Return Values Research Articles

Investment Feasibility Analysis Of Resort X Development Project In Labuan Bajo Using Discounted Cash Flow And Monte Carlo Method

In general, tourist destinations in Labuan Bajo are synonymous with Komodo National Park which is one of the seven wonders of the world. This is the background for the developer to invest in the construction of resort X in Labuan Bajo. It is hoped that the X resort area can become an attraction for tourists in Labuan Bajo. In this study, a financial feasibility analysis was carried out to find out whether or not the investment to be made was feasible. The methods used are discounted cash flow (DCF) and Monte Carlo. The X resort construction project is built on an area of 113,396 m2 and is planned to build 263 units of shop houses and 1 unit of 4-star hotels as well as supporting buildings such as back of house (BOH) buildings, public toilets, floating libraries, arrival pavilions. The results of the analysis using the DCF method show a positive NPV value of Rp 87,895,000,000 and an IRR value of 17.66% greater than the Minimum Attractive Rate of Return (MARR) value of 10%, so that the project can be declared feasible to be implemented, as well as the Monte Carlo method which shows a positive NPV value of Rp 35,423,000,000 and an IRR value of 12.82% greater than the MARR value. Therefore, based on the results of the analysis of these two methods, the X resort project in Labuan Bajo can be declared feasible to be implemented.

A Comparative Study of The Application of NPV And IRR To Real Estate Investment Decision- Making

The China real estate industry is undergoing a fluctuation nowadays. And this paper is going to investigate this industry by researching a leading enterprise, CHINA VANKE CO., LTD. with the assistance of two financial instruments, Net Present Value (NPV) and Internal Rate of Return (IRR). Furthermore, the paper has another purpose that is comparing the properties between NPV and IRR. This paper uses literature research method and case study to analyse the situation of Vanke and list the possible considerations while having the analysis. After a series of research, the author considers NPV as a better indicator compared to IRR. NPV has the advantage of simple marginal determination, however, the related indicator, discount rate cannot be determined easily and accurately in the real estate analysis since it can be affected by many factors. For advantage of IRR, it is simple to calculate the value because it is only decided by the cashflows. On the other hand, IRR cannot clarify the real estate investment decision-making by itself, it must have the assistance of NPV versus discount rate graph. Consequently, the mixed employment would present a clearer and more comprehensive analysis for investors.

Assessing extreme significant wave height in China’s coastal waters under climate change

Accurately estimating the return values of significant wave height is essential for marine and coastal infrastructure, particularly as climate change intensifies the frequency and intensity of extreme wave events. Traditional models, which assume stationarity in wave data, often underestimate future risks by neglecting the impacts of climate change on wave dynamics. Combining time series decomposition and recurrence analysis, the research develops a nonstationary framework to predict significant wave height. The stochastic component is modelled using a stationary probability distribution, while the deterministic component is predicted based on sea surface temperature projections from CMIP6 climate scenarios. The model evaluation demonstrates strong predictive capability for both stochastic and deterministic components. Application of the model to China’s coastal waters reveals significant discrepancies between stationary and nonstationary return value estimates. Compared to conventional distribution models, the nonstationary model predicts substantial increases in extreme wave heights. These findings underscore the importance of adopting nonstationary models to more accurately assess future risks posed by extreme wave events in a changing climate.

Stock Returns and Volatility: Comparative Investigation from SAARC Countries

This research article analyses the stock return and volatility of three selected SAARC countries (Pakistan, India, and Bangladesh), focusing on the role of natural resources in influencing market performance. This study aims to determine the robustness of the performance, considering the economic significance of these countries' natural resources. The daily data from the PSX 100 index (Pakistan Stock Exchange), BSE index (Bombay Stock Exchange), and DSE index (Dhaka Stock Exchange) for the years 2019 to 2021 has been obtained from their websites. The data is treated in MS Excel to compute the daily Geometric Returns for BSE, PSX, and DSE indices. Furthermore, the data were analyzed using the specialized package EViews for Time Series Graph, Descriptive Statistics, Unit Root Test, and GARCH Model. The findings of this research show that the BSE index has the highest return value of 6.78 percent compared with 4.56 percent and 3.75 percent for DSE and PSX, respectively. The Half-life model has been applied to calculate the volatility observed by the indices. The result shows that BSE is the most volatile and takes the highest time of 45 days to revert to its mean position, followed by PSX and DSE, which take 19 and 10 days, respectively. Therefore, investing in the BSE index is better for risk-lover investors. At the same time, DSE has the fastest mean reversion compared to the other two indices. Therefore, it is feasible for risk-averse investors.

