International Journal of Environmental Engineering (10 papers in press)

Regular Issues

• Measurement and Influencing Factors of Green Total Factor Productivity of Cities in the Yangtze River Economic Zone  
  by Bizhen Chen, Fengjiao Ye 
  Abstract: The Yangtze River Economic Zone is an important pattern of China’s economic planning. This paper collected the data of cities in the Yangtze River Economic Zone from 2006 to 2018, by applying the Epsilon-Based Measure (EBM) method and Global-Malmquist-Luenberger (GML) index, calculated the green total factor productivity (GTFP), and analysed the distribution and influencing factors of GTFP in time and space. The main conclusions are as follows: (1) the overall GTFP of cities in the Yangtze River Economic Zone has shown an upward trend over time, while the development of cities in the middle and lower reaches is better than that of upstream cities; (2) the per capita GDP, the proportion of the tertiary industry, and the centralized treatment rate of sewage treatment plants can significantly promote the growth of GTFP, while the proportion of secondary industry and the number of students in colleges and universities have negative effects.
  Keywords: Yangtze River Economic Zone; GTFP; EBM; GML; fixed effect model; undesirable output; the industrial structure; urban construction; air and water pollution; environmental governance.
  DOI: 10.1504/IJEE.2025.10073782
   
  
• The Manifestation of the Complex Structure of Plant Communities in the Tourism Landscape Planning of Traditional Villages  
  by Yi Lin 
  Abstract: Traditional villages are important tourism resources, and the study of their landscape planning has certain value. This paper mainly studied the plant communities of traditional villages. It took three traditional villages, A, B, and C, in Guangdong and three traditional villages, D, E, and F, in Guangxi as the subjects to analyse their plant communities and investigate the species situation in these villages. Moreover, the importance and diversity of the species were calculated. The results showed that Villages A, B, and C had fewer arbour families and genera than Villages D, E, and F, and the villages in Guangxi had more abundant species resources. Importance calculation revealed that the dominant species in Villages A, B, and C were mostly evergreen trees, while those in Villages D, E, and F were mostly deciduous trees. Diversity calculation showed that Village F had better diversity and uniformity in all layers.
  Keywords: Plant community; complex structure; traditional village; landscape planning; tourism.
  DOI: 10.1504/IJEE.2025.10074049
   
  
• Low-Carbon Logistics and Distribution Scheme for the Smart City by Integrating Internet of Things Technology and Improved Genetic Hybrid Algorithm  
  by Yanyan Jin, Xia Chen, Kaiyuan Zhang 
  Abstract: To find the optimal path for distribution, the experiment proposes a low-carbon logistics and distribution path optimisation method for smart cities based on the Internet of Things (IoT) technology and ant colony genetic hybrid algorithm. On dataset A, when iterating up to 62 times, the average running time of this method (5.54 s) is less than other algorithms. When the full load factor was only 30%, the research method started to get the minimum total cost. The overall cost did not increase noticeably with the tax when the carbon tax was between 0 and 100 yuan/ton, indicating that the businesses in this range found the carbon tax to be acceptable. The research technique yielded a shorter optimal distribution path distance of 18.69 kilometres, as demonstrated by the application comparison. These results can provide a new theoretical basis for the low-carbon logistics and distribution in the smart city.
  Keywords: Low carbon; Logistics and distribution; Ant colony algorithm; Genetic algorithm; Smart city.
  DOI: 10.1504/IJEE.2025.10074171
   
  
• ENVI-met Simulation-Based Study on the Impact of Urban Small-Scale Public Green Spaces on Airborne Particulate Matter (APM) Concentration  
  by Jie Zhao, Lei Feng 
  Abstract: Airborne particulate matter (APM) concentration significantly affects urban environmental quality and residents health. To explore how small-scale public green spaces regulate APM, a site in Zhengzhou, Henan Province, was studied using field measurements and ENVI-met simulations. Results show a diurnal APM pattern characterised by a morning peak, early afternoon trough, and evening rebound. Microclimate factors wind speed, temperature, and humidity have greater influence than vegetation structure, with APM negatively correlated with wind speed and temperature, and positively with humidity. Spatial configuration, vegetation structure, and the ratio of green to hard surfaces notably affect APM reduction efficiency. Wind-permeable vegetation layouts enhance APM dispersion. Optimisation strategies include reducing hard pavement, adopting open and connected spatial layouts, and increasing mixed plant communities around the area, offering practical guidance for improving air quality and microclimate in high-density urban environments.
  Keywords: Small-scale public green space; airborne particulate matter (APM) concentration; variation pattern; ENVI-met simulation.
  DOI: 10.1504/IJEE.2025.10075312
   
  
• Sustainable scheduling algorithm for social green assets based on big data  
  by Jia Jiao, Junmei Li 
  Abstract: The current methods used cannot effectively improve the economic benefits of green assets. Given this, this study innovatively combines big data with sustainable scheduling algorithms, proposes a new scheduling algorithm for green assets, and verifies its effectiveness through simulation experiments. In the comparison of the system penetration rate between the improved scheduling algorithm and the two commonly used scheduling algorithms, the improved algorithm always maintained a range of 70%-86%, and its variation amplitude was relatively gentle. This indicated that the reliability of this algorithm was significantly higher than other algorithms. In the same scenario, the cost of the improved algorithm remained within the range of 1,600-2,800, and its usage time remained within the range of 150 s-230 s for all three algorithms. This indicated that the algorithm consumed significantly less time and cost in job scheduling compared to other scheduling algorithms. The experiment shows that the improved sustainable scheduling algorithm can effectively improve the economic benefits of green asset enterprises.
  Keywords: social green assets; big data; sustainable scheduling algorithm; improved sustainable scheduling algorithm; ISSA; simulation model.
  DOI: 10.1504/IJEE.2025.10075702
   
