This study introduces a novel methodological approach in environmental finance by developing a machine learning-enhanced green insurance indicator integrated within a System GMM framework. Leveraging data from 20 OECD countries during 2012–2022, we examine the drivers of green investment, focusing on economic capacity, financial innovation, and environmental pressures. The hybrid design combines XGBoost-based feature selection with dynamic panel econometrics, improving precision in measurement and causal inference. By constructing this new quantitative indicator, the study provides a more nuanced and accurate assessment of green insurance’s role across countries. Our findings reveal that GDP per capita is the strongest driver, green insurance contributes positively as a secondary factor, and CO₂ emissions negatively affect investment. The results highlight how financial innovation and environmental policy mechanisms interact with economic conditions to shape sustainable investment patterns. By employing this innovative hybrid modeling approach, the study offers both methodological advancement and practical insights for policymakers aiming to align economic growth with ecological sustainability. Overall, the integration of machine learning with advanced econometric techniques demonstrates strong potential for improving the understanding of complex, cross-country environmental investment dynamics.
This study examines the relationship between economic growth, energy consumption, greenhouse gas emissions, and waste management in OECD countries from 2000 to 2021 using spatial panel models. The findings indicate that energy consumption and pollutant emissions significantly impact waste production, emphasizing the need for effective policies to mitigate these effects. The study highlights the importance of developing comprehensive policies and fostering regional cooperation to enhance waste management and accelerate the transition toward a circular economy. Reducing energy consumption and emissions plays a vital role in minimizing waste generation. Therefore, governments should adopt sustainable strategies that include expanding renewable energy sources, investing in advanced recycling technologies, and improving infrastructure for waste collection, sorting, and disposal. Strengthening collaboration between public and private sectors, implementing stricter environmental regulations, and increasing public awareness of sustainable waste management practices are essential steps toward achieving long-term ecological and economic resilience. By integrating circular economy principles into policy-making, OECD countries can reduce environmental damage, optimize resource utilization, and contribute to global climate objectives.
Solar thermal systems depend on efficient heat integration strategies to minimize energy loss. The present study proposes a novel multigeneration configuration. It integrates a heliostat field–driven power source with compressed air energy storage (CAES) for long-term and flexible energy storage. The system also includes a supercritical CO₂ cycle, an absorption chiller, a heating unit, and a multi-effect desalination (MED). A key innovation of this work is the introduction of a three-state operation method. This method was developed using engineering equation solver (EES) and enables adaptive operation across charging, discharging, and storage states, enhancing overall system efficiency. The study also provides a comprehensive sensitivity analysis and uses artificial intelligence–assisted multi-objective optimization based on the non-dominated sorting genetic algorithm II (NSGA-II) combined with the linear programming technique for multidimensional analysis of preference (LINMAP). The exergetic round-trip efficiency (ERTE), unit cost of products, and CO₂ emissions served as objective functions, yielding optimized baseline values of 28.47%, 0.1158 $/kWh, and 31.52 kg/MWh, respectively. Simulation results, using 2024 meteorological data, demonstrate system feasibility under two Iranian climates: Tehran and Yazd. For Tehran, the optimal ERTE, cost, and emission values are 26.97%, 0.1280 $/kWh, and 30.21 kg/MWh. For Yazd, these values are 27.80%, 0.1174 $/kWh, and 30.09 kg/MWh. Compared with conventional CAES-integrated solar systems, the proposed design improves ERTE by about 8–12%. This confirms superior performance and adaptability across diverse operating conditions.
In fossil-fuel-dependent less developed economies, persistent industrial energy inefficiencies significantly undermine both sustainable economic growth and climate-mitigation objectives. This study explores the institutional and structural determinants of energy inefficiency in Iran’s industrial sector using Stochastic Frontier Analysis (SFA), a method that isolates inefficiency from random noise. Based on provincial data spanning 2012–2023, we estimate energy efficiency scores and identify factors associated with inefficiency. The results reveal substantial regional disparities, with over half of the provinces scoring below the national average efficiency (mean= 0.7883), indicating an annual energy-saving potential equivalent to 739,358 tons of crude oil. Key inefficiency factors include ownership structure, market structure, and workforce education gaps. Our findings inform a policy framework for reforming industrial sectors, emphasizing: (1) targeted privatization incentives; (2) investment in vocational training; and (3) the promotion of energy-efficiency cooperatives among SMEs to leverage scale benefits, advancing progress toward SDG 7 and net-zero transitions in Iran and other emerging economies facing similar institutional and market distortions.
The rapid expansion of metropolitan areas and the resulting fragmentation of green spaces have intensified concerns regarding ecological functionality and spatial justice. This study assesses the structural connectivity of the green space network in the Tehran Metropolitan Area by applying core principles of landscape ecology, with an emphasis on horizontal linkages across micro, meso, and macro spatial scales. Using Sentinel-2 satellite imagery and object-based classification, three key landscape metrics—Percentage of Landscape (PLAND), Proximity Index (PROX), and Landscape Shape Index (LSI)—were calculated within a 500-meter moving window and integrated into a Composite Connectivity Index (CCI). Spatial modeling and analysis were performed using eCognition Developer, GIS, FRAGSTATS, and Expert Choice software. Results indicate pronounced spatial inequalities in green space connectivity. Northern neighborhoods such as Baharan, Darband, Niavaran, Darakeh, Ghasemabad Dehnarmak, Jamaran, Tajrish, and Shahrak-e Gharb demonstrate the highest connectivity levels. In contrast, neghborhoods such as Northern Afsariyeh, Karun, Salsabil, Imam Khomeini, Vahidieh, Majidieh, and Dehghan reveal extremely low connectivity, underscoring severe fragmentation. A sharp north-south divide is evident, with districts 1, 2, 3, 4, and 5 showing high connectivity, while fourteen central districts exhibit limited structural integration. Southern districts (15, 16, and 20) display moderate conditions. The new index—capturing patch proportion, spatial proximity, and shape complexity—offers a practical and scalable tool for assessing and enhancing the performance of urban green infrastructure. Addressing these connectivity inequalities requires prioritizing structural enhancement over mere expansion to advance both ecological resilience and equitable planning.