The Economic Efficiency of Micro Biogas Plants: A Sustainable Energy Solution in Slovenia—Case Study

This paper presents a simulation model for determining the most suitable type of microbiogas plant for small local communities on Slovenian farms, focusing on the efficient processing of organic waste. This model uses various input parameters, including different types and quantities of slurry and corn silage. Four different scenarios were developed to represent potential plant sizes, each evaluated using key economic indicators: net present value, breakthrough price, and internal rate of return. A scenario sensitivity analysis was conducted to assess the effects of changes in investment costs, fluctuations in energy prices, and the addition of corn silage to the anaerobic digestion process. The results highlight significant differences in economic viability across the scenarios, with some demonstrating positive financial outcomes and shorter payback periods and others indicating potential financial risks and longer recovery times under certain conditions. The analysis suggests that smaller micro biogas plants may struggle to achieve profitability without optimizing input ratios or reducing costs, whereas larger plants show more favorable economic indicators, provided certain conditions are met. Furthermore, the economic efficiency improves when adding maize silage to the fermentor mixture.

Research on robust decision making for intelligent connected vehicle at highway on-ramp

Reinforcement learning has demonstrated its potential in the decision-making field of autonomous driving. By engaging in trial-and-error learning and interacting with the environment, we can adapt our behavioral strategies based on reward and enhance driving decisions. Nevertheless, real-world vehicle decision-making involves intricate and diverse information, and the limited state information hinders agents from making optimal decisions, which may result in catastrophic consequences like collisions. Therefore, this paper proposes a robust deep reinforcement learning method based on the Soft Actor-Critic (SAC) algorithm for highway intelligent connected vehicle ramp merging decision-making. The model represents the vehicle features of the target lane and its adjacent lanes as an environmental state space. Additionally, a hybrid action space is designed, which combines discrete lateral actions and continuous longitudinal actions. Finally, an on-ramp simulation platform is built using actual roads and SUMO to verify the feasibility of the model. Multiple sets of comparative analysis experimental results demonstrate that the proposed method outperforms others in terms of the average reward return value, robustness value, collision rate, and success rate. Moreover, the model exhibits a stable lane change success rate under various road traffic density conditions, which indicates its robustness. The code can be obtained at https://github.com/ColinFanghz/sac-on-ramp.git .

Comprehensive Analysis of 100 MW Wind Farm Losses and Their Financial Impacts: A Study on Array Losses Across Multiple Turbines Using a RETScreen Expert Software

Abstract This study aimed to analyze and evaluate the influence of array losses on the financial sustainability and economic viability of wind farm projects with a capacity of 100 MW. The Al-Fajer site has been proposed for a feasibility study to assess the viability of building an onshore wind farm. The assessment of investment costs was conducted using the RETScreen program. The findings demonstrated that alterations in array losses impact the amount of energy exported to the grid, influencing changes in revenue, pre-tax internal rate of return (IRR), and net present value (NPV). When array losses in (case 1) decrease by 2%, that will positively impact financial feasibility factors. Therefore, it will lead to a gain in income for all turbines; the net present value (NPV) and pre-tax internal rate of return (IRR) values experienced an increase, indicating a positive impact on the project’s profitability. When array losses in (case 2) increase by 2%, it will lead to negative results on the wind farm and, thus, reduce the energy exported to the grid; wind turbine revenue will experience a decline. This increase substantially affects the NPV and IRR, leading to decreases. The capacity factor experienced a drop, resulting in significant changes to the project’s financial returns. The levelized cost of energy (LCOE) has increased due to decreased production, leading to higher prices. The simple payback likewise experienced a boost beyond its usual norms.

