Environmental Indicators Research Articles

AI-Based Environmental Monitoring System with Farm Automation

This project presents an innovative approach to environmental monitoring in agricultural settings using AI-based solutions integrated with IoT technology. The system leverages data collected from various sensors such as soil moisture, gas sensors, and other environmental indicators to monitor the conditions in real-time. By utilizing an Arduino microcontroller, ESP module, and ThingSpeak server, the project is able to capture, store, and transmit sensor data effectively. The highlight of the project is the AI-based weather prediction module implemented using a Convolutional Neural Network (CNN) in Python, providing critical insights into weather conditions and allowing for data-driven decision-making. The system also includes a user-friendly web interface to display real-time monitoring and predictions, enabling farmers to better manage their resources and plan ahead for changes in environmental conditions

Carbon Footprint of Electric Vehicles—Review of Methodologies and Determinants

The carbon footprint of a product and organization is one of the most important environmental indicators in many sectors, including transport. Consequently, electric vehicles (EV) are being introduced as an alternative to achieve decarbonization targets. This article presents an overview of methodologies for assessing the carbon footprint of electric vehicles, including a review of concepts, methods, standards, and calculation models based on the life cycle of the carbon footprint. The article also includes a systematic review of the results of EV carbon footprint analyses. The analysis of current knowledge on the carbon footprint focuses on road transport vehicles: Battery Electric Vehicles (BEV), Fuel Cell Electric Vehicles (FCEV), Hybrid Electric Vehicles (HEV), and Plug-in Hybrid Electric Vehicles (PHEV). Additionally, a review of factors determining the carbon footprint assessment of electric vehicles, considering their entire life cycle, has been conducted.

Evaluation of solar distiller performance using steel wool pads, internal reflectors, and woven wire mesh: economic, environmental, and sustainability analysis

Abstract Different researches were performed by using thermal storage materials, solar concentrators, reflectors, and other technologies to increase the productivity of solar stills. The main objective of the present study is to enhance the basin solar still productivity by using ecomaterials from the wastes of workshops and factories. So, this study presents an experimental investigation of basin solar still performance using steel wool pads, internal reflectors, and woven wire mesh. The experimental setup was performed at Suez Canal University, Ismailia City, Egypt. Two solar stills were included: one represents the conventional design (CSS), and the second one is the modified with steel wool pads, internal reflectors, and woven wire mesh (MSS). Both solar stills were evaluated under identical climate conditions, considering water depths of 1, 2, 3, and 4 cm, and the required parameters were measured and tabulated during the working days. The results indicated that incorporating these modifications led to a notable increase in accumulative productivity and daily thermal efficiency with minimum cost per liter. The MSS enhanced the daily yield and thermal efficiency by 22.1% and 28.0%, respectively, versus CSS at a water depth of 1 cm. In addition, the energy production factor (EPF), energy payback time (EPT), and the average exergy waste were 1.2 years, 0.85, and 400.2 J, respectively, for MSS. The annual exergy output, environmental effect factor (EEF), improvement potential (IP), and sustainability index were 462.7 kWh, 10.4, 290.6 W, and 1.09, respectively, for MSS. Finally, the cost per liter and the annual CO2 reduction over a lifetime for CSS and MSS were 0.023 $/l, 17.4 tons, and 0.11 $/l, 20.4 tons, respectively. These results align with and reinforce previous publications on desalination, highlighting the potential of these modifications for addressing the pressing challenges of affordable and efficient water using solar energy.

