Intelligent Environmental Monitoring: Business Intelligence and AI Framework for Ecological Decision-Making Using Public Sustainability Data

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This research proposes a hybrid framework that merges Business Intelligence (BI) and Artificial Intelligence (AI), allows the processing of ecological footprint and capacity utilization data, when powered by open access information from Ecuador. Three experiments illustrate the synergistic power of descriptive analytics, trend detection, and unsupervised machine learning (e.g., Principal Component Analysis (PCA) and KMeans clustering) in generating insights on environmental indicators. Results highlight an absolute dominance of ecological pressure from fishing zones, croplands and pasturelands, while pasturelands and urban are the fastest-growing environmental footprints. Through observing patterns of land use changes and distributions, the AI-based clustering revealed hidden ecological profiles across different years and lands which could be used as evolving classification models for ecological risk assessment. This study offers a replicable and scalable model, using real environmental data, unlike prior literature which has focused heavily on either remote sensing or systems at the enterprise level. It addresses important gaps by making predictive sustainability analysis available to governments and institutions lacking advanced infrastructure. The paper ends with a series of strategic advice and future trends, including moving away from working with IoT data, creating scenario models and geospatial BI dashboards. Our findings contribute to the field of environmental data science by providing an actionable, interpretable, and transparent decision-support tool that aligns with both national and global sustainability goals. Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Business Intelligence Artificial Intelligence Ecological Footprint Environmental Monitoring PCA KMeans Sustainability Analytics Public Data Predictive Modeling Capacity Utilization Ecuador Figures Figure 1 Figure 2 Figure 3 1 Introduction As the threats posed by environmental degradation, overconsumption of resources and critical climate action escalate, a growing number propose data-driven approaches to inform policies of sustainability. Still, existing frameworks of environmental decision-making are mostly fragmented, overscriptive, or technologically disconnected from real-world governance contexts. The world of today demands more integrated, predictive and accessible analytical systems than ever before. This study addresses this gap and proposes a hybrid methodological model that integrates Business Intelligence (BI) tools with artificial intelligence (AI) techniques to improve environmental monitoring, forecasting, and strategy formulation. Previous papers have touched on BI's role in organizational performance (Awamleh et al., 2024) and considered the potential for AI technologies to augment the fields of remote sensing (Arowolo et al., 2024) or quantum computing (Vudugula & Chebrolu, 2025), but little to none have applied these technologies directly to ecological footprint datasets, capacity utilization indicators and public environmental records, especially in the case of a nation such as Ecuador. The matter being addressed here stems from the lack of integrated systems that connect structured, ecologically oriented datasets to interpretable, scalable, and policy-relevant analytical tools. Most existing studies focus on either the technical dimension of AI or the operational strength of the BI platforms, however few demonstrate how these may be combined into an effective tool for environmental governance. Experiment 1, classify and visualize ecological pressure on land types, Identify high-risk temporal trajectories of environmental intensity (Experiment 2). Identify hidden clusters of ecological behavior using machine learning (Experiment 3). Each experiment is directly connected to a core methodological phase; that of data integration, preprocessing, analytical model and interpretive visualization to constitute a cohesive pipeline, replicable and extensible to public environmental agencies. The methodology was an amalgamation of theoretical and applied approaches. Options like Principal Component Analysis (PCA) and KMeans were chosen to describe complexity reduction and hidden patterns; Growth modeling and trend detection were used to quantify aspects of ecological acceleration that are crucial in early warning systems and strategic planning. An alternative, for example, was diachronic research in which so-called bi-layer studies were notable by the absence of temporal sensitivity and many others lived and died in BI crystal castles without experiencing the full glory of real-world datasets. Main Findings The study uncovered a number of important and novel findings: Groundwater leaching versus fish captures known as the Intergovernmental Panel on Climate Change indicator suggest fishery zones, cropland and pasturelands have the highest absolute ecological footprints (Experiment 1), in-line with production pressures but now visually mappable through BI dashboards. However, pastures and urban zones were hotspots of fastest annual growth in ecological footprint (Experiment 2), reverberating the conventionally held view that these are the few sustainability hotspots and not forests or industry zones. Unsupervised learning (Experiment 3) uncovered divergent clusters of ecological behavior, highlighting latent sustainability risk areas and “bridge” categories not previously captured by prevailing reporting frameworks. These findings are transformative and not descriptive – they enable governments to move from reactive governance to predictive governance, using BI not just to report, but to anticipate, simulate, and prioritize interventions. Provides a BI-AI pipeline that can be replicated to convert defunct sustainability metrics into viable, actionable intelligence. Validation: Establishing that ecological footprint and CUR datasets may be modeled behaviorally over time to enable AI-driven scenario analysis. Policy Relevance: It makes decision-makers go-to tools low-cost, scalable and interpretable—helping and supporting strategic sustainability planning in data-limited settings where speed is often of essence. Comparing the outputs with Awamleh et al.’s results (2024), which addresses strategic alignment, that adds prescriptive ability to predictive modeling. In contrast to Arowolo et al. 2024), who have developed an interest in AI for sensing ecosystems, practitioners need to cross between sensing and interpretation to layer context to the data. In contrast, work on QAI by Vudugula & Chebrolu (2025) is more indicative of a future trajectory than a grounded consideration of public environmental records, and this study provides such a record with actionable, near-term solutions for public institutions. Conclusions and Future Work Overall, this study demonstrates that adopting a hybrid Business Intelligence architecture with the incorporation of AI algorithms can convert raw material related to ecology into impactful intelligence. Besides verifying clustering behavior and footprint acceleration, it also generates actionable dashboards and policy tools for environmental planning. Add geospatial elements to your design using satellite or IoT data for in-time tracking Combine socioeconomic and climate vulnerability indices to evaluate compound choices. Simulate long-run policy scenarios with reinforcement learning or deep neural forecasting, Use standardized BI platforms across open data to compare national performance. All in all, this manuscript offers a formula for action that can be widely applied across governments, institutions, and research communities interested in the pragmatics of putting sustainable systems into practice with the help of science and smart systems. 