Analisis Kinerja Keuangan Untuk Mengukur Tingkat Kesehatan Perusahaan Manufaktur Sub Sektor Farmasi pada Bursa Efek Indonesia Periode Tahun 2018-2022

The aim of this research is to analyze the company’s financial performance to measure the health level of pharmaceutical sub-sector manufacturing companies listed on the IDX in terms of liquidity, solvency, profitability and activity ratios for the 2018-2022 period. The sample used was 9 pharmaceutical sub-sector companies. The data analysis method used is quantitative descriptive. The research results show that the average ratio of liquidity, solvency, profitability and activity for pharmaceutical sub-sector companies fluctuates. Based on the liquidity ratio, Kimia Farma Tbk has the highest average value of the current ratio, Sido Muncul Herbal & Pharmaceutical Industry Tbk has a quick ratio and Indofarma Tbk has a cash ratio so it is classified as a company with the highest level of health. The solvency ratio in the debt to equity ratio and debt to asset ratio of the Sido Muncul Herbal & Pharmaceutical Industry Tbk has the lowest average value ratio, while the long-term debt to equity ratio of Darya Varia Laboratoria Tbk is classified as a company with the highest level of health. Tempo Scan Pacific Tbk’s profitability ratio has the highest average ratio value for net profit margin and return on assets and Pyridam Farma Tbk for return on equity so it is classified as a company with the highest level of health. The activity ratio in the inventory turnover ratio and the fixed asset turnover ratio of Darya Varia Laboratoria Tbk and Organon Pharma Indonesia Tbk in the total asset turnover ratio each have the highest average ratio values so they are classified as companies with the highest level of health.

Analisis Kelayakan Investasi Smart Homestay Bu Ai Di Situs Wisata Gunung Padang

The research aims to assess the investment feasibility of Bu Ai's smart homestay. Bu Ai's smart homestay investment feasibility assessment is one of the Matching Fund Grant programs. The subject of this research is Bu Ai's Smart Homestay Business located in Kp.Gunung Padang, Karyamukti Kec.Campaka Kab.Cianjur West Java. This research was conducted for 5 months starting from August-December. The data used in this study are primary data. The methods used in this research are Payback Period, Net Present Value, Profitability Index and Internal Rate of Return. The results found that the feasibility assessment using PP, NPV, PI, & IRR is not feasible. However, at this time the investment is still accepted due to other factors including the location of the homestay which is located in the Gunung Padang site tourism area which has the potential to continue to increase.

Predicting storm surge extremes on the Southeast Brazilian Coast: Long-term projections with neural networks

Extreme sea level events, resulting from the confluence of tides and storm surges, pose a significant threat to coastal populations and economies. The escalating risks associated with these events are exacerbated by climate change, manifesting in heightened storm intensity, increased frequency, and rising sea levels. Precise estimation of the probability of extreme storm surges is crucial for effective coastal management and adaptation. However, utilizing historical storm data is challenging due to data scarcity and the imperative to consider potential non-stationarity induced by climate change in predicting such events. This study addresses these challenges by introducing two neural network-based machine learning systems: a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM). Leveraging local and remote atmospheric and oceanic conditions, these systems project storm surges until 2060, incorporating climate projections. Trained and evaluated using sea level data from Imbetiba Port in Macaé, Rio de Janeiro, Brazil, the models utilize dynamic regionalization data from the RegCM4 and WW3 models, forced by HadGEM2-ES and MPI climate models. Both neural network models exhibited similar performance patterns, demonstrating high agreement in predicting storm surge heights with a 100-year return value, based on Imbetiba Port data. Projections utilized peaks-over-threshold (POT) methods, and extremes were calculated using a generalized Pareto distribution (GPD). Long-term projections indicated a 28 % increase (MLP ANN) and a substantial 70 % increase (LSTM RNN) in estimating extreme values, surpassing the observed storm surge of 0.67 m. Projected mean values were 0.86 m for the MLP network and 1.15 m for the LSTM network, providing valuable insights into the potential amplification of extreme sea level events in the studied region.

Practical non-stationary extreme value analysis of peaks over threshold using the generalised Pareto distribution: Estimating uncertainties in return values

