IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i15p6285-d1440883.html
   My bibliography  Save this article

Data-Driven Net-Zero Carbon Monitoring: Applications of Geographic Information Systems, Building Information Modelling, Remote Sensing, and Artificial Intelligence for Sustainable and Resilient Cities

Author

Listed:
  • Jilong Li

    (School of Built Environment, Kensington Campus, The University of New South Wales, Sydney, NSW 2052, Australia)

  • Sara Shirowzhan

    (School of Built Environment, Kensington Campus, The University of New South Wales, Sydney, NSW 2052, Australia)

  • Gloria Pignatta

    (School of Built Environment, Kensington Campus, The University of New South Wales, Sydney, NSW 2052, Australia)

  • Samad M. E. Sepasgozar

    (School of Built Environment, Kensington Campus, The University of New South Wales, Sydney, NSW 2052, Australia)

Abstract

NZCCs aim to minimise urban carbon emissions for healthier cities in line with national and international low-carbon targets and Sustainable Development Goals (SDGs). Many countries have recently adopted Net-Zero Carbon City (NZCC) policies and strategies. While there are many studies available on NZCC cities’ definitions and policymaking, currently, research is rare on understanding the role of urban data-driven technologies such as Building Information Modelling (BIM) and Geographic Information Systems (GIS), as well as AI, for achieving the goals of NZCCs in relation to sustainable development goals (SDGs), e.g., SDGs 3, 7,11, 13, and 17. This paper aims to fill this gap by establishing a systematic review and ascertaining the opportunities and barriers of data-driven approaches, analytics, digital technologies, and AI for supporting decision-making and monitoring progress toward achieving NZCC development and policy/strategy development. Two scholarly databases, i.e., Web of Science and Scopus databases, were used to find papers based on our selected relevant keywords. We also conducted a desktop review to explore policies, strategies, and visualisation technologies that are already being used. Our inclusion/exclusion criteria refined our selection to 55 papers, focusing on conceptual and theoretical research. While digital technologies and data analytics are improving and can help in the move from net-zero carbon concepts and theories to practical analysis and the evaluation of cities’ emission levels and in monitoring progress toward reducing carbon, our research shows that these capabilities of digital technologies are not used thoroughly yet to bridge theory and practice. These studies ignore advanced tools like city digital twins and GIS-based spatial analyses. No data, technologies, or platforms are available to track progress towards a NZCC. Artificial Intelligence, big data collection, and analytics are required to predict and monitor the time it takes for each city to achieve net-zero carbon emissions. GIS and BIM can be used to estimate embodied carbon and predict urban development emissions. We found that smart city initiatives and data-driven decision-making approaches are crucial for achieving NZCCs.