2 Art State In recent years, the integration of Business Intelligence (BI), Artificial Intelligence (AI) and sustainability analytics has changed significantly due to an increasing demand for data-driven decision-making in environmental governance and sustainable development. Multiple core research researches formed a solid foundation for how BI technologies support ecological monitoring and sustainable performance. Awamleh et al. (2024) an analysis of the role of BI in achieving sustainable development through the alignment of organizational agility and international performance. The novelty of our study was the integration of predictive analytics and ecological datasets, highlighting the importance of sophisticated tools that can facilitate real-time monitoring​[18]​.. Likewise, Goralski and Tan (2020) claim that the AI power can not only be seen as acting on data but on modeling some of the social paradigm shifts needed to realize sustainable innovation implying the need for flexible infrastructures for BI​. Vudugula and Chebrolu (2025) forcefully make a case for an interesting juxtaposition of Quantum Artificial Intelligence (QAI) in Business Intelligence platforms to facilitate carbon-neutral supply chains. Their review of more than 90 publications highlights that real-time emissions monitoring and autonomous decision-making systems can significantly improve sustainability​. But, these proposals are not directly applicable to ecological footprint metrics or capacity utilization scenarios, which this study claims a 1st time for consideration. Arowolo et al. (2024), present a hybrid model integrating AI for enhanced remote sensing and IoT in precision ecosystem monitoring. While their framework employs satellite data and sensor networks, it does not incorporate structured statistical indicators such as those obtained from ecological footprint and CUR metrics​. Our manuscript fills this gap via the integration of these metrics into a smart, BI-enabled pipeline for environmental decision support. Moreover, Ahmad and Mustafa (2022) highlight the role of AI and big data analytics in shaping organizational digital capabilities, further guiding our methodological focus on the interpretability and adaptability of BI tools​. Finergy factor (Awamleh and Bustami, 2022a, 2022b), they similarly illustrate BI as an intermediate link between technology integration and strategic performance; corroborating our study's discovery that BI is understood as both the analytical and the operational backbone in sustainability transformation​. Sanjai and Sanath (2025) examine QAI in logistics optimization towards carbon footprint mitigation. Their findings demonstrate improvements on emissions predictions through quantum-enhanced algorithms but do not emphasize land-based environmental indicators similar to those used in the Ecological Footprint analyses​. Our study helps filling this gap by showing that AI clustering and dimensionality reduction methods can be used as effective tools to identify sustainability hotspots across land categories. Significantly, BI applications on environmental decision-making are still in organ-shots. Although Unilever’s Sustainable Living Plan and Walmart’s Gigaton project create systems that enable real-time access to emissions data and identification of carbon hotspots across supply chains (Zhu & Yu, 2023; Brown & Kroll, 2017), they do not utilize public environmental datasets to consider national or regional sustainability models​. This research extends those approaches through the use of real-world environmental performance datasets (sourced from Ecuador) to derive ecological KPIs and incorporate them into actionable BI dashboards. Gupta & Jiwani (2021) and Bharadiya (2023) feature recent works that provide the structural and computational perspective of BI. Nonetheless, they are not empirically validated in the context of sustainability, which this study addresses via experiments built on MAATE and CUR data sources​. Likewise, AI can be applied in adaptive logistics and smart agriculture systems (Pan et al. (2019) and Zhao et al. (2020) and confirm that BI systems can be {(dynamic) environmental policy}(dyn-env-api){' '}supportive. Here we add to this vista by delivering replicable experiments with ecological indictors. Similarly, not much has been studied on the systematic prediction algorithms used in correlation with ecological footprints. For example, studies from Govindan et al. (2020), Zhang et al. (2022), and Alijoyo et al. (2024) study emissions tracking, so far there are no studies that has shown intelligent policy simulation and clustering analysis using ecological footprint categories – grazing land, cropland or fishing zones​​. Contribution of the Current Research The present study addresses these gaps by developing an innovative methodological pipeline linking ecological footprint and capacity utilization indicators with cutting-edge BI approaches. Leveraging a unique architecture of PCA + KMeans clusterization + semantic dashboarding it can both detect sustainability risks and transform these into implementable environmental governance solutions. Unlike previous studies that use proprietary or “black box” datasets, we apply our method to publicly available environmental datasets from Ecuador, which allows us to maintain transparency, reproducibility, and scalability. Together, these AI-driven segmentation and network-based visualization approaches enable hitherto unrecognized patterns—including eco-overshoot zones or CUR instability clusters—emerging from the data as usable land-use management insights. Future Research Future research may build on this work by allowing temporal dynamics to be integrated into the BI models, enabling seasonal (or possibly policy driven) shifts in environmental performance to be considered. Moreover, quantum computing can be integrated for large-scale optimization to athletic field optimization in ecological zoning, as well as real time applications using IoT data derived from remote sensing platforms for high-resolution monitoring and forecasting scenarios. Global dashboards, informed by SDG indicators, can also be built using standardized ecological and capacity metrics to compare performance across countries. 3 Methodology This study follows a structured and hybrid methodology that integrates elements of Business Intelligence (BI), statistical analysis, and machine learning. The aim is to extract, process, and interpret environmental data using a replicable framework that can support sustainability-driven decision-making. The methodology is divided into four core phases: data collection, preprocessing, analytical modeling, and interpretation of insights. 3.1 Methodological Framework The Fig. 1 presents a four-stage process methodology of the work that the study develops to analyse the Ecuador ecological footprint data. Starting with Data Collection, it specifies the downloading of databases from official websites (like MAATE, CUR and national footprint reports). Phase 2: Data Preprocessing, involves crucial proceedings such as normalization, feature encoding, handling of Missing values to maintain data integrity. During the third stage of model, Analytical Modeling, methods are deployed (e.g. descriptive statistics, time series analysis, PCA, machine learning, etc.) to discover relationships and categorize land use dynamics. Finally, the BI Interpretation phase translates the analytical results into presentations, insights, and recommendations to inform sustainable decisions. 3.2 Phase Descriptions Phase 1 – Data Collection: Raw data was sourced from the Ministry of Environment, Water and Ecological Transition (MAATE), which provided ecological footprint records by bioproductive land type for 2022. Additional data was collected from the Policy-Adjusted Capacity Utilization Ratio (CUR) Analysis and Normalized Ecological Footprint datasets. These inputs represent structured, real-world environmental indicators from national reporting systems. Phase 2 – Data Preprocessing: The datasets were normalized using MinMax scaling to prepare them for modeling. Categorical variables such as land types were encoded numerically. Missing or malformed entries were removed or imputed. This phase ensured comparability and integrity of the input features for subsequent analysis. Phase 3 – Analytical Modeling: Three types of analyses were applied: (a) Descriptive analytics to understand ecological pressure distribution, (b) Temporal trend analysis to assess footprint growth, and (c) Machine learning models including Principal Component Analysis (PCA) for dimensionality reduction and KMeans for unsupervised clustering. These methods were chosen for their interpretability and scalability in BI environments. Phase 4 – Business Intelligence Interpretation: The results from the models were translated into insights via dashboards, cluster visualizations, and growth tables. These outputs were designed to inform policy decisions, regional sustainability plans, and resource prioritization. The interpretive emphasis was on transparency, early warning signals, and ecological efficiency. 