International Journal of Global Warming (27 papers in press)

Regular Issues

• Fuel Consumption and CO2 Emission Comparison of Conventional and Hybrid Vehicles Using Data-Based Models and ECMS  
  by Zeynep Burcu Acunas, Ovun Isin, Ozgun Balci 
  Abstract: In this study, data was obtained via the OBD2 output of a sample hybrid vehicle. Then, electric motor, battery, and internal combustion engine (ICE) maps were created. Vehicle fuel consumption and CO2 emission results over NEDC cycle and a local route were compared between conventional vehicle model equipped with spark ignition engine and hybrid vehicle model with ECMS energy management, developed in Matlab/Simulink environment. Vehicle fuel consumption decreased by 7.59% with the ECMS controlled hybrid system over the NEDC cycle, and by 12.03% under the local route conditions. Similar significant improvements obtained in CO2 emission results as well.
  Keywords: Hybrid vehicle; ECMS; CO2; fuel consumption.
  DOI: 10.1504/IJGW.2026.10077159
   
  
• Farmland and plane (Platanus orientalis L.) stand accumulated more phytolith-occluded organic carbon in the ancient Yellow River channel  
  by Xinru Gu, Baoxian Tao, Yuqing Jiang, Shijun Zhu 
  Abstract: Response of phytolith-occluded organic carbon (PhytOC) to different land use types remains unclear. Since the 1950s, the ancient Yellow River channel (ACYR) has been converted into farmland (FL), forest (dominated by species such as Sophora japonica L, Populus nigra, Fraxinus chinensis, and Platanus orientalis L. (PO), etc), and orchard. This study firstly identified the land use types associated with the greatest soil PhytOC storage in the AYRC. Results showed that FL and PO exhibited the largest soil PhytOC storage. Firstly, the substantial PhytOC storage in the aboveground litter layer of FL and PO enhanced the return of PhytOC to the soil. Secondly, elevated concentrations of soil Si fractions in FL and PO may promote the phytolith production, while increased concentrations of soil amorphous iron oxide may inhibit the phytolith dissolution. Overall, these findings suggest that FL and PO are conducive to the PhytOC accumulation in the ACYR.
  Keywords: Phytolith-occluded organic carbon; soil Si fractions; land use types; sandy land; ancient Yellow River channel.
  DOI: 10.1504/IJGW.2026.10077414
   
  
• Time series forecasting of global temperature anomalies using machine learning  
  by Ravi Patel, Aditya Kumar, Jainath Yadav, Mrityunjay Singh 
  Abstract: Accurate forecasting of global temperature anomalies is essential for understanding long-term climate variability and guiding climate change policies. This study compares classical machine learning (ML) and deep learning (DL) models for predicting global mean temperature anomalies using data from 1880-2024. The framework incorporates extensive pre-processing with several ML and DL architectures. Results show that ML models, especially SVR and Lasso, achieve higher accuracy and efficiency than DL approaches. Forecasts for 2025-2034 indicate a consistent warming trend, with DL models projecting anomalies surpassing 1.5 C by 2031, while regression-based methods estimate more moderate increases of 1.1-1.3C .
  Keywords: global temperature anomalies; climate forecasting; machine learning; deep learning; time series prediction; climate change.
  DOI: 10.1504/IJGW.2026.10078018
   
  
• Indoor environment design of university classrooms based on improved genetic algorithm and lighting conditions  
  by Wenting Chang, Zhaofeng Wang, He Dong 
  Abstract: University classrooms face challenges such as insufficient coordination between natural and artificial lighting, glare, and uneven illuminance. This study deploys sensors to collect light and heat data, and conducts parametric modelling based on Grasshopper. Ladybug simulates daylight performance indicators and uses an improved genetic algorithm for multi-objective optimisation to improve overall daylight environment performance. The results showed that after optimisation, in classrooms with 5070 people, the DA increased from 62.3% to 72.5%, and the DGP decreased from 0.52 to 0.44. In classrooms with 120150 people, the DA improved by 9.75%. These findings demonstrate that the improved genetic algorithm enhances daylight environment performance, providing a basis and pathway for design.
  Keywords: improved genetic algorithm; indoor lighting environment design; university classrooms; multi-objective optimisation; dynamic lighting index.
  DOI: 10.1504/IJGW.2026.10078113
   