Correlations between environmental justice indicators and community-level COVID-19 vaccination rates among older adults

Correlations between environmental justice indicators and community-level COVID-19 vaccination rates among older adults

Research on the Index Calculation Method for the Impact of Drought on Water Quality in the Nakdong River, Korea

The impact of drought is intensifying due to climate change, leading to significant environmental consequences, particularly concerning river water quality. While drought is typically classified as meteorological or hydrological, studies assessing its environmental impacts remain limited. Drought-induced hydrological alterations in rivers often degrade water quality, necessitating the development of an environmental drought index. This study introduces a novel methodology for calculating an index to evaluate the effects of drought on river water quality, specifically applied to tributaries of the Nakdong River in South Korea. The index was constructed by reviewing existing water quality and drought indices, selecting relevant parameters, and weighting each factor following the National Sanitation Foundation Water Quality Index (NSFWQI) methodology. Factors integrated into the index encompass both meteorological and hydrological indicators, with priority given to variables measurable in real time. Real-time parameters—such as flow rate, cumulative precipitation, days without rainfall, and sensor-based metrics (pH, electrical conductivity [EC], dissolved oxygen [DO], and total organic carbon [TOC])—were incorporated. Additionally, for rivers with upstream dams, dam discharge data were included to reflect its influence on flow conditions. The applicability of the calculated index was assessed by comparing index values to observed water quality data. A class interval structure was implemented to enhance the index’s usability across diverse riverine conditions. Furthermore, the utility of the index was validated by comparing it to the basin’s target water quality, thereby assessing its sensitivity to drought-induced water quality deterioration. The environmental drought index proposed in this study enables the proactive and real-time monitoring of water quality under drought conditions. When applied to 10 tributaries of the Nakdong River, the index demonstrated a clear correlation between drought conditions and water quality deterioration. This index provides a practical tool for river management, facilitating early response strategies to mitigate water quality impacts associated with environmental drought.

Forecasting Electric Vehicle Adoption in the USA Using Machine Learning Models

Electric vehicles (Electric Vehicles) are at the vanguard of the global dispensation to sustainable transportation, depicting a pivotal step toward diminishing greenhouse gas emissions and reliance on fossil fuels. Notwithstanding, the adoption of Electric Vehicles has been growing in the USA, but their future remains at a crossroads. The objective of the research is to design and execute machine learning models capable of providing accurate predictions of future trends in electric vehicle adoption in the USA. The dataset gathered for analyzing EV adoption in the USA comprises data across three primary categories: environmental data, economic indicators, and policy-related data. The economic indicators include household income, fuel prices, electricity rates, and lithium battery costs that affect EV purchasing power obtained from the U.S. Census Bureau and the U.S. Energy Information Administration (EIA). Environmental data include greenhouse gas emissions and air quality indices from the EPA, providing information on regional environmental conditions that might affect EV attractiveness. Other policy data included federal and state incentives such as tax credits, rebates, and EV infrastructure data, collected from the U.S. Led by the U.S. Department of Energy's Alternative Fuels Data Center and the Energy Laboratory, additional EV sales trends were pulled from databases of the automotive industry. In this research project, credible and proven machine learning models were employed, most notably, Linear Regression, Random Forest, and XG-Boost. The performance of the models was tested for EV adoption prediction by considering a few important metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). From the model performance metrics presented, the Gradient Boosting Regressor and Random Forest models performed far better by a big margin than Linear Regression. The prediction models, particularly, the Random Forest regressors and Gradient Boosting regressors demonstrated incredible forecasting of electric vehicle adoption. The model works excellently on the premise that historical data with relevant features can be utilized to gain some valuable insight into future trends. Policy-makers interested in stimulating the wider use of electric vehicles can ensure that targeted policies address both current barriers and future demands. Results of this analysis suggest incentives, such as tax credits, rebates, and subsidies, are some of the most common actions to reduce the upfront cost of an EV, a key circumventing factor in the choice that many consumers face.

Evaluating Environmental Risks and Waste Management in Chemistry Laboratories: Integrating FMEA and Cleaner Production

The issue of environmental waste production has increased substantially, and educational institutions have become initiating agents because of their research and practical activities. This research work has the following objectives: To assess and analyze the environmental aspects and environmental impacts created by the chemistry laboratories at the Institute of Health and Biotechnology (IHB) in Coari, Amazonas, using management methodologies. The FMEA and Cleaner Production (CP) approaches were applied for risk assessment and decision-making to identify risks and propose preventive measures. It becomes clear that the Environmental Risk Index (ERI) guides activities toward managing or reducing risk. In the 67 cases of the process, 32 of them were assessed as priorities, two of which are high risk and require action. The results include the following objectives for a Waste Management System (WMS) suited to the environment in the institution: compliance with current environmental laws or regulations and the creation of a safer environment for students to work and learn.

