Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus

Spatial–temporal distribution measurement of input–output efficiency of the water–energy–food nexus of the Yangtze River Economic Belt, China

Water, energy, and food are important factors affecting people’s lives and socio-economic development, and their production and consumption processes are closely related, so it is necessary to do research on input–output efficiency as a whole. The research is conducive to promoting the effective utilization and rational allocation of relevant resources in the Yangtze River Economic Belt, thereby promoting sustainable development. This study calculates the input–output efficiency of the water–energy–food (WEF) nexus of 11 provincial administrative regions in the Yangtze River Economic Belt utilizing the DEA-BCC model. Then, new indicators called area expansion degree and the subsystem’s influence degree are proposed with the aid of the standard deviation ellipse model to analyze the characteristics and trends of spatial–temporal distribution of input–output efficiency. The standard deviation ellipse model starts from the basic spatial elements, including point, line, and surface, and is used to study the spatial distribution and trend change of efficiency according to the center of gravity and area. The shift of the center of gravity shows the direction of significant improvement in the effect of resource allocation, and the change of area shows the differences in the speed of efficiency improvement between regions and the future development trend. The results mainly indicate that the resource allocation in the middle and lower reaches of the Yangtze River Economic Belt is more reasonable than that in the upper reaches, and the efficiency distribution is obviously concentrated in the northeast direction. It is suggested that the provincial administrative regions in the upper reaches should optimize the industrial structure, the regions in the middle and lower reaches should improve the resource structure, and the flow of talents and technology of regions should be promoted.

Modeling Analysis on Coupling Mechanisms of Mountain–Basin Human–Land Systems: Take Yuxi City as an Example

The result of a human–land relationship in geographical environment systems is a human–land coupling system, which is a comprehensive process of interaction and infiltration between human economic and social systems and the natural ecosystem. Based on the recognition that the human–land system is a nonlinear system coupled by multiple factors, a time delay fractional order dynamics model with a Holling-II-type transformation rate was constructed, the stability analysis of the system was carried out, the transformation times of different land classes were clarified, and the coupled dynamics model parameters of mountainous areas and basin areas were obtained by using the land-use change survey data and socio-economic statistical data in Yuxi City, respectively: the transformation parameter of the production and living land to the unused land in mountainous areas and basin areas (aM, 0.0486 and aB, 0.0126); the transformation parameter of unused land to production and living land in mountainous areas and basin areas (bM 0.0062 and bB, 0.0139); the transformation parameter of unused land to the forest and grass land in mountainous areas and basin areas (sM, 0.0051 and sB, 0.0028); the land area required to maintain the individual unit in mountainous areas and basin areas (hM, 0.0335 and hB, 0.0165); the average reclamation capacity in mountainous areas and basin areas (dM, 0.03 and dB, 0.05); the inherent growth rate of populations in mountainous areas and basin areas (rM, 0.0563 and rB, 0.151). Through analyzing the coupling mechanisms of human–land systems, the countermeasures for the difference between mountainous areas and basin areas in the future development are put forward. The mountainous area should reduce the conversion of forest and grass land to production and living land by reducing the average reclamation or development capacity, reducing the excessive interference of human beings on unused land, and speeding up its natural recovery and succession to forest and grass land. In addition to reducing the average reclamation or development capacity in basin areas, the reclamation or development rate of the idle land and degraded land should be increased, and the conversion of idle land and degraded land into productive and living land should be encouraged by certain scientific and technological means.

Developing a Deep Neural Network with Fuzzy Wavelets and Integrating an Inline PSO to Predict Energy Consumption Patterns in Urban Buildings

Energy has been one of the most important topics of political and social discussion in recent decades. A significant proportion of the country’s revenues is derived from energy resources, making it one of the most important and strategic macro policy and sustainable development areas. Energy demand modeling is one of the essential strategies for better managing the energy sector and developing appropriate policies to increase productivity. With the increasing global demand for energy, it is necessary to develop intelligent forecasting methods and algorithms. Different economic and non-economic indicators can be used to estimate the energy demand, including linear and non-linear statistical methods, mathematics, and simulation models. This non-linear relationship between these indicators and energy demand has led researchers to search for intelligent solutions, such as artificial neural networks for non-linear modeling and prediction. The purpose of this study was to use a deep neural network with fuzzy wavelets to predict energy demand in Iran. For the training of the presented components, a hybrid training method incorporating both an inline PSO and a gradient-based algorithm is presented. The provided technique predicts energy consumption in Tehran, Mashhad, Ahvaz, and Urmia from 2010 to 2021. This study shows that the presented method provides high-performance prediction at a lower level of complexity.

Agricultural Land or Photovoltaic Parks? The Water–Energy–Food Nexus and Land Development Perspectives in the Thessaly Plain, Greece

Water, energy, land, and food are vital elements with multiple interactions. In this context, the concept of a water–energy–food (WEF) nexus was manifested as a natural resource management approach, aiming at promoting sustainable development at the international, national, or local level and eliminating the negative effects that result from the use of each of the four resources against the other three. At the same time, the transition to green energy through the application of renewable energy technologies is changing and perplexing the relationships between the constituent elements of the nexus, introducing new conflicts, particularly related to land use for energy production vs. food. Specifically, one of the most widespread “green” technologies is photovoltaic (PV) solar energy, now being the third foremost renewable energy source in terms of global installed capacity. However, the growing development of PV systems results in ever expanding occupation of agricultural lands, which are most advantageous for siting PV parks. Using as study area the Thessaly Plain, the largest agricultural area in Greece, we investigate the relationship between photovoltaic power plant development and food production in an attempt to reveal both their conflicts and their synergies.

An Integrated Economic, Environmental and Social Approach to Agricultural Land-Use Planning

Agricultural land-use change is a dynamic process that varies as a function of social, economic and environmental factors spanning from the local to the global scale. The cumulative regional impacts of these factors on land use adoption decisions by farmers are neither well accounted for nor reflected in agricultural land use planning. We present an innovative spatially explicit agent-based modelling approach (Crop GIS-ABM) that accounts for factors involved in farmer decision making on new irrigation adoption to enable land-use predictions and exploration. The model was designed using a participatory approach, capturing stakeholder insights in a conceptual model of farmer decisions. We demonstrate a case study of the factors influencing the uptake of new irrigation infrastructure and land use in Tasmania, Australia. The model demonstrates how irrigated land-use expansion promotes the diffusion of alternative crops in the region, as well as how coupled social, biophysical and environmental conditions play an important role in crop selection. Our study shows that agricultural land use reflected the evolution of multiple simultaneous interacting biophysical and socio-economic drivers, including soil and climate type, crop and commodity prices, and the accumulated effects of interactive decisions of farmers.