  
• Impacts of temperature-driven climate change on agricultural pest population dynamics: neural network-based simulation and risk prediction  
  by Hongfei Zhang, Zhengbing Wang, Mingsheng Yang, Yan Zhang, Zhen Li 
  Abstract: Global climate change alters agricultural pest population dynamics. Existing neural network prediction models lack biological interpretability. This study constructs the BIT-Net framework integrating biological mechanisms and data-driven channels to simulate the temperature-pest dynamic relationship. Experiments in the North China Plain demonstrate a high prediction accuracy. The framework identifies the optimum developmental temperature and the high-temperature inhibition threshold. The risk warning system provides advance warnings for multiple climate scenarios. Cross-regional migration tests confirm the framework maintains high prediction accuracy across varying conditions.
  Keywords: climate change; agricultural pests; population dynamics; neural network; risk prediction.
  DOI: 10.1504/IJGW.2026.10078114
   
  
• Machine learning modelling of coordinated development of environment and economy in climate change  
  by Wenjuan Li, Ming-Hung Shu, Jui-Chan Huang, Chih-Chin Kuo 
  Abstract: Traditional linear models fail to capture policy synergies between environment and economy, leading to low predictive accuracy. This paper proposes a machine learning framework for coordinated development of the environment and the economy (CDEE). It employs a bidirectional long short-term memory (Bi-LSTM) to capture long-term dependencies in key indicators, such as carbon emission intensity. The extreme gradient boosting-light gradient boosting machine (XGBoost-LightGBM) model combines XGBoosts feature filtering with LightGBMs high-dimensional data processing. Bi-LSTM achieved a root mean square error (RMSE) of 0.82, while XGBoost-LightGBM had a variance of only 0.0001 for environment-economy coordination index (EECI).
  Keywords: climate change; environment and economy; collaborative development; machine learning; extreme gradient boosting; XGBoost; bidirectional long short-term memory; BiLSTM.
  DOI: 10.1504/IJGW.2026.10078115
   
  
• Intelligent assessment of low-carbon landscape design effects based on transformer and temporal point cloud analysis  
  by Shu Wang 
  Abstract: Amid urbanisation and climate change, low-carbon landscape design is vital for sustainability, yet traditional assessments rely on subjective, static methods. This study introduces a transformer-based framework analysing temporal point clouds to intelligently quantify carbon sequestration, urban heat island mitigation, and biodiversity. Validated on multi-temporal LiDAR datasets from urban parks, our model achieves 92.5% carbon accuracy (18.7% improvement over baselines), thermal comfort prediction RMSE of 0.42, and biodiversity correlation of 0.89. The results demonstrate significant advancements in accuracy and interpretability for dynamic landscape performance evaluation, advancing AI applications in sustainable urban design.
  Keywords: low-carbon landscape design; temporal point cloud; transformer; spatiotemporal feature extraction; carbon sequestration assessment; urban heat island mitigation; biodiversity evaluation; intelligent assessment.
  DOI: 10.1504/IJGW.2026.10078184
   
  
• Advanced machine learning framework for predicting municipal solid waste generation trends toward resilient and environmentally sustainable urban development  
  by Ankit Bansal, Umang Soni, Aqueel Ahmad 
  Abstract: Due to the rapid urbanisation and industrial growth, municipal solid waste (MSW) generation becomes a global challenge. The present study applies machine learning (ML)-based prediction models to estimate MSW generation trends in Singapore spanning the period 2003-2020. Model performance was evaluated using the various error metrics, while variable importance was assessed via the Pearson correlation coefficient (PCC) and partial dependence plots. The findings demonstrates that ensemble-modelling approaches offers high predictive accuracy and can be generalised to various regions for effective MSW forecasting, contributing to environmental resilience in line with sustainable development goals (SDG) 12 and 13.
  Keywords: municipal solid waste; MSW; waste generation prediction; machine learning; sustainable urban development; environmental sustainability.
  DOI: 10.1504/IJGW.2026.10078191
   
  
• Climate Mitigation Implications of Small Modular Reactor-Integrated Microgrids in Baseload Power-Centric Smart Cities  
  by T.A.E. H.O. Woo, Chang Hyun Baek, Kyung B.A.E. Jang 
  Abstract: This study uses the System Dynamics method with Vensim to analyze microgrids integrating fossil fuels, Small modular reactor-based nuclear, and renewable energy for climate mitigation. Across four 50-year scenarios, Current2 shows the strongest results - its Power Grid rises nearly 800 times, and Smart City Integrity increases over 200 times, making it most suitable for smart city applications. Statistical analysis confirms that Current2 has the highest variability. As AI-driven industries increase power demand, nuclear energy becomes increasingly vital to stabilize supply and complement intermittent renewables, supporting sustainable energy transition and climate change response.
  Keywords: Climate Mitigation; Microgrid; Baseload; Energy; Small Modular Reactor (SMR).
  DOI: 10.1504/IJGW.2026.10078292
   
  
• Dynamic Evolution Analysis of River Ecological Economic Belt at Regional and Urban Scales  
  by Yeming Lyu, Yuxiao Shang, Zhenghui Chen, Yihui Xu 
  Abstract: Taking the Huaihe River Basin as a case study, this study examines the dynamic changes in water resources resulting from global urbanization and economic development from 2010 to 2024. This study develops an adaptive evaluation index system that includes social, economic, and ecological dimensions. The results showed that the adaptability of social systems has been enhanced, the adaptability of ecosystems has remained stable but with significant spatial differences, and the overall adaptability has improved. Research has shown that the dynamic balance between society and ecosystems is crucial for the adaptive stability of watersheds.
  Keywords: Huaihe River basin; Ecological economic belt; Dynamic evolution; Adaptive evaluation; Social system.
  DOI: 10.1504/IJGW.2026.10078465
   
  
• An Integrated Modelling Approach for Sustainable Development of the UNESCO classified ecosystem (Ichkeul Lake, North Africa)
  by Liping Ouyang, Zhirui Huang, Enen Chen, Ying Bai 
  Abstract: This paper presents the results of a simulation study on the impact of integrating water intake and output for and from Lake Ichkeul, a Ramsar and UNESCO reserve wetland in North Tunisia. Three scenarios were simulated over nine years, with varying amounts of freshwater injected into the lake from different dams. The study showed that a non-active lake management policy would result in severe ecosystem degradation, with the lake eventually becoming a salt marsh. Under the status quo, the area and density of Potamogeton would decrease dramatically and the number of migratory birds would rapidly decline. The second scenario would allow the ecosystem to be barely resilient and maintain itself, while the third scenario would lead to long-term sustainability and stabilize all ecosystem components. The results of the present study have implications for water management policies and the preservation of the unique biodiversity of the Ichkeul Lake ecosystem.
  Keywords: Ichkeul Lake; Water management; Ecosystem resilience; Wetland conservation; Scenarios.
  
  
• Collaborative System of Low-Carbon Sustainable Energy and Grassroots Governance Based on Big Data Analysis  
  by Binghui Lei, Peng Xu 
  Abstract: Low-carbon energy operation at the grassroots level suffers from disconnected energy-governance data, feedback lag, and no computable coupling mechanism. This study constructs a collaborative system based on big data, forming a heterogeneous temporal graph through unified mapping and time alignment, and introducing attention-driven dynamic graph representation learning to model energy and governance behaviour evolution. Low-carbon constraints and policy gradient updates drive energy scheduling and governance incentives within a shared closed loop. Experiments show carbon emission intensity stabilises at 0.39 kgCO2kWh, supply-demand deviation at 2.39%, and governance response delay at 1.8 time windows, with a collaborative stability index converging to 0.87.
  Keywords: Big Data Analysis; Low-Carbon Sustainable Energy; Grassroots Governance; Dynamic Graph Representation Learning; Collaborative Optimization.
  DOI: 10.1504/IJGW.2026.10078595
   
  
• Energy-daylight interaction perspective: effects of natural shading, photosensor and orientation  
  by Yasin Kaya, Kadir Zengin, Sebahattin Ünalan 
  Abstract: Buildings account for a substantial share of global energy consumption and related carbon emissions, making the building sector a key component of global warming mitigation strategies. Therefore, while improving energy efficiency remains highly important, this study additionally investigates daylight comfort criteria, which also play a critical role in sustainable building design.In this study, the combined effects of natural shading, daylight-responsive photosensors, and building orientation on energy consumption and daylighting performance were comprehensively analyzed using a reference building model. These parameters were examined under different scenarios through comparative energy assessments that explicitly considered heat transfer processes.The main findings indicate that integrating photosensors with optimized building orientation significantly improves daylight comfort. Consequently, total energy consumption was reduced by up to 6.3% (4.11 MWh/year), accompanied by a 7.6% reduction in CO? emissions (2,175 kgCO?/year).
  Keywords: building; energy-daylight interaction; natural shading; photosensor; orientation.
  DOI: 10.1504/IJGW.2026.10078658
   
  
• Decarbonizing Algeria's Phosphate Industry: A Life Cycle Assessment of Environmental Hotspots and Policy-Ready Solutions  
  by Ali Makhlouf, Hamza Cheniti, Hani Amir Aouissi, Marc Azab 
  Abstract: Algeria's phosphate sector faces the challenge of increasing production while limiting environmental impacts in a water-scarce and fossil-fuel-dependent context. We applied a cradle-to-gate Life Cycle Assessment using the ReCiPe2016 method to evaluate the environmental footprint of beneficiated phosphate. Calcination accounts for 58% of greenhouse gas emissions, while diesel-based logistics represent 47.09% of fuel consumption. Scenario analysis indicates that electrified conveyors could reduce emissions by 6.83% and crusher relocation can lower transport impacts. Although Algeria shows lower energy intensity than Morocco and Tunisia, its water recycling rate remains limited. Sustainable pathways include renewable energy integration, improved water circularity, and SDGs alignment.
  Keywords: Algeria; Electrified conveyor systems; Groundwater depletion; LCA; Phosphate production; Renewable energy integration; SDGs.
  DOI: 10.1504/IJGW.2026.10078662
   
  
• Machine Learning Simulation Analysis of the Impact of Extreme Environmental Events Driven by Global Warming on Economic Dynamics  
  by Zhimin Wu, Ye Tian 
  Abstract: This paper proposes an XGBoost-SHAP framework to model nonlinear climate-economy linkages under extreme events. Using multi-source panel data, it constructs a hierarchical intensity index for complex extremes. Bayesian-optimised XGBoost minimises cross-validated MSE to capture climate shocks nonlinear economic impacts. TreeSHAP yields global feature rankings and local attributions, revealing key drivers: high temperature (SHAP = 0.42), precipitation frequency (0.38), and long-term drought (0.35). In agriculture-heavy economies (> 40% share), extreme heat suppresses annual growth by 0.57 on average. The model achieves RMSE 1.52.1 and MAPE 4.8%8.2%, offering high accuracy and interpretability for differentiated climate adaptation policy.
  Keywords: Climate Warming; Extreme Environmental Events; Extreme Gradient Boosting; SHapley Additive exPlanations; Nonlinear Modeling.
  DOI: 10.1504/IJGW.2026.10078664
   
  
• Spatiotemporal Evolution and Multi-Scenario Simulation of Urban Green Space Carbon Sinks in the Guangdong-Hong Kong-Macao Greater Bay Area from Accounting to Prediction  
  by Liping Ouyang, Zhirui Huang, Enen Chen, Ying Bai 
  Abstract: This study couples the InVEST and PLUS models to analyse spatiotemporal patterns and future carbon stock in the Guangdong-Hong Kong-Macao Greater Bay Area from 1995 to 2050 under natural, ecological and economic scenarios. Results show that carbon stock decreased from 1995 to 2025, with a high periphery and low centre pattern, primarily due to the conversion of arable and forest land to construction. Ecological protection increases future stocks while economic development accelerates loss. The study provides a quantitative basis for land optimisation and carbon neutrality pathways.
  Keywords: Urban Green Space Carbon Sinks; Spatiotemporal Evolution; Multi-Scenario Simulation; Guangdong-Hong Kong-Macao Greater Bay Area; InVEST-PLUS Model Coupling.
  DOI: 10.1504/IJGW.2026.10078803
   
  
• Legal Liability under Mode of Rural Environmental Pollution Control Based on Internet Technology  
  by Ting Yuan 
  Abstract: As a major agricultural nation, China faces rural environmental pollution control as a critical issue for agricultural and rural development. In the context of the rural revitalization strategy, rural environmental protection has become especially urgent. This study takes rural environmental improvement as its starting point, leverages Internet technology to develop a new online trial system for rural environmental pollution cases, and refines the legal liability framework for pollution control. Using core principles of game theory, the study constructs a dynamic tripartite game model involving enterprises, environmental regulators, and villagers, derives the equilibrium solution, and explores corresponding legal countermeasures. Experimental results demonstrate that implementing the Internet-based case trial system in rural environmental pollution governance significantly increases villagers' satisfaction by 9.63% and effectively advances rural pollution control efforts.
  Keywords: Rural Environmental Pollution Control; Legal Liability Issues; Internet Technology; Game Theory.
  DOI: 10.1504/IJGW.2026.10078805
   
  
• Tracking the Effectiveness of Marine Ecological Assessment and Restoration Around Nuclear Power Plants Based on Deep Learning  
  by Zhixian Wang, Depeng Li 
  Abstract: This deep learning system assesses marine ecosystem restoration around nuclear plants. Multi-source data form spatiotemporal tensors. A spatio-temporal convolutional neural network-transformer extracts parameters like chlorophyll-a. A graph neural network models ecological connections. Attention long short-term memory integrates data to quantify contributions. Experiments show: root mean square error of 0.85 and Nash-Sutcliffe model efficiency coefficient of 0.92 for chlorophyll-a; intersection over union of 0.405 for sea surface temperature anomalies; suspended matter prediction errors of 3.36.74%. The model enables high-precision dynamic assessment and reliable restoration tracking.
  Keywords: Deep Learning; Marine Ecological Assessment; Nuclear Power Plant Environment; Ecological Restoration Effectiveness Tracking; Spatio-Temporal Convolutional Neural Network.
  DOI: 10.1504/IJGW.2026.10078806
   
  
• Analysis of Digital Marketing Strategies in Enterprise Supply Chain under the Background of Low Carbon Economy  
  by Xiaoyu Yan 
  Abstract: To study the digital marketing strategies of enterprises under the background of low-carbon economy, this paper proposes a fuzzy neural network to evaluate the digital marketing of enterprises. Evaluation is the foundation for ensuring the effective operation of enterprise work methods. Scientific, effective, and fair evaluation can stimulate the enthusiasm of enterprises. On the other hand, it can also improve the digital marketing performance of enterprises. The experimental results showed that 68 people believed shopping was very convenient under traditional marketing, while 160 people believed shopping was very convenient under digital marketing. Only 55 people believed that the service quality of traditional marketing was high, but 189 people believed that the service quality of digital marketing was high. It can be seen that under digital marketing, not only is shopping convenient, but people are also very satisfied with the service quality of digital marketing.
  Keywords: Low-carbon Economy; Fuzzy Neural Network; Enterprise Digital Marketing; Enterprise Supply Chain; Enterprise Evaluation.
  DOI: 10.1504/IJGW.2026.10078809
   
