Land Use Conflict Detection and Multi-Objective Optimization Based on the Productivity, Sustainability, and Livability Perspective

Land use affects many aspects of regional sustainable development, so insight into its influence is of great importance for the optimization of national space. The book mainly focuses on functional classification, spatial conflict detection, and spatial development pattern optimization based on productivity, sustainability, and livability perspectives, presenting a relevant opportunity for all scholars to share their knowledge from the multidisciplinary community across the world that includes landscape ecologists, social scientists, and geographers. The book is systematically organized into the optimization theory, methods, and practices for PLES (production–living–ecological space) around territorial spatial planning, with the overall planning of PLES as the goal and the promotion of ecological civilization construction as the starting point. Through this, the competition and synergistic interactions and positive feedback mechanisms between population, resources, ecology, environment, and economic and social development in the PLES system were revealed, and the nonlinear dynamic effects among subsystems and elements in the system identified. In addition, a series of optimization approaches for PLES is proposed

Green Economy and Sustainable Development

Considering the importance of the challenges for sustainable development, this Book is intended to disseminate the results of cutting-edge research and broadcast the opinions of scientists from around the world, providing technological breakthroughs in green energy and urbanism, recycling and modernization of basic industries, conducting fundamental research on the economic problems of the transition to sustainable development

Developing an economic estimation system for vertical farms

The concept of vertical farming is nearly twenty years old, however, there are only a few experimental prototypes despite its many advantages compared to conventional agriculture. Significantly, financial uncertainty has been identified as the largest barrier to the realization of a ‘real’ vertical farm. Some specialists have provided ways to calculate costs and return on investment, however, most of them are superficial with calculations based on particular contextual circumstances. To move the concept forwards a reliable and flexible estimating tool, specific to this new building typology, is clearly required. A computational system, software named VFer, has therefore been developed by the authors to provide such a solution. This paper examines this highly flexible, customised system and results from several typical vertical farm configurations in three mega-cities (Shanghai, London and Washington DC) are used to elucidate the potential economic return of vertical farms

Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil

The simulation and analysis of future land use and land cover—LULC scenarios using artificial neural networks (ANN)—has been applied in the last 25 years, producing information for environmental and territorial policy making and implementation. LULC changes have impacts on many levels, e.g., climate change, biodiversity and ecosystem services, soil quality, which, in turn, have implications for the landscape. Therefore, it is fundamental that planning is informed by scientific evidence. The objective of this work was to develop a geographic model to identify the main patterns of LULC transitions between the years 2000 and 2018, to simulate a baseline scenario for the year 2036, and to assess the effectiveness of the Chapecó River ecological corridor (an area created by State Decree No. 2.957/2010), regarding the recovery and conservation of forest remnants and natural fields. The results indicate that the forest remnants have tended to recover their area, systematically replacing silviculture areas. However, natural fields (grassland) are expected to disappear in the near future if proper measures are not taken to protect this ecosystem. If the current agricultural advance pattern is maintained, only 0.5% of natural fields will remain in the ecological corridor by 2036. This LULC trend exposes the low effectiveness of the ecological corridor (EC) in protecting and restoring this vital ecosystem.info:eu-repo/semantics/publishedVersio

Geo Data Science for Tourism

This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations.

A decision support system for urban infrastructure inter-asset management employing domain ontologies and qualitative uncertainty-based reasoning

Urban infrastructure assets (e.g. roads, water pipes) perform critical functions to the health and well-being of society. Although it has been widely recognised that different infrastructure assets are highly interconnected, infrastructure management in practice such as planning, installation and maintenance are often undertaken by different stakeholders without considering these dependencies due to the lack of relevant data and cross-domain knowledge, which may cause unexpected cascading social, economic and environmental effects. In this paper, we present a knowledge based decision support system for urban infrastructure inter-asset management. By considering various infrastructure assets (e.g. road, ground, cable), triggers (e.g. pipe leaking) and potential consequences (e.g. tra c disruption) as a holistic system, we model each sub-domain using a modular ontology and encapsulate the interdependence between them using a set of rules. Moreover, qualitative likelihood is assigned to each rule by domain experts (e.g. civil engineers) to encode the uncertainty of knowledge, and an inference engine is applied to predict the potential consequences of a given trigger with location specific data and the encoded rules. A web-based prototype system has been developed based on the above concept and demonstrated to a wide range of stakeholders. The system can assist in the process of decision making by aiding data collation and integration, as well as presenting potential consequences of possible triggers, advising on whether additional information is needed or suggesting ways of obtaining such information. The work shows an intelligent approach to integrate and process multi-source data to pioneer a novel way to aid a complex decision process with a high social impact

Decarbonisation of seaports: A review and directions for future research

Marine activities in seaports account for circa 3% of total carbon emissions worldwide, prompting several initiatives to decarbonise their energy systems and make seaports smarter and greener. This paper provides a thorough and authoritative review of the vast array of research in this field, including past and ongoing initiatives. The study reveals that existing research leverages recent advances in digital technologies while focusing on one or several of the following themes: carbon reduction, use of renewable energy resources, cost-performance optimisation, deployment of smart control technologies, the regulatory landscape for greening seaports, and implementing green port practices guidelines. As such, the paper provides a critical review of existing technologies and concepts that promote and contribute to the decarbonisation of seaports, including Smart Grids and Virtual Power Plants. Several avenues for future research are then discussed, including (a) total life cycle approach to seaport energy management, (b) Semantic-based modelling, forecasting and optimisation of seaports energy systems, (c) Secure and reliable seaports energy services, and (d) Transition towards prosumer-driven seaport energy communities. The paper concludes by emphasising the importance of an adapted energy regulatory landscape at a national and EU-wide level to meet EU phased energy reduction targets

Belt & Road Initiative in Times of ‘Synchronized Downturn’

Nearly ten years since the official launch of the Belt and Road Initiative (BRI), an understanding of what the initiative’s objectives are consolidated. However, the short-, mid-, and long-term implications of the initiative are less clear. This is reflected in academic research, as well as in policy-oriented publications stemming from the global think-tank sector. This collection adds to this debate by offering a glimpse into selected aspects of BRI and its development, including the applicability of existing theories of trade to the case of BRI, the specificity of investment modes associated with BRI, sustainability, SDGs, socio-cultural issues, and many other implications. Due to its focus on diverse aspects of BRI, this collection will be of interest to students of international economics, international relations, and related subjects

Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System

As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated