2026-04-01
Ajeesh Puthusserry Paulose, Felix Augustin
IntroductionSelecting suitable sites for solid waste management (SWM) facilities is a complex decision-making problem involving conflicting socio-geographical criteria and inherent uncertainty.MethodTo address this challenge, this study proposes a novel Bipolar Association between Preference and Performance with Satisfactory Score (BiAPPSS) framework by extending the classical APPSS model into a bipolar triangular fuzzy environment. The proposed approach simultaneously captures supportive and conflicting assessments through positive and negative membership degrees, enabling a more realistic evaluation of alternative locations. Furthermore, a Bidirectional Associative Memory (BAM) network is integrated to model the interrelationships between evaluation criteria and suitable regions, enhancing the interpretability of the decision process.Results and DiscussionThe applicability of the BiAPPSS–based framework is demonstrated through a real-world case study on SWM site selection in Ernakulam district, Kerala, India, where Brahmapuram is identified as the most suitable location. The robustness and validity of the proposed model are verified using comparative analysis with existing fuzzy MCDM methods, sensitivity analysis, and interpretability assessment. The results indicate that the proposed framework provides a reliable and data-driven decision-support tool for sustainable SWM planning and policy formulation.
DOI: 10.3389/fenvs.2026.17568742026-03-30
Jussen Facuy Delgado, Daniela Muñoz-Vera, Suri Ballesteros-Montesdeoca, Lia Seminario-Espinoza
Wildfires represent an increasing environmental challenge in many regions of the world due to the combined effects of climate variability and anthropogenic pressures on natural ecosystems. Understanding the drivers of wildfire occurrence is essential for improving prevention strategies and environmental management. This study analyzes the spatial and temporal patterns of forest fires in Ecuador between 2010 and 2025 using geospatial analysis and statistical modeling approaches. Wildfire occurrence data were obtained from official governmental records, while climatic and demographic variables were derived from NASA’s POWER Data platform and national census data. A province–month panel dataset comprising 4,608 observations was constructed to evaluate wildfire dynamics across the 24 provinces of Ecuador. Spatial analysis was conducted using Geographic Information Systems, while statistical relationships were examined through correlation analysis and generalized linear models, including Poisson and Negative Binomial regressions. Additionally, wildfire severity was evaluated using a multiple linear regression model, where burned area was considered the dependent variable. The results reveal clear spatial, temporal, and seasonal patterns of wildfire activity, with higher wildfire frequencies concentrated in the Andean and coastal regions and during the dry season months between July and October. Statistical analyses indicate that maximum temperature alone does not appear to significantly explain wildfire occurrence patterns, whereas population density shows a stronger positive association with wildfire frequency. These findings suggest that anthropogenic pressure may play a more important role than climatic variability in shaping wildfire dynamics in Ecuador. Overall, the study provides a comprehensive spatio-temporal characterization of wildfire activity and highlights the importance of integrating socio-environmental factors into wildfire risk management and prevention strategies.
DOI: 10.3389/fenvs.2026.17643952026-03-30
Nan Liu, Shuyu Chen, Jun Kong, Tianze Zhang
As the penetration rate of new energy vehicles in China continues to rise, the incentive effect of the CAFC-NEV credits policy has gradually weakened. The Ministry of Industry and Information Technology therefore proposes to transform the CAFC-NEV credits policy into carbon emission management policy. Focusing on the transition plan for CAFC-NEV credits of automotive industry in China, this paper constructs a differential game model of the supply chain that simultaneously manufactures and sells internal combustion engine vehicle (ICEV) and new energy vehicle (NEV), so as to examine the transitional effect of carbon credit on CAFC-NEV credits. The results show that: (1) Implementing carbon credit for NEV on the basis of the current CAFC-NEV credits does not affect capital stock or goodwill. (2) Within a specific range of NEV standard type credit and proportional requirement, the optimal equilibrium solutions for manufacturer and retailer are higher under the carbon credit compared to the CAFC-NEV credits scenario. (3) Neither the single-channel nor the dual-channel sales model affects the basic conclusions of the research. (4) The abolition of CAFC-NEV credits exerts a greater impact on manufacturer’s profit than on retailer’s. Empirical analysis reveals that the profit margins for manufacturer and retailer under the CAFC-NEV credits and carbon credit differ by less than 7% and 1% respectively. This study provides a theoretical basis for promoting the transformation of automotive industry policy from CAFC-NEV credits to carbon emission management in China.