Choice of tuning parameters influences the performance of non-stationary extreme value modelling for peaks over threshold using the generalised Pareto (GP) distribution. We examine the effect of tuning parameter choice on maximum roughness-penalised likelihood estimation of GP models, the shape and scale parameters of which are assumed to vary smoothly on a one-dimensional “directional” covariate domain, under a B-spline representation. We examine the effect of (a) extreme value model parameterisation, (b) relative roughness penalty of GP parameters as a function of covariate, (c) cross-validation strategy for roughness parameter tuning, and (d) estimator for return value, on the estimation of return values corresponding to return periods 1000× the period of a sample of size 1000. Bootstrap resampling is used for thorough uncertainty quantification. We also compare results with those from stationary inference.Results from a large simulation study of 16 cases broadly representative of North Sea conditions for significant wave height with direction, indicate that (i) multiple two-group cross-validation yields lower return value estimates that ten-group cross-validation (leading to negative bias on average, for the case studies considered), (ii) the quantile of the bootstrap predictive estimator yields larger values than the mean over bootstraps of the quantile estimate (leading to reduced omni-directional bias for the case studies considered). Further, (iii) the use of stationary models for non-stationary tails is only reasonable when a high extreme value threshold is set for the stationary analysis. However, (iv) the relative performance of different modelling strategies is sensitive to the specific characteristics of the case study.

Risk-return analysis of clean energy grid project investment based on integrated ISM and Monte Carlo model

To solve the problem of complex and difficult to quantify factors affecting investment returns and risks in clean energy power grids, this study comprehensively applies the interpretive structural model and Monte Carlo model to the analysis of investment risk-returns in clean energy power grid projects. The interpretive structural model is utilized to analyze project investment returns, while the Monte Carlo model is used to analyze project investment risks. The project investment risk is based on the factor analysis of project investment returns, and key risk factors are identified through 1000 simulations, and the impact of these risks on project returns is quantified. By combining the two, the investability of the project is analyzed. The results showed that grid electricity prices, kilowatt hour subsidies, technology learning rates, total annual sunshine hours, and system power generation efficiency were key factors driving investment returns. The average expected value of investment return was about 20%, and the probability of investment return below 6% was close to 0. The overall project is worth investing in. From this, it can be seen that the research designed investment risk-return analysis methods for clean energy grid projects can effectively distinguish the main factors affecting investment returns and risks, and pre simulate the risk situation of returns. This study can provide reference for investor decision-making.

Improving sub-seasonal extreme precipitation forecasts over China through a hybrid statistical-dynamical framework

Skillful and reliable sub-seasonal extreme precipitation forecasts are crucial for disaster prevention and mitigation. In this study, we introduce a hybrid statistical-dynamical framework to predict monthly maximum one-day precipitation (Rx1D) and monthly maximum five-day precipitation (Rx5D) over China from May to October. In the hybrid statistical-dynamical framework, the ECMWF forecasts of precipitation and boreal summer intraseasonal oscillation (BSISO) indices are used as predictors to establish calibration model and bridging models, separately. The calibration model and bridging models are then merged to generate probabilistic forecasts of Rx1D and Rx5D. Our results suggest that the bridging models show better performance in predicting Rx1D and Rx5D than calibration model in May, June, and July when the BSISO indices are used as predictors. The forecast skill of calibration model is higher compared to bridging models in August, September, and October. The BMA merged forecasts take advantage of both calibration model and bridging models, and can provide skilful and reliable forecasts for both Rx1D and Rx5D prediction. To have a more comprehensive assessment, we also evaluate the prediction skill of the occurrence of extreme precipitation events with exceedance probabilities of 50%, 20%, and 5% for both Rx1D and Rx5D. The Brier skill score of merged forecasts indicates that the hybrid statistical-dynamical framework can also provide skilful forecasts for the occurrence of extreme precipitation events greater than one-in-5-year return value of Rx1D (5Rx1D) and one-in-5-year return value of Rx5D (5Rx5D) in comparison to long-term climatology. These findings demonstrate the great potential of combining dynamical models and statistical models in improving sub-seasonal extreme precipitation forecasts.

Global estimates of 100-year return values of daily precipitation from ensemble weather prediction data

Abstract. High-impact river floods are often caused by very extreme precipitation events with return periods of several decades or centuries, and the design of flood protection measures thus relies on reliable estimates of the corresponding return values. However, calculating such return values from observations is associated with large statistical uncertainties due to the limited length of observational time series, uneven spatial distributions of rain gauges and trends associated with anthropogenic climate change. Here, 100-year return values of daily precipitation are estimated on a global grid based on a large data set of model-generated precipitation events from ensemble weather prediction. In this way, the statistical uncertainties in the return values can be substantially reduced compared to observational estimates due to the substantially longer time series. In spite of a general agreement in spatial patterns, the model-generated data set leads to systematically higher return values than the observations in many regions, with statistically significant differences, for instance, over the Amazon, western Africa, the Arabian Peninsula and India. This might be linked to an overestimation of tropical extreme precipitation in the model or an underestimation of extreme precipitation events in observations, which, if true, would have important consequences for practical water management.

