Document Type : Review Paper

Authors

1 Department of Agricultural Extension, Sylhet Agricultural University, Sylhet, Bangladesh

2 Department of Psychology, National University, Gazipur, Bangladesh

3 Department of Environmental Science, Bangladesh Agricultural University, Mymensingh, Bangladesh

10.52293/WES.4.2.619

Abstract

This study examines the concept of data-driven smart ecological urbanism, which aims to promote sustainable and environmentally conscious urban growth through the use of data-driven technology. The authors offer a thorough review of the current literature on the topic, particularly highlighting the advantages and challenges of data-driven methodologies in urbanism. They argue that data-driven urbanism can offer invaluable insights into ecological concerns and pinpoint areas that require development. However, they also underscore concerns related to data privacy, over-reliance on technology, and potential unexpected outcomes. This study reveals the potential of data-driven smart ecological urbanism, emphasizing the necessity of a cautious and analytical approach to ensure its sustainable and socially equitable application. This paper suggests that adopting data-driven smart ecological urbanism can substantially improve a city's sustainability and livability by providing deeper insights into the ecological ramifications of urban expansion. The integration of data analytics and machine learning bolsters cities' capacity to deliver more accurate environmental forecasts, paving the way for targeted interventions that mitigate negative impacts and promote urban resilience, and sustainability. Nevertheless, the incorporation of data-driven technology in urban planning is not without challenges. For data-driven urbanism to truly embody social justice and sustainability, it is crucial to address issues related to data privacy and security, unforeseen consequences, and dependency on technology. Without a comprehensive analysis of these concerns, the application of data-driven smart ecological urbanism might inadvertently introduce adverse effects on marginalized communities and fall short of its potential in supporting sustainable urban growth.

Keywords

 

OPEN ACCESS

©2024 The author(s). 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit: http://creativecommons.org/licenses/by/4.0/

PUBLISHER NOTE

Forscher Publisher remains neutral concerning jurisdictional claims in published maps and institutional affiliations.

CURRENT PUBLISHER

Forscher Publisher

 

