Simulation and impact analysis of behavioral and socioeconomic dimensions of energy consumption

Abstract

Human-oriented factors present unavoidable challenges and uncertainties in building energy strategic planning. The uncertainties escalate when the target society is not fully known to the decision-maker and can create performance gaps between the expected and actual outcomes of sustainability targets. This article aims to investigate the role of socioeconomic and behavioral dimensions in residential energy consumption patterns among regions that host high proportions of migrant communities with diverse cultural and ethnic traits. This study evaluates the patterns in human-building interactions and energy behaviors among local and migrant communities based on empirical evidence and survey analysis. The survey data are investigated via machine learning approaches to identify the interdependencies between and feature importance of critical factors that influence human-building interactions and to determine elements that help to discern the energy behavior of locals and migrants. A simulation analysis is conducted to analyze residential energy consumption under different human indoor thermal comfort preferences in multiple case scenarios to demonstrate how improvements in human-building interaction can create saving opportunities. The findings capture the main socioeconomic and behavioral contributors in residential energy consumption and demonstrate the impact of human factor at a high level in regions with imbalanced demographics and societies in transition.

Smart cities and communities in the GCC region: from top-down city development to more local approaches

“…The smart planned city model deployed through technologies can fail to engage people in sustainability, which is crucial for making these solutions successful and ultimately accomplishing improvements in urban sustainable performance (Ghofrani et al, 2022). In contrast to top-down planned cities, community-level smart initiatives are largely lacking in the Gulf region despite the multiple benefits attested to them by the experts interviewed; e.g., energy democratization, community building through engagement and participation, improvements in environmental and energy literacy, and the promotion of environmental lifestyles.…”

“…It is represented by people who collaboratively and collectively address and optimize the community's social, environmental and cultural needs (Zaidan et al, 2022). It involves open participatory practices that are built on community members' involvement and their diverse local perspectives (Ghofrani et al, 2022). In particular, the community must address the reduction of fossil fuel usage creating the means (e.g., production) of providing clean energy.…”

Assessment of the Impact of Anthropogenic Evolution and Natural Processes on Shoreline Dynamics Using Multi-Temporal Satellite Images and Statistical Analysis

“…The population of the country increased exponentially from 0.46 million in 1960 to 2.8 million in 2019 due to the influx of the expatriate population driven by the need of workers to satisfy economic development and, recently, organise the 2022 FIFA World Cup [54][55][56][57][58]. Furthermore, the country adopted an ambitious 2030 development vision for human, economic, environmental, and social development, which has led to large-scale investment projects, massive urban development, a significant growth rate in real gross domestic product (GDP), and rising governmental expenditure [59][60][61][62][63]. The estimated length of Qatar's shoreline is 560 km and, due to the harsh climatic and geographical conditions, residents have always settled around the country's shoreline.…”

Accelerating the Change to Smart Societies- a Strategic Knowledge-Based Framework for Smart Energy Transition of Urban Communities

“…Furthermore, age is also a critical factor because older individuals are more skeptical regarding the return on investments in energy solutions (Nair et al, 2010). In contrast, some studies have reported that the older population is more likely to use than younger homeowners [ (Long, 1993;Barr et al, 2005;Ghofrani et al, 2021)]. Research on the relationship between income and attitudes toward technology adoption has demonstrated that the inclination to adopt renewable energy technologies is in correlation with monthly income (Sardianou and Genoudi, 2013).…”