DOI: 10.3389/fenvs.2026.17378402026-03-27
David Antonio Buentello-Montoya, Ivanna Robledo-Hernández, Andrew Sebastián Larrea-Cedeño, Christian Enrique García-García, Karina Guadalupe Coronado-Apodaca
The increasing generation of agro waste presents major environmental and management challenges, driven by improper disposal practices that contribute to soil degradation, pollution, and greenhouse gas emissions. Among available waste valorization pathways, thermochemical and hydrothermal conversion technologies enable the production of biochar and hydrochar with significant potential for soil amendment and carbon sequestration. This review focuses on the production and application of biochar and hydrochar derived from agro waste for soil amendment. The characteristics of agro waste are discussed in relation to their suitability for thermochemical and hydrothermal conversion; the physicochemical properties of biochar and hydrochar are discussed with respect to surface chemistry, stability, nutrient retention, and environmental performance. Recent advances in soil application are reviewed, highlighting effects on soil physicochemical properties, microbial biomass, crop productivity, pollutant immobilization, and carbon sequestration. Overall, biochar generally exhibits superior stability, surface area, and soil amendment performance compared to hydrochar; however, it is not suitable for all biomasses, particularly wet residues or sludges. In contrast, hydrochar can be effectively upgraded through post-treatments such as washing or secondary pyrolysis, enabling competitive soil amendment performance. Challenges for both chars include feedstock heterogeneity, scalability, long-term environmental impacts, and standardization for carbon accounting. Addressing these issues is critical to align agro waste–derived biochar and hydrochar with climate mitigation and net-zero targets.
DOI: 10.3389/fenvs.2026.18049842026-03-27
Xin Feng, Ke Li, Liming Yang, Tianhao Song, Tianning Wang
As a critical engine of China’s economic growth and ecological security, the Yangtze River Economic Belt (YREB) faces the dual challenge of sustaining development while mitigating carbon footprint pressure (CFP). This study examines the driving forces and spatiotemporal heterogeneity of CFP in the YREB from 2000 to 2021, aiming to support the region’s contribution to the United Nations Sustainable Development Goals. By integrating an extended STIRPAT model with Geographically and Temporally Weighted Regression (GTWR) and General Dominance Analysis (GDA), we isolate the specific contributions of population, affluence, and technology. The results indicate that: (1) economic inertia remains the dominant stressor, with population growth and economic expansion significantly increasing CFP, outweighing the mitigation effects of energy efficiency improvements; (2) the industrial structure has shifted roles, transitioning from a primary driver of carbon pressure to an inhibitor in later years, reflecting effective policy interventions in the tertiary sector; and (3) spatial heterogeneity is pronounced, with the midstream region identified as the critical “governance bottleneck” due to high industrial intensity, whereas the downstream region exhibits advanced decoupling trends. These findings suggest that single-dimensional energy transitions are insufficient for the YREB; a differentiated regional strategy focusing on midstream industrial upgrading is essential for achieving carbon neutrality.
DOI: 10.3389/fenvs.2026.17647622026-03-27
Cheng Peng, Hao Zhang
Residential solar photovoltaic (PV) systems have emerged as a key source of household energy consumption, contributing to urban sustainable development. Contrary to the commonly observed monotonic negative relationship between urban population density and PV installation, this study identifies an inverted U-shaped relationship. Based on the empirical evidence, we further develop an urban model that incorporates residential PV adoption to explore two opposing effects of population agglomeration. On one hand, agglomeration reduces installation costs, thereby promoting PV adoption; on the other hand, it leads to more households residing in high-rise buildings, where residential PV installation is often unfeasible. The varying trade-offs between these two effects across different population densities give rise to the observed non-monotonic pattern.