Decision support tools for water reuse: a systematic review

ABSTRACT This article provides a comprehensive review of decision support tools for water reuse (DST4WR), focusing on microbiological risk assessment (MRA), life cycle analysis (LCA), life cycle cost (LCC), and multi-criteria decision analysis (MCDA). A systematic review of 35 articles published between 2020 and 2024, plus one from 2019, was conducted. The studies were categorised based on the DST4WR applied, with each tool discussed individually. MRA tools assess public health risks in different case studies. LCA identifies key environmental indicators, and its integration with LCC facilitates comprehensive cost analysis. MCDA, applied in various case studies, uses criteria like environmental, social, economic, technical, public health, and functional aspects. Integrating DST4WR tools identifies synergies and trade-offs between criteria, aiding informed decision-making. Combining MRA, LCA/LCC, and MCDA is especially beneficial, as each tool provides a distinct perspective. Using these tools together offers a holistic view of water reuse management, ensuring that all relevant factors are balanced. This approach enhances decision-making and builds stakeholder confidence and acceptance by transparently addressing public health, environmental, economic, and social concerns.

Financial management of an environmental enterprise

The article examines financial management of environmental enterprises from the standpoint of evaluating the results of financial analysis and specifics of its implementation. Increasing the competitiveness of environmental organisations in the Russian Federation and ensuring their prospects in the face of sanctions and restrictions at this stage of development is becoming increasingly relevant. The authors propose a methodology for financial analysis of industrial and environmental activities of an enterprise based on the calculation of key financial and private environmental indicators of environmental enterprises. The novelty of this study lies in an integrated approach to financial management based on the results of the analysis of financial and private environmental indicators of environmental organisations, covering the financial and environmental aspects of their activities. The integrated approach to financial management is innovative because it is founded on the close relationship between financial and environmental activities of enterprises. The presented article develops analytical support and methodological tools of the analysis for use by environmental enterprises, with consideration to their specifics, in order to improve the efficiency of financial management. The practical significance of this article lies in the analytical provision of effective financial management for the sustainable development of environmental enterprises.

Apple Pomace Integrated Biorefinery for Biofuels Production: A Techno-Economic and Environmental Sustainability Analysis

The combination of techno-economic process modelling and life cycle assessment is an integrated methodology that addresses quantitative operational data, and evaluates the emissions associated with any process under development. In particular, the valorisation of waste streams within the context of the circular economy could be considered a valid and promising approach, especially regarding techno-economic and environmental indicators. This manuscript aims to evaluate the integral valorisation of apple pomace from the processing industry into bioethanol, and vinasses (a byproduct of the distillation process) into biogas and digestate as biofertiliser. In addition to biogas production, lagooning and composting were considered as strategies for vinasse management. After the conceptual design of the process options was completed, the environmental profile of bioethanol production was estimated across different scenarios. When biogas production was integrated to reduce the biorefinery’s energy demand, the carbon footprint was 1.13 kg CO2eq∙kg−1. This footprint increased to values around four when lagooning and composting were used as vinasse management strategies. Although the economic dimension posed a significant limitation due to high investment costs, the eco-efficiency analysis showed that the scenario of the co-production of bioethanol and biogas is the best alternative. Despite the promising results, further research is needed to explore the recovery of additional co-products to develop a high-potential strategy for apple pomace.

