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The Saudi 2030 Housing Program Vision aims to increase the population of Riyadh City, the capital of the Kingdom of Saudi Arabia, to between 15 and 20 million people. This paper aims to predict the demand for residential units in Riyadh City by 2030 in line with this vision.

This paper adopts a statistical modeling approach to estimate the residential demands for Riyadh City. Several population growth models, including the nonlinear quadratic polynomial spline regression model, the sigmoidal logistic power model and the exponential model, are tested and applied to Riyadh to estimate the expected population in 2030. The growth model closest to the Kingdom’s goal of reaching between 15 and 20 million people in 2030 is selected, and the paper predicts the required number of residential units for the population obtained from the selected model. Desktop database research is conducted to obtain the data required for the modeling and analytical stage.

The exponential model predicts a population of 16,476,470 in Riyadh City by 2030, and as a result, 2,636,235 household units are needed. This number of housing units required in Riyadh City exceeds the available residential units by almost 1,370,000, representing 108% of the available residential units in Riyadh in 2020.

This study provides valuable insights into the demand for residential units in Riyadh City by 2030 in line with the Saudi 2030 Housing Program Vision, filling the gap in prior research. The findings suggest that significant efforts are required to meet the housing demand in Riyadh City by 2030, and policymakers and stakeholders need to take appropriate measures to address this issue.

The purpose of this paper is to develop a statistical-based model to forecast future domestic water demand in the context of climate change, population growth and technological development in Yellow River.

The model is developed through the analysis of the effects of climate variables and population on domestic water use in eight sub-basins of the Yellow River. The model is then used to forecast water demand under different environment change scenarios.

The model projected an increase in domestic water demand in the Yellow River basin in the range of 67.85 × 10⁸ to 62.20 × 10⁸ m³ in year 2020 and between 73.32 × 10⁸ and 89.27 × 10⁸ m³ in year 2030. The general circulation model Beijing Normal University-Earth System Model (BNU-ESM) predicted the highest increase in water demand in both 2020 and 2030, while Centre National de Recherches Meteorologiques Climate Model v.5 (CNRM-CM5) and Model for Interdisciplinary Research on Climate- Earth System (MIROC-ESM) projected the lowest increase in demand in 2020 and 2030, respectively. The fastest growth in water demand is found in the region where water demand is already very high, which may cause serious water shortage and conflicts among water users.

The simple regression-based domestic water demand model proposed in the study can be used for rapid evaluation of possible changes in domestic water demand due to environmental changes to aid in adaptation and mitigation planning.

We analyze several dimensions of food security in Ethiopia, taking into account projected population growth, economic growth, and price information to estimate future food consumption by income decile. The analysis looks at the potential impact of large consumer price increases on food security metrics. We use the new USDA/ERS demand-based modeling framework in order to carry out this study. The modeling approach captures economic behavior by making food demand systematically responsive to income and price changes based on a demand specification well-grounded in microeconomic foundations. The projected change in food consumption can be apportioned to population growth, income growth, and changes in food prices and real exchange rates. We found that Ethiopia is highly food insecure, with 54% of the population consuming less than 2,100 calories a day at calibration levels. Income growth under unchanged prices mitigates food insecurity with the number of food-insecure people falling to 42.5 million in 2016. If domestic prices were free to fall with world market prices, the food-insecure population would decrease farther to 36.1 million. If domestic prices increased because of domestic supply shocks and constrained imports, the food-insecure population could rise to 64.7 million. The food gap (i.e., the amount of food necessary to eliminate Ethiopia’s food insecurity) would reach 3.6 million tons. The practical implications of this are that measures of food security are sensitive to changes in prices. Maintaining higher prices when global prices are low maintains higher levels of food insecurity than would otherwise prevail. Expanded access to lower cost imports could significantly improve food security in Ethiopia.

