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Research Product Types: Journal Article

peer-reviewed article in academic journal

From Tweets to Energy Trends (TwEn): An exploratory framework for machine learning-based forecasting of urban-scale energy behavior leveraging social media data

Abbasabadi, N., & Ashayeri, M. (2024). From Tweets to Energy Trends (TwEn): An exploratory framework for machine learning-based forecasting of urban-scale energy behavior leveraging social media data. Energy and Buildings, 317, 114440-. https://doi.org/10.1016/j.enbuild.2024.114440

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Abstract

TwEn framework links tweet frequency with urban energy use patterns. AI models forecast energy from social media data sourced from X. Framework predicts NYC electricity use with high accuracy. Tweet frequency’s seasonal stability enhances energy research. Real-time social data can aid sustainable urban energy policy. Understanding energy behavior is crucial in addressing climate change, yet the accuracy of energy predictions is often limited by reliance on oversimplified occupancy data. This study develops an exploratory framework, from Tweets to Energy Trends (TwEn), leveraging machine learning and geo-tagged social media data to investigate the social dynamics of urban energy behavior. TwEn explores the relationship between social media interactions, specifically the frequency of tweets using data from the X Platform, and energy use patterns on an hourly basis. Employing various machine learning models, including artificial neural networks (ANN), decision tree (DTREE), random forest (RDF), and gradient boosting machine (GBM), the study evaluates their efficiency in both static and time-series forecasting of energy use trends and investigates the capability of social media data in predicting urban energy patterns. In addition, the study carries out a series of sensitivity analyses to provide an examination of the data and models. Furthermore, comprehensive data acquisition methods are developed and implemented. Tested on New York City using actual hourly electricity consumption data, the framework demonstrates significant predictive power of tweet frequency on urban electricity use. The framework also exhibits significant seasonality in X data, identifying patterns and trends that can inform time series urban building energy models (UBEM). The results offer new insights into the determinants of urban energy behavior and provide crucial perspectives for augmenting UBEMs, ensuring they are closely aligned with the complex social dynamics of contemporary urban environments. By integrating both digital and physical data, this study sheds light on urban energy behavior, supporting the formulation of effective and sustainable energy policies for urban futures.

Keywords

Occupancy; Social Media; Time Series Forecast; Urban Building Energy Modeling (UBEM); Urban Energy Behavior

Awareness, adoption readiness and challenges of railway 4.0 technologies in a developing economy

Awodele, I. A., Mewomo, M. C., Municio, A. M. G., Chan, A. P. C., Darko, A., Taiwo, R., Olatunde, N. A., Eze, E. C., & Awodele, O. A. (2024). Awareness, adoption readiness and challenges of railway 4.0 technologies in a developing economy. Heliyon, 10(4), e25934–e25934. https://doi.org/10.1016/j.heliyon.2024.e25934

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Abstract

The railway industry has witnessed increasing adoption of digital technologies, known as Railway 4.0, that is revolutionizing operations, infrastructure, and transportation systems. However, developing countries face challenges in keeping pace with these technological advancements. With limited research on Railway 4.0 adoption in developing countries, this study was motivated to investigate the awareness, readiness, and challenges faced by railway professionals towards implementing Railway 4.0 technologies. The aim was to assess the level of awareness and preparedness and identify the key challenges influencing Railway 4.0 adoption in Nigeria's railway construction industry. A questionnaire survey (was distributed to professionals in the railway construction sector to gather their perspectives on awareness of, preparation for, and challenges associated with the use of Railway 4.0 technologies. The results revealed that awareness of Railway 4.0 technologies was moderate, while readiness was low among the professionals. Using exploratory factor analysis, 10 underlying challenge constructs were identified including lack of technical know-how, resistance to change, infrastructure limitations, and uncertainty about benefits, amongst others. Partial Least Square Structural Equation Modelling (PLS-SEM) confirmed these constructs, with reliability and availability, lack of technical know-how, lack of training and resources, and uncertainties in benefit and gains having significant influence on awareness and readiness. The study concludes that focused efforts in training, infrastructure improvement, supportive policies, and communicating the advantages of Railway 4.0 are critical to drive adoption in Nigeria and other developing economies. The findings provide insights into tailoring Railway 4.0 implementation strategies for developing contexts.

