International Journal of Environment and Pollution (13 papers in press)

Regular Issues

• Open Access

  Evaluation and trend prediction of the relationship between carbon emissions, energy, and sustainable growth based on neural networks ( Free Full-text Access )

  by Tingting Tan 
  Abstract: This study investigates the relationship between carbon emissions (CE), energy, and sustainable growth using neural networks. Data from five regions North America, South America, Europe, Asia Pacific, and Africa were analysed to model CE trends based on energy structure and consumption. A neural network model was trained and optimised to predict correlations among CE, energy use, and economic growth. Focusing on China, the study examines vehicle emissions, fuel-powered versus new energy vehicle sales, and their impact on CE and the economy. Results show a strong correlation between energy consumption and CE (R = 0.99), with energy efficiency and composition also influencing emissions. As new energy vehicle adoption rises, fossil fuel demand declines, helping curb total CE, support carbon neutrality, and promote sustainable development. The model demonstrates that optimising energy structure is key to balancing economic growth and environmental protection.
  Keywords: carbon emissions; neural networks; energy mix; energy consumption; data analysis; sustainable development; climate change.
  DOI: 10.1504/IJEP.2025.10072992
   
  

• Open Access

  Promoting the transformation of digital economy structure based on artificial intelligence in the low carbon economy environment ( Free Full-text Access )

  by Fan Chen, Aijun Liu 
  Abstract: This study investigates the impact of artificial intelligence (AI) on economic structure (ES) transformation within a low-carbon economy. Focusing on ES advancement and rationalisation, an empirical model is established incorporating control variables such as policy, openness, informatisation, and population density. Using dynamic panel analysis, results show that AI significantly promotes both ES advancement and rationalisation at the 1% level in the first lagged period. The findings indicate that AI enhances industrial efficiency and supports green development, playing a crucial role in driving sustainable, high-quality economic growth. This research provides valuable insights for policymakers seeking to integrate AI into low-carbon economic strategies.
  Keywords: economic restructuring; artificial intelligence; low-carbon economic environment; economic model analysis; expert system.
  DOI: 10.1504/IJEP.2025.10073301
   
  

• Open Access

  Empowering sustainable growth: the transformative impact of environmental protection inspections on heavy polluters ( Free Full-text Access )

  by Yingbo Gu, Chang Liu 
  Abstract: This study finds that environmental protection inspections (EPI) significantly improve the green total factor productivity (GTFP) of Chinas A-share heavy polluters, primarily through modest gains in green innovation and compliance behaviour. Using panel data spanning from 2011 to 2019, this study employs the super-slacks-based measure (SBM) model and the MalmquistLuenberger (ML) index to quantify GTFP, and applies a differencein-differences (DID) approach to examine the dynamic relationship between EPI and GTFP. The empirical results indicate that EPI exerts a statistically significant and positive effect on GTFP among heavily polluting enterprises, with green technological innovation identified as a key mediating mechanism. Furthermore, regional heterogeneity analysis reveals that the positive impacts of EPI are primarily concentrated in Chinas eastern and central regions, underscoring the influence of regional economic development levels on the effectiveness of environmental governance.
  Keywords: environmental protection inspections; green total factor productivity; heavy polluters; green technological innovation; environmental governance; sustainable economic development.
  DOI: 10.1504/IJEP.2025.10073694
   
  

• Open Access

  Green logistics distribution route algorithm based on carbon emissions optimisation ( Free Full-text Access )

  by Lin Zhu, Chulv Sun 
  Abstract: Aiming at the problems of high carbon emissions (CE) and low optimisation efficiency in green logistics distribution (LD) path optimisation, this paper takes CE as the goal and introduces an adaptive genetic algorithm (AGA) to dynamically adjust the crossover and mutation probabilities, reduce CE, and improve the global search capability and convergence speed. This paper first constructs an optimisation model based on the basic data of the LD network, and then constructs a carbon emission optimisation model based on fuel consumption and CE taking into account time windows and traffic constraints. Finally, this paper analyses the performance of genetic algorithm (GA), ant colony algorithm (ACO), particle swarm algorithm (PSO) and AGA algorithm in carbon emission reduction and path optimisation by comparing their optimisation results. The results show that the AGA algorithm performs well in all test scenarios, successfully reduces CE, and significantly shortens the delivery route.
  Keywords: green logistics; distribution route; route optimisation; carbon emissions; AGA; adaptive genetic algorithm; optimise efficiency.
  DOI: 10.1504/IJEP.2025.10074264
   
  

• Open Access

  Digital economy and global value chain restructuring from a cross-country sustainable industry analysis ( Free Full-text Access )

  by L.I. Zhou, Mohd Azlan Shah Zaidi, Zhanxue Wang 
  Abstract: The unprecedented expansion of the digital economy is fundamentally altering the architecture and operational dynamics of global value chains (GVCs). This study investigates the impact of digital economy development on the restructuring of global value chains (GVCs). This analysis develops a theoretical framework and employs empirical analysis utilizing cross-country industry data. These findings show that digital economy development catalyzes GVC restructuring; this kind of restructuring is mainly reflected in the changes in the network status of the global value network and with notable heterogeneity across industries, input origins, and country classifications. Mechanism analysis shows that the digital economy promotes restructuring global value chains by improving production efficiency and innovation capabilities.
  Keywords: GVC; global value chain restructuring; digital economy; production efficiency; innovation capability; mediation effect.
  DOI: 10.1504/IJEP.2025.10074674
   