DOI: 10.3389/fenvs.2026.17888772026-03-27
Raza Ahmed, Wenjiang Huang, Zeenat Dildar, Hafiz Adnan Ashraf, Muhammad Ateeq, Zahid Ur Rahman
Grasshopper outbreaks pose serious ecological and economic threats to temperate grasslands, reducing vegetation productivity and destabilizing steppe ecosystems. To enhance monitoring and prediction, this study integrated remote-sensing indicators with ensemble machine learning and a degree-day (DD) model to assess grasshopper habitat suitability in the Xilingol steppe of Inner Mongolia from 2018 to 2022. Field-based occurrence data of Oedaleus decorus asiaticus and Dasyhippus barbipes were combined with 28 environmental variables encompassing meteorological, vegetative, soil, topographic, and landscape factors. Following multicollinearity screening using tolerance, variance inflation factor (VIF), and correlation thresholds, independent predictors were used to train four algorithms—Random Forest, Multilayer Perceptron, XGBoost, and MaxEnt—whose weighted integration formed the ensemble model. Spatial autocorrelation and hotspot analyses were employed to examine distributional clustering, while suitability maps were classified into low, moderate, and high categories. The ensemble achieved the highest predictive accuracy (AUC = 0.923–0.945), outperforming individual models. Results indicated persistent spatial clustering of grasshopper occurrences, with stable hotspots around Xilinhot and East and West Ujumqin and notable expansions during favorable years (2020, 2022). Minimum temperature during the egg stage (22.6%) and fractional vegetation cover during the nymph stage (19.8%) emerged as dominant environmental drivers, followed by precipitation, elevation, and above-ground biomass. These findings underscore the central role of temperature and vegetation dynamics in shaping grasshopper habitat suitability. The integration of DD-based phenological modeling with ensemble learning provides a robust, ecologically coherent framework for regional pest monitoring, early warning, and sustainable grassland management in semi-arid ecosystems.
DOI: 10.3389/fenvs.2026.17453952026-03-26
Fatih Boz, Burcu Yilmaz, Halil Özekicioğlu, Hüseyin Topuz, Ulaş Ünlü
This study investigates how recent global crises have reshaped the sunflower oil trade network and what these shifts mean for the environmental sustainability and financial resilience of agri-food systems. Focusing on the COVID-19 pandemic and the Russia–Ukraine conflict, we analyze export flows from 2019 to 2022 using complex network techniques to identify changes in structural connectivity, core–periphery patterns and country-level influence. The findings show that Ukraine and India remained the most central actors in the network throughout the period, while 2022 marked a significant reorganization: Türkiye and Russia rose in prominence following the Grain Corridor initiative, signaling the emergence of alternative regional trade pathways. Despite these geopolitical shocks, the network preserved a dense and highly interconnected structure, revealing persistent interdependence among major producers and import-dependent economies. However, several key trade relationships weakened or shifted, illustrating the sensitivity of global edible-oil supply chains to geopolitical disruptions. These dynamics are closely linked to climate-sensitive agricultural systems, with implications for land-use pressures, food security risks, and the vulnerability of environmentally exposed importing countries. The study further highlights how sustainable finance mechanisms—including risk-responsive investment strategies and resilience-oriented funding models—can support the stability of edible-oil supply chains by addressing the concentration, dependency and reconfiguration patterns identified in the trade network under rising climate and geopolitical uncertainty. Network results were validated through cross-year structural consistency checks, ensuring methodological robustness. Overall, the analysis provides timely evidence on the reconfiguration of sunflower oil trade and offers insights relevant to SDG2 (Zero Hunger) and SDG13 (Climate Action), contributing to efforts to build more resilient and environmentally sustainable food systems.
DOI: 10.3389/fenvs.2026.17571812026-03-26
Yaoru Li, Zhi Qu, Yingying Liu, Chunmei Deng, Shengtao Chen, Weihan Yin
Coastal areas such as bays, characterized by intense land-sea interactions and frequent human activities, are confronting ecological pressures induced by trace metal(loid) pollution in aquatic environments. In this study, trace metal(loid)s (Cu, Pb, Zn, Cr, Cd, and As) in surface water, bottom water, and surface sediments of northern Liaodong Bay were analyzed to assess their spatial distribution, contamination status, and controlling factors. The concentrations and distribution patterns of these metal(loid)s were consistent between surface and bottom water, yet varied substantially among different elements, with high-concentration areas distributed in a patchy manner. All trace metal(loid) concentrations in the water column were within the Class I seawater quality standard. However, no significant correlation was observed between trace metal(loid) contents in sediments and those in the water column (correlation coefficients R2 are all less than 0.1), and the distribution patterns of individual metal(loid)s in sediments were also inconsistent. Sediments were unpolluted by Cu and Cr, while Pb, Zn, Cd, and As exhibited unpolluted to moderately polluted conditions. Comprehensive evaluation indicated that the overall trace metal(loid) pollution in surface sediments was low. Trace metal(loid)s in both water and sediments were not predominantly controlled by natural processes; instead, point source pollution derived from riverine transport, coastal industrial and agricultural activities, offshore oil extraction, and shipping emerged as a crucial influencing factor. This study provides fundamental data for in-depth research on trace metal(loid) pollution in coastal areas and emphasizes the need for adequate attention to metal(loid) pollution in coastal ecosystems.
DOI: 10.3389/fenvs.2026.1777750