Life cycle assessment and synergistic effects of sustainable strategies to improve environmental sustainability in arid climates

Adapting sowing date (SD) based on environmental sustainability and increasing productivity is crucial for creating flexibility in agriculture and maintaining food security. This study aimed to optimize SD and nitrogen (N) management using environmental indices in an arid climate. Yield, N use efficiency (NUE), water productivity (WP), and total environmental impact (EI) were assessed for five SDs for barley and triticale crops, along with three levels of N fertilizer application. The AquaCrop model was used for crop yield prediction, and EI was evaluated using Life Cycle Assessment (LCA). Delayed SD led to a shorter growing period (10.6–12.8 %), decreased WP (76.4–87.5 %), and decreased yield (58.3–87.6 %) for both crops compared to conventional SD. In contrast, earlier SD resulted in an extended growing period (1.7–3.6 %), increased WP (14.8–20.4 %), and increased yield (8.3–10.2 %). NUE showed an inverse relationship with increasing fertilizer levels. The most significant EIs were due to N consumption (33.4 %), followed by diesel (28.5 %) and potassium fertilizer (13.7 %) for both crops. Early SD could reduce EI by an average of 12.5 %. Combining early SD with moderate N levels was the most sustainable approach to maximize yield, minimize water use, and reduce EI, thereby informing sustainable practices for cereal cultivation in similar climates.

Geoenvironmental and Health Indices to Assess the Hazardousness of Heavy Metals in Urban Dust in Schoolyards in Murcia, Spain

The aim of this study was to evaluate the possible contamination of urban dust in the schoolyards of 27 schools in an urban area of the city of Murcia (SE Spain). The color and degree of magnetism, as well as the heavy metal content (Cd, Cu, Cr, Ni, Pb, and Zn), were determined to establish the absence or the degree of contamination, if present, using environmental and health indices. It was established that the concentrations of heavy metals in the dust samples followed the order Zn (454 mg kg−1) > Cu (77 mg kg−1) > Cr (68 mg kg−1) > Pb (56 mg kg−1) > Ni (19 mg kg−1) > Cd (0.4 mg kg−1). Dark-colored dust showed the highest concentrations of contaminants associated with medium or high magnetism. An analysis of the magnetic and non-magnetic fractions indicated the highest concentrations of all heavy metals in the magnetic fraction. According to the geoenvironmental indices used, the ecological risk in these schoolyards is moderate overall. Based on the health indices, ingestion is the main route of entry of dust particles into the body, which poses the main health risk for adults and children for all heavy metals. Regarding the hazard index (HI) for all elements and the cancer risk (CR) for children and adults, the results indicate that there is no health risk.

Improving the potential of fifth-generation district heating and cooling networks through robust design and operational optimization under future energy market and demand uncertainties

Network investments and projected energy prices greatly impact the financial viability and environmental impact of fifth-generation district heating and cooling (5GDHC) networks. Compared to the previous generation, the upfront investment costs in 5GDHC networks are relatively high due to the decentralized energy demand and supply of the participating prosumers. The transition to 5GDHC is also hindered by uncertain energy markets and the fluctuating energy demands of the prosumers. It makes investors hesitant to commit, even for promising business cases. In this work, a framework is presented to assess the economic and environmental performance of a 5GDHC business case, based on a multi-objective robust optimization algorithm (MO-RDO), to identify the optimal trade-offs between environmental and economic designs. The proposed methodology consists of four steps. In the first two steps, a specific business case is modeled, and techno-economic and environmental performances are analyzed against uncertain energy markets. In the third step, the techno-economic and environmental indicator responses are used to develop a data-driven machine-learning model. This model offers better computational efficiency than a high-fidelity nonlinear model and assesses the most influential parameters driving economic and environmental performance via a global sensitivity analysis. This aids in refining the multi-objective robust design optimization (MO-RDO) framework by pinpointing key design variables and objectives amid data uncertainties. Additionally, the formulated MO-RDO provides a mix of environmentally and economically optimal network designs and operational parameters and highlights the trade-offs between them. The designs and trade-offs are evaluated using financial metrics like net present value (NPV), return on investment, and discounted payback period. These metrics are calculated over the project life, considering the initial investment and net savings. For the considered case, the environmental design has a 10% higher investment cost compared to the economically optimal solution but reduces total emissions (TE) by 13% and improves operational cost savings, which is crucial for better financial indicators. The proposed framework aligns with the EU transition plan to incorporate waste energy sources and decarbonization paths, estimating only a 3% increase in NPV as a risk for the environmentally optimal solution on a financial scale.