  
• Assessment of Decadal Rainfall Prediction Skill Across Southeast Asia Using CMIP6 Models  
  by Dara Kasihairani, Supari Supari, Rahmat Hidayat 
  Abstract: This study evaluates decadal climate prediction (DCP) skill over Southeast Asia using CMIP6 hindcasts initialised during 19602010. Rainfall prediction skill is assessed for winter (DJF) and summer (JJA) using the anomaly correlation coefficient (ACC), root mean square error (RMSE), and normalised standard deviation (NSD). Results show spatial clustering into three subregions (MCSEA, MLSEA, and PLSEA). Domain-averaged ACC remains weak (0.01 to 0.07) but exhibits substantial spatial variability (0.5 to 0.5). RMSE is typically 140160 mm. Skill is higher at early lead times (LY2) and in DJF, decreases at intermediate leads, and partially recovers at longer leads (LY8LY9).
  Keywords: forecast skill; decadal prediction; region; lead time; ensemble model.
  DOI: 10.1504/IJGW.2026.10078906
   
  
• A Simulation Study on the Coordination of Green Finance and Fiscal Policy to Support Regional Low-Carbon Transition  
  by Lingli Yang 
  Abstract: The current policy practice often relies on a single tool, while green finance and fiscal policy complement each other in multiple aspects, and their synergistic effects and regional differences have not been fully explored in terms of quantity. To this end, this paper constructs a stock flow consistency (SFC) model that combines heterogeneous regions and energy preference industries, introduces endogenous policy variables based on a subject based macro financial model, and incorporates multi period games and risk contagion mechanisms between regional governments, financial institutions, and high carbon enterprises. The experiment used data from eight provinces in China to simulate the dynamic feedback of fiscal and monetary incentives under different industrial endowments. The results indicate that under the sole effect of baseline fiscal policy, the green capital formation rate in underdeveloped areas only increased by 2.1%, while the coordinated strategy optimized by the coupled model increased this figure to 5.8%.
  Keywords: Green Finance; Fiscal Policy; Regional Low-Carbon Transition; Policy Coordination; Financial Risk Contagion; Regional Heterogeneity; Policy Synergy Threshold.
  DOI: 10.1504/IJGW.2026.10079409
   
  
• Spatial Assessment of Heat Extremes with a Risk-Based Approach for the Districts of Ankara, the Capital of Turkiye  
  by Sevilcan Basak Unal, Cavit Isık Yavuz 
  Abstract: Extreme heat is a public health threat for cities. This study presents spatial assessment of the vulnerability, exposure, hazard, and the overall risk of heat extremes across the districts of Ankara, Turkiye, by using remote sensing and secondary data. Results showed that the northern districts of the city exhibited the lowest hazard levels, while exposure was highest in the metropolitan core, and vulnerability was greater in peripheral areas. One urban and two rural districts experienced the highest risk with different underlying characteristics, highlighting the need for addressing multiple dimensions of the risk management for public health actions.
  Keywords: heat stress; extreme heat; risk; vulnerability; spatial assessment.
  DOI: 10.1504/IJGW.2026.10079760
   
  
• Copula-Based Wind-solar joint output simulation for carbon capture economic dispatch optimisation in power systems  
  by Yunyu Yang 
  Abstract: To address insufficient correlation modeling of wind-solar outputs and limited economic regulation of carbon capture in conventional power dispatch, this study constructs a carbon-integrated economic dispatch model via wind-solar joint simulation. Vine Copula is adopted to estimate the joint probability distribution and characterize the correlation of wind and solar outputs, while marginal distribution fitting and the K-Nearest Neighbor method generate high-precision joint scenarios. A stepped carbon trading mechanism is embedded to dynamically control carbon costs. Results demonstrate that the model yields a determination coefficient of 0.9557 and scenario coverage of 92.09%, superior to benchmark models. It reduces power loss to 69.35 kW, wind/solar curtailment rates to 1.40% and 1.27%, carbon emissions to 6876 t, and total operating costs. The model effectively balances economic and low-carbon performance, supporting low-cost, low-carbon operation of high-renewable power systems.
  Keywords: Copula; Wind and solar output; Carbon capture; Economic dispatch; Low carbon.
  DOI: 10.1504/IJGW.2027.10080040
   