  

• Open Access

  Using soil water content and energy balance model to promote sustainable development of green and low-carbon economy ( Free Full-text Access )

  by Junyi Cao 
  Abstract: This study aims to evaluate the effectiveness of low-carbon economy (LCE) policies and explore their practical applications in reducing greenhouse gas emissions and promoting efficient resource utilisation. This study uses an ecological footprint calculation method based on the energy balance (EB) model to conduct a comparative experimental analysis on two regions that implement general economic policies and low-carbon economic policies, respectively. The research results show that between 2020 and 2024, carbon dioxide emissions from manufacturing and agriculture in regions implementing low-carbon economic policies increased by 3.97% and 4.02% respectively, significantly lower than the 7.78% and 8.97% increases in regions implementing general economic policies. Meanwhile, the growth rate of water consumption was also slower. Furthermore, low-carbon energy policies had a relatively small impact on total output, indicating that they can effectively reduce the environmental burden while ensuring economic growth.
  Keywords: LCE; low-carbon economy; energy balance; sustainable development; green industry.
  DOI: 10.1504/IJEP.2025.10074681
   
  

• Open Access

  Research on green trade data prediction under global economic shock based on ConvLSTM model oriented towards reducing carbon emissions ( Free Full-text Access )

  by Nianjie Shang, Yue Zhang, Yan Zhang, GaiRong Dai 
  Abstract: Existing green trade data prediction models only focus on the temporal characteristics of the data, while ignoring the spatial relationships of the data, resulting in large prediction errors for trade volume (M and X). This paper takes Sino-Korean trade as the main research object, and uses the convolutional long short-term memory (ConvLSTM) model to predict trade volume (M and X) data by combining the advantages of spatiotemporal features. This paper first collects and preprocesses relevant green trade data, then constructs a ConvLSTM model, and finally uses the model to output the predicted values of trade volumes M and X for the next year and compares them with the actual data. Experimental results show that the RMSE and MAE of the ConvLSTM model are 16,300 and 20,500, respectively, which are 1900 and 2300 lower than those of the LSTM model.
  Keywords: economic shock; green trade; data prediction; ConvLSTM model; spatial features; low-carbon economy; trade volume.
  DOI: 10.1504/IJEP.2025.10074875
   
  

• Open Access

  Urban environmental sustainability and welfare management optimisation based on ant colony optimisation and embedded systems ( Free Full-text Access )

  by Min Tang, Yanmin Cheng, Jie Deng 
  Abstract: Urban welfare management optimisation encompasses the full lifecycle of support for vulnerable populations, from initial intervention to long-term rehabilitation. In many economies, the number of employment and income opportunities is limited for destitute individuals, because of their low levels of skills, education, and capital. Rapid urbanisation has transformed many rural areas into metropolitan zones lacking open spaces, farmland, and water resources. This study investigates an enhanced ant colony optimisation (ACO) algorithm for path optimisation, combining global and local pheromone updates to improve computational efficiency and convergence. Spatial compression modification further accelerates optimisation by reducing developmental complexity. Embedded system applications promote the reuse of open spaces, offering income opportunities such as gardening and freshproduce supply for the urban poor. Although they lack certain metropolitan amenities, they maintain rural ties through visits, relocation, and participation in social and economic institutions. Integrating indigenous knowledge into urban development can empower communities, alleviate poverty, and support environmentally sustainable growth.
  Keywords: welfare management; urban; ant colony optimization; embedded systems.
  DOI: 10.1504/IJEP.2025.10074969
   
  

• Open Access

  Research on emergency monitoring methods for landslide disasters based on improved atmospheric correction GB-SAR and multi-source data geocoding ( Free Full-text Access )

  by Hao Zhang, Xiaolin Yang, Xiangtian Zheng, Zhenan Yin, Guiwen Ren, Shanshan Hou 
  Abstract: Ground-based synthetic aperture radar (GB-SAR) has become a key technical equipment in the field of geological disaster prevention. Existing monitoring methods have limitations: contact sensors offer limited coverage, while optical remote sensing struggles in adverse weather. This study presents an integrated remote sensing system combining improved GB-SAR, terrestrial laser scanning (TLS), and unmanned aerial vehicles (UAVs) for emergency monitoring. Innovations include an atmospheric correction model accounting for range, elevation, and azimuth angles, and a point cloud filtering method enhancing Permanent Scatterer selection. These reduce GB-SAR monitoring errors by 30%. A multi-source fusion framework integrates TLSs highresolution 3D modelling and UAVs rapid imaging for dynamic deformation analysis. The system enables fast risk area identification within 72 h to support emergency decisions. Experiments validate the improved GB-SARs accuracy and the fusion strategys effectiveness in landslide scenarios, offering a robust solution for real-time hazard assessment and mitigation.
  Keywords: landslide monitoring; GB-SAR; ground-based synthetic aperture radar; TLS; terrestrial laser scanning; UAVs; unmanned aerial vehicles.
  DOI: 10.1504/IJEP.2025.10075064
   