Assessing the impact of socio-economic and environmental factors on COVID-19 spread in urban neighborhoods: evidence from Urmia, Iran

ABSTRACT The majority of the world’s population has been living in cities for the past two decades, making these areas particularly vulnerable to various stressors, including natural and man-made disasters. A growing literature suggests that socio-economic and environmental indicators significantly affect the epidemic. As a result, this research aims to investigate the effect of these indicators on the rate of infection with COVID-19 in the neighborhoods of Urmia City in Iran. This study uses Moran’s analysis, cluster and outlier analysis to identify high-risk and low-risk areas, as well as weighted spatial regression analysis. The results indicate that the number of employees, the number of elderly people, construction density, and road density have a significant relationship in predicting the epidemic. Based on the study of the effect of variables on the number of COVID-19 patients in Urmia City in Iran, the predicted values for the number of coronavirus patients indicate that the risk of contracting COVID-19 in the southern and part of the western areas is higher than in the northern and northeastern areas.

Pre-emergence herbicides used in urban and agricultural settings: dissipation and ecological implications

Herbicides are widely recognized as the most cost-effective solution for weed control, but their extensive use in both urban and agricultural settings raise serious concerns about nontarget effects. We assessed the possible hazards associated with pre-emergence herbicides such as dimethenamid–P, metazachlor, and pyroxasulfone, which are frequently applied in both urban and agricultural soils. The dissipation rate constant values (k day−1: 0.010–0.024) were positively linked to total organic carbon (TOC), silt, clay, soil pH, and Al and Fe oxides, but negatively correlated with sand content. In contrast, half-life values (DT50: 29–69 days) of the herbicides showed negative correlations with TOC, clay, silt, soil pH, and Fe and Al oxides, while sand content showed a positive impact. The selected herbicides showed minimal impact on soil dehydrogenase activity (DHA). Mostly, soils with higher organic matter (OM) content exhibited increased DHA levels, highlighting the role of OM in influencing this soil enzyme across different soils. Assessment of environmental indicators like groundwater ubiquity score (GUS:1.69–6.30) and leachability index (LIX: 0.23–0.97) suggested that the herbicides might reach groundwater, posing potential risks to nontarget biota and food safety. Human non-cancer risk evaluation, in terms of hazard quotient (HQ < 1) and hazard index (HI < 1), suggests minimal or no risks from exposure to soil containing herbicide residues at 50% of the initial concentrations. Our data thus help the stakeholders and regulatory agencies while applying these pre-emergence herbicides in soils and safeguarding human and environmental health.

Enhancing environmental sustainability through lean construction principles: an industry-based SEM analysis

PurposeIn the modern era, the concept of sustainability has become pervasive, permeating various sectors, including the construction industry. Driven by growing environmental concerns, construction organizations are increasingly compelled to seek operational excellence while realigning their practices towards environmental sustainability goals. Hence, this paper investigates the capability of lean construction (LC) principles in enhancing environmental performance (EP) within the construction industry using a structural equation modeling (SEM) approach.Design/methodology/approachThe study employs SEM to explore the relationship between LC components and environmental performance factors in the construction context. This methodology integrates both empirical and qualitative analyses within a structured framework to explore the relationship between LC components and EP factors in the construction context. By collecting and statistically testing data from 307 Moroccan construction companies, the study evaluates the impact of five LC principles – process transparency and standardization, continuous improvement, waste elimination, planning and scheduling and material flow and pull – on four environmental performance indicators: material use, energy consumption, non-product output and pollutant releases.FindingsThe analysis reveals that, among the five LC principles – process transparency and standardization, continuous improvement, waste elimination, planning and scheduling and material flow and pull – the principles of waste elimination and planning and scheduling significantly contribute to environmental improvements. These practices were shown to impact positively four environmental performance indicators: material use, energy consumption, non-product output and pollutant releases.Originality/valueThis research fills a critical gap in empirical studies on the beneficial effects of LC practices on environmental sustainability in the construction industry. It provides theoretical insights and practical recommendations for managers and practitioners to effectively implement LC principles, thereby promoting more efficient and sustainable practices, particularly in developing countries.