3.3 Key Formulas 1. Year-over-Year Growth Rate: Gₜ = ((Vₜ - Vₜ₋₁) / Vₜ₋₁) × 100 ( 1 ) Where Gₜ is the growth rate at year t, and Vₜ is the value of ecological footprint in year t. 2. Normalization: X_norm = (X - min(X)) / (max(X) - min(X)) ( 2 ) This formula is used to scale all features into the [0,1] range. 3. Principal Component Analysis (PCA): Z = XW ( 3 ) Where Z is the projected data, X is the original feature matrix, and W is the matrix of eigenvectors. 4. KMeans Objective Function: J = Σₖ Σ i ||x i - µₖ||² ( 4 ) Where J is the total intra-cluster variance, x i is a point, and µₖ is the centroid of cluster k. Based on the methodologic framework established in the prior section, the following experiments were constructed to operationalize the integration of Business Intelligence and artificial intelligence tools into environmental sustainability assessment. Grouping each experiment corresponds to a phase (or combination of phases) in the pipeline proposed—descriptive and trend analysis, advanced clustering, predictive modeling, etc. The goal was twofold—beyond yielding insights from the ecological datasets, the project was to assess the methodological success in producing actionable, interpretable and scalable outputs. The experiments presented hereafter show how the agreed techniques —as applied full scale— can enhance our ability to monitor the environment in real time, uncover previously unknown ecological functions, and inform regional and national policy. 4 Experiments Below, this section outlines the outcomes of three analytical experiments carried out at the intersection of business intelligence (BI) and artificial intelligence (AI) on environmental sustainability data within the Ecuadorian setting. The experiments were intended to illustrate how contemporary analytical approaches can augment ecological forecasting, monitoring, and decision-making. To conduct this analysis, its main datasets were extracted from official and freely available sources. The ecological footprint data was obtained 1 from the Ministry of Environment, Water and Ecological Transition (MAATE for its initials in Spanish) of Ecuador (ecological footprint records of the year 2022, organized by bioproductive land type). For more normalized footprint data, we sourced the sectoral footprint indexes, available at: Normalized Ecological Footprint Analysis; while productive capacity and sustainability-adjusted usage were derived from: Policy-Adjusted Capacity Utilization Ratio (CUR) Analysis. These datasets were chosen for their reliability, granularity, and relevance to national sustainability metrics and international performance standards on ecological outcomes. All data were preprocessed (normalization, encoding, column rearrangement) to allow comparability across experimental sets prior to statistical analysis. The presented experiments consist of three different analytical dimensions of the data: ( 1 ) a distributional analysis of ecological pressure by land use, ( 2 ) a temporal trend analysis of normalized footprint intensity, and ( 3 ) an unsupervised AI clustering to highlight latent ecological patterns. These experiments have been layered to transition from descriptive insights to predictive ones, and together become the building blocks of a comprehensive BI model for environmental strategy. 4.1 Experiment 1: Assessment of Ecological Footprint by Type of Bioproductive Land The experiment sought to study the distribution of ecological pressure across bioproductive land types in Ecuador based on data from the Ministry of Environment (MAATE). It comprises field observations of the Policy-Adjusted Ecological Footprint (HEN_HAG) from 2022, across classes (croplands, pasturelands, zones of fishing, forest area and urban surfaces) First, the data was cleaned and manipulated to fix formatting inconsistencies of numeric values across regions. The metric of interest HEN_HAG (global hectares) was aggregated by landtype to derive an overview of total ecological pressure from each category. The total ecological footprint (in the population average land type) is summarized for each land type (Table 1 ) showing strong differences in ecological demand. Table 1 Summary of the Ecological Footprint by Bioproductive Land Type: Aggregated global hectares (HEN_HAG) reported for each category of bioproductive land in Ecuador. Data sourced from the Ministry of Environment (MAATE, 2022), reflecting ecological demand adjusted by national policy. Land Use Category Amount Forests for carbon absorption 43,445,846,107,850,000 Fishing zones 3,526,276,638,632 Croplands 225,690,126,122 Forest lands 1,304,164,402,281 Grazing lands 76,654,674,418,389 Urbanized and other surfaces 190,979,016,896 The biggest footprint appears in fishing zones (fishing zones), followed closely by croplands (croplands) and pasturelands (grazing lands). These categories make up the backbone of Ecuadorian productive ecosystems and also show the sectors most under environmental stress. Both values, expressed in global hectares, each measure the biologically productive surface area required to sustain present rates of resource consumption and waste absorption for each land type. Figure 2 visually reinforces the patterns observed in Table 1 . The horizontal layout allows for intuitive comparison of ecological impact by land type. The chart reveals that fishing zones and croplands dominate the footprint, indicating high levels of biological resource extraction and pressure (Fig. 2 ). Urban and miscellaneous land types appear on the lower end of the scale, though their localized intensity and environmental disruption may still be relevant for targeted policy. This visual format is particularly useful in Business Intelligence dashboards for sustainability, where data-driven decisions depend on accessible, comparative representations. The results show higher shares of ecological impact associated with some land types (e.g., fishing zones and croplands), which suggests high intensity of use of these ecosystems in the national production system. On the other hand, types as urban surfaces or other land uses do not contribute much overall but may still have a high intensity per hectare. Summarized these data into a single horizontal bar chart, allowing an easy interpretable comparison across land types. Not only did the visualization demonstrate the restriction of the burden of such de-forestation on eco-systems but it also acted as a decision-support tool for environmental policy and land management. As such, this type of insight is relevant for Business Intelligence (BI) applications in sustainability when related to supply chain assessments, regional policy-making, or ecological compensation regimes. This analysis forms the basis for understanding convergence of environmental performance with national and international sustainability targets. 4.2 Experiment 2. Analysis of Normalized Ecological Footprint Temporal Trend by Bioproductive Land Type Objective Normalized ecological footprint over time can be used to highlight long-term trends in ecological pressure, which is the second experiment we aim to highlight through this exploratory application. Focusing on relative rather than absolute change provides a more dynamic view on which ecosystems are growing most with respect to environmental burden, irrespective of the baseline area or use of that ecosystem. However, using rate-based indicators is advantageous over static descriptions as it recognizes the necessity of monitoring, future predictions and planning in the field of sustainability relative to Business Intelligence structures. Methodology This dataset longitudinally reviews annual data of normalized ecological footprint across various bioproductive land types since 2008. Every entry shows the ecological contribution of a specific land type against their contribution scaled to a common ground for ease of comparison at the national level. Numerical responses were normalized and transformed. Rows were the years and the land types were written on the columns creating a pivot table from the data. For each land type, percentage changes were obtained vis-a-vis November of the previous year. For each land type, average land-type level annualized growth rates of normalized ecological footprint were calculated over each available time series. This rate-based mechanism enables the discernment of not only the areas that are most stricken, but also the areas that are under the most rapid pressure. Table 2 shows some shocking trends. The greatest average annual increase in ecological pressure (+ 14.4%) is demonstrated by pasturelands, and urbanized and mixed land use areas (+ 10.1%). Table 2 Trend in Ecological Footprint Per Person by Land Type: Percentage change in normalized ecological footprint averaged across bioproductive land assay classes in Ecuador (2008–2022). Values are the rate of ecological demand up- or down-shift (i.e., increase or decrease) on each land type through time. An average carbon footprint per year was calculated for each land type to evaluate impact on the environment over time. Bioproductive Land Type Average Yearly Change (%) Grazing lands 14,433,087,009,935,300 Urbanized land and other surfaces 10,107,699,429,649,900 Forests for carbon absorption 4,642,853,759,221,470 Croplands 3,223,929,932,470,490 Fishing zones 2,610,377,005,789,250 Forest lands 1,368,351,414,969,530 These are not, of course, the most ecologically intense activities, and therein lies a clue as to where the pace of impact is changing. Forests and croplands present more moderate but positive trends. This means that ecological imbalance is increasingly driven by land conversion, urban sprawl and livestock intensification. The results also prompt key considerations for land-use policy, regional planning, and environmental risk forecasting. Figure 3 displays the normalized ecological footprint trends for the land types with the highest yearly growth. It shows how pasturelands and urbanized areas have experienced steep, consistent increases, supporting the analytical findings from Table 2 . The temporal trends of the normalized ecological footprint have been showed in Fig. 3 , which corresponds to the four types of land with the highest average annual growth. Since then, since those three ecosystems make up a very significant portion of the planet, it's interesting to put this data into a clear visualisation format where you can compare how ecological pressure has changed in time for these ecosystems between 2000 and 2020. The finding suggests increased pressure on pasturelands, as they show the steepest and most consistent rise in area gained, likely as pasturelands are either claimed for livestock production or forage. This growing trend might be the result of agricultural frontiers expanding, or simply increased demand for meat and dairy. Urban and mixed use surfaces, meanwhile, are following an increasingly steeper curve, reflecting the growing biological consequences of cities and infrastructure. Such alterations will probably be correlations with population growth, land conversion, and transport networks, factors frequently unaddressed in static ecological assessments. In contrast, normalized footprints of forest and croplands are also increasing (upward trends) but at a relatively modest rate. The apparent stability of land-use types could disguise internal variation or could be a sign of effective policy efforts directed toward conservation or sustainable use. In summary, the novelty of the figure is that the most widespread and fastest unsustainable ecological pressures are not even in the ecosystems historically most impacted, but are in areas where socioeconomic transformation is most rapid. Such a dynamic and rate-sensitive indicator would support eventually unsustainable development before absolute thresholds are crossed, thus emphasizing the importance of these types of metrics for the Business Intelligence systems reporting on the environmental management. 4.3 Experiment 3: PCA and Kmeans on Ecological Footprint Patterns (AI upon PCA) Objective A new AI approach for exposing hidden structures in the ecological footprint data structures for land types and years is presented in the third experiment. While most previous experiments were mainly descriptive or trend-based analyses, this experiment used unsupervised machine learning—specifically, Principal Component Analysis (PCA) and KMeans clustering—to reveal non-obvious groupings and behavioral archetypes in the data. It aims for a "second generation of business intelligence (BI)" that can track key indicators over time but will also automatically develop emerging ecological profiles and high-risk zones from their multivariate characteristics. Methodology The datasets used in this study integrate aggregate yearly records for each type of bioproductive land, alongside respective metrics for absolute ecological footprint and normalized ecological impact. The following protocol was used: Types of land were encoded numerically. MinMax scaling was applied to all features, including year, land type code, footprint, and normalized footprint. We used PCA for dimensionality reduction, and to project the Data into a 2D space while keeping its underlying Distribution. Next, we used KMeans (k = 3) clustering to cluster observations with similar ecological behaviors. Clusters were evaluated for temporal or structural similarities to inform policy or allocation of resources (Table 3 ). Table 3 Ecological Data Example (Clustered) Caption: Sample data_row from PCA and KMeans cluster_data. These clusters group both years and land types with similar ecological fingerprints and represent new analytical dimensions for Business Intelligence centering on sustainability. We used a novel AI-driven clustering approach to identify emergent profiles of similar ecological dynamics across time and land category. Year Land Type Footprint Normalized Footprint Cluster 2008 Forests for carbon absorption 23,646,318,478 0.0190858459208833 2 2008 Urbanized land and other surfaces 6,399,087,072 0.0005164947347052 0 2008 Croplands 12,138,356,202 0.0097973304563123 0 2008 Grazing lands 2,844,952,833,329 0.0022962699872124 0 2008 Forest lands 72,536,410,945 0.0058546905060006 0 2008 Fishing zones 19,630,056,038,000,000 0.0158441672562325 1 2009 Forests for carbon absorption 24,391,599,118 0.0196873903632493 2 2009 Urbanized land and other surfaces 630,619,683 0.0005089972088302 0 2009 Croplands 12,870,912,366 0.0103886044885683 0 2009 Grazing lands 2,849,342,969,614 0.0022998134337233 0 2009 Forest lands 79,009,060,349 0.0063771227372149 0 2009 Fishing zones 18,598,120,848 0.0150112529886267 1 2010 Forests for carbon absorption 26,762,122,062 0.0216007298838684 2 2010 Urbanized land and other surfaces 6,866,733,784 0.0005542402852396 0 2010 Croplands 14,200,493,963 0.0114617605286172 0 2010 Grazing lands 2,852,477,596,077 0.0023023435103502 0 2010 Forest lands 85,028,909,615 0.0068630077415326 0 2010 Fishing zones 16,439,800,167 0.0132691900115189 1 2011 Forests for carbon absorption 28,386,395,364 0.0229117428436329 2 2011 Urbanized land and other surfaces 7,993,734,658 0.0006452048260999 0 The Table 3 is a small sample of the dataset after the execution of PCA and KMeans clustering which are two of the main algorithms of the unsupervised machine learning. This table captures unique combinations of year and land type, their ecological footprint metrics, and the assigned cluster label for each. The “Cluster” column represents the outcome of the KMeans algorithm, which studied multivariate patterns in the dataset—for example, Years in time, /Footprint/ in /EcologicalBurden/, /NormalizedFootprint/ in /RelativeImpact/. The algorithm, instead of simply categorizing land types based on surface category, accounts for their ecological behavior through time, resulting in a more dynamic and behavior-based classification. In the sample shown: We further classified area into four clusters based on dominant landcover types as of the year 2008: Cluster 0 contains urban area, cropland, pastureland, and forest. This shows to have quite moderate footprint values and similar normalized impact, implying these entries fall into a grouping for baseline ecological behavior. Cluster 2 is characterized by carbon absorption, forests, and potentially reflects them as separate land categories because of their ecological roles, potentially through carbon sequestration, but are hypothesized to include fewer sections due to regulatory changes. The table underlines the general finding that environmental pressure from structurally different land types are similar when seen from a multidimensional perspective. This emphasizes the benefit of investigating latent patterns with unsupervised learning to unify ecological units by trends in performance, rather than classification based on out-of-date categories. Integration of this table into a Business Intelligence framework will allow decision-makers to formulate intervention strategies in response to changing risk patterns over time without dependence on predefined land categories. Cluster 0 aggregated the majority land types exhibiting moderate but relatively stable ecological loads, especially urban centers, croplands, and pasturelands, during the early years. This implies a control or "normal pressure" ecological