Suggested Citation

  • Jilong Li & Sara Shirowzhan & Gloria Pignatta & Samad M. E. Sepasgozar, 2024. "Data-Driven Net-Zero Carbon Monitoring: Applications of Geographic Information Systems, Building Information Modelling, Remote Sensing, and Artificial Intelligence for Sustainable and Resilient Cities," Sustainability, MDPI, vol. 16(15), pages 1-26, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6285-:d:1440883
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/15/6285/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/15/6285/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ehab Shahat & Chang T. Hyun & Chunho Yeom, 2021. "City Digital Twin Potentials: A Review and Research Agenda," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    2. Li, Francis G.N. & Bataille, Chris & Pye, Steve & O'Sullivan, Aidan, 2019. "Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?," Applied Energy, Elsevier, vol. 239(C), pages 991-1002.
    3. Luo, Haizhi & Li, Yingyue & Gao, Xinyu & Meng, Xiangzhao & Yang, Xiaohu & Yan, Jinyue, 2023. "Carbon emission prediction model of prefecture-level administrative region: A land-use-based case study of Xi'an city, China," Applied Energy, Elsevier, vol. 348(C).
    4. Huiping Wang & Zhun Zhang, 2022. "Forecasting CO 2 Emissions Using A Novel Grey Bernoulli Model: A Case of Shaanxi Province in China," IJERPH, MDPI, vol. 19(9), pages 1-22, April.
    5. B. Dan Wood & Arnold Vedlitz, 2007. "Issue Definition, Information Processing, and the Politics of Global Warming," American Journal of Political Science, John Wiley & Sons, vol. 51(3), pages 552-568, July.
    6. Ziyu Duan & Seiyong Kim, 2023. "Progress in Research on Net-Zero-Carbon Cities: A Literature Review and Knowledge Framework," Energies, MDPI, vol. 16(17), pages 1-27, August.
    7. Omer Saud Azeez & Biswajeet Pradhan & Helmi Z. M. Shafri, 2018. "Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    8. Hui Jin, 2021. "Prediction of direct carbon emissions of Chinese provinces using artificial neural networks," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-17, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jeremiah Bohr, 2017. "Is it hot in here or is it just me? Temperature anomalies and political polarization over global warming in the American public," Climatic Change, Springer, vol. 142(1), pages 271-285, May.
    2. Donatella Baiardi, 2021. "What do you think about climate change?," Working Papers 477, University of Milano-Bicocca, Department of Economics, revised Aug 2021.
    3. Delshad, Ashlie B. & Raymond, Leigh & Sawicki, Vanessa & Wegener, Duane T., 2010. "Public attitudes toward political and technological options for biofuels," Energy Policy, Elsevier, vol. 38(7), pages 3414-3425, July.
    4. Lawrence C. Hamilton, 2016. "Public Awareness of the Scientific Consensus on Climate," SAGE Open, , vol. 6(4), pages 21582440166, November.
    5. Maowen Sun & Boyi Liang & Xuebin Meng & Yunfei Zhang & Zong Wang & Jia Wang, 2024. "Study on the Evolution of Spatial and Temporal Patterns of Carbon Emissions and Influencing Factors in China," Land, MDPI, vol. 13(6), pages 1-24, June.
    6. Vittorio Astarita & Giuseppe Guido & Sina Shaffiee Haghshenas & Sami Shaffiee Haghshenas, 2024. "Risk Reduction in Transportation Systems: The Role of Digital Twins According to a Bibliometric-Based Literature Review," Sustainability, MDPI, vol. 16(8), pages 1-26, April.
    7. Mohamed M. Mostafa, 2016. "Post-materialism, Religiosity, Political Orientation, Locus of Control and Concern for Global Warming: A Multilevel Analysis Across 40 Nations," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 128(3), pages 1273-1298, September.
    8. Raya Muttarak & Thanyaporn Chankrajang, 2015. "Who is concerned about and takes action on climate change? Gender and education divides among Thais," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 13(1), pages 193-220.
    9. Shahzaib Alam, 2025. "Digital Twins as the Predictive Core of Smart Cities: A Systematic Review and Conceptual Framework for Sustainable Urban Futures," Frontiers in Computational Spatial Intelligence, 50sea, vol. 3(3), pages 154-164, September.
    10. Xianwang Li & Zhongxiang Huang & Saihu Liu & Jinxin Wu & Yuxiang Zhang, 2023. "Short-Term Subway Passenger Flow Prediction Based on Time Series Adaptive Decomposition and Multi-Model Combination (IVMD-SE-MSSA)," Sustainability, MDPI, vol. 15(10), pages 1-30, May.
    11. Rosenfelder, Markus & Wussow, Moritz & Gust, Gunther & Cremades, Roger & Neumann, Dirk, 2021. "Predicting residential electricity consumption using aerial and street view images," Applied Energy, Elsevier, vol. 301(C).
    12. Joe F. Bozeman & Shauhrat S. Chopra & Philip James & Sajjad Muhammad & Hua Cai & Kangkang Tong & Maya Carrasquillo & Harold Rickenbacker & Destenie Nock & Weslynne Ashton & Oliver Heidrich & Sybil Der, 2023. "Three research priorities for just and sustainable urban systems: Now is the time to refocus," Journal of Industrial Ecology, Yale University, vol. 27(2), pages 382-394, April.
    13. An, Yimeng & Dang, Yaoguo & Wang, Junjie & Zhou, Huimin & Mai, Son T., 2024. "Mixed-frequency data Sampling Grey system Model: Forecasting annual CO2 emissions in China with quarterly and monthly economic-energy indicators," Applied Energy, Elsevier, vol. 370(C).
    14. Yaxin Tian & Xiang Ren & Keke Li & Xiangqian Li, 2025. "Carbon Dioxide Emission Forecast: A Review of Existing Models and Future Challenges," Sustainability, MDPI, vol. 17(4), pages 1-29, February.
    15. Jian Zhang & Jingyang Liu & Li Dong & Qi Qiao, 2022. "CO 2 Emissions Inventory and Its Uncertainty Analysis of China’s Industrial Parks: A Case Study of the Maanshan Economic and Technological Development Area," IJERPH, MDPI, vol. 19(18), pages 1-14, September.
    16. Deng Ai & Da Kuang & Yiqi Tao & Fanbo Zeng, 2025. "Integrating Image Recognition, Sentiment Analysis, and UWB Tracking for Urban Heritage Tourism: A Multimodal Case Study in Macau," Sustainability, MDPI, vol. 17(17), pages 1-21, August.
    17. Yamei Chen & Chao Zhang, 2024. "Characteristics of Spatial–Temporal Evolution of Carbon Emissions from Land Use and Analysis of Influencing Factors in Hubao-Eyu Urban Agglomerations, China," Sustainability, MDPI, vol. 16(17), pages 1-25, September.
    18. Zhongwei Liu & William Smith & Ahmad Safi, 2014. "Rancher and farmer perceptions of climate change in Nevada, USA," Climatic Change, Springer, vol. 122(1), pages 313-327, January.
    19. Mohamed M. Mostafa, 2020. "Catastrophe Theory Predicts International Concern for Global Warming," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(3), pages 709-731, September.
    20. Issam A. R. Moghrabi & Sameer Ahmad Bhat & Piotr Szczuko & Rawan A. AlKhaled & Muneer Ahmad Dar, 2023. "Digital Transformation and Its Influence on Sustainable Manufacturing and Business Practices," Sustainability, MDPI, vol. 15(4), pages 1-35, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6285-:d:1440883. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.