Multiple surface lattice resonances in symmetric nanocuboid dimer arrays

Abstract Surface lattice resonances based on nanoparticle arrays have significant characteristics such as localized field enhancement and high quality factor, and can be applied in fields such as optical sensors and lasers. In this work, we propose a symmetric Si/SiO2 nanocuboid dimer array that can generate and regulate two surface lattice resonances. One of the surface lattice resonances (named SLR1) is mainly due to the coupling between the electric dipole resonance of single Si/SiO2 nanocuboid dimers and the diffraction waves perpendicular to the applied electric field. Another surface lattice resonance (named SLR2) mainly originates from the coupling between the magnetic dipole resonance of single Si/SiO2 nanocuboid dimers and the diffraction waves parallel to the direction of the applied electric field. The research results indicate that the polarization direction of the incident field, the period of the array, the gap between the nanocuboids in the dimer, particle size, and the medium environment are all important for regulating the two surface lattice resonances. The sensing application of multiple surface lattice resonances is also investigated. The results show that under appropriate structural parameters, SLR1 can provide good stability for sensing applications, its sensitivity and figures of merit are 472 nm/RIU and 104, respectively. However, SLR2 is very weak or suppressed when the refractive index of the medium environment is greater than or equal to 1.2. This characteristic limits the application range of SLR2 in sensing. This work is of great significance for the design of micro-nano photonic devices based on multiple surface lattice resonances.

Scrutinizing the Statistical Distribution of a Composite Index of Soil Degradation as a Measure of Early Desertification Risk in Advanced Economies

Using descriptive and inferential techniques together with simplified metrics derived from the ecological discipline, we offer a long-term investigation of the Environmental Sensitive Area Index (ESAI) as a proxy of land degradation vulnerability in Italy. This assessment was specifically carried out on a decadal scale from 1960 to 2020 at the province (NUTS-3 sensu Eurostat) level and benefited from a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic (‘what … if’) approach. The spatial distribution of the ESAI was investigated at each observation year (1960, 1970, 1980, 1990, 2000, 2010, 2020, 2030) calculating descriptive statistics (central tendency, variability, and distribution shape), deviation from normality, and the increase (or decrease) in diversification in the index scores. Based on nearly 300 thousand observations all over Italy, provinces were considered representative spatial units because they include a relatively broad number of ESAI measures. Assuming a large sample size as a pre-requisite for the stable distribution of the most relevant moments of any statistical distribution—because of the convergence law underlying the central limit theorem—we found that the ESAI scores have increased significantly over time in both central values (i.e., means or medians) and variability across the central tendency (i.e., coefficient of variation). Additionally, ecological metrics reflecting diversification trends in the vulnerability scores delineated a latent shift toward a less diversified (statistical) distribution with a concentration of the observed values toward the highest ESAI scores—possibly reflecting a net increase in the level of soil degradation, at least in some areas. Multiple exploratory techniques (namely, a Principal Component Analysis and a two-way hierarchical clustering) were run on the two-way (data) matrix including distributional metrics (by columns) and temporal observations (by rows). The empirical findings of these techniques delineate the consolidation of worse predisposing conditions to soil degradation in recent times, as reflected in a sudden increase in the ESAI scores—both average and maximum values. These trends underline latent environmental dynamics leading to an early desertification risk, thus representing a valid predictive tool both in the present conditions and in future scenarios. A comprehensive scrutiny of past, present, and future trends in the ESAI scores using mixed (parametric and non-parametric) statistical tools proved to be an original contribution to the study of soil degradation in advanced economies.