Keywords

Railway 4.0; Awareness; Readiness; Challenges; Technologies

Key performance indicators for hospital planning and construction: a systematic review and meta-analysis

Liu, W., Chan, A.P.C., Chan, M.W., Darko, A. and Oppong, G.D. (2024), “Key performance indicators for hospital planning and construction: a systematic review and meta-analysis”, Engineering, Construction and Architectural Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ECAM-10-2023-1060

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Abstract

Purpose
The successful implementation of hospital projects (HPs) tends to confront sundry challenges in the planning and construction (P&C) phases due to their complexity and particularity. Employing key performance indicators (KPIs) facilitates the monitoring of HPs to advance their successful delivery. This study aims to comprehensively investigate the KPIs for hospital planning and construction (HPC).

Design/methodology/approach
The KPIs for HPC were identified through a systematic review. Then a comprehensive assessment of these KPIs was performed utilizing a meta-analysis method. In this process, basic statistical analysis, subgroup analysis, sensitive analysis and publication bias analysis were performed.

Findings
Results indicate that all 27 KPIs identified from the literature are significant for executing HPs in P&C phases. Also, some unconventional performance indicators are crucial for implementing HPs, such as “Project monitoring effectiveness” and “Industry innovation and synergy,” as their high significance is reflected in this study. Despite the fact that the findings of meta-analysis are more trustworthy than those of individual studies, a high heterogeneity still exists in the findings. It highlights the inherent uncertainty in the construction industry. Hence, this study applied subgroup analysis to explore the underlying factors causing the high level of heterogeneity and used sensitive analysis to assess the robustness of the findings.

Originality/value
There is no consensus among the prior studies on KPIs for HPC specifically and their degree of significance. Additionally, few reviews in this field have focused on the reliability of the results. This study comprehensively assesses the KPIs for HPC and explores the variability and robustness of the results, which provides a multi-dimensional perspective for practitioners and the research community to investigate the performance of HPs during the P&C stages.

Keywords

Key performance indicators; hospital projects; planning and construction; systematic review; meta-analysis; project monitoring effectiveness; industry innovation and synergy

Driving factors for the adoption of green finance in green building for sustainable development in developing countries: The case of Ghana

Debrah, C., Chan, A. P. C., Darko, A., Ries, R. J., Ohene, E., & Tetteh, M. O. (2024). Driving factors for the adoption of green finance in green building for sustainable development in developing countries: The case of Ghana. Sustainable Development, https://doi.org/10.1002/sd.3022

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Abstract

While there are many motivating factors for green finance (GF) implementation, a comprehensive taxonomy of these variables is lacking in the literature, especially for green buildings (GBs). This study aims to analyze the criticality and interdependence of GF-in-GB's driving factors. This study develops a valid set of factors to justify the interrelationships among the drivers. The drivers of GF-in-GB are qualitative in nature, and uncertainties exist among them due to linguistic preferences. This study applies the fuzzy Delphi method to validate eight drivers under uncertainties. Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) with qualitative information is used to determine the interrelationships among the drivers. The drivers were grouped under two categories: prominent drivers and cause-effect drivers. The findings revealed that “increased awareness of GF models in GB” and “preferential capital requirements for low-carbon assets” are the top two most prominent/important drivers of GF-in-GB. In Ghana, the top three cause group drivers are “climate commitment,” “improved access to and lower cost of capital,” and “favorable macroeconomic conditions and investment returns.” Drivers with the highest prominence values have the potential to affect and/or be affected by other drivers; therefore, managers and policymakers should prioritize promoting or pursuing these drivers in the short term. On the other hand, it is important to pay more than equal attention to the drivers with the highest net cause values because they have the largest long-term impact on the entire system. The theoretical and practical implications of the study are discussed, enhancing understanding and decision-making in GF-in-GB.

