Efficient management of water and energy resources has become a strategic requirement across integrated mining and phosphate complexes due to rising operating costs, environmental pressures, and sustainability targets. This study proposes an artificial intelligence (AI)-driven framework for optimizing water and energy consumption across the complete phosphate value chain, from phosphate rock mining and beneficiation to slurry transport and downstream processing units including sulfuric acid, phosphoric acid, ammonia utility integration, and di-ammonium phosphate (DAP) production. The framework applies advanced machine learning models, including Long Short-Term Memory (LSTM), Random Forest, and Extreme Gradient Boosting (XGBoost), to predict water demand, energy use, and process efficiency using real-time industrial and operational data. By integrating predictive analytics with optimization techniques, the proposed system supports dynamic decision-making, improved water recycling, reduced energy intensity, and better production coordination across upstream and downstream units. The study further highlights how AI-enabled optimization contributes to lower carbon emissions by reducing excess pumping, thermal losses, and inefficient process loads. The proposed approach supports sustainable industrial development, aligns with digital transformation trends, and provides a scalable pathway for intelligent resource management in integrated phosphate complexes.

This study examines how artificial intelligence can be used to assess Maqasid al-Shariah outcomes in the development of Saudi Arabia’s debt market under Vision 2030. While Saudi debt-market expansion has strengthened financing channels through sukuk, bonds, and government debt instruments, limited research has examined whether such development advances broader Islamic ethical objectives, including justice, transparency, wealth circulation, risk-sharing, social benefit, and protection from excessive uncertainty. The study proposes an AI-enabled assessment framework that links debt-market indicators with Maqasid-based outcome dimensions. Using conceptual analysis, policy review, and proposed machine-learning indicators, the paper explains how artificial intelligence can support real-time monitoring of market depth, investor inclusion, Shariah compliance, sustainability alignment, and financing contribution to national development. The framework is positioned within Saudi Arabia’s Financial Sector Development Program, which seeks to deepen capital markets and diversify financing sources in support of Vision 2030. The paper contributes to Islamic finance literature by moving beyond product-level Shariah compliance toward measurable outcome-based governance. It also provides practical value for regulators, issuers, investors, and policymakers seeking to integrate ethical finance, digital intelligence, and market development in Saudi Arabia’s evolving financial ecosystem.

Cloud analytics is actively applied in the field of real estate investment and portfolio optimizations in today's business environment. Solutions created by AWS, Microsoft Azure, and Google Cloud Platform (GCP) provide the opportunities to manage the portfolio by ingesting data, performing predictive modeling, and visualizing data sets. The current paper aims at analyzing the role of cloud analytics in data-driven portfolio optimization in seven markets: Saudi Arabia, UAE, Qatar, Oman, India, the United Kingdom, and the United States. There are a lot of developments concerning the application of predictive analytics and machine learning in recent years. The results of the JLL 2025 Global CRE Technology Survey report that 92% of the companies in the CRE sectors are running AI pilots, whereas the share was 5% in 2023 [13]. The accuracy rate of estimates made with machine learning algorithms in relation to property prices and rental demand stands between 82% and 91% [22]. It is claimed that there are potential profits worth $1.3 trillion in the global real estate sector [17]. IoT devices installed in smart buildings allow saving up to 25% on operating expenses [3]. Moreover, 60% of all sales in Dubai involve blockchain technology [30]. In each market, cloud analytics drivers have their peculiarities. The total value of the Vision 2030 program implemented in Saudi Arabia is over $819 billion, while the average annual growth rate of proptech market is 16.09% [1]. The Real Estate Data Cube in Dubai contains information that can be accessed publicly; it is used to train algorithms, and the accuracy rate of predictions reached 92% [3]. The HM Land Registry in the UK invested £72 million into digital transformation, using AI to save about £59 million annually due to fraud [10, 11]. The Indian proptech market assessed at $1.72 billion makes use of cloud business intelligence software; over 70% of large companies apply such solutions [8]. Statista reports that the total market size in 2025 equals $752 billion and it is going to reach $2.39 trillion by 2030 [25]. In terms of the real estate sector, 89% of firms use multiple clouds, whereas 52% of enterprise workloads happen in public clouds [19-25]. In this paper, the architecture of cloud analytics will be analyzed along with the comparison of the products by the leading providers, several case studies in seven countries, and the implementation roadmap.

The rapid urbanization of cities worldwide has intensified environmental challenges such as air pollution, water contamination, noise pollution, and climate variability. These challenges necessitate the adoption of intelligent and scalable monitoring systems to support sustainable urban development. This study proposes an AI-based Environmental Monitoring System designed to enhance real-time data collection, analysis, and decision-making within smart city ecosystems. The proposed system integrates Internet of Things (IoT) sensors, cloud computing, and artificial intelligence algorithms to monitor environmental parameters including air quality, temperature, humidity, water quality, and noise levels. Machine learning models are employed to analyze large-scale environmental data, detect anomalies, and generate predictive insights for proactive environmental management. The framework introduces a multi-layer architecture comprising data acquisition, data processing, analytics, and decision-support layers. This architecture enables efficient data flow, scalability, and real-time responsiveness. The system also incorporates sustainability indicators aligned with global environmental standards to support policy formulation and urban planning. The findings highlight that AI-driven monitoring significantly improves environmental awareness, reduces response time to environmental hazards, and supports data-driven governance. This research contributes to the development of sustainable smart cities by providing a scalable and intelligent environmental monitoring framework that enhances urban resilience and environmental sustainability.