THE INFLUENCE OF MARKET RATIO ON CUMULATIVE ABNORMAL RETURNS IN COMPANIES LISTED ON THE JAKARTA ISLAMIC INDEX (JII)

This research was conducted to test whether there is an influence of earnings per share and price earnings ratio on the Cumulative Abnormal Return of 14 companies listed on the Jakarta Islamic index during 2020-2022. This research uses field research, this research uses quantitative research methods. The approach used in this research is an associative approach which aims to determine the relationship between two or more variables. With data collection techniques in the form of documentation using primary data sourced from secondary data, and processing using E-Eviws 12 (X64). There is a statistical analysis technique with 14 companies selected using the purposive sampling method registered in the Jakarta Islamic Index during 2020-2022. The research results obtained show 1) Earning Per Share has no positive influence and is not significant on Cumulative Abnormal Return. This is proven by the t-test carried out, where a value of 0.9314 > 0.05 was obtained, so it can be concluded that H1 is rejected. 2) Price Earning Ratio has no positive influence and is not significant on the Cumulative Abnormal Return value. This is proven by the t-test carried out, where a value of 0.9287 > 0.05 was obtained, so it can be concluded that H2 is rejected. 3) Simultaneously Earning Per Share and Price Earning Ratio do not have a positive and insignificant influence on Cumulative Abnormal Return which has been proven from the F-Test results where a value of 0.986304 > 0.05 is obtained, so it can be concluded that H4 is rejected.

Pricing drinking water testing in northern Haiti: Financial sensitivity to operating costs, user demand, and economic conditions

Safe drinking water availability is a concern in Haiti. Public systems have limited coverage and reliability. Private wells and local water sources are often of unknown or poor quality. Public health events, such as the 2010 cholera outbreak, demonstrate vulnerability to water contamination. To address these concerns, a drinking water laboratory was established at the Campus Henri Christophe in Limonade, a branch of the State University of Haiti, to meet water testing demands from local clients such as for-profit kiosks, institutions, industries, and municipal water systems. This study assessed the financial viability of a university-based drinking water laboratory in Haiti by calculating Internal Rate of Return and Net Present Value. Sensitivity analysis was used to identify the range of conditions under which laboratory revenues would cover operating costs. To achieve an acceptable profitability level, the laboratory must perform microbiological testing for routine monitoring samples and test an average of five samples per day. Price-based incentives for new clients have relatively small impacts on profitability. Finally, international and Haitian inflation cause some variation in profitability. These economic factors will be among the key drivers of laboratory operation costs. The results underscore the main factors that must be considered to make the laboratory successful and the importance of strategic marketing for laboratory managers to encourage clients to regularly test drinking water and emphasize microbiological testing.

The Model of Stock Pricing Banking Sector on the Indonesia Stock Exchange during the Government of Susilo Bambang Yudhoyono and Joko Widodo

Differences in macroeconomic conditions between the SBY and JKW government periods can significantly impact stock prices within the banking sector, especially when the economy experiences fluctuations, leading to increased stock price volatility. This research aims to assess the influence of various macroeconomic factors, including the Global index, macroeconomics, world oil prices, China index, Arab index, competitive resources, and inflation, on banking sector returns from October 2004 to October 2014. The analysis utilizes the arbitrage pricing theory, difference test, and mean absolute deviation. The study divides the analysis into high-return and low-return groups for each government period. Findings indicate that macroeconomic factors exert a simultaneous influence on return groups during both SBY and Jokowi's administrations. However, for JKW high returns, these factors do not significantly impact them. Specifically, the Global Index, China Index, Arab Index, Resource Competitiveness, and Inflation show a positive correlation with both high and low returns during SBY term. Conversely, during JKW tenure, only the Global Index and China Index exhibit positive effects on high and low returns. The difference test reveals disparities in the Arab index for high and low returns of SBY, while only the Global index displays differences for JKW. The variations in high returns between SBY and Jokowi stem from discrepancies in the Global and Arab indices, while no distinctions exist in low returns between the two administrations. Additionally, the mean absolute return values for high returns during JKW era demonstrate the smallest accuracy, indicating potential limitations in the predictive power of the model during this period.

User guide for Social Determinants of Health Survey data in the All of Us Research Program.