This chapter presents research and analysis on the Institute for Economics and Peace’s (IEP’s) index in the Ecological Threat Report (ETR). In the analysis, 178 countries are examined at the sub-national level, accounting for 99.9% of the global population. The estimate consists of five indicators that aggregate to yield an index of ecological threats. These five indicators are water risk, the prevalence of stunting, the impact of natural disasters, projected population growth and projected temperature rise. The ETR is a tool that can be used to identify the countries that are at the highest risk of ecological threats. The index identifies that 30 countries facing the highest level of ecological threats as well as low levels of resilience are home to 1.26 billion people. At the end of 2020, in these 30 countries, 68% of the total people were forcibly displaced beyond their borders. As these 30 countries suffer collectively from the highest ecological threats and without the reversal of ecological degradation, displacement is very likely to continue. Without urgent development, ecological threats will continue to create humanitarian emergencies and will likely increase without a sustained effort to reverse the current trend.

In this chapter, we explore the future global supply and demand trends for key agricultural products under baseline assumptions of socioeconomic changes in population and income. We examine nutritional trends under this baseline to highlight countries that lag behind in attaining key dietary sufficiency targets.

Using a global multimarket agricultural model, we disaggregate the key macronutrients within food commodities to understand how progress toward target dietary intake levels of nutrients compares across various regions. We look particularly at those regions whose populations fall into the bottom sixth of nutritional attainment (the Bottom Billion) and note their slow projected progress toward achieving dietary sufficiency in key macronutrients.

Many countries falling into the Bottom Billion category of nutritional attainment are in Africa and Asia. Colombia is the only Latin American country that fell into this category. Most populations in the Bottom Billion are deficient in carbohydrate, protein, and fiber intake.

Policies aimed at eliminating hunger and improving the nutritional status of populations must be aligned with evolving socioeconomic patterns and changes that shape food consumption and dietary patterns. This analysis evaluates regions of the world in greatest need of attaining sufficient dietary intake of important nutrients, and sets the stage for a deeper discussion of policy options for improving these regions’ food security.

The purpose of this study was to investigate the relationship between electricity access, population growth and economic growth in Pakistan.

Phillips–Perron unit root test was applied to check the stationarity of the variables and an Autoregressive Distributed Lag (ARDL) bounds testing approach to co-integration was used to investigate the causality link between the study variables. Finally, a projection method was applied to check the future trend of the variables.

The study results show the long-term connections among the variables; further, the results illustrate that the electricity access to the urban population and the urban population growth has a significant impact on the economic growth, while the electricity access to the rural population and the rural population growth has a negative impact on the economic growth in Pakistan.

The electricity sector needs further attention from the Government of Pakistan to boost the production from different energy sources, such as oil, gas, solar, nuclear and hydropower to be able to fulfill the country’s growing demand.

By using the ARDL bounds testing approach to co-integration, this study addressed the literature gap regarding electricity access, population growth and economic growth in Pakistan.

With the proliferation of e-commerce companies, express delivery companies must increasingly maintain the efficient expansion of their networks in accordance with growing demands and lower margins in a highly uncertain environment. This paper provides a framework for leveraging demand data to determine sustainable network expansion to fulfill the increasing needs of startups in the express delivery industry.

While the literature points out several hub assignment methods, the authors propose an alternative spherical-clustering algorithm for densely urbanized population environments to strengthen the accuracy and robustness of current models. The authors complement this approach with straightforward mathematical optimization and simulation models to generate and test designs that effectively align environmentally sustainable solutions.

To examine the effects of different degrees of demand variability, the authors analyzed this approach's performance by solving a real-world case study from an express delivery company's primary market. The authors structured a four-stage implementation framework to facilitate practitioners applying the proposed model.

Previous investigations explored driving distances on a spherical surface for facility location. The work considers densely urbanized population and traffic data to simultaneously capture demand patterns and other road dynamics. The inclusion of different population densities and sustainability data in current models is lacking; this paper bridges this gap by posing a novel framework that increases the accuracy of spherical-clustering methods.