This study examines the influence of China–Zambia bilateral trade relations on Zambia's economic growth over the period 1998–2023, grounded in four theoretical frameworks the Export-Led Growth Hypothesis, the Heckscher-Ohlin Factor Endowment Theory, Mercantilist Trade Theory, and Dependency Theory that collectively capture the growth-enhancing and structurally constraining dimensions of the bilateral trade relationship. Using annual time-series data sourced from the World Bank World Development Indicators and the World Integrated Trade Solution database, three Autoregressive Distributed Lag (ARDL) bounds testing models were estimated, each incorporating a distinct bilateral trade indicator export to China, imports from China, and the bilateral trade balance alongside inflation, exchange rate, and external debt as macroeconomic control variables. The ARDL bounds test confirms the existence of a stable long-run cointegrating relationship across all three model specifications, with F-statistics of 5.214, 4.882, and 5.637 respectively exceeding the 5% upper critical bound of 4.01. Long-run estimates reveal that exports to China exert a positive and statistically significant effect on economic growth (β = 0.0048, p < 0.01), while imports from China exhibit a negative and significant long-run effect (β = −0.0031, p < 0.05), and the bilateral trade balance carries the strongest positive coefficient (β = 0.0057, p < 0.01), collectively confirming a structurally asymmetric trade relationship in which commodity export revenues drive growth while manufactured import dependency constrains domestic value addition. Short-run error correction estimates further confirm that between 61.7% and 70.1% of short-run disequilibrium is corrected within one year, reflecting moderately fast adjustment dynamics, while inflation and exchange rate depreciation consistently exert negative short-run and long-run pressures on growth. A comprehensive battery of post-estimation diagnostics including the Breusch–Godfrey LM, Breusch–Pagan, Jarque–Bera, and Ramsey RESET tests confirms that all models satisfy the classical regression assumptions, and CUSUM and CUSUMSQ stability tests verify structural parameter stability throughout the sample period despite major macroeconomic shocks including the Global Financial Crisis, the 2015–2016 commodity price collapse, and the COVID-19 contraction. The study concludes that while China has become a central engine of Zambia's trade expansion and a significant contributor to GDP growth particularly during commodity super-cycle phases Zambia's persistent structural dependence on primary commodity exports and manufactured goods imports renders its growth path highly vulnerable to external demand shocks and copper price volatility, underscoring the urgent need for export diversification, domestic value addition, and strategic trade policy reform to translate bilateral trade engagement into more inclusive and sustainable long-run economic development.

This study develops a hybrid econometric and deep learning framework for forecasting global commodity price dynamics using multi-commodity time series data over the period 2000-2026. The proposed framework integrates Autoregressive Integrated Moving Average (ARIMA) models to capture linear dependencies, Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to model volatility persistence, and machine learning techniques, including Random Forest and Extreme Gradient Boosting, to extract nonlinear structures from residual components. In addition, Long Short-Term Memory (LSTM) networks are employed to capture long-term temporal dependencies. Empirical results across major commodities such as crude oil, gold, copper, natural gas, platinum, and silver demonstrate that the hybrid model significantly outperforms standalone econometric and deep learning models. The framework achieves consistent reductions in forecasting errors, with improvements in predictive accuracy ranging from approximately 5.5% to 12% across commodities and generates positive out-of-sample R² values in rolling-window evaluations, indicating strong temporal stability. The results further reveal pronounced volatility persistence (α + β ≈ 0.98-0.99) across all commodities and confirm that nonlinear residual components contain substantial predictive information. While LSTM models capture general temporal patterns, their standalone predictive performance remains limited, with relatively low or negative R² values in several cases. The findings indicate that commodity price dynamics are inherently multidimensional, characterized by linear dependencies, volatility clustering, and nonlinear interactions. By integrating these components within a unified structure, the proposed hybrid framework provides a robust and scalable approach for commodity price forecasting, with important implications for financial modeling, investment strategies, and policy analysis.

Large infrastructure projects such as ports, electricity infrastructure, transportation infrastructure, water infrastructure, and digital infrastructure are seen as the backbone of the logistics infrastructure of a country or its long-run economic competitiveness. A significant change was noted in the market for project finance from 2020 to 2025. The period started with a phase of capital retrenchment following the pandemic, which was followed by a phase of high base rates, where the interest rate was regulated by the US Federal Reserve at a level of 5.25%-5.50%, which is the fastest increase in forty years. In addition, a reduction in balance sheets for banks, an increase in the speed of adoption of ESG factors, and an increase in awareness about climate-related factors were noted. A reduction in private participation in infrastructure (PPI) was noted from USD 96.8 billion in 2019 to USD 45.7 billion in 2020, which then increased to USD 86 billion in 2023. This was a volatile period for the market for project finance. The purpose of this paper is to outline a structured narrative review of the published research between 2020 and 2025 on debt structuring and investment strategies for large-scale infrastructure investments. The argument is that the key to capital efficiency in infrastructure finance is not "leverage," but rather the concurrent optimization of the three interdependent design elements of risk-aligned contract architecture, a capital structure that is aligned with cash flow quality, and structural control mechanisms. The combination of the three elements of a well-structured infrastructure finance solution is referred to as the Infrastructure Finance Efficiency Triangle (IFET). It is a new approach to making decisions based on the synthesis of the published research. The paper reviews the following eight categories of financial instruments along the three dimensions of risk position, return sources, and capital efficiency. The benchmarks are as follows: the yield on infrastructure debt is 4.9-5.1%, five-year default rate is 2.4% versus 9.6% for non-financial corporates; DSCR is 1.20x-1.50x; DSRA commitments are six months' debt service; debt equity ratio is 70/30 for contracted assets [12]. The paper provides a structured review of the following three case studies: a solar power purchase agreement, a toll road concession, a RAB-regulated water utility. A seven-step implementation roadmap is provided.