International Journal of Global Warming (18 papers in press)

Regular Issues

• Identification The Impact of Climate Change on Extreme Weather Events in Indonesia Maritime Continent Using NEX-GDDP-CMIP6 Data  
  by Amalia Nurlatifah, Prawira Yudha Kombara, Aisya Nafiisyanti, Rahmawati Syahdiza, Haries Satyawardhana, Aulia Arip Rakhman, Finkan Rahma Yuditya, Latifa Hanum Zain, Rifda Amara Aulia, Khodja Ummi Medina, Aulia Rahma Effendi, Nurul Hidayati, Rehanda Umamil Hadi 
  Abstract: Global climate change challenges regions like the Indonesia Maritime Continent (IMC), where greenhouse gas emissions drive extreme weather. This study examines future changes in extreme rainfall using NEX-GDDP-CMIP6 data. Historical (19842014) and projected (20152060) data reveal that under high-emission scenarios (SSP58.5), consecutive dry days (CDD) may rise 15%, from 10 to 12 days annually by 2060. Very heavy precipitation days (R20mm) could increase by 25%, from 11 to 14 days. These trends pose dual risks: drought and heightened flooding. Adaptation strategies are critical to address threats to agriculture, infrastructure, and public health in vulnerable IMC areas.
  Keywords: Climate change; NEX-GDDP-CMIP6; Extreme rainfall; Consecutive Dry Days (CDD); Number of days with rainfall exceeding 20mm or more (R20mm).
  DOI: 10.1504/IJGW.2026.10074647
   
  
• Algorithm for Mining the Relationship between Carbon Emissions and Financial Ecosystem under the Background of Climate Change  
  by Yan Hou, Shuling Yang 
  Abstract: To address the shortcomings of traditional mining algorithms in handling the non-stationary and high-dimensional nature of carbon finance data, this paper employs a PSO-FP-Growth algorithm. This algorithm uses PSO to adaptively search for optimal support and confidence, combined with an FP-tree to efficiently mine frequent itemsets. Through incremental updates and local fine-tuning, the algorithm avoids rebuilding the entire tree to accommodate time-varying data. Experiments show that the algorithm achieves a mining efficiency of 92.3% with an average confidence level of 0.78. Even in an 80-dimensional scenario, it maintains an efficiency of 85.7%, demonstrating high practicality
  Keywords: Carbon Emissions; Financial Ecosystem; Correlation Mining; PSO-FP-Growth Algorithm; Mining Efficiency.
  DOI: 10.1504/IJGW.2026.10074883
   
  
• Electrochemical Performance of Polyaniline/ZnO Composite and Bilayer Electrodes for High-Power Supercapacitors  
  by Ayşe Evrim Bulgurcuoğlu  
  Abstract: This study investigates the electrochemical performance of polyaniline (PANI), zinc oxide (ZnO), and their bilayer and composite electrodes for supercapacitors. Cyclic voltammetry, galvanostatic charge-discharge, and Ragone plot analyses were used to evaluate energy and power characteristics. PANI exhibits high energy density but limited stability, while ZnO maintains performance at high power densities. The PANI/ZnO bilayer shows moderate improvement; however, the PANI/ZnO composite achieves the highest energy density of 6.39 Wh kg1 at 0.27 W kg1 and retains nearly 100% capacitance after 1,500 cycles, demonstrating superior stability and efficiency
  Keywords: polyaniline (PANI); zinc oxide (ZnO); Bilayer; Supercapacitor.
  DOI: 10.1504/IJGW.2026.10075662
   
  
• Double Materiality and SWOT Analysis: A Practical Framework for Environmental Impact and Sustainability Assessment  
  by Özlem Yurtsever, Mustafa Cem Çelik, Tanay Sidki Uyar 
  Abstract: Double materiality is becoming a necessity in sustainability reporting, as it is essential for aligning organisational strategies with ESG priorities. This study proposes a framework to embed SWOT analysis within sustainability reporting, focusing on double materiality. By positioning SWOT as a bridge, the framework enables a holistic evaluation of internal capabilities and external ESG-related risks and opportunities. It encourages periodic updates to question sets, supporting an evolving, stakeholder-inclusive process aligned with annual reporting cycles. This approach offers organisations a practical reference to develop transparent, credible, and adaptable sustainability disclosures, reinforcing their strategic decision-making and compliance with increasingly complex reporting standards.
  Keywords: SWOT Analysis; Sustainability Reporting; Double Materiality; Environmental Sustainability; Climate Change.
  DOI: 10.1504/IJGW.2026.10075747
   
