International Journal of Environment and Pollution (5 papers in press)

Regular Issues

• Open Access

  Marginal emission reduction costs of regional energy system transformation enabled by digital economy from the perspective of supply chain ( Free Full-text Access )

  by Wenjuan Liu, Wenbo Ma 
  Abstract: The existing supply chain management model is difficult to effectively control the unit marginal emission reduction cost. This paper constructs a multi-level integrated framework based on the Extreme Gradient Boosting (XGBoost) algorithm to capture the nonlinear relationship between electricity prices, weather, industrial activities and historical loads, and dynamically output node loads and carbon emission boundaries. The prediction results are transmitted to the Mixed-Integer Linear Programming (MILP) model in real time, which optimizes the carbon emission path through piecewise linear control and global constraints to minimize the unit marginal emission reduction cost. The node carbon emission data is written into the Hyperledger Fabric blockchain after hash encryption, so as to carry out full-link encrypted evidence storage and traceability. Experiments show that when the carbon quota is compressed to 50% of the system's baseline emissions, the unit marginal emission reduction cost is reduced from 278 yuan/ton to 245 yuan/ton.
  Keywords: digital economy integration; marginal abatement cost; regional energy system; supply chain coordination; carbon information governance.
  DOI: 10.1504/IJEP.2026.10078072
   
  

• Open Access

  Research on the development of low-carbon digital economy transformation based on artificial intelligence and fuzzy synthesis algorithm ( Free Full-text Access )

  by Yong Zhang, Hailing Jiang, Yun Liu, Yusri Kamin 
  Abstract: Due to low-carbon and digital transformation (LCDT), the economic and social system is expected to undergo significant changes in the coming decades, which will have a significant impact on employment. Artificial intelligence (AI) has been proven to significantly reduce the frequency of carbon dioxide emissions in small, medium-sized, and less advanced cities by optimising economic processes, improving the efficiency of transportation networks, and predicting and preventing emissions. This paper proposes an artificial intelligence assisted fuzzy synthesis algorithm to achieve the development of low-carbon digital transformation (AI-FSA-LCDT). This paper conducted experimental research on the relationship between digital economy expansion and low-carbon emissions using intelligent multi criteria algorithms. The results indicated that developing a digital economy may significantly reduce the carbon reduction intensity of the region and its neighboringcountries. Therefore, it is crucial to promote the construction process of the digital economy as a development strategy in a low-carbon environment.
  Keywords: digital transformation; artificial intelligence; CO2 emission; fuzzy synthesis algorithm; low carbon economy; carbon reduction intensity.
  DOI: 10.1504/IJEP.2026.10078281
   
  

• Open Access

  Coupling coordination level and sustainable development of ecological environment and network economy based on data mining ( Free Full-text Access )

  by Rong Wang, Zhenpeng Di, Xia Rong 
  Abstract: Traditional studies on the coupling and coordination of economics and ecology often overlook the emerging network economy and fail to clarify its role in regional sustainable development. To fill this research gap, this study takes Lanzhou as the research object and combines data mining technology with a coupled coordination model to construct a multi-dimensional evaluation index system covering the ecological environment, traditional economy and network economy. By collecting and processing relevant data from Lanzhou, this study quantifies the coupling degree and coordination level between the ecological environment and the economic system (including the network economy), and dynamically tracks its evolution trend. The results show that the coupling coordination degree calculated in 2019 was 0.43, indicating that Lanzhou is at a low but improving level. This study shows that implementing a data-driven strategy can further strengthen the coupling and coordination between the ecological environment and the economy in Lanzhou.
  Keywords: data mining; ecosystem and network economy; sustainable development; coupling and coordination.
  DOI: 10.1504/IJEP.2025.10078282
   
  

• Open Access

  Calculation of carbon emission footprint of ecotourism energy driven by big data ( Free Full-text Access )

  by Shuang Li 
  Abstract: Aiming at the problems of coarse data granularity, incomplete coverage, and lack of dynamics in the calculation of carbon emissions in ecotourism, a dynamic calculation framework driven by big data is constructed, and an empirical study is conducted using A rural tourism scenic spot as a case. Ecotourism is different from mass tourism. Its carbon emissions show the characteristics of decentralised transportation modes, a clean energy structure, and high dependence on surrounding facilities. This paper integrates heterogeneous multi-source data, including global positioning system (GPS) trajectories, energy consumption at scenic spots, transportation use, and social media comments, to build a unified data model and enrich the data dimension. For data preprocessing, the Kalman filter is used to smooth the GPS trajectory and eliminate jump-point errors.
  Keywords: big data driven; ecotourism energy; carbon footprint calculation; multi-regional input-output model; LSTM; long short-term memory; multiregional input output.
  DOI: 10.1504/IJEP.2026.10079105
   
  
• Exploring the association between air pollutants and meteorological parameters using canonical correlation analysis  
  by M.R. Sindhumol, S. Suresh, M. Sangeetha, D. Kalvinithi 
  Abstract: Meteorological parameters play a significant role in exacerbating air pollution, as proved by the existing literature work. This research primarily attempts to identify the pivotal air and meteorological parameters that influence the air quality within the Chennai region. It also entails a comprehensive assessment of the strength and direction of the relationship between air pollutants and meteorological parameters by employing a Canonical Correlation Analysis. From squared correlation estimates, three canonical functions are identified as significant. Notably, the outcome of this study reveals that the variations in natural conditions such as Atmospheric Temperature, Solar Radiation, and Relative Humidity are associated with the presence of particulate matter and gaseous emissions, including Ammonia, Nitrogen Dioxide, and Sulfur Dioxide. The conclusions drawn from this study will assist in framing policies and regulations that can be implemented to control the alarming levels of air pollution in Chennai.
  Keywords: CCA; canonical correlation analysis; particulate matter; ammonia; nitrogen dioxide; atmospheric temperature; relative humidity; wind speed.
  DOI: 10.1504/IJEP.2026.10079197