In this research, we employed multivariate statistical methods to investigate the perspectives of small and medium-sized enterprises (SMEs) concerning the Extended Producer Responsibility (EPR) regulation and their apprehensions related to EPR compliance. The EPR regulation, which places the responsibility of waste management on producers, has significant financial and administrative implications, particularly for SMEs. A sample of 114 businesses was randomly selected, and the collected data underwent comprehensive analysis. Our findings highlight that a notable proportion of businesses (44.7%) possess knowledge of the EPR regulation’s provisions, whereas only a marginal fraction (1.8%) lacks sufficient familiarity. We also explored the interplay between opinions on the EPR regulation and concerns regarding its financial and administrative implications. Our results establish a significant correlation between EPR regulation opinions and concerns, with adverse opinions prominently influencing concerns, particularly regarding financial burdens and administrative workloads. These outcomes, derived from the application of multivariate statistical techniques, provide valuable insights for enhancing the synergy between environmental regulations and business practices. EPR regulation significantly affects SMEs in terms of financial, administrative, and legal obligations, thus our study highlights that policymakers may need to consider additional support mechanisms to alleviate the regulatory burden on SMEs, fostering a more effective and sustainable implementation of the EPR regulation.

The physical-mechanical characteristics of leather are crucial in the tanning industry since they determine whether the leather satisfies quality standards for various product manufacture. This study’s goal was to assess the physical-mechanical characteristics of leather that could be washed and used for garments after the Zetestan-GF polymer was added during the tanning process. The data gathered from the physical-mechanical analysis of two treatments—one a control with white leather (T1) and the other with leather treated with Zetestan-GF polymer (T2)—were compared for the development of this work. Each treatment was performed in triplicate, undergoing three washes, yielding a total of 24 samples for analysis. Following the acquisition of the leather, a control was applied and the various treatments were compared. SAS software version 9.0 was utilized for the data’s statistical analysis. The physical-mechanical properties of the control leather and the leather treated with Zetestan-GF polymer were compared using a one-way ANOVA, and any differences in the means (p < 0.05) were assessed using the Tukey test. The findings showed that while the polymer’s application during the tanning process affects the parameters of softness, tensile strength, elongation percentage, and dry and wet flexometry, it has no effect on the lastometry parameter. In conclusion, the physical-mechanical characteristics of the product made by tanning cow hides can be greatly impacted by the inclusion of a polymer.

In Côte d’Ivoire, the government and its development partners have implemented a national strategy to promote agroforestry and reforestation systems as a means to combat deforestation, primarily driven by agricultural expansion, and to increase national forest cover to 20% by 2045. However, the assessment of these systems through traditional field-based methods remains labor-intensive and time-consuming, particularly for the measurement of dendrometric parameters such as tree height. This study introduces a remote sensing approach combining drone-based Airborne Laser Scanning (ALS) with ground-based measurements to enhance the efficiency and accuracy of tree height estimation in agroforestry and reforestation contexts. The methodology involved two main stages: first, the collection of floristic and dendrometric data, including tree height measured with a laser rangefinder, across eight (8) agroforestry and reforestation plots; second, the acquisition of ALS data using Mavic 3E and Matrice 300 drones equipped with LiDAR sensors to generate digital canopy models for tree height estimation and associated error analysis. Floristic analysis identified 506 individual trees belonging to 27 genera and 18 families. Tree height measurements indicated that reforestation plots hosted the tallest trees (ranging from 8 to 16 m on average), while cocoa-based agroforestry plots featured shorter trees, with average heights between 4 and 7 m. A comparative analysis between ground-based and LiDAR-derived tree heights showed a strong correlation (R² = 0.71; r = 0.84; RMSE = 2.24 m; MAE = 1.67 m; RMSE = 2.2430 m and MAE = 1.6722 m). However, a stratified analysis revealed substantial variation in estimation accuracy, with higher performance observed in agroforestry plots (R² = 0.82; RMSE = 2.21 m and MAE = 1.43 m). These findings underscore the potential of Airborne Laser Scanning as an effective tool for the rapid and reliable estimation of tree height in heterogeneous agroforestry and reforestation systems.

This study examines the spatial distribution of consumption competitiveness and carrying capacity across regions, exploring their interrelationship and implications for sustainable regional development. An evaluation index system is constructed for both consumption competitiveness and carrying capacity using a range of economic, social, and environmental indicators. We apply this framework to regional data in China and analyze the resultant spatial patterns. The findings reveal significant regional disparities: areas with strong consumption competitiveness are often concentrated in economically developed regions, while high carrying capacity is notable in less populated or resource-rich areas. Notably, a mismatch emerges in some regions—high consumer demand is not always supported by adequate carrying capacity, and vice versa. These disparities highlight potential sustainability challenges and opportunities. In the discussion, we address reasons behind the spatial mismatch and propose policy implications to better align consumer market growth with regional resource and environmental capacity. The paper concludes that integrating consumption-driven growth strategies with carrying capacity considerations is essential for balanced and sustainable regional development.

Mapping land use and land cover (LULC) is essential for comprehending changes in the environment and promoting sustainable planning. To achieve accurate and effective LULC mapping, this work investigates the integration of Geographic Information Systems (GIS) with Machine Learning (ML) methodology. Different types of land covers in the Lucknow district were classified using the Random Forest (RF) algorithm and Landsat satellite images. Since the research area consists of a variety of landforms, there are issues with classification accuracy. These challenges are met by combining supplementary data into the GIS framework and adjusting algorithm parameters like selection of cloud free images and homogeneous training samples. The result demonstrates a net increase of 484.59 km² in built-up areas. A net decrement of 75.44 km² was observed in forest areas. A drastic net decrease of 674.52 km² was observed for wetlands. Most of the wastelands have been converted into urban areas and agricultural land based on their suitability with settlements or crops. The classifications achieved an overall accuracy near 90%. This strategy provides a reliable way to track changes in land cover, supporting resource management, urban planning, and environmental preservation. The results highlight how sophisticated computational methods can enhance the accuracy of LULC evaluations.