  
• Impacts of Climate Mitigation Actions on Crop Yields in South Asia  
  by Lishuai Zhao, Fekadu Tadege Kobe, Zhihua Zhang, M. James C. Crabbe, Hao Zhang 
  Abstract: Under simultaneous pressures from explosive population growth, outdated agricultural facilities and constrained arable land, South Asia is facing mounting challenges in food security. In this study, we investigate yield changes of major crops (maize, rice, soybean and wheat) in four agro-ecological zones of South Asia under three future emission scenarios. We find that strong climate mitigation actions in the future would have consistent damage crop yields by 9.6%-31.4% in the poorest Northwest region of South Asia and have very diverse impacts on crop yields with ranging from -15.1% to 6.2% for the Ganges Plain, the Indus Plain and the Deccan Plateau, in particular, such actions would damage annual mean rice yield by 8.3%-15.1% while benefiting annual mean maize yield by 5.3%-6.2% in the 21st century. These findings give valuable insights into the sustainable development of South Asia and enable mitigation of food security challenges in the whole 21st century
  Keywords: Climate Change Impacts; Crop yields; Agro-ecological zones; South Asia.
  DOI: 10.1504/IJGW.2026.10075810
   
  
• Transformation of Low-carbon Economy Development Model Based on Machine Learning Algorithm  
  by Ling Xie, Jun Yang 
  Abstract: The current low-carbon economy (LCE) development model has problems such as low transformation efficiency and an unclear path. Based on machine learning algorithms, this paper constructs a model to analyse the transformation willingness of enterprises and explores the application of supervised and unsupervised learning in LCE. Simulation experiments show that the transformation enthusiasm of Enterprise B (65%) is higher than that of Enterprise A (55%), which verifies the effectiveness of the model. Studies have shown that machine learning can provide scientific decision-making support for LCE development and help enterprises and regions achieve efficient low-carbon transformation.
  Keywords: Low-carbon Economy; Development Model; Machine Learning Algorithms; Supervised and Unsupervised.
  DOI: 10.1504/IJGW.2026.10075815
   
  
• Digital Transformation and Collaborative Management of Energy Enterprise Carbon Emissions Using Neural Networks in Climate Change Context  
  by Tingting Tan, Liang Chen 
  Abstract: In response to global warming, improving carbon emission management of energy enterprises is essential. This paper proposes a C-LSTM (Collaborative Long Short-Term Memory) model to clean historical carbon data, extract key features, and construct an LSTM neural network for capturing time series characteristics. The model is trained using the Adam optimizer with parameter tuning. Integrated with a collaborative innovation platform, it enables real-time data and prediction sharing. Experiments show the model achieves high accuracy (MSE reduced to 0.006), with acceptable time costs. Carbon emissions dropped by 21.59%, and energy efficiency increased by 10.02%, confirming the model's effectiveness.
  Keywords: Climate Warming; Carbon Emission; Energy companies; Digital Transformation; Collaborative Innovation; Long Short-term Memory; Energy efficiency.
  DOI: 10.1504/IJGW.2026.10075889
   
  
• Analysis of Carbon Emissions in China's Manufacture of Electrical Machinery and Apparatus: a Perspective of Time and Space, Decomposition and Decoupling  
  by Hao Lu, Wenzhuo Sun, Qinwei Wang, Long Sun 
  Abstract: The study aims to analyse carbon emissions in China's manufacture of electrical machinery and apparatus from 2012 to 2022. By constructing an accounting model and applying the LMDI and Tapio models, the study found that the industry's carbon emissions depend heavily on purchased electricity, which accounts for nearly 90% of total emissions, and that their geographical distribution was highly concentrated in Guangdong, Jiangsu, and Zhejiang provinces. Economic growth was the primary driver of the increase in carbon emissions, leading to a deterioration in the decoupling between carbon emissions and economic development, from strong decoupling to expansive negative decoupling.
  Keywords: carbon emission; manufacture of electrical machinery and apparatus; LMDI model; Tapio model.
  DOI: 10.1504/IJGW.2026.10076013
   
  
• Analysis of In-River Flood Measures on Flood Risk and Stream Ecosystem by Paired-Watershed Approach: a Case Study  
  by Hurem Dutal 
  Abstract: This study investigated the effects of small-sized in-river flood control structures on both flood susceptibility and stream ecosystems using the paired-watershed approach in Turkiye. Differences in daily streamflows among the pre-treatment, treatment, and post-treatment periods were determined using analysis of variance (ANOVA). The effects of flood control structures on the stream ecosystem were evaluated using the low flow and flashiness indicators, whereas their impact on flood susceptibility was assessed using high flow. The results showed that flood susceptibility increased by 19.8%, while low flows increased by 13.4% and flashiness decreased by 6% after the flood control measures.
  Keywords: flood risk; small-sized in-river structures; weir; paired-watershed; streamflow; low flows; Türkiye.
  DOI: 10.1504/IJGW.2026.10076016
   