The impact of penalties, incentives, and monitoring costs on the stakeholders’ decision-making behaviors in non-compliance drone operations

Wang, X., Yang, Y., Darko, A., Chan, A. P. C., & Chi, H.-L. (2024). The impact of penalties, incentives, and monitoring costs on the stakeholders’ decision-making behaviors in non-compliance drone operations. Technology in Society, 77. https://doi.org/10.1016/j.techsoc.2024.102589

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Abstract

As an automated assistive tool, drones have revolutionized industrial activities and brought numerous potential benefits to society. However, irresponsible drone users often disregard compliance with regulations, leading to new challenges in drone usage. Although governments have implemented punishment and incentive mechanisms to prevent non-compliant drone operations, the extent to which they can effectively deter such activities remains unclear. To address this gap, the study employed evolutionary game theory to assess the impacts of penalties for non-compliance, incentives for public monitoring, and monitoring costs for the government on the multiple stakeholders' decision-making behaviors (SDBs). The study also used the Chinese construction market data to simulate how penalties, incentives, and monitoring costs influence SDBs. The numerical simulations reveal that penalties and incentives could reduce drone users' non-compliant operations, but this effect is useful only if the penalties and incentives exceed a certain value. In China, drone users' non-compliant operations can be controlled when penalties for drone users exceed 12,000 yuan, and incentives for the public's monitoring exceed 170 yuan/day. The current Chinese government's penalties that were administered for non-compliant drone operations have not achieved a deterrent effect, but the incentive is feasible. These findings provide a fresh insight into the decision-making behaviors of stakeholders in non-compliant drone operations. Additionally, the tripartite evolutionary game model developed in this study can assist other countries in determining optimal values for penalties, incentives, and monitoring costs to mitigate non-compliant drone operations effectively.

Driving factors for the adoption of green finance in green building for sustainable development in developing countries: The case of Ghana

Debrah, C., Chan, A. P. C., Darko, A., Ries, R. J., Ohene, E., & Tetteh, M. O. (2024). Driving factors for the adoption of green finance in green building for sustainable development in developing countries: The case of Ghana. Sustainable Development., 1–22. https://doi.org/10.1002/sd.3022

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Abstract

While there are many motivating factors for green finance (GF) implementation, a comprehensive taxonomy of these variables is lacking in the literature, especially for green buildings (GBs). This study aims to analyze the criticality and interdependence of GF‐in‐GB's driving factors. This study develops a valid set of factors to justify the interrelationships among the drivers. The drivers of GF‐in‐GB are qualitative in nature, and uncertainties exist among them due to linguistic preferences. This study applies the fuzzy Delphi method to validate eight drivers under uncertainties. Fuzzy Decision‐Making Trial and Evaluation Laboratory (FDEMATEL) with qualitative information is used to determine the interrelationships among the drivers. The drivers were grouped under two categories: prominent drivers and cause‐effect drivers. The findings revealed that “increased awareness of GF models in GB” and “preferential capital requirements for low‐carbon assets” are the top two most prominent/important drivers of GF‐in‐GB. In Ghana, the top three cause group drivers are “climate commitment,” “improved access to and lower cost of capital,” and “favorable macroeconomic conditions and investment returns.” Drivers with the highest prominence values have the potential to affect and/or be affected by other drivers; therefore, managers and policymakers should prioritize promoting or pursuing these drivers in the short term. On the other hand, it is important to pay more than equal attention to the drivers with the highest net cause values because they have the largest long‐term impact on the entire system. The theoretical and practical implications of the study are discussed, enhancing understanding and decision‐making in GF‐in‐GB.

Keywords

fuzzy Delphi method; fuzzy DEMATEL; green building; green finance; sustainable development

Challenges to energy retrofitting of existing office buildings in high-rise high-density cities: The case of Hong Kong

Linyan Chen, Amos Darko, Mayowa I. Adegoriola, Albert P.C. Chan, Yang Yang, Mershack O. Tetteh, “Challenges to energy retrofitting of existing office buildings in high-rise high-density cities: The case of Hong Kong,” Energy and Buildings, Volume 312, 2024, 114220, ISSN 0378-7788, https://doi.org/10.1016/j.enbuild.2024.114220.

