies have been demonstrated in previous studies [104]. On the other hand, environmental considerations can be evaluated to assess building sustainability. As a result, the entire decision-making process can be optimized [105]. Simultaneously, DSS, combined with the predictive capabilities of ML to investigate the proper concrete mix proportions, can aid in assessing the impact of a building over its full life cycle, both in terms of environmental and financial expenses [72,106].
3.4. Document Analysis
4. Discussion
4.1. Mainstream Research Topics on AI in NZCEs for Sustainable Building Projects
4.1.1. Life Cycle Assessment and Carbon Footprint
4.1.2. Practical Applications of AI Techniques in Sustainable Buildings
4.1.3. Multi-Objective Optimization
4.1.4. Energy Management and Energy Efficiency
4.1.5. Carbon Emissions from Buildings
4.1.6. Decision Support System (DSS) and Sustainability
4.2. Research Gaps of AI in NZCEs for Sustainable Buildings
4.2.1. Existing Problems of the Life Cycle Assessment Method
4.2.2. Opportunities and Challenges Faced by AI Techniques in Sustainable Buildings
4.2.3. Scope of Application of Multi-Objective Modeling
4.2.4. Improvements in Energy Management and Efficiency
4.2.5. Raise Awareness of Reducing Carbon Emissions
4.2.6. Sustainable Development of Buildings
4.3. Research Trends of AI in NZCEs for Sustainable Building Projects
- Various factors, such as energy savings, emissions reduction, and the feasibility of financial costs, should be considered when adopting LCA methods.
- Improving the legal framework and international regulatory regime for the application of AI techniques to reduce carbon emissions.
- Balancing carbon emission reduction with other sustainability objectives in response to changes in building parameters.
- Empirical research on energy optimization strategies for different building scenarios.
- Construction industries and practitioners should actively implement carbon-neutral policies.
- Countries can share their experiences and work together to promote the development of sustainable buildings.
- Using DSS to provide data analyses and forecasts should incorporate more environmental parameters to enable decision-makers to make sustainable development decisions.
- Increased attention to decision-making processes and the implementation of program design to reduce carbon emissions.
5. Conclusions
5.1. Study Implications and Contributions
5.2. Limitations and Future Research Directions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Journal Name | Number of Relevant Articles | % Total Publications |
---|---|---|
Journal of Cleaner Production | 24 | 15.58 |
Applied Energy | 17 | 11.04 |
Energy and Buildings | 13 | 8.44 |
Energy | 10 | 6.49 |
Sustainability (Switzerland) | 9 | 5.84 |
Building and Environment | 5 | 3.25 |
Buildings | 5 | 3.25 |
Energies | 5 | 3.25 |
Sustainable Cities and Society | 5 | 3.25 |
Construction and Building Materials | 3 | 1.95 |
Engineering Applications of Artificial Intelligence | 3 | 1.95 |
Sensors | 3 | 1.95 |
Computers and Industrial Engineering | 2 | 1.30 |
International Journal of Low-Carbon Technologies | 2 | 1.30 |
Journal of Building Engineering | 2 | 1.30 |
Others | 46 | 29.87 |
Total | 154 |
Keywords | Occurrences | Average Publication Year | Links | Average Citations | Average Normalized Citations | Total Link Strength |
---|---|---|---|---|---|---|
Machine learning | 14 | 2021 | 12 | 29.79 | 1.08 | 14 |
Artificial intelligence | 15 | 2022 | 9 | 16.40 | 0.67 | 10 |
Life cycle assessment | 8 | 2019 | 7 | 28.25 | 0.79 | 9 |
Sustainability | 9 | 2020 | 8 | 32.78 | 1.31 | 9 |
Optimization | 5 | 2020 | 7 | 19.00 | 1.65 | 7 |
Carbon footprint | 6 | 2019 | 4 | 28.00 | 0.78 | 6 |
Energy consumption | 4 | 2019 | 6 | 55.00 | 1.96 | 6 |
Artificial neural network | 12 | 2021 | 5 | 28.25 | 1.47 | 6 |
Sensitivity analysis | 4 | 2021 | 5 | 29.25 | 1.46 | 5 |
Concrete | 3 | 2021 | 4 | 33.67 | 1.78 | 5 |
Energy efficiency | 12 | 2020 | 5 | 19.83 | 0.69 | 5 |
Renewable energy | 3 | 2023 | 4 | 0.67 | 0.54 | 4 |
Carbon emission | 6 | 2021 | 3 | 14.83 | 1.45 | 4 |
Climate change | 5 | 2016 | 4 | 36.40 | 0.88 | 4 |
Embodied carbon | 5 | 2021 | 3 | 16.60 | 0.70 | 3 |
Buildings | 3 | 2018 | 3 | 25.67 | 0.71 | 3 |
Building energy performance | 3 | 2020 | 1 | 58.00 | 2.19 | 2 |