IMPACTO AMBIENTAL POR USO DE PLAGUICIDAS Y SUSTENTABILIDAD DEL SISTEMA PRODUCTIVO DEL MAÍZ AMARILLO DURO EN HUAURA, PERÚ

&lt;p&gt;&lt;strong&gt;Background:&lt;/strong&gt; Hard yellow maize (MAD) is one of the main crops in economic, social and environmental terms worldwide, however, it is managed through conventional agriculture which entails a high dependence on agrochemical inputs, which cause a negative impact on the agroecosystem and through this study it will be possible to identify the critical points and establish solutions for them. &lt;strong&gt;Objective: &lt;/strong&gt;To determine the coefficient of environmental impact of pesticides in the field and find the general sustainability index of the MAD production units. &lt;strong&gt;Objective:&lt;/strong&gt; Determine the coefficient of environmental impact of pesticides in the field and find the general sustainability index of the MAD production units. &lt;strong&gt;Methodology:&lt;/strong&gt; The environmental impact (EI ha&lt;sup&gt;-1&lt;/sup&gt;) of pesticides in the field was evaluated by calculating the environmental impact coefficient (EIQ), concentration of the active ingredient, dose and number of applications. In addition, sustainability was determined through multicriteria analysis by applying 85 questionnaires with structured questions to farmers whose main activity is MAD, with economic, social and environmental indicators standardized on a scale of 1 to 5. &lt;strong&gt;Results:&lt;/strong&gt; The study showed that the EI ha&lt;sup&gt;-1&lt;/sup&gt; of MAD was 105.11 rated as high, which generates a negative impact on the environment and a high risk for farmers who apply these pesticides. It was also found that the sustainability index of the social and economic dimensions was 3.56 and 3.18 respectively, while the environmental index (2.64) obtained a value below the minimum sustainable threshold, therefore the MAD production system in Huaura is not sustainable. The analysis found the critical points: levels and types of fertilization (2.74), incorporation of organic matter (2.33), crop residue management (1.51), living barriers (1.36), pest control method (2.49), frequency of pesticide applications (2.53), irrigation system (2.54), level of education (2.96), quality of technical assistance and training (2.8), income from other activities (2.9) and marketing channel (1.82). &lt;strong&gt;Implications:&lt;/strong&gt; Timely intervention on critical points allows establishing solution options and improving pesticide management.&lt;strong&gt; Conclusion: &lt;/strong&gt;The study showed that the IE ha&lt;sup&gt;-1&lt;/sup&gt; of MAD was high, indicating that the pesticides used for chemical pest and disease control generate a negative impact on the environment and a high risk for farmers who apply these pesticides. Likewise, it was found that the MAD production system is not sustainable. Therefore, the identification of the critical points allows the establishment of strategies to strengthen the MAD agroecosystem in Huaura, Peru.&lt;/p&gt;

ISO 14000 as a driver of sustainability: Exploring its alternative drivers beyond ISO 9000 standards

This study replicates and extends Corbett and Kirsch (2001) and Vastag (2004) using a new data set to investigate the drivers of ISO 14000 certification diffusions using decision tree analysis. The findings indicate that at the national level, ISO 14000 certification diffusions are influenced by factors other than ISO 9000 certification diffusions, such as the number of environmental treaties signed and ratified, industrial activities as a percentage of GDP, and GDP per capita, thus provides a range of managerial insights and enhances scholarly understanding of sustainability beyond the influence of ISO 9000. Future studies might extend the countries included in this study to see if the results are the same. Future research may include other factors like a country’s Environmental, Social, and Governance (ESG) indicators to better understand its commitment to sustainability, including environmental sustainability. The country’s culture may influence customers, investors, and other stakeholders’ knowledge and desire for sustainable practices and inspire firms to obtain ISO 14000 certifications. Since larger firms may seek ISO 14000 certification, future studies may evaluate the influence of the number of large firms in various countries as drivers of ISO certification diffusions.