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Abstract

Achieving carbon neutrality by 2050 has become a global goal, sparking concerns regarding energy consumption and carbon emissions in building operations. Office buildings in high-rise high-density cities serve as central business districts, contributing significantly to the city’s economic activity and consuming a lot of energy. The process of retrofitting existing office buildings for energy efficiency in high-rise high-density cities tends to be challenging. However, there is a lack of comprehensive understanding of the challenges involved in office buildings’ energy retrofitting, as they have not been thoroughly explored. This study aims to investigate the challenges to the existing office building energy retrofitting (EOBER) in high-rise high-density cities with real cases in Hong Kong. Initially, a systematic literature review was undertaken to identify 49 potential EOBER challenges and categorized into seven groups: technical, financial, institutional, social, environmental, regulatory, and other categories. Afterward, 23 EOBER challenges were identified through 24 semi-structured interviews with 36 real office building energy retrofitting cases in Hong Kong. Moreover, these challenges were quantified by the Z-numbers-based Delphi survey and analysis. Results show that regulatory challenges are the primary challenges, followed by financial challenges. The lack of government incentives, policies, legislation and regulations significantly hinders practitioners’ ability to engage in energy retrofitting initiatives. The long payback period of building energy retrofitting poses a critical financial concern for practitioners embracing such initiatives. In the end, this research proposed integrated strategies to tackle these challenges and increase building energy efficiency, including launching financial and regulatory incentives, shortening the interval for mandatory energy audits, disseminating knowledge, and diversifying finance channels of building energy retrofitting. The findings contribute to the body of knowledge by employing systems thinking to identify and evaluate EOBER challenges in high-rise high-density cities through empirical methodologies. Moreover, this study provides valuable references for practitioners in navigating these challenges and minimizing risks associated with the retrofitting process.

Post-pandemic transit commute: Lessons from focus group discussions on the experience of essential workers during COVID-19

Ashour, L. A., Shen, Q., Moudon, A., Cai, M., Wang, Y., & Brown, M. (2024). Post-pandemic transit commute: Lessons from focus group discussions on the experience of essential workers during COVID-19. Journal of Transport Geography, 116. https://doi.org/10.1016/j.jtrangeo.2024.103832

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Abstract

Public transit services, which provide a critical lifeline for many essential workers, were severely interrupted during the COVID-19 pandemic. As institutions gradually return to normal in-person operations, it is critical to understand how the pandemic affected essential workers' commute and what it will take to ensure the effective recovery of transit ridership and enhance the long-term resiliency and equity of public transportation systems for those who need it the most. This study used focus group discussions with essential workers who were pre-pandemic transit riders to understand how the COVID-19 pandemic has impacted their commute perceptions, experiences, motives, and challenges and explore the potential changes in their travel behavior post-pandemic. We used NVivo 12 Pro to conduct a thematic analysis of the transcripted discussion data and examined patterns of commute mode change with respect to participants' attributes, including job type, home location, and gender. The results show that public transit had multiple reliability and frequency challenges during the pandemic, which resulted in most participants switching away from public transportation. With the increased availability of hybrid remote work and pandemic-related parking policies, driving emerged as a safer and more affordable commute mode for many pre-pandemic transit riders, rendering transit services less efficient for those who continued to rely on it. Planning for post-COVID resilient and reliable mobility requires a major rethinking of providing an efficient and effective transport system and a more fundamental approach to long-term public transport policy. To recover transit ridership, transit agencies need to ensure transit service availability and provide reliable transit information through smartphone apps. Similarly, transit agencies need to coordinate with other employers to provide free or heavily subsidized transit passes, to facilitate the recovery of transit demand effectively.

Keywords

Essential workers; Commute; Public transit; Focus group discussions; COVID-19 pandemic; Post-pandemic

Interactions between climate change and urbanization will shape the future of biodiversity

Urban, M.C., Alberti, M., De Meester, L. et al. Interactions between climate change and urbanization will shape the future of biodiversity. Nat. Clim. Chang. (2024). https://doi.org/10.1038/s41558-024-01996-2

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Abstract

Climate change and urbanization are two of the most prominent global drivers of biodiversity and ecosystem change. Fully understanding, predicting and mitigating the biological impacts of climate change and urbanization are not possible in isolation, especially given their growing importance in shaping human society. Here we develop an integrated framework for understanding and predicting the joint effects of climate change and urbanization on ecology, evolution and their eco-evolutionary interactions. We review five examples of interactions and then present five hypotheses that offer opportunities for predicting biodiversity and its interaction with human social and cultural systems under future scenarios. We also discuss research opportunities and ways to design resilient landscapes that address both biological and societal concerns.