Experts confirm that 85% of the world’s population is expected to live in cities by 2050. Therefore, cities should be prepared to satisfy the needs of their citizens and provide the best services. The idea of a city of the future is commonly represented by the smart city, which is a more efficient system that optimizes its resources and services, through the use of monitoring and communication technology. Thus, one of the steps towards sustainability for cities around the world is to make a transition into smart cities. Here, sensors play an important role in the system, as they gather relevant information from the city, citizens, and the corresponding communication networks that transfer the information in real-time. Although the use of these sensors is diverse, their application can be categorized in six different groups: energy, health, mobility, security, water, and waste management. Based on these groups, this review presents an analysis of different sensors that are typically used in efforts toward creating smart cities. Insights about different applications and communication systems are provided, as well as the main opportunities and challenges faced when making a transition to a smart city. Ultimately, this process is not only about smart urban infrastructure, but more importantly about how these new sensing capabilities and digitization developments improve quality of life. Smarter communities are those that socialize, adapt, and invest through transparent and inclusive community engagement in these technologies based on local and regional societal needs and values. Cyber security disruptions and privacy remain chief vulnerabilities.

This study presents a neuroengineering-based machine learning tool developed to predict students’ performance under different learning modalities. Neuroengineering tools are used to predict the learning performance obtained through two different modalities: text and video. Electroencephalographic signals were recorded in the two groups during learning tasks, and performance was evaluated with tests. The results show the video group obtained a better performance than the text group. A correlation analysis was implemented to find the most relevant features to predict students’ performance, and to design the machine learning tool. This analysis showed a negative correlation between students’ performance and the (theta/alpha) ratio, and delta power, which are indicative of mental fatigue and drowsiness, respectively. These results indicate that users in a non-fatigued and well-rested state performed better during learning tasks. The designed tool obtained 85% precision at predicting learning performance, as well as correctly identifying the video group as the most efficient modality.

Cities demand urgent transformations in order to become more affordable, livable, sustainable, walkable and comfortable spaces. Hence, important changes have to be made in the way cities are understood, diagnosed and planned. The current paper puts urban accessibility into the centre of the public policy and planning agenda, as a transferable approach to transform cities into better living environments. To do so, a practical example of the City of Monterrey, Mexico, is presented at two planning scales: the metropolitan and local level. Both scales of analysis measure accessibility to main destinations using walking and cycling as the main transport modes. The results demonstrate that the levels of accessibility at the metropolitan level are divergent, depending on the desired destination, as well as on the planning processes (both formal and informal) from different areas of the city. At the local level, the Distrito Tec Area is diagnosed in terms of accessibility to assess to what extent it can be considered a part of a 15 minutes city. The results show that Distrito Tec lacks the desired parameters of accessibility to all destinations for being a 15 minutes city. Nevertheless, there is a considerable increase in accessibility levels when cycling is used as the main travelling mode. The current research project serves as an initial approach to understand the accessibility challenges of the city at different planning levels, by proving useful and disaggregated data. Finally, it concludes providing general recommendations to be considered in planning processes aimed to improve accessibility and sustainability.

Urban planning has a crucial role in helping cities meet the United Nations’ Sustainable Development Goals and robust datasets to assess mobility accessibility are central to smart urban planning. These datasets provide the information necessary to perform detailed analyses that help develop targeted urban interventions that increase accessibility in cities as related to the emerging vision of the 15 Minute City. This study discusses the need for such data by performing a comparative urban accessibility analysis of two university campuses and their surrounding urban areas, here defined as the Stanford District, located in the San Francisco Bay Area in the United States, and Distrito Tec in Monterrey, Mexico. The open-source tool Urban Mobility Accessibility Computer (UrMoAC) is used to assess accessibility measures in each district using available data. UrMoAC calculates distances and average travel times from block groups to major destinations using different transport modes considering the morphology of the city, which makes this study transferable and scalable. The results show that both areas have medium levels of accessibility if cycling is used as the primary mode of transportation. Hence, improving the safety and quality of cycling in both cities emerges as one of the main recommendations from the research. Finally, the results obtained can be used to generate public policies that address the specific needs of each community’s urban region based on their accessibility performance.

A lack of data collection on conscious mobility behaviors has been identified in current sustainable and smart mobility planning, development and implementation strategies. This leads to technocentric solutions that do not place people and their behavior at the center of new mobility solutions in urban centers around the globe. This paper introduces the concept of conscious mobility to link techno-economic analyses with user awareness on the impact of their travel decisions on other people, local urban infrastructure and the environment through systematic big data collection. A preliminary conscious mobility indicator framework is presented to leverage behavioral considerations to enhance urban-community mobility systems. Key factors for conscious mobility analysis have been derived from five case studies. The sample offers regional diversity (i.e., local, regional and the global urban contexts), as well as different goals in the transformation of conventional urban transport systems, from improving public transport efficiency and equipment electrification to mitigate pollution and climate risks, to focusing on equity, access and people safety. The case studies selected provide useful metrics on the adoption of cleaner, smarter, safer and more autonomous mobility technologies, along with novel people-centric program designs to build an initial set of conscious mobility indicators frameworks. The parameters were applied to the city of Monterrey, Nuevo Leon in Mexico focusing on the needs of the communities that work, study and live around the local urban campus of the Tecnologico de Monterrey’s Distrito Tec. This case study, served as an example of how conscious mobility indicators could be applied and customized to a community and region of interest. This paper introduces the first application of the conscious mobility framework for urban communities’ mobility system analysis. This more holistic assessment approach includes dimensions such as society and culture, infrastructure and urban spaces, technology, government, normativity, economy and politics, and the environment. The expectation is that the conscious mobility framework of analysis will become a useful tool for smarter and sustainable urban and mobility problem solving and decision making to enhance the quality of life all living in urban communities.

This chapter presents a brief overview of the policy design and theoretical environmental economic principles that underpin the concept of emissions trading systems (ETS) as a policy approach to address climate change. It discusses basic environmental economic principles pertinent to the development of market-based solutions to mitigate greenhouse gas (GHG) and co-pollutants. The chapter serves as the technical basis for the broader discussion that this book as a whole presents on the launch of the pilot phase of the Mexican ETS on January 1, 2020. Understanding international program design experiences, theoretical principles, and implementing best practices is key to ensuring Mexico’s success in the transition from the pilot or learning phase to an operational ETS compliance system. This will ensure Mexico fulfills its national climate policy goals and nationally determined contributions (NDC) under the Paris Agreement in a cost-effective manner, while also providing compliance flexibility to the industrial sectors covered under the program. A well-designed ETS ultimately provides the right incentives for industrial carbon emission reductions to drive cost-effective abatement and clean innovation. Secondly, this chapter presents a more in-depth review of policy developments focusing specifically on key implementation lessons from the two most advanced ETS systems in operation to date: (1) the European Union ETS and (2) California’s cap-and-trade program. In short, this chapter outlines a set of key policy lessons and design parameters to support the transition from the pilot Mexican ETS to an operational compliance phase in a socially just, environmentally sound, and cost-effective manner.