  
• Coordinated Development of Carbon Emissions, Energy, and Financial Sustainable Growth Based on Fuzzy System Theory  
  by Lisha Zheng 
  Abstract: Aiming at goal conflicts and uncertainties in the coordinated development of carbon emissions, energy, and financially sustainable growth, this study integrates triangular fuzzy numbers (to address uncertainties from incomplete data/subjective judgment), the analytic hierarchy process (AHP, to determine indicator weights), and a coordination degree model to build a comprehensive evaluation model. Taking City S as an example, its coordination index increased from 0.46 to 0.81 between 2018 and 2024. Green finance and renewable energy are key drivers, while carbon emission control and energy efficiency are bottlenecks that require support to address related issues.
  Keywords: Carbon Emissions; Financial Sustainability; Coordinated Development; Triangular Fuzzy Numbers; Analytic Hierarchy Process.
  DOI: 10.1504/IJGW.2026.10076019
   
  
• Optimisation of Multi-Source Meteorological Data Fusion Algorithms Based on Artificial Intelligence  
  by Yingkui Yang, Hongliang Han, Chao Li, Yang Dong 
  Abstract: Frequent extreme weather events demand high-resolution forecasts, yet current multi-source fusion models struggle with small-scale accuracy. This study aligns multi-source meteorological data via timestamp synchronization and spatial interpolation, then employs a multi-scale CNN to capture both local precipitation disturbances (small kernels) and large-scale atmospheric circulation (large kernels). A self-attention mechanism strengthens extreme weather signals, while a GNN-based fusion subnetwork models cross-source associations. A multi-task loss further enhances prediction accuracy and spatiotemporal consistency. Experiments show clear advantages: the proposed method achieves lower MAE (0.25 vs. 0.68, 0.49, 0.35 for baselines), higher spatial consistency (0.96), extreme event recognition (>0.9), and strong correlation (0.983), demonstrating its effectiveness in high-resolution small-scale extreme weather prediction.
  Keywords: Multi-Source Meteorological Data Fusion; Artificial Intelligence; Deep Convolutional Network; Attention Mechanism; Extreme Weather Prediction.
  DOI: 10.1504/IJGW.2026.10076108
   
  
• A Study on Optimising the Green Coverage of Low-Carbon Building Landscapes Using Deep Learning Algorithms  
  by Yuyao Zhang, Jiahao Zhang 
  Abstract: This study proposes a boundary-cross supervised segmentation network (BCS-SegNet) for accurate green coverage segmentation in urban street view images. Integrating decoupled residual self-attention and boundary cross-supervision, BCS-SegNet outperforms models like R-CNN, U-Net, and TransUNet in mIoU and dice coefficients. The method supports low-carbon building landscape optimisation by precisely quantifying green view rates, offering a robust tool for urban green planning.
  Keywords: Deep Learning; Building landscapes; SegNet; Green visual index.
  DOI: 10.1504/IJGW.2026.10076109
   
  
• Low-carbon Supply Chain Emission Reduction Decision-making Model Based on the Perspective of Big Data Green Technology Adoption  
  by Chengyuan Xia, Zhenpeng Di, Wei Chen 
  Abstract: This paper proposes a low-carbon supply chain emission reduction (LC SCER) decision-making model from the perspective of big data-driven green technology (GT) adoption. The model quantitatively assesses the degree of GT adoption and integrates it into a profit-maximisation framework for supply chain partners. Leveraging big data analytics enables real-time monitoring, predictive analysis, and optimised decision-making across the supply chain. Experimental validation shows that the model achieves an average of 1.0521 million tons of carbon emissions, an average emission cost of 259,700 yuan, and an average energy efficiency of 92.98%, significantly outperforming traditional SCER models across environmental and economic performance
  Keywords: Green Technology; Adoption Perspective; Low-carbon Supply Chain; Emission Reduction Decision Model; Big Data.
  DOI: 10.1504/IJGW.2026.10076111
   