Letters to Editor

WES Journal welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in WES should be sent to the editorial office of WES within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.
[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.
[3] Letters can be no more than 300 words in length.
[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.
[5] Anonymous letters will not be considered.
[6] Letter writers must include their city and state of residence or work.
[7] Letters will be edited for clarity and length.
Bibri, S. E. (2018). Transitioning from Smart Cities to Smarter Cities: The Future Potential of ICT of Pervasive Computing for Advancing Environmental Sustainability. Urban Book Series. https://doi.org/10.1007/978-3-319-73981-6_10
Bibri, S. E. (2021a). Data-driven smart sustainable cities of the future: urban computing and intelligence for strategic, short-term, and joined-up planning. Computational Urban Science, 1(1), 1–29. https://doi.org/10.1007/s43762-021-00008-9
Bibri, S. E. (2021b). The underlying components of data-driven smart sustainable cities of the future: a case study approach to an applied theoretical framework. European Journal of Futures Research, 9(1). https://doi.org/10.1186/s40309-021-00182-3
Bibri, S. E., & Krogstie, J. (2020). Data-Driven Smart Sustainable Cities of the Future : A Novel Model of Urbanism and Its Core Dimensions , Strategies , and Solutions. Journal of Futures Studies, 25(2), 77–94. https://doi.org/10.6531/JFS.202012
Branny, A., Møller, M. S., Korpilo, S., McPhearson, T., Gulsrud, N., Olafsson, A. S., … Andersson, E. (2022). Smarter greener cities through a social-ecological-technological systems approach. Current Opinion in Environmental Sustainability, 55, 1–11. https://doi.org/10.1016/j.cosust.2022.101168
Chao, K., Sarker, M. N. I., Ali, I., Firdaus, R. B. R., Azman, A., & Shaed, M. M. (2023). Big data-driven public health policy making: Potential for the healthcare industry. Heliyon, 9(9), e19681. https://doi.org/10.1016/j.heliyon.2023.e19681
Cheng, Z., Wang, L., & Zhang, Y. (2022). Does smart city policy promote urban green and low-carbon development? Journal of Cleaner Production, 379(P1), 134780. https://doi.org/10.1016/j.jclepro.2022.134780
Cugurullo, F. (2018). Exposing smart cities and eco-cities: Frankenstein urbanism and the sustainability challenges of the experimental city. Environment and Planning A: Economy and Space, 50(1), 73–92. https://doi.org/10.1177/0308518X17738535
D’Amico, G., Taddeo, R., Shi, L., Yigitcanlar, T., & Ioppolo, G. (2020). Ecological indicators of smart urban metabolism: A review of the literature on international standards. Ecological Indicators, 118(August). https://doi.org/10.1016/j.ecolind.2020.106808
Dong, F., Li, Y., Li, K., Zhu, J., & Zheng, L. (2022). Can smart city construction improve urban ecological total factor energy efficiency in China? Fresh evidence from generalized synthetic control method. Energy, 241, 122909. https://doi.org/10.1016/j.energy.2021.122909
Dunlap, A. (2023). The green economy as counterinsurgency, or the ontological power affirming permanent ecological catastrophe. Environmental Science and Policy, 139(October 2022), 39–50. https://doi.org/10.1016/j.envsci.2022.10.008
Engin, Z., van Dijk, J., Lan, T., Longley, P. A., Treleaven, P., Batty, M., & Penn, A. (2020a). Data-driven urban management: Mapping the landscape. Journal of Urban Management, 9(2), 140–150. https://doi.org/10.1016/j.jum.2019.12.001
Engin, Z., van Dijk, J., Lan, T., Longley, P. A., Treleaven, P., Batty, M., & Penn, A. (2020b). Data-driven urban management: Mapping the landscape. Journal of Urban Management, 9(2), 140–150. https://doi.org/10.1016/j.jum.2019.12.001
Galychyn, O., Fath, B. D., Shah, I. H., Buonocore, E., & Franzese, P. P. (2022). A multi-criteria framework for assessing urban socio-ecological systems: The emergy nexus of the urban economy and environment. Cleaner Environmental Systems, 5(December 2021), 100080. https://doi.org/10.1016/j.cesys.2022.100080
Geropanta, V., Karagianni, A., Mavroudi, S., & Parthenios, P. (2021). Exploring the relationship between the smart-sustainable city, well-being, and urban planning: An analysis of current approaches in Europe. Smart Cities and the un SDGs. Elsevier Inc. https://doi.org/10.1016/b978-0-323-85151-0.00010-5
Kremer, P., Haase, A., & Haase, D. (2019). The future of urban sustainability: Smart, efficient, green or just? Introduction to the special issue. Sustainable Cities and Society, 51(July). https://doi.org/10.1016/j.scs.2019.101761
Kutty, A. A., Wakjira, T. G., Kucukvar, M., Abdella, G. M., & Onat, N. C. (2022). Urban resilience and livability performance of European smart cities: A novel machine learning approach. Journal of Cleaner Production, 378(November 2021), 134203. https://doi.org/10.1016/j.jclepro.2022.134203
Li, X., Ma, C., & Lv, Y. (2022). Environmental Cost Control of Manufacturing Enterprises via Machine Learning under Data Warehouse. Sustainability, 14(18), 11571. https://doi.org/10.3390/su141811571
Liu, W., Wei, W., Yan, X., Dong, D., & Chen, Z. (2020). Sustainability risk management in a smart logistics ecological chain: An evaluation framework based on social network analysis. Journal of Cleaner Production, 276, 124189. https://doi.org/10.1016/j.jclepro.2020.124189