Integration of social determinants of health into health outcomes research will allow researchers to study health inequities. The All of Us Research Program has the potential to be a rich source of social determinants of health data. However, user-friendly recommendations for scoring and interpreting the All of Us Social Determinants of Health Survey are needed to return value to communities through advancing researcher competencies in use of the All of Us Research Hub Researcher Workbench. We created a user guide aimed at providing researchers with an overview of the Social Determinants of Health Survey, recommendations for scoring and interpreting participant responses, and readily executable R and Python functions. This user guide targets registered users of the All of Us Research Hub Researcher Workbench, a cloud-based platform that supports analysis of All of Us data, who are currently conducting or planning to conduct analyses using the Social Determinants of Health Survey. We introduce 14 constructs evaluated as part of the Social Determinants of Health Survey and summarize construct operationalization. We offer 30 literature-informed recommendations for scoring participant responses and interpreting scores, with multiple options available for 8 of the constructs. Then, we walk through example R and Python functions for relabeling responses and scoring constructs that can be directly implemented in Jupyter Notebook or RStudio within the Researcher Workbench. Full source code is available in supplemental files and GitHub. Finally, we discuss psychometric considerations related to the Social Determinants of Health Survey for researchers.

Optimizing the bidding strategy and assessing profitability of over-install renewable plants equipped with battery energy storage systems

The over-installation of renewable energy sources (RES) can enhance the profits of RES producers by increasing the total exporting energy; however, RES power curtailments must be applied under over-production of RES plants to ensure their contracted maximum export capacity limits. Battery energy storage systems (BESSs) can be used to reduce the RES curtailments and therefore enhance the profits of producers. This work develops a bidding strategy as a scenario-based stochastic optimization scheme to maximize the expected profits of producers in electricity markets considering battery degradation and maximum export capacity constraints. Using the proposed scheme, a financial analysis is performed to assess the profitability for over-installing hybrid RES plants by calculating the net present value and internal rate of return of the project under different BESS costs. Simulation results, using real data from a wind and photovoltaic plant, demonstrate the effectiveness of the proposed stochastic scheme in enhancing the profit of producers compared to the corresponding deterministic scheme. The performed financial analysis, using the net present value metric, indicates that a maximum over-installation of 150%–170% and 160%–180% for the wind and photovoltaic plant, respectively, is profitable, while the usage of a BESS is profitable when its cost is under 200,000 €/MWh.

Leveraging atmospheric moisture recycling in Saudi Arabia and neighboring countries for irrigation and afforestation planning

Effective irrigation planning is crucial for sustainable agricultural development and ecosystem restoration projects in arid regions. With respect to ambitious greening initiatives, Saudi Arabia is establishing a national strategy toward a more sustainable and eco-friendly future not only for itself but also for the broader Middle East region. Thus, comprehensively understanding the water cycle in the region is essential to identify the most suitable target locations for afforestation and reforestation while considering the potential role of irrigation. Herein, in addition to traditional pedoclimatic factors, we introduce a complementary consideration—“irrigation recycling.” Building on the well-established concept of atmospheric moisture recycling and taking advantage from an atmospheric trajectory dataset, we track the path of evaporated water from current or potential irrigated sites to the location where the evaporated water eventually falls as precipitation. Our analysis offers two key benefits. First, it helps pinpoint the regions in which and the periods during which water recycling is maximum within the country, aiding more precise calculations of the investment return value for irrigation infrastructures. Second, it helps identify the land-use change patterns that contribute to international efforts such as drought mitigation in East Africa as an example. We found that one-third of the actual precipitation in the current Saudi irrigated sites originated from evapotranspiration over land, mainly from Saudi Arabia and surrounding countries. Interestingly, most of the evapotranspiration from these irrigated sites will eventually fall somewhere over land (primarily in Iran). Controlling the seasonality and spatial distribution of the future irrigation expansion will allow controlling the atmospheric moisture recirculation in Saudi Arabia and nearby drought-prone regions such as Eastern Africa. The outcomes of this study will be the subject of future integrated assessments to account for the climatic feedbacks of the land-use change scenarios. At present, they provide crucial insights to support the decision-making process surrounding the Saudi and Middle East Green Initiatives. Further, the presented methodology offers a pragmatic framework that can be applied to similar greening projects for other regions, making it a viable and valuable approach for global sustainability programs.