  
• Discussion on Indoor Environment Optimisation and Low-Carbon Ener-gy-Saving Strategies Based on Machine Learning  
  by Mengqi Shen 
  Abstract: Building energy consumption accounts for a significant proportion of global energy use, necessitating efficient and intelligent control strategies. Traditional rule-based methods suffer from limited adaptability, while data-driven ap-proaches face challenges in generalization. This study proposes the Machine Learning-Based Indoor Environment Control System (MIECS), integrating reinforcement learning, deep learning, and edge computing within a three-layer architecture. By modeling device-environment interactions using heterogeneous graphs, MIECS enhances sample efficiency and convergence speed. Experimental results demonstrate a 23.7% reduction in energy consumption and an 18.5% improvement in user comfort compared to conventional methods, offering a scalable and adaptive solution for intelligent building management.
  Keywords: Indoor Environment Optimisation; Machine Learning; Reinforcement Learning; Energy Efficiency; Smart Building Control.
  DOI: 10.1504/IJGW.2026.10076112
   
  
• Impact of the Carbon Trading Market on the Energy Economy and E-commerce Enterprise Operations  
  by Guangbo Lin, Shanyu Chen, Ninggui Duan 
  Abstract: To address fragmented carbon management caused by the lack of a unified quota mechanism and decentralised accounting standards for e-commerce, this study constructs a dynamic carbon source accounting model that integrates the spatio-temporal correlation algorithm (STCA), the extended vehicle routing problem (E-VRP), and the Kalman filter. STCA matches e-commerce orders with logistics routes; E-VRP optimises transportation to reduce per-order emissions; the Kalman filter dynamically corrects regional power grid emission factors. The model addresses accounting distortions caused by data volatility and missing values in static methods, enabling accurate, real-time carbon flow tracking across electricity and logistics systems.
  Keywords: Carbon Trading; Energy Economy; Electronic Commerce; Carbon Accounting; Dynamic Model.
  DOI: 10.1504/IJGW.2026.10076296
   
  
• Low Carbon Planning Model for the Development of Distributed Green Energy Industry Under the Background of Urban Digital Transformation  
  by Wei Liu, Wenxia Tong 
  Abstract: This paper proposes a digital twin-driven carbon flow dynamic planning framework (DT-CFDP) to align multi-source real-time data with static low-carbon urban planning. Integrating city information modelling (CIM), real-time data fusion, proximal policy optimisation and extended Kalman filter-based calibration, DT-CFDP enables 5-minute adaptive energy scheduling under carbon intensity constraints. Validated on five pilot cities (20202023), it reduces carbon emissions per unit GDP by 32.34% versus LEAP, MPC, and DQN baselines, improves response time by 32.8%, reduces lifecycle costs by 18.7%, and maintains 69.30% median renewable penetration, offering a real-time, adaptive low-carbon planning approach for urban green energy systems.
  Keywords: Smart City; Renewable Energy; Low Carbon Planning; Digital Twin; Reinforcement Learning.
  DOI: 10.1504/IJGW.2026.10076618
   
  
• Evaluation of Porous Aluminum Materials Used in Crash Boxes in Automobiles with AHP-BORDA in terms of Sustainable Production  
  by Samet K?rm?z?tepe, Nil Toplan, Alparslan Serhat Demir 
  Abstract: Today, the increase in global warming caused the automotive industry to develop more sustainable, lightweight and safe components, leading to rise in environmental concerns. Crash boxes, especially in the front the vehicle parts, are critical to ensuring passenger safety, absorbing energy in the event of an impact. In this study, aluminum-based porous materials were evaluated in terms of sustainable production for use in crash boxes using the Analytical Hierarchy Process (AHP) and BORDA methods. The results obtained by the AHP-BORDA methods reveal that Sintered Porous Aluminum was determined as the most suitable material in terms of environmental sustainability.
  Keywords: Sustainable Production; Emission;Automotive;Crash Boxes;AHP;BORDA.
  DOI: 10.1504/IJGW.2026.10076829
   
  
• Accurate Calculation Model for Carbon Sequestration Efficiency of Urban Green Spaces by Integrating Lidar and Multispectral Images  
  by Qianhe Xiang 
  Abstract: Accurately measuring urban green space carbon sequestration efficiency is challenging due to the limitations of single remote sensing sources. This paper proposes the dual-modality-collaborative sensing network (DM-CSN), integrating LiDAR and multispectral imagery. Using CNNs for feature extraction and a cross-modal transformer attention module for dynamic fusion, the model optimises contribution weights for carbon sink estimation. Experimental results demonstrate high precision, with an R2 of 0.89 and RMSE of 0.17 kg C/(m2
  Keywords: LiDAR-Multispectral Fusion; Urban Green Space; Carbon Sequestration Efficiency; DM-CSN Model; Cross-Modal Attention.
  DOI: 10.1504/IJGW.2026.10076839