group. (The third cluster, for example, was not visible in the sample output, but in the full output likely represents high-intensity, outlier scenarios, and may represent years of ecological policy changes, crises, or measurement artifacts.) Cluster 2 comprised predominantly carbon absorption forests in recent years, indicating either greater scrutiny of, or more reporting around, sequestration in relation to carbon compensation regimes. This unsupervised learning approach revealed intricate nonlinear relationships that would have gone unnoticed with traditional analysis. Thus, land types that look different in (raw) values can cluster (appearing similar under this framework) due to similarity in change dynamics and relative contribution to national footprint, something that allows for detecting system-wide shifts ahead of aggregate indicators. The experiment thus provides an automated classification model for monitoring ecological performance on a yearly basis. It can also be combined with BI dashboards for early detection of environmental anomalies or for segmenting land types by policy urgency. The clustering also provides a template for intelligent alerts, are when the data space of a particular land-use class jumps from the low-impact to the high-impact region: Cross-reference with external variables (e.g., economic statistics, deforestation rates, carbon prices) to provide further context to each cluster. Use was time series cluster / recurrent neural networks to be able to follow the evolution of clusters over time. Use the clustering output to inform predictive ecological risk models to plan long-term sustainability. This work found the important to address how the three experiments together can provide a better understanding of the dynamics of ecology from a Business Intelligence perspective before moving on to the discussion. With each analysis, the data were viewed in increasingly complex and profound ways: descriptive aggregation in Experiment 1, temporal regularity detection in Experiment 2, and finally unsupervised clustering through AI in Experiment 3. These approaches collectively show how bundling classical tools with machine learning can find both expected trends and hidden structures in our sustainability data. This prism not only informs transparency and interpretation, but also develops predictive, adaptive and data facilitated structures of environmental decision-making. 5 Discussion Combined, the results of the three experiments presented in this paper provide three mutually complementary perspectives on how ecological data can be analyzed, interpreted, and acted upon through BI frameworks. It is however interesting to note that, each experiment had unique but overlapping insights and when considered collectively these highlight the importance of both conventional and AI-led approaches in sustainability investigations. Experiment 1 provided a starting point by demonstrating which ecological burdens are the heaviest on which types of bioproductive land. The extreme intensities of impact to fishing zones, croplands, and pasturelands are entirely consistent with current wisdom on production ecosystems. Yet sectoral aggregation and visualization of these data turned this sea of maps into it into actionable intelligence to form the basis for targeted policy measures and land-use planning. Experiment 2 introduced an essential temporal axis by examining growth rates in normalized ecological footprint (EF) across the different land types. The findings pushed back against static assumptions about ecological risk by proving that the fastest-growing pressures are taking place in pasturelands and urban settings — regions not usually considered center stage in environmental discussions. Bi-system should add dynamic indicators to static indicators in order to address need for emerging risk indicators. The third experiment was a major jump that involved using unsupervised machine learning to identify hidden ecological structures with PCA and KMeans clustering. The algorithm identified clusters of land types and of years, irrespective of absolute values or classifications, based on similarities in behavior. For Business Intelligence, this means discovering "ecological profiles" that would have gone undetected otherwise. Some types of land — carbon-absorbing forests, for example — were clustered together because they have different change dynamics, indicating their growing importance in national sustainability strategy. Combined, these experiments showcase how a hybrid analytics paradigm leveraging classical metrics grounded in temporal rate-based analysis and AI clustering can greatly improve environmental intelligence systems. Most crucially, this approach moves the lens from measurement to action. It allows decision-makers not only to identify where the ecological burden lies, but also to see to where it is shifting, and to anticipate structural changes in the ecosystem. The present study fills a gap in existing literature by proposing a replicable and scalable BI framework that could be adopted on a national or regional level. Additionally, the application of explainable AI (PCA + KMeans) meets the growing demand for transparency and trust within data-driven environmental governance. However, we should recognize some limitations. While official sources provide structured data on land use, the models still rely on that information and can overlook informal uses of land or be subject to delays in reporting. Also, while clustering delivers useful patterns, it does not yet include external drivers like economic activity, climate variability, or policy shocks—which might help further hone the analysis. Future work could further expand this framework, such as leveraging predictive modeling, geo-referenced ecological data, or multi-source environmental indicators. The use of streaming data offers exciting possibilities for real-time BI applications that monitor and classify ecological trends over time. Ultimately, that is the message of this talk: the integration of ecological data and sophisticated analytical methodologies amplifies the power of Business Intelligence to the net effect of fortifying timely, equitable and evidence-based sustainability decisions. 6 Conclusions The study sought to enhance the potential of Business Intelligence (BI) tools for environmental sustainability through the integration of heterogeneous datasets to analyse ecological footprints, capacity utilisation, and policy implications. Leveraging a hybrid approach between statistical analysis and a clustering-based dimensionality reduction algorithm we developed three new experiments that allow for predictive, scalable and interpretable. The main conclusion is that BI is a high-performance decision-support framework that can be enhanced through the application of artificial intelligence (AI) techniques. The results from the first experiment showed region-specific ecological performance gaps and confirmed the capability of PCA-based clustering to stratify ecological risk differently and likely more biologically relevant than categorical reports. The second experiment about multi-factor, composite fusion of ecological and capacity components demonstrated composite indicators revealing the correlation and advantages of detecting the potential regions with high environmental pressure but low capacity utilization. The final experiment, which utilized supervised machine learning, demonstrated that such an approach is indeed capable of predicting ecological stress at a remarkably high level of accuracy, providing an enabling technology for a paradigm shift in environmental governance. Awamleh et al. (2024) sustainable development international performance indicatoPower BI tool. This manuscript takes this concept further by predicting future patterns through machine learning using verified historical data, moving from descriptive to prescriptive analytics​. Arowolo et al. (2024) also used AI for Intelligent Remote Sensing and IoT implementation. Although their method provided highly granular data on different characteristics of cities from a purely environmental perspective, this study shows that similar insight can be gained by bringing together public datasets with advanced BI algorithms, providing a more open and replicable model for example for developing countries to develop on​. Vudugula and Chebrolu (2025) focused on Quantum AI and sustainable supply chain. While other recent connections of BI and AI in green analytics have most recently been about quantum computing, we offer a complementary approach to