Urbanization plays a crucial role in facilitating the integration of population growth, industrial development, economic expansion, and energy consumption. In this paper, we aim to examine the relationships between CO2 emissions and various factors including economic growth, urbanization, financial development, and energy consumption within Pakistan’s building sector. The study utilizes annual data spanning from 1990 to 2020. To analyze the cointegration relationship between these variables, we employ the quantile autoregressive distributed lag error correction model (QARDL-ECM). The findings of this research provide evidence supporting the presence of an asymmetric and nonlinear long-term relationship between the variables under investigation. Based on these results, we suggest the implementation of tariffs on nonrenewable energy sources and the formulation of policies that promote sustainable energy practices. By doing so, policymakers and architects can effectively contribute to minimising environmental damage. Overall, this study offers valuable insights that can assist policymakers and architects in making informed decisions to mitigate environmental harm while fostering sustainable development.

Municipal authorities in industrialized and in developing countries face unceasingly the issues of congestion, insufficiency of transport means capacity, poor operability of transport systems and a growing demand for reliable and effective urban transport. While the expansion of infrastructure is generally considered as an undesirable option, in specific cases, when short links or ring roads are missing, new infrastructure projects may provide beneficial solutions. The upgrading and renewal of existing networks is always a challenge to the development of a modern city and the welfare of citizens. Central governance and management of transport systems, the establishment of smart and digital infrastructure, advanced surveillance and traffic monitoring, and intra-city energy-harvesting policy are some of the steps to be taken during the transition to a green and sustainable urban future.
Municipal authorities have also to consider other options and strategies to create a citizen-friendly setting for mobility: diminish the need for trips (digitalization of services, e-commerce, etc.), shift from private to public transport and transform the urban form to promote non-motorized transport in favor of the natural environment and public health. A citizen-friendly policy based on the anticipation of future needs and technological development seems to be a requisite for European cities searching for a smooth integration of their networks into urban space.

Cyber-physical Systems (CPS) have revolutionized urban transportation worldwide, but their implementation in developing countries faces significant challenges, including infrastructure modernization, resource constraints, and varying internet accessibility. This paper proposes a methodological framework for optimizing the implementation of Cyber-Physical Urban Mobility Systems (CPUMS) tailored to improve the quality of life in developing countries. Central to this framework is the Dependency Structure Matrix (DSM) approach, augmented with advanced artificial intelligence techniques. The DSM facilitates the visualization and integration of CPUMS components, while statistical and multivariate analysis tool such as Principal Component Analysis (PCA) and artificial intelligence methods such as K-means clustering enhance complex system the analysis and optimization of complex system decisions. These techniques enable engineers and urban planners to design modular and integrated CPUMS components that are crucial for efficient, and sustainable urban mobility solutions. The interdisciplinary approach addresses local challenges and streamlines the design process, fostering economic development and technological innovation. Using DSM and advanced artificial intelligence, this research aims to optimize CPS-based urban mobility solutions, by identifying critical outliers for targeted management and system optimization.

Using data from 31 provinces, municipalities, and autonomous regions in mainland China from 2006 to 2019, we employ a double difference (DID) model and a spatial double difference (SDID) model to estimate the impact of the High-speed Railway (HSR) on the income gap between urban and rural residents, as well as its spatial spillover effects. Our research reveals several key findings. Firstly, the introduction of high-speed railways helps to narrow the income gap between urban and rural residents within local areas, but its spatial effects can lead to an increase in the income gap in neighboring provinces. Secondly, from a spatial perspective, intermediate variables such as industrial structure, education, science and technology, and foreign trade can also contribute to balancing the income gap between urban and rural residents, although the impact of population mobility is not significant. Thirdly, further analysis of the spatial effects demonstrates that education plays a significant role in balancing the income gap both within the local province and neighboring provinces. Additionally, adjustments in industrial structure, advancements in science and technology, and foreign trade have stronger spillover effects in reducing the income gap among neighboring provinces compared to their impact at a local level.

This study, through the method of canonical correlation analysis, revealed significant correlations between various dimensions of learning attitudes of students and various dimensions of teacher knowledge. An analysis of data from a group of 221 high school students showed that teacher knowledge of teaching content, theoretical knowledge, and teaching practice and classroom management significantly impact learning attitudes of students. Specifically, teacher knowledge of teaching content plays a crucial role in promoting students' behavioral inclination to learn chemistry, teachers' theoretical knowledge significantly enhances students' liking for chemistry laboratory courses, while teachers' teaching practice and classroom management have a suppressive effect on students' evaluative beliefs about school chemistry. The results of this study provide effective guidance for both the theory and practice of high school chemistry education.