Sustainable development | 3 | 2018 | 2 | 8.33 | 0.94 | 2 |
Energy conservation | 3 | 2020 | 2 | 14.33 | 0.86 | 2 |
Multi-objective optimization | 3 | 2023 | 2 | 0.67 | 0.54 | 2 |
Decision support system | 3 | 2012 | 1 | 118.00 | 1.80 | 1 |
Thermal energy storage | 3 | 2017 | 1 | 11.00 | 1.18 | 1 |
Compressive strength | 3 | 2020 | 1 | 19.33 | 0.78 | 1 |
Article | Title | Total Citations | Normalized Citations |
---|---|---|---|
[112] | Green IoT and edge AI as key technological enablers for a sustainable digital transition toward a smart circular economy: An industry 5.0 use case | 65 | 3.60 |
[110] | A hybrid decision support system for sustainable office building renovation and energy performance improvement | 238 | 2.88 |
[101] | Modeling carbon emission intensity: Application of artificial neural network | 125 | 2.82 |
[74] | An integrated approach of BIM-enabled LCA and energy simulation: The optimized solution toward sustainable development | 48 | 2.66 |
[114] | Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption, and CO2 emissions | 100 | 2.63 |
[117] | Modeling heating and cooling energy demands for building stock using a hybrid approach | 47 | 2.61 |
[90] | Machine learning modeling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making | 73 | 2.54 |
[118] | Designing sustainable concrete mixture by developing a new machine learning technique | 66 | 2.29 |
[111] | Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network | 115 | 2.25 |
[80] | Developing novel 5th generation district energy networks | 63 | 2.19 |
[119] | Design and implementation of cloud analytics-assisted smart power meters considering advanced artificial intelligence as edge analytics in demand-side management for smart homes | 97 | 2.19 |
[120] | Life cycle greenhouse gas emissions and energy use of polylactic acid, bio-derived polyethylene, and fossil-derived polyethylene | 58 | 2.02 |
[113] | A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behavior | 55 | 1.91 |
[70] | Grading buildings on energy performance using city benchmarking data | 84 | 1.9 |
[121] | Analyzing the influence factors of the carbon emissions from China’s building and construction industry from 2000 to 2015 | 81 | 1.83 |
[122] | The hourly life cycle carbon footprint of electricity generation in Belgium, bringing a temporal resolution in life cycle assessment | 84 | 1.76 |
[93] | Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads | 47 | 1.63 |
[123] | Development of electrochromic evacuated advanced glazing | 49 | 1.38 |
[124] | Low-carbon cold chain logistics using ribonucleic acid-ant colony optimization algorithm | 61 | 1.38 |
[125] | Data-driven strategic planning of building energy retrofitting: The case of Stockholm | 50 | 1.13 |
[115] | Life cycle assessment of a wooden single-family house in Sweden | 48 | 1.08 |
[116] | Development of a model for urban heat island prediction using neural network techniques | 69 | 1.00 |
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Li, Y.; Antwi-Afari, M.F.; Anwer, S.; Mehmood, I.; Umer, W.; Mohandes, S.R.; Wuni, I.Y.; Abdul-Rahman, M.; Li, H. Artificial Intelligence in Net-Zero Carbon Emissions for Sustainable Building Projects: A Systematic Literature and Science Mapping Review. Buildings 2024, 14, 2752. https://doi.org/10.3390/buildings14092752
Li Y, Antwi-Afari MF, Anwer S, Mehmood I, Umer W, Mohandes SR, Wuni IY, Abdul-Rahman M, Li H. Artificial Intelligence in Net-Zero Carbon Emissions for Sustainable Building Projects: A Systematic Literature and Science Mapping Review. Buildings. 2024; 14(9):2752. https://doi.org/10.3390/buildings14092752
Chicago/Turabian StyleLi, Yanxue, Maxwell Fordjour Antwi-Afari, Shahnawaz Anwer, Imran Mehmood, Waleed Umer, Saeed Reza Mohandes, Ibrahim Yahaya Wuni, Mohammed Abdul-Rahman, and Heng Li. 2024. "Artificial Intelligence in Net-Zero Carbon Emissions for Sustainable Building Projects: A Systematic Literature and Science Mapping Review" Buildings 14, no. 9: 2752. https://doi.org/10.3390/buildings14092752
APA StyleLi, Y., Antwi-Afari, M. F., Anwer, S., Mehmood, I., Umer, W., Mohandes, S. R., Wuni, I. Y., Abdul-Rahman, M., & Li, H. (2024). Artificial Intelligence in Net-Zero Carbon Emissions for Sustainable Building Projects: A Systematic Literature and Science Mapping Review. Buildings, 14(9), 2752. https://doi.org/10.3390/buildings14092752