  

• Open Access

  Evaluation of petroleum safety management system based on embedded intelligent image sensor ( Free Full-text Access )

  by Shunzheng Jia, Nan Sun, Fangting Jia 
  Abstract: With the development of optical engineering, the application of embedded sensors in petroleum development has become a research hotspot in the field of petroleum extraction. Embedded intelligent image sensors can detect differences in oil reflection in the visible light wavelength range. Based on the test results, a program for the petroleum safety management system can be designed. This paper uses embedded intelligent image sensor technology to construct a petroleum safety management system. This paper first introduces the requirements of the petroleum safety management system and optimises it using embedded intelligent image sensors. Finally, the feasibility of the system is verified through experiments. Experimental data shows that the system achieves functional efficiencies of 0.825, 0.814, 0.793, 0.841, and 0.832 in personnel safety management, equipment safety management, material safety management, operational process safety management, and scheduling safety management, respectively. These data indicate that the system can allocate different security management contents reasonably
  Keywords: petroleum development; security management system; intelligent image sensor; efficiency index; petroleum development; embedded sensors.
  DOI: 10.1504/IJEP.2026.10075433
   
  

• Open Access

  State diagnosis technology of metal enclosed gas insulation equipment based on Apriori algorithm in cloud computing environment ( Free Full-text Access )

  by Jiayi Wang, Yuan Fang, Shaoqing Chen, Zongxi Zhang, Dianbo Zhou, Yuhang He, Jing Zhang 
  Abstract: In recent years, with the increasing failure rate of GIS, there has been a growing need for people to understand the most common insulator issues. Therefore, real-time monitoring of its operating status is crucial to ensure the safe and reliable operation of power lines. This study proposes the use of Apriori algorithm (CFSA-AA) for cloud based fault state analysis to predict discharge faults, mechanical faults, and abnormal mechanical vibrations in metal enclosed GIS. This data is sourced from the Kaggle repository used for VSB power line fault detection. This study summarises GIS abnormal heating faults, including circuit breakers, isolating switches, shell grounding, and disc insulator bolts. The experimental results show that compared with other existing models, the proposed CFSA-AA model improves the accuracy of fault diagnosis to 98.9%, pattern discovery rate to 97.4%, operating state detection rate to 95.6%, Mattew correlation coefficient ratio to 96.4%, and error rate to 7.4%.
  Keywords: state diagnosis; metal enclosed gas insulation equipment; Apriori algorithm; cloud computing; fault detection; transmission lines.
  DOI: 10.1504/IJEP.2026.10075463
   
  

• Open Access

  Carbon reduction coordination and pricing strategy of a four-level supply chain under demand uncertainty ( Free Full-text Access )

  by Qiang Shen, Xiuyun Hou 
  Abstract: This study centres on how demand uncertainty impacts emission reduction in supply chains, premised on the Stackelberg game model. It delves into the collaborative emission reduction and pricing strategies of a four echelon supply chain, including suppliers, producers, retailers and consumers, and meticulously examines the function of producers carbon emission reduction subsidies and low carbon product promotion subsidies in this process. The findings indicate that producers reduction subsidies to suppliers and promotion subsidies to retailers play a coordinating role in supply chain emission reduction and product pricing, either directly or indirectly. The promotion subsidy for products with lower carbon emissions strengthens retailers efforts to promote them and increases retail prices. Although these subsidies might lower producers emission reduction levels and raise wholesale prices, they enhance the entire supply chain systems emission reduction.
  Keywords: four-level supply chain; mission reduction subsidies; promotion subsidies; collaborative emission reduction.
  DOI: 10.1504/IJEP.2025.10075469
   
  

• Open Access

  Investigation into sustainable development of ecological environment and economic technology in the context of supply chain management ( Free Full-text Access )

  by Yanlong Zhao, Xuebo Yan 
  Abstract: This study aims to explore the synergistic optimisation effect of green supply chain management on ecological environment and economic sustainable development, and reveal its practical value in reducing resource consumption and environmental pollution. This paper compares the environmental-economic sustainable development under traditional supply chain management models and green supply chain management models. The experimental results show that in the Yangtze River Delta region, the average wastewater discharge of the traditional supply chain management model is 1.745 billion tons, while the average wastewater discharge of the green supply chain management model is 1.42 billion tons; in the Yunnan-Guizhou Plateau region, the average wastewater discharge of the traditional supply chain management model is 1.45 billion tons, while the average wastewater discharge of the green supply chain management model is 965 million tons. Therefore, applying green supply chain management in the sustainable development of environment and economy can effectively reduce wastewater emissions.
  Keywords: sustainable development; economic technology; ecological environment; supply chain management; analytic hierarchy process.
  DOI: 10.1504/IJEP.2026.10075610