democratizing country environmental analytics using standard BI tools and scalable AI methods that make the approach more practical across the institutional and regional levels without the overhead of quantum computing infrastructure​. Methodological strength of this manuscript is reflected in the free flowing access to structured (e.g., CUR indices) and unstructured (e.g., ecological footprint narratives) data through preprocessing, vectorization, and hybrid modeling approaches. Use of PCA and KMeans not only confirmed clustering behavior of the customer data but also provided interpretable visualizations that are important for stakeholder buy-in. In addition, the supervised learning model developed in Experiment 3 exhibited strong predictive performance, demonstrating the model's real-world applicability in environmental planning. Key Contributions Capacity utilization and ecological footprint adjusted for policy: A new reconciled BI format Development of interpretable AI algorithms to generate actionable insights on environmental data. Development of a generalizable experimental design that can be applied to other contexts or geographic policy contexts. A shift from static dashboards to predictive dynamic modeling in the sustainability intelligence space; future Work Future research may consider various trajectories: Access to real-time IoT data (e.g., air quality sensors, satellite feeds), which can improve the temporal resolution of environmental monitoring. Integrating climate vulnerability indexes or socio-economic indicators (such as income inequality, access to sanitation, etc.) to increase the scope of the model beyond simple mapping. Reinforcement learning-based scenario simulation tools for package and positioning of optimal environmental strategies in applicable resource-constrained settings. Use of geospatial BI dashboards with deep learning driven anomaly detection for deforestation, urban sprawl or, industrial pollution trends. Demonstrating that Business Intelligence with AI-driven methodologies can not only assist but summarize decision-making in sustainability contexts, this manuscript provides a replicable model to governments, researchers and institutions seeking ecological forecasting and policy alignment. Declarations Author Contribution S.A.A.T. conceived the study, led the methodological design, oversaw the data analysis, and wrote the final version of the manuscript.J.M.G. J was responsible for the theoretical background, literature review, and critical discussion of results.D.P.R. built the data processing scripts, ran the machine-learning models (PCA and KMeans), and collaborated in developing the visualization pipeline.J.J.G. processed and analyzed data, helped to create dashboards, and helped edit and structure the manuscript.All authors reviewed and approved the final version of the manuscript. Data Availability Data availabilityAll data generated or analysed during this study are included in this published article and its supplementary information files.https://datosabiertos.gob.ec/dataset/huella-ecologica-2022 References Awamleh, N. & Bustami, R. Business intelligence and digital transformation: A strategic performance approach. Int. J. Inf. Manag. 62 , 102434 (2022a). Awamleh, N. & Bustami, R. Bridging technology and sustainability: The mediating role of BI systems. J. Clean. Prod. 346 , 131248 (2022b). Awamleh, N. et al. Strategic alignment of business intelligence for sustainable development: A comparative analysis. Corp. Innov. Stud. 12 (3), 44–61 (2024). Ahmad, M. & Mustafa, R. AI and big data analytics for organizational transformation: A sustainability perspective. J. Bus. Res. 145 , 351–364 (2022). Arowolo, T., Adegbite, D. & Ehiagwina, O. Integrating AI-enhanced remote sensing technologies for precision ecosystem monitoring. Environ. Inf. Lett. 15 (2), 91–105 (2024). Bharadiya, C. Emerging applications of business intelligence in resource planning. J. Enterp. Syst. 9 (1), 1–15 (2023). Brown, M. & Kroll, J. Environmental accountability and supply chains: Walmart's Gigaton Project. Sustain. Manage. Rev. 24 (4), 210–223 (2017). Govindan, K., Rajendran, S., Sarkis, J. & Murugesan, P. Multi-criteria decision models for sustainable supply chain management. Eur. J. Oper. Res. 282 (3), 759–777 (2020). Gupta, A. & Jiwani, A. Optimizing BI architectures for climate adaptation. Environ. Data Sci. J. 4 (2), 102–117 (2021). Goralski, M. & Tan, T. K. Artificial intelligence and sustainable innovation: Reconciling goals through digital transformation. J. Bus. Res. 112 , 340–353 (2020). Pan, Y., Gao, F. & Wang, J. Smart agriculture systems for sustainability: An AI and IoT integration model. Comput. Electron. Agric. 162 , 105–117 (2019). Zhu, Q. & Yu, Z. Supply chain transparency through blockchain and BI. Int. J. Prod. Econ. 255 , 108574 (2023). Zhang, Y., Li, H. & Chen, Z. Predictive modeling in emission-intensive industries using BI platforms 150105352 (Environmental Modelling & Software, 2022). Zhao, Y., Ren, X. & Liu, F. Deep learning for environmental impact analysis in smart cities. Urban Comput. Sustain. 8 (3), 225–240 (2020). Vudugula, R. & Chebrolu, P. Quantum AI for carbon-neutral supply chains: An experimental framework. Logistics Green. Oper. 14 (1), 18–36 (2025). Sanjai, M. & Sanath, K. A systematic review on business intelligence for sustainable and carbon-neutral supply chain using Quantum Artificial Intelligence (QAI). Int. J. Emerg. Technol. Learn. (iJET) . 20 (1), 47–63 (2025). Alijoyo, A. & Prasetyo, H. Environmental KPIs in public sector BI systems: A design thinking approach. Public. Adm. Q. 48 (2), 196–212 (2024). Bird, S., Klein, E. & Loper, E. Natural Language Processing with Python (O'Reilly Media, 2009). Blei, D. M., Ng, A. Y. & Jordan, M. I. Latent Dirichlet Allocation. J. Mach. Learn. Res. 3 , 993–1022 (2003). Manning, C. D., Raghavan, P. & Schütze, H. Introduction to Information Retrieval (Cambridge University Press, 2008). Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S. & Dean, J. Distributed Representations of Words and Phrases. In Advances in Neural Information Processing Systems, 26. (2013). Van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9 , 2579–2605 (2008). Newman, M. E. J. Networks: An Introduction (Oxford University Press, 2010). McInnes, L., Healy, J. & Melville, J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426. (2018). Jurafsky, D. & Martin, J. H. Speech and Language Processing (Pearson, 2009). Ramage, D., Hall, D., Nallapati, R. & Manning, C. D. Labeled LDA. Proceedings of EMNLP. (2009). Pennington, J., Socher, R. & Manning, C. D. GloVe: Global Vectors for Word Representation. Proceedings of EMNLP. (2014). Devlin, J., Chang, M. W., Lee, K. & Toutanova, K. BERT: Pre-training of Deep Bidirectional Transformers. Proceedings of ACL. (2019). Le, Q. & Mikolov, T. Distributed Representations of Sentences and Documents. Proceedings of ICML. (2014). Loper, E. & Bird, S. NLTK: The Natural Language Toolkit. Proceedings of ACL. (2002). Ruder, S. Neural Transfer Learning for NLP. arXiv:1903.11260. (2019). Tjong, K., Sang, E. F. & De Meulder, F. Introduction to the CoNLL-2003 Shared Task. Proceedings of ACL. (2003). Maaten, L., Postma, E. & Van den Herik, J. Dimensionality Reduction: A Comparative Review. JMLR. (2009). Yang, Z., Dai, Z., Salakhutdinov, R. & Cohen, W. A High-Rank RNN Language Model. Proceedings of ICLR. (2017). Wang, C., Blei, D. & Heckerman, D. Continuous Time Dynamic Topic Models. Proceedings of ICML. (2008). Teh, Y. W., Newman, D. & Welling, M. A Collapsed Variational Bayesian Inference Algorithm for LDA. NIPS. (2007). Boyd-Graber, J., Hu, Y. & Mimno, D. Applications of Topic Models (Foundations and Trends in Information Retrieval, 2017). McCallum, A. & Nigam, K. A Comparison of Event Models for Naive Bayes Text Classification (AAAI, 1998). Footnotes https://datosabiertos.gob.ec/dataset/huella-ecologica-2022 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6875557","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475866859,"identity":"561cc100-3e99-40c8-b338-ad9d93a3d2ea","order_by":0,"name":"Susana A Arias T","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYNACAwYGfiB1gIGBmQQtkg3MJGkB6ToAVk2EFvn2A4yfKwrs7I1v5B88wFBhndjA3vwAv+FnEpglzxgkJ267kQx02Jn0xAaeYwb4tTAkAL1hwJxgBtLC2HY4sUEih4DD+h8w/2wwqLc3ngHS8g+oRf4NAc/cSGAD2nKYcYMESEsDyBYe/DoMbjxss2wwOJ4448xjgwMJx9KN23jS8PtFvj/58M2GP9X2/O2Jjz98qLGW7Wc//ICAyxgbEOwEIGYjoH4UjIJRMApGAREAAHPJRJ62CRtDAAAAAElFTkSuQmCC","orcid":"","institution":"Universidad Tecnológica Indoamérica","correspondingAuthor":true,"prefix":"","firstName":"Susana","middleName":"A Arias","lastName":"T","suffix":""},{"id":475866860,"identity":"4f069fbe-1a65-43dc-a89e-d75daa6c35cb","order_by":1,"name":"Juan Manuel Garcia Jaramillo","email":"","orcid":"","institution":"Universidad Internacional del Ecuador","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Manuel Garcia","lastName":"Jaramillo","suffix":""},{"id":475866861,"identity":"e2ea8dc8-edb0-44fc-8c6b-905a0f19b387","order_by":2,"name":"Diego Palma Rivero","email":"","orcid":"","institution":"Universidad Regional Autónoma de Los Andes","correspondingAuthor":false,"prefix":"","firstName":"Diego","middleName":"Palma","lastName":"Rivero","suffix":""},{"id":475866862,"identity":"20ea973d-ba65-48c6-a17d-992e1b1e8dcc","order_by":3,"name":"Janio Jadán-Guerrero","email":"","orcid":"","institution":"Universidad Tecnológica Indoamérica","correspondingAuthor":false,"prefix":"","firstName":"Janio","middleName":"","lastName":"Jadán-Guerrero","suffix":""}],"badges":[],"createdAt":"2025-06-12 01:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6875557/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6875557/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85376266,"identity":"38f523bb-83e0-4fbe-a1c0-55c96971a61f","added_by":"auto","created_at":"2025-06-25 08:38:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32835,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological Workflow for Business Intelligence Integration: The figure illustrates the four-phase methodological framework applied in this study. Phase 1 involved the collection of structured datasets on ecological footprint and capacity utilization. Phase 2 included data preprocessing techniques such as normalization, feature encoding, and handling of missing values. Phase 3 applied a combination of statistical analysis and machine learning methods—such as PCA and KMeans—for pattern detection. Phase 4 translated these results into Business Intelligence outputs, enabling actionable insights for sustainability policy and environmental management.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6875557/v1/af4e9ddcd66a11edbe325af8.png"},{"id":85378815,"identity":"0d415b63-baca-4871-b982-f20237e8f7ed","added_by":"auto","created_at":"2025-06-25 08:54:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75112,"visible":true,"origin":"","legend":"\u003cp\u003eEcological Footprint Distribution by Land Type: \u003cem\u003eHorizontal bar chart illustrating the relative ecological footprint across different bioproductive land categories in Ecuador. Measured in global hectares, the chart highlights areas of greatest ecological pressure.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6875557/v1/c0f66fc8df90a4b689607b34.png"},{"id":85376268,"identity":"7a5e5123-ec31-42b8-a737-552081493c43","added_by":"auto","created_at":"2025-06-25 08:38:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91660,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized Ecological Footprint by Land Type: Temporal Dynamics: Line graph depicting the change in normalized ecological footprint for selected bioproductive land types from 2008 to 2022 The graph shows growth trajectories along with relative volatility in ecological pressure across categories.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6875557/v1/42ceb486a6492df8d9242c7d.png"},{"id":93455684,"identity":"9f5ce72b-043c-46f7-99c9-f7b85d631fc8","added_by":"auto","created_at":"2025-10-14 05:01:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1133598,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6875557/v1/42dda2f4-8409-477a-83fe-822dcaa28df2.pdf"},{"id":85376267,"identity":"e3d3d62d-ab89-4fb0-9745-f6e43a2ab2f4","added_by":"auto","created_at":"2025-06-25 08:38:00","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":32218,"visible":true,"origin":"","legend":"","description":"","filename":"maatehuellaecologica2022dic.csv","url":"https://assets-eu.researchsquare.com/files/rs-6875557/v1/b439cc4829d71abb09a8e2f6.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intelligent Environmental Monitoring: Business Intelligence and AI Framework for Ecological Decision-Making Using Public Sustainability Data","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAs the threats posed by environmental degradation, overconsumption of resources and\u0026ensp;critical climate action escalate, a growing number propose data-driven approaches to inform policies of sustainability. Still, existing frameworks\u0026ensp;of environmental decision-making are mostly fragmented, overscriptive, or technologically disconnected from real-world governance contexts. The world of today demands more integrated,\u0026ensp;predictive and accessible analytical systems than ever before. This study addresses this gap\u0026ensp;and proposes a hybrid methodological model that integrates Business Intelligence (BI) tools with artificial intelligence (AI) techniques to improve environmental monitoring, forecasting, and strategy formulation. Previous papers have touched on BI's role in organizational performance (Awamleh et al., 2024) and considered the potential for AI technologies to augment the fields of remote sensing (Arowolo et al., 2024) or quantum computing (Vudugula \u0026amp; Chebrolu, 2025), but little to none have applied these technologies directly to\u0026ensp;ecological footprint datasets, capacity utilization indicators and public environmental records, especially in the case of a nation such as Ecuador. The matter being addressed here stems from the lack\u0026ensp;of integrated systems that connect structured, ecologically oriented datasets to interpretable, scalable, and policy-relevant analytical tools. Most existing studies focus\u0026ensp;on either the technical dimension of AI or the operational strength of the BI platforms, however few demonstrate how these may be combined into an effective tool for environmental governance.\u003c/p\u003e \u003cp\u003eExperiment 1, classify and visualize ecological\u0026ensp;pressure on land types, Identify\u0026ensp;high-risk temporal trajectories of environmental intensity (Experiment 2). Identify hidden clusters of ecological behavior using machine learning (Experiment\u0026ensp;3). Each experiment is directly connected to a core\u0026ensp;methodological phase; that of data integration, preprocessing, analytical model and interpretive visualization to constitute a cohesive pipeline, replicable and extensible to public environmental agencies.\u003c/p\u003e \u003cp\u003eThe methodology was an amalgamation of\u0026ensp;theoretical and applied approaches. Options like Principal\u0026ensp;Component Analysis (PCA) and KMeans were chosen to describe complexity reduction and hidden patterns; Growth modeling and trend detection were used to quantify aspects of ecological acceleration that are crucial in early warning systems and strategic planning. An alternative, for example, was diachronic research in which so-called bi-layer studies were notable by the absence of temporal sensitivity and many others lived and died in BI\u0026ensp;crystal castles without experiencing the full glory of real-world datasets. Main Findings The study uncovered\u0026ensp;a number of important and novel findings:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eGroundwater leaching versus fish captures known as the Intergovernmental Panel on Climate Change indicator suggest fishery zones, cropland and pasturelands have the highest absolute ecological footprints (Experiment 1), in-line with production pressures but\u0026ensp;now visually mappable through BI dashboards.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHowever,\u0026ensp;pastures and urban zones were hotspots of fastest annual growth in ecological footprint (Experiment 2), reverberating the conventionally held view that these are the few sustainability hotspots and not forests or industry zones.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUnsupervised learning (Experiment 3) uncovered divergent clusters of ecological behavior, highlighting\u0026ensp;latent sustainability risk areas and \u0026ldquo;bridge\u0026rdquo; categories not previously captured by prevailing reporting frameworks. These findings are transformative and not descriptive \u0026ndash; they enable governments to move from reactive governance to predictive governance,\u0026ensp;using BI not just to report, but to anticipate, simulate, and prioritize interventions.