  
• Research on opportunities and challenges of ecological environment protection in Hebei Province from the perspective of big data from 2013 to 2022  
  by Ying Zhao 
  Abstract: From 2013 to 2022, the ecological environment in Hebei Province has undergone historic, transformative, and global changes, which is one of the most significant areas of change in the past decade. This article takes Hebei Province as the research object, comprehensively evaluates environmental carrying capacity of the region over the past decade, analyses its spatiotemporal evolution characteristics, and studies the structural and governance system of the ecological environment. The research results indicate that while the carrying capacity of the ecological e surroundings continues to improve, the foundation for a stable and positive ecological environment in Hebei Province is not yet solid. The turning point from quantitative change to qualitative change has not yet arrived, and it is still during a vital stage of pressure accumulation and progress under serious loads. Hebei Province has significant geographical advantages and a good development foundation.
  Keywords: big data; ecological environment; protection.
  DOI: 10.1504/IJEE.2025.10073862
   
  
• Automatic extraction method of water body boundaries in remote sensing images based on deep residual network  
  by Qianchen Yang 
  Abstract: Water body extraction, which classifies each pixel in an image as either water or background, is a fundamental task in land - cover categorisation. Accurate identification of water bodies is critical for urban hydrology applications such as water resource management and flood warning systems. While traditional index-based methods like NDWI and MNDWI have been widely used, deep convolutional neural networks (DCNNs) have recently shown promising improvements. However, training these networks requires large volumes of high-quality labelled data, which is often limited in remote sensing. PAN improves performance across various models and datasets, yielding an average increase of 0.749 in mean Intersection over Union (m Io U). Moreover, PAN surpasses previous benchmarks without altering model architecture and demonstrates the added value of incorporating multispectral data.
  Keywords: patch adaptive network; PAN; StyleGAN2; Dandelion optimisation; DO; RGB/NIR; binary cross entropy; BCE.
  DOI: 10.1504/IJEE.2025.10073857
   
  
• Correlation between digital economy development and regional carbon emissions through spatial spillover effects  
  by Bing Xia 
  Abstract: In order to study the correlation between the digital economy and carbon emissions, this paper constructed a multiple regression model involving the digital economy and carbon emissions for cities above the prefecture level in China. Moreover, the spatial spillover effect was analysed by introducing the spatial weights among cities during the construction process. It was found that the development of the digital economy had a spatial spillover effect and can effectively reduce local carbon emissions and drive green transition of industries. The research findings of this paper offer theoretical support and empirical evidence for developing interregional collaborative carbon reduction strategies.
  Keywords: digital economy; regional carbon emissions; spatial spillover effect; regression analysis.
  DOI: 10.1504/IJEE.2025.10073468
   
  
• Optimum and multi-objective operation based on the economic and pollution aspects of a multiple energy system in connection with the upstream electricity and natural gas networks, considering renewable sources  
  by Fan Bu, Weihao Ren 
  Abstract: This study addresses the environmental challenges posed by growing reliance on fossil fuels, highlighting the urgent need for sustainable energy systems amid rising fuel consumption and limited non-renewable resources. The research focuses on improving the efficiency of a multi-energy system integrated with both electric and natural gas networks, aiming to meet thermal and electrical demands sustainably. The proposed model pursues dual objectives: minimising operational costs and reducing greenhouse gas emissions. Renewable sources, including solar panels and wind turbines, are employed, while a scenario generation technique manages the uncertainties of renewable energy production. Optimisation is performed using the simple augmented ε-constraint method (ECM), and the fuzzy min-max approach selects the most balanced outcome. Simulation results demonstrate superior performance over traditional methods by avoiding suboptimal solutions. This work offers valuable insights into integrating renewable energy with advanced optimisation techniques for environmentally and economically efficient multi-energy systems.
  Keywords: multi-energy systems; renewable energy integration; multi-objective optimisation; load management program; LMP; simple augmented ε-constraint method; fuzzy min-max decision method; greenhouse gas emissions reduction; scenario-based uncertainty modelling.
  DOI: 10.1504/IJEE.2025.10073618
   
  
• Urban and rural landscape design analysis technology based on image semantic segmentation deep learning and remote sensing image processing  
  by Xuen Hou, Qiuyue Shan 
  Abstract: This study addresses the rising demand for high-quality urban and rural environments by proposing a deep learning-based landscape analysis method. This study proposes a landscape analysis method based on deep learning to address the growing demand for high-quality urban and rural environments. This method first uses Deeplab-v3+as the model foundation. Then, two-dimensional decomposition is used for segmentation to reduce the number of convolution parameters, and depth wise separable convolution operations are optimised to improve the accuracy of the training model. Finally, select two representative deep and shallow layers for feature fusion operations at different levels. The results showed that compared with X-DeepLab-v3+ and the original Deeplab-v3+, the optimised model achieved up to 58.3% higher intersection-over-union and 47.6% higher pixel accuracy. Additionally, it demonstrated strong alignment with real-world compactness and connectivity values, reaching 99% and 98% respectively. The model enables precise analysis of landscape fragmentation and ecological connectivity, offering valuable insights for environmental protection and landscape design.
  Keywords: image semantic segmentation; ISS; Deeplab-v3+; deep learning; landscape analysis; remote sensing image processing.
  DOI: 10.1504/IJEE.2025.10073759