The impact of infrastructure and entrepreneurship support programs on the Female TEA Ratio: A PMG-ARDL approach in an urban context

The purpose of this article is to improve our understanding of the factors that influence the Female/Male Total Early-stage Entrepreneurial Activity (TEA) ratio in urban environments. Its goal is to provide policymakers with a solid scientific foundation as well as to suggest new research directions in the field of female entrepreneurship. The study focuses on how macroenvironmental factors influence the Female/Male TEA Ratio in urban contexts within BRICS countries. Several key factors are considered in the analysis, including entrepreneurial finance, government support policies, entrepreneurship support programs, commercial and legal infrastructure, and physical infrastructure. The research findings confirm the hypothesis that commercial and legal infrastructure, as well as government measures to support entrepreneurship, have a negative long-term influence on the Female/Male TEA Ratio by using the PMG-ARDL method and examining data from the Global Entrepreneurship Monitor (GEM). Furthermore, the findings show that entrepreneurship support programs and physical infrastructure have a positive impact on the ratio. However, no significant impact on entrepreneurial finance has been observed in both the short and long term. The article emphasizes the importance of taking into account the regional dimension when developing entrepreneurship support policies, as well as the unique characteristics of each urban area

Exploring the Relationship between Infrastructure and Entrepreneurial Development in BRICS Countries “PMGARDL” approach

This article examines the significance of infrastructure in the emergence and development of entrepreneurship, focusing on policies and their interaction with other public support programs. The central issue is to emphasize the importance of considering various types and specificities of infrastructure. The article posits multiple hypotheses regarding the impact of infrastructure on entrepreneurship, with an emphasis on long-term influence. It assumes that incorporating infrastructure into public policies is essential for fostering entrepreneurial growth. Additionally, it suggests that diverse types of infrastructure play a crucial role in the development of an entrepreneurial ecosystem, particularly in technology sectors. The methodology employed in this study is based on the PMG-ARDL method. Researchers used the “TEA” proxy to measure entrepreneurial dynamics and examine the impact of specific infrastructure characteristics. The sample consists of data from BRICS countries, enabling comparisons across institutional contexts and development levels. The main contributions of this study lie in highlighting the importance of infrastructure for entrepreneurship, especially in the long term. Results suggest that public policies should pay special attention to planning and improving infrastructure to energize the entrepreneurial ecosystem. The research also underscores the need for developing new indices to better measure the various specificities of infrastructure

Crowdsourcing Public Engagement for Urban Planning in the Global South: Methods, Challenges and Suggestions for Future Research

Crowdsourcing could potentially have great benefits for the development of sustainable cities in the Global South (GS), where a growing population and rapid urbanization represent serious challenges for the years to come. However, to fulfill this potential, it is important to take into consideration the unique characteristics of the GS and the challenges associated with them. This study provides an overview of the crowdsourcing methods applied to public participation in urban planning in the GS, as well as the technological, administrative, academic, socio-economic, and cultural challenges that could affect their successful adoption. Some suggestions for both researchers and practitioners are also provided.CEA

Artificial Intelligence Based Methods for Smart and Sustainable Urban Planning: A Systematic Survey

The world's cities are facing various challenges such as rapid urbanization, poverty, climate change, pollution, sustainable and inclusive development. Building futuristic smart and sustainable cities is proving to be a response to these challenges. Recent publications show that urban planning decision support is increasingly using artificial intelligence through machine learning methods to address these challenges. 172 papers were published in 2020 compared to 8 before 2010 with a forecast of at least 200 papers in 2021. Despite the explosive number of scientific publications on applications artificial intelligence for urban planning decision support, few studies have made a systematic assessment of the state of the art to inform future research. This would help focus on the approaches used, the planning problems most commonly addressed, the data used and even the study areas. We found that the top 5 most addressed urban planning issues include: land use/cover, urban growth, urban buildings, urban mobility and urban environment. Furthermore, a large amount of data was used from sensors and simple or ensemble machine learning methods were more used in this case. Deep learning methods are more used for land use/cover, buildings and climate issues which are mostly based on satellite image data. On the other hand, China and the United States are the most studied territories while Africa is almost not. A high intensity of collaboration between researchers affiliated with Chinese, American and English institutions was observed. Thus, urban planning researchers should benefit from this synthesis work by understanding the general idea of the application of machine learning methods in urban planning, its trends, issues, current challenges and future research directions

Crowdsourcing Public Engagement for Urban Planning in the Global South: Methods, Challenges and Suggestions for Future Research

Crowdsourcing could potentially have great benefits for the development of sustainable cities in the Global South (GS), where a growing population and rapid urbanization represent serious challenges for the years to come. However, to fulfill this potential, it is important to take into consideration the unique characteristics of the GS and the challenges associated with them. This study provides an overview of the crowdsourcing methods applied to public participation in urban planning in the GS, as well as the technological, administrative, academic, socio-economic, and cultural challenges that could affect their successful adoption. Some suggestions for both researchers and practitioners are also provided

Towards an Understanding of Hydraulic Sensitivity: Graph Theory Contributions to Water Distribution Analysis

Water distribution systems (WDSs) are complex networks with numerous interconnected junctions and pipes. The robustness and reliability of these systems are critically dependent on their network structure, necessitating detailed analysis for proactive leak detection to maintain integrity and functionality. This study addresses gaps in traditional WDS analysis by integrating hydraulic measures with graph theory to improve sensitivity analysis for leak detection. Through case studies of five distinct WDSs, we investigate the relationship between hydraulic measures and graph theory metrics. Our findings demonstrate the collective impact of these factors on leak detection and system efficiency. The research provides enhanced insights into WDS operational dynamics and highlights the significant potential of graph theory to bolster network resilience and reliability

Identifying and Classifying Urban Data Sources for Machine Learning-Based Sustainable Urban Planning and Decision Support Systems Development

With the increase in the amount and variety of data that are constantly produced, collected, and exchanged between systems, the efficiency and accuracy of solutions/services that use data as input may suffer if an inappropriate or inaccurate technique, method, or tool is chosen to deal with them. This paper presents a global overview of urban data sources and structures used to train machine learning (ML) algorithms integrated into urban planning decision support systems (DSS). It contributes to a common understanding of choosing the right urban data for a given urban planning issue, i.e., their type, source and structure, for more efficient use in training ML models. For the purpose of this study, we conduct a systematic literature review (SLR) of all relevant peer-reviewed studies available in the Scopus database. More precisely, 248 papers were found to be relevant with their further analysis using a text-mining approach to determine (a) the main urban data sources used for ML modeling, (b) the most popular approaches used in relevant urban planning and urban problem-solving studies and their relationship to the type of data source used, and (c) the problems commonly encountered in their use. After classifying them, we identified the strengths and weaknesses of data sources depending on several predefined factors. We found that the data mainly come from two main categories of sources, namely (1) sensors and (2) statistical surveys, including social network data. They can be classified as (a) opportunistic or (b) non-opportunistic depending on the process of data acquisition, collection, and storage. Data sources are closely correlated with their structure and potential urban planning issues to be addressed. Almost all urban data have an indexed structure and, in particular, either attribute tables for statistical survey data and data from simple sensors (e.g., climate and pollution sensors) or vectors, mostly obtained from satellite images after large-scale spatio-temporal analysis. The paper also provides a discussion of the potential opportunities, emerging issues, and challenges that urban data sources face and should overcome to better catalyze intelligent/smart planning. This should contribute to the general understanding of the data, their sources and the challenges to be faced and overcome by those seeking data and integrating them into smart applications and urban-planning processes