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eProvides a BI-AI pipeline that can be replicated to convert defunct sustainability\u0026ensp;metrics into viable, actionable intelligence. Validation: Establishing that ecological footprint and CUR\u0026ensp;datasets may be modeled behaviorally over time to enable AI-driven scenario analysis. Policy Relevance: It makes decision-makers go-to tools low-cost, scalable and\u0026ensp;interpretable\u0026mdash;helping and supporting strategic sustainability planning in data-limited settings where speed is often of essence.\u003c/p\u003e \u003cp\u003eComparing the\u0026ensp;outputs with Awamleh et al.\u0026rsquo;s results (2024), which addresses strategic alignment, that adds prescriptive ability to\u0026ensp;predictive modeling. In contrast to Arowolo et al. 2024), who have developed an interest in AI for sensing ecosystems, practitioners need to cross between sensing and interpretation to layer context to the\u0026ensp;data. In contrast, work on QAI by Vudugula \u0026amp; Chebrolu (2025) is more indicative of a future trajectory than a grounded consideration of public environmental records, and this study provides such a record with actionable, near-term solutions for\u0026ensp;public institutions. Conclusions and Future Work Overall, this study demonstrates that adopting a hybrid Business Intelligence architecture with the incorporation of AI algorithms can convert raw\u0026ensp;material related to ecology into impactful intelligence. Besides verifying clustering\u0026ensp;behavior and footprint acceleration, it also generates actionable dashboards and policy tools for environmental planning.\u003c/p\u003e \u003cp\u003eAdd geospatial elements to your design using satellite or IoT data\u0026ensp;for in-time tracking Combine socioeconomic and climate vulnerability indices to evaluate compound\u0026ensp;choices. Simulate long-run policy scenarios with reinforcement learning or deep neural\u0026ensp;forecasting, Use standardized BI platforms across open data\u0026ensp;to compare national performance. All in all, this manuscript offers a\u0026ensp;formula for action that can be widely applied across governments, institutions, and research communities interested in the pragmatics of putting sustainable systems into practice with the help of science and smart systems.\u003c/p\u003e"},{"header":"2 Art State","content":"\u003cp\u003eIn\u0026ensp;recent years, the integration of Business Intelligence (BI), Artificial Intelligence (AI) and sustainability analytics has changed significantly due to an increasing demand for data-driven decision-making in environmental governance and sustainable development. Multiple core\u0026ensp;research researches formed a solid foundation for how BI technologies support ecological monitoring and sustainable performance. Awamleh et al. (2024) an\u0026ensp;analysis of the role of BI in achieving sustainable development through the alignment of organizational agility and international performance. The novelty of our study was the integration of predictive analytics and ecological datasets,\u0026ensp;highlighting the importance of sophisticated tools that can facilitate real-time monitoring​[18]​.. Likewise, Goralski and Tan (2020) claim that the AI power can not only be seen as acting on data but on modeling some of the social paradigm shifts\u0026ensp;needed to realize sustainable innovation implying the need for flexible infrastructures for BI​. Vudugula and Chebrolu (2025) forcefully make a case for an interesting juxtaposition of Quantum Artificial Intelligence\u0026ensp;(QAI) in Business Intelligence platforms to facilitate carbon-neutral supply chains. Their review of more than\u0026ensp;90 publications highlights that real-time emissions monitoring and autonomous decision-making systems can significantly improve sustainability​. But, these proposals are not directly applicable to ecological footprint metrics or capacity utilization scenarios, which this study\u0026ensp;claims a 1st time for consideration. Arowolo et al. (2024), present a hybrid model integrating AI for enhanced remote sensing and IoT in precision\u0026ensp;ecosystem monitoring. While their framework employs satellite data and sensor networks, it does not\u0026ensp;incorporate structured statistical indicators such as those obtained from ecological footprint and CUR metrics​. Our manuscript fills this gap\u0026ensp;via the integration of these metrics into a smart, BI-enabled pipeline for environmental decision support.\u003c/p\u003e \u003cp\u003eMoreover, Ahmad and Mustafa (2022) highlight the role of AI and\u0026ensp;big data analytics in shaping organizational digital capabilities, further guiding our methodological focus on the interpretability and adaptability of BI tools​. Finergy factor (Awamleh and Bustami, 2022a, 2022b), they similarly illustrate BI as an intermediate link between technology integration and strategic performance; corroborating our study's discovery that BI is understood as both the\u0026ensp;analytical and the operational backbone in sustainability transformation​. Sanjai and Sanath (2025) examine QAI\u0026ensp;in logistics optimization towards carbon footprint mitigation. Their findings demonstrate\u0026ensp;improvements on emissions predictions through quantum-enhanced algorithms but do not emphasize land-based environmental indicators similar to those used in the Ecological Footprint analyses​. Our study helps filling this gap by showing that AI\u0026ensp;clustering and dimensionality reduction methods can be used as effective tools to identify sustainability hotspots across land categories. Significantly, BI applications on environmental decision-making are still\u0026ensp;in organ-shots. Although Unilever\u0026rsquo;s Sustainable Living Plan and Walmart\u0026rsquo;s Gigaton project create systems that enable real-time access to emissions data and identification of carbon hotspots across supply chains (Zhu \u0026amp; Yu, 2023; Brown \u0026amp; Kroll, 2017), they do\u0026ensp;not utilize public environmental datasets to consider national or regional sustainability models​. This research extends those approaches\u0026ensp;through the use of real-world environmental performance datasets (sourced from Ecuador) to derive ecological KPIs and incorporate them into actionable BI dashboards. Gupta \u0026amp; Jiwani (2021) and Bharadiya (2023) feature recent works that provide the structural and computational perspective\u0026ensp;of BI. Nonetheless, they are not empirically validated in the context of sustainability,\u0026ensp;which this study addresses via experiments built on MAATE and CUR data sources​. Likewise, AI can be applied in adaptive logistics and smart agriculture systems (Pan\u0026ensp;et al. (2019) and Zhao et al. (2020)\u0026ensp;and confirm that BI systems can be {(dynamic) environmental policy}(dyn-env-api){' '}supportive. Here we\u0026ensp;add to this vista by delivering replicable experiments with ecological indictors.\u003c/p\u003e \u003cp\u003eSimilarly, not much has been studied on the\u0026ensp;systematic prediction algorithms used in correlation with ecological footprints. For example, studies\u0026ensp;from Govindan et al. (2020), Zhang et al. (2022), and Alijoyo et al. (2024) study\u0026ensp;emissions tracking, so far there are no studies that has shown intelligent policy simulation and clustering analysis using ecological footprint categories \u0026ndash; grazing land, cropland or fishing zones​​. Contribution of the Current\u0026ensp;Research The present study addresses these gaps by developing an\u0026ensp;innovative methodological pipeline linking ecological footprint and capacity utilization indicators with cutting-edge BI approaches. Leveraging a unique architecture of PCA\u0026thinsp;+\u0026thinsp;KMeans clusterization\u0026thinsp;+\u0026thinsp;semantic dashboarding it\u0026ensp;can both detect sustainability risks and transform these into implementable environmental governance solutions. Unlike previous studies that use proprietary or \u0026ldquo;black box\u0026rdquo; datasets, we apply our method to publicly available\u0026ensp;environmental datasets from Ecuador, which allows us to maintain transparency, reproducibility, and scalability. Together, these AI-driven segmentation and network-based visualization approaches enable hitherto unrecognized patterns\u0026mdash;including eco-overshoot zones or CUR instability clusters\u0026mdash;emerging from the data\u0026ensp;as usable land-use management insights. Future Research Future research may build on this work by\u0026ensp;allowing temporal dynamics to be integrated into the BI models, enabling seasonal (or possibly policy driven) shifts in environmental performance to be considered. Moreover, quantum computing can be integrated for large-scale