Lv, Y., Ma, C., Li, X., & Wu, M. (2021). Big data driven COVID-19 pandemic crisis management: Potential approach for global health. Archives of Medical Science, 17(3), 829–837. https://doi.org/10.5114/aoms/133522
Lv, Y., Ma, C., Wu, M., Li, X., & Hao, X. (2022). Assessment of Preschool’s Inclusive Participation in Social Responsibility Program Under Institutional Pressure: Evidence From China. Frontiers in Psychology, 13(March). https://doi.org/10.3389/fpsyg.2022.810719
Lv, Y., Wu, M., Ma, C., Hao, X., & Zeng, X. (2022). Assessment of the status quo of social responsibility performance of inclusive kindergartens: Evidence from China. PloS One, 17(11), e0272742. https://doi.org/10.1371/journal.pone.0272742
Ma, C., & Ding, L. (2020a). A research on the seasonal difference of air pollution in Chengdu. IOP Conference Series: Earth and Environmental Science, 569(1). https://doi.org/10.1088/1755-1315/569/1/012071
Ma, C., & Ding, L. (2020b). Empirical research on influential factors of air pollution in Chinese provincial capital cities. IOP Conference Series: Earth and Environmental Science, 601(1), 012011. https://doi.org/10.1088/1755-1315/601/1/012011
Ma, C., & Qirui, C. (2023). Spatial-temporal evolution pattern and optimization path of family education policy: An LDA thematic model approach. Heliyon, 9(7), e17460. https://doi.org/10.1016/j.heliyon.2023.e17460
Ma, C., Wang, F., & Lv, Y. (2023). Teaching effects of using bullet-screen technology during classes on students’ learning: The mediating effect of perceived interactivity. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12054-3
Macke, J., Rubim Sarate, J. A., & de Atayde Moschen, S. (2019). Smart sustainable cities evaluation and sense of community. Journal of Cleaner Production, 239. https://doi.org/10.1016/j.jclepro.2019.118103
Mortaheb, R., & Jankowski, P. (2022). Smart city re-imagined: City planning and GeoAI in the age of big data. Journal of Urban Management, 12(1), 4–15. https://doi.org/10.1016/j.jum.2022.08.001
Sarker, M. N. I., Yang, B., Lv, Y., Huq, M. E., Kamruzzaman, M. M., M, M. K., … M, M. K. (2020). Climate Change Adaptation and Resilience through Big Data. International Journal of Advanced Computer Science and Applications, 11(3), 533–539. https://doi.org/10.14569/IJACSA.2020.0110368
Sato, A., Tani, S., Sasaki, K., Lin, W., Furuya, J., & Hirai, C. (2019). People-Centric City Enabled by Digital Platforms. Accelerating Social Innovation through Global Open Collaborative Creation. Retrieved from https://www.vitacastle.com/rev/archive/2019/r2019_04/pdf/P074-080_R4a05.pdf
Sheikh, H., Mitchell, P., & Foth, M. (2023). More-than-human smart urban governance: A research agenda. Digital Geography and Society, 4(October 2022), 100045. https://doi.org/10.1016/j.diggeo.2022.100045
Song, M., Tan, K. H., Wang, J., & Shen, Z. (2022). Modeling and evaluating economic and ecological operation efficiency of smart city pilots. Cities, 124(October 2021), 103575. https://doi.org/10.1016/j.cities.2022.103575
Wahab, A. M., & Mohamed, L. (2022). Making Kuantan As People Centric City: Enabling Data-Driven Decisions Via Smart City Implementation. Journal of Public Security and Safety, 13(1), 103–133.
Wang, J., Huang, K., Liu, H., & Yu, Y. (2022). The ecological boundary gap is gradually tightening in China’s megacities: Taking Beijing as a case. Science of the Total Environment, 806, 151484. https://doi.org/10.1016/j.scitotenv.2021.151484
Wu, D., Xie, Y., & Lyu, S. (2023). Disentangling the complex impacts of urban digital transformation and environmental pollution: Evidence from smart city pilots in China. Sustainable Cities and Society, 88(October 2022), 104266. https://doi.org/10.1016/j.scs.2022.104266
Wu, M., Hao, X., Wan, X., Ma, C., & Wu, Y. (2022). Opportunities and Challenges of Joint Training of Postgraduate Students by the University-Industry Collaboration Institutions in Big Data Era. ACM International Conference Proceeding Series, 194–198. https://doi.org/10.1145/3524383.3524431
Wu, M., Yan, B., Huang, Y., & Sarker, M. N. I. (2022). Big Data-Driven Urban Management: Potential for Urban Sustainability. Land, 11(5), 680. https://doi.org/10.3390/land11050680
Wu, Y. (2022). Ecological Smart City Construction Based on Ecological Economy and Network Governance. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/5682965
Xiao, Q., Zhao, L., Ji, L., & Xia, J. (2022). Ecological characteristics of distributed energy planning in ecological urban renewal design. Energy Reports, 8, 13037–13046. https://doi.org/10.1016/j.egyr.2022.09.114
Yao, Z. H. O. U., Jiang, C., & Shan-shan, F. E. N. G. (2022). Effects of urban growth boundaries on urban spatial structural and ecological functional optimization in the Jining Metropolitan Area, China. Land Use Policy, 117(May 2021), 106113. https://doi.org/10.1016/j.landusepol.2022.106113
Zeng, X., Yu, Y., Yang, S., Lv, Y., & Sarker, M. N. I. (2022). Urban Resilience for Urban Sustainability: Concepts, Dimensions, and Perspectives. Sustainability, 14(5), 2481. https://doi.org/10.3390/su14052481