  
• Optimisation Algorithms for Green Protection, Renovation and Reconstruction of Historical Buildings Aiming at Low Carbon Goals  
  by Sheng-Nan Li, Yan He 
  Abstract: This study proposes a constrained NSGA-II algorithm balancing cultural authenticity and low-carbon performance in historic building preservation. By encoding appearance fidelity and structural safety as hard constraints, the method minimizes lifecycle carbon emissions and economic costs via hybrid discrete-continuous encoding. A constraint-priority mechanism ensures regulatory compliance. This paper tests the approach across various scenarios; it achieves minimum lifecycle carbon emissions of 301.3 kgCO?e/m? and costs of 2164.9 CNY/m?. Operational carbon intensity falls below 43 kgCO?e/m??year, with over 50% energy savings. This framework provides a robust decision-making tool for sustainable historic urban renewal without compromising heritage integrity.
  Keywords: Historical Buildings; Green Protection; Low-Carbon Goals; Renovation and Upgrading; Multi-Objective Optimization; NSGA-II Algorithm.
  DOI: 10.1504/IJGW.2026.10080132
   
  
• Assessment of Climate Change Effects on Precipitation and Temperature in the Aras River Basin  
  by Simin Ganjei, Jalal Shiri, Sepideh Karimi 
  Abstract: Climate change represents a major challenge for water resource management through its impacts on precipitation and temperature regimes. This study assesses the effects of climate change on precipitation and temperature in the Aras River Basin using three CMIP6 GCM (MPI-ESM1-2-LR, CNRM-CM6-1, and MRI-ESM2-0). Statistical downscaling was performed using the LARS-WG model, and future projections were evaluated under SSP1-2.6 and SSP5-8.5 scenarios. Based on its superior performance during the historical period, MPI-ESM1-2-LR was selected for detailed analysis. The results project increasing temperatures and decreasing precipitation by 2080, with more pronounced changes under SSP5-8.5, particularly in the northwestern basin.
  Keywords: Aras; Climate Change; CMIP6; LARS-WG; SSP.
  DOI: 10.1504/IJGW.2026.10080186
   
  
• Optimisation Framework for Residential Landscape Design Based on the Synergistic Benefits of Carbon Sequestration and Biodiversity  
  by Ru An 
  Abstract: In the current field of residential landscape design, there is a lack of tools for comprehensive quantitative evaluation and optimisation feedback on the synergistic benefits of carbon sequestration and biodiversity, resulting in the inability to predict and balance the comprehensive ecological performance of the landscape during the design phase. This paper developed a design optimisation framework that integrates parameter modelling and multiobjective optimisation algorithms. This framework first establishes a parameter model of landscape design elements and combines it with a fixed quantum model of core benefits such as carbon storage increment and habitat structure complexity index. The experimental results showed that compared with the traditional baseline scheme, the balanced collaborative scheme generated by the optimisation framework improved carbon efficiency and biodiversity efficiency by 20.5% and 19.0%, respectively. Pareto front analysis indicates that the balanced collaborative approach achieves the optimal balance between two objectives.
  Keywords: Carbon-Biodiversity Synergy; Residential Landscape; Multi-Objective Optimization; Parametric Design; Non-Dominated Sorting Genetic Algorithm.
  DOI: 10.1504/IJGW.2026.10080294
   
  
• Simulation of the Path for Green Finance and Industrial Policy Synergy to Support the Transformation of High-Carbon Industries  
  by Xi Zhang, Xinmiao Jiang 
  Abstract: This paper simulates a high carbon industry transition by coupling heterogeneous agent modelling with deep deterministic policy gradient. Green finance and industrial policy parameters are encoded into state vectors. A multi objective reward function drives adaptive enterprise decisions. Monte Carlo simulations reveal a phase transition in year 12 under synergistic optimisation, with carbon intensity converging to below 1.50 tons/10,000 CNY and financing cost volatility converging to below 4.0%. The optimal policy mix trajectory balances economic stability and emission reduction efficiency.
  Keywords: Green Finance; Industrial Policy; Agent Modeling; Deep Deterministic Policy Gradient; High-Carbon Transition.
  DOI: 10.1504/IJGW.2027.10080296