Municipal Solid Waste (MSW) management presents a significant challenge for traditional separation practices, due to a considerable increase in quantity, diversity, complexity of types of solid waste, and a high demand for accuracy in classification. Image recognition and classification of waste using computer vision techniques allow for optimizing administration and collection processes with high precision, achieving intelligent management in separation and final disposal, mitigating environmental impact, and contributing to sustainable development objectives. This research consisted of evaluating and comparing the effectiveness of four Convolutional Neural Network models for MSW detection, using a Raspberry Pi 4 Model B. To this end, the models YOLOv4-tiny, YOLOv7-tiny, YOLOv8-nano, and YOLOv9-tiny were trained, and their performance was compared in terms of precision, inference speed, and resource usage in an embedded system with a custom dataset of 1883 organic and inorganic waste images, labeled with Roboflow by delimiting the area of interest for each object. Image preprocessing was applied, with resizing to 640 × 640 pixels and contrast auto-adjustments. Training considered 85% of images and testing considered 15%. Each training stage was conducted over 100 epochs, adjusting configuration parameters such as learning rate, weight decay, image rotation, and mosaics. The precision results obtained were as follows: YOLOv4-tiny, 91.71%; YOLOv7-tiny, 91.34%; YOLOv8-nano, 93%; and YOLOv9-tiny, 92%. Each model was applied in an embedded system with an HQ camera, achieving an average of 86% CPU usage and an inference time of 1900 ms. This suggests that the models are feasible for application in an intelligent container for classifying organic and inorganic waste, ensuring effective management and promoting a culture of environmental care in society.

In Latin America, the energy crisis has worsened due to the dependence on energy services and fossil fuel imports from highly industrialized countries at prices established by the international market; this is particularly relevant to the construction industry, which presents a significant deficit in optimal energy consumption. Hence, some governments have established public policies to maximize the efficiency of these services and, at the same time, minimize the carbon footprint. In this research study, we reviewed the public policies, strategies, and incentives for energy efficiency (EE) implementation in the residential sector established by the Mexican government. A scoping review methodology was chosen and implemented in the following steps: 1. Research inquiry identification. 2. Determination of the relevant literature and studies. 3. The literature selection. 4. Data graphing. 5. Results collection, overview, and submission. In this systematic review, we identified five mandatory standards (NOM-008-ENER-2001, NOM-009-ENER-2014, NOM-018-ENER-2011, NOM-020-ENER-2011, and NOM-024-ENER-2012), six optional standards, four strategies (Green Mortgage, Integral Sustainable Improvement in Existing Housing, ECOCASA, and NAMA), and three kinds of incentives (green bonds, credit and interest rates (Green Mortgage, FIDE, and Ecocasa), and taxes (Income Tax Reduction)). As a result of the implementation of the above, as of December 2020, NAMA financed 5106 developers of 38 projects in 15 states; contributed to a reduction of 126,779 tons of CO₂; and aided 19,913 people. From 2013 to December 2023, EcoCasa subsidized 71,440 households for a total of 224 projects in 25 states; contributed to a reduction of 2.6 million tons of CO₂; aided 285,760 Mexicans; and issued EcoCasa certificates for 3,473,556 m². The results of the EE indicators in residential buildings showed an increase in the housing unit number as well as an increase in household appliances, with those based on power consumption prevailing. The residential sector ranks third in power consumption in Mexico, consuming an estimated 790 pj, of which 76% corresponds to thermal energy and 24% to electric power. Among countries in Latin America and the Caribbean, Mexico has achieved an Energy Transition Index of 62%.

Municipal solid waste (MSW) accumulation is a critical global challenge for society and governments, impacting environmental and social sustainability. Efficient separation of MSW is essential for resource recovery and advancing sustainable urban management practices. However, manual classification remains a slow and inefficient practice. In response, advances in artificial intelligence, particularly in machine learning, offer more precise and efficient alternative solutions to optimize this process. This research presents the development of a light deep neural network called R3sNet (three “Rs” for Reduce, Reuse, and Recycle) with residual modules trained end-to-end for the binary classification of MSW, with the capability for faster inference. The results indicate that the combination of processing techniques, optimized architecture, and training strategies contributes to an accuracy of 87% for organic waste and 94% for inorganic waste. R3sNet outperforms the pre-trained ResNet50 model by up to 6% in the classification of both organic and inorganic MSW, while also reducing the number of hyperparameters by 98.60% and GFLOPS by 65.17% compared to ResNet50. R3sNet contributes to sustainability by improving the waste separation processes, facilitating higher recycling rates, reducing landfill dependency, and promoting a circular economy. The model’s optimized computational requirements also translate into lower energy consumption during inference, making it well-suited for deployment in resource-constrained devices in smart urban environments. These advancements support the following Sustainable Development Goals (SDGs): SDG 11: Sustainable Cities and Communities, SDG 12: Responsible Consumption and Production, and SDG 13: Climate Action.

Since 2019, the Mexican Government has implemented the Sembrando Vida Program (SVP) to address environmental degradation and social marginalization in rural areas. This research analyzed the SVP’s targeting mechanisms to assess the fulfillment of its objectives in the central region of the state of Guerrero, Mexico. The methodology included an analysis of the Operating Rules; a review of the beneficiary registry; the construction of environmental, social, and economic indicators; proportional stratified sampling with a 90% confidence level; and an SVP evaluation using indicators and confidence intervals. Targeting mechanisms identified agrarian subjects aged ≥ 18 years with 2.5 hectares living below the poverty line. Changes in the indicator matrix highlight a focus on food self-sufficiency and poverty levels, among others. Key results from the indicators include 100% social recovery, 62.3% of beneficiaries increased their agricultural income, and 100% application of learned environmental techniques. The intervals showed that between 26.49–42.19% of individuals joined the agricultural sector; 55.68–71.58% diversified their crops; and 86.15–95.65% made improvements in health, housing, or education. The evaluation of the SVP demonstrated its impact on environmental preservation and the improvement of the socioeconomic well-being of the rural population in the study area.

Inadequate Municipal Solid Waste (MSW) management constitutes a critical environmental challenge, as approximately 40% of waste reaches uncontrolled disposal sites where open-air incineration generates significant air, soil, and water pollution. The objective of this study was to evaluate the MSW Environmental Management System (EMS) in Chilpancingo de los Bravo, Guerrero, Mexico, through sustainability indicators and applicable Mexican environmental regulations to identify operational and structural deficiencies that guide a comprehensive improvement in its management. The methodology comprised an analysis of the EMS via the Municipal Development Plan, the identification of environmental indicators and applicable Mexican standards, and an evaluation of the EMS through waste characterization and sustainability metrics. A sample of 208 kg was defined in accordance with standards NMX-AA-015-1985 and NMX-AA-022-1985. The results indicate a generation rate of approximately 350 tons per day (1.2 kg/capita/day), with municipal collection coverage of 70% of the territory across 24 daily routes operated by 30 vehicles. Indicators revealed a recycling rate of 4.86%, collection coverage of 79.66%, a 0% treatment rate due to the absence of composting or material recovery facilities, and 95% of waste directed to the Final Disposal Site (FDS). These findings demonstrate substantial deficiencies in the current EMS, highlighting that the systematic application of indicators is an effective diagnostic tool for identifying gaps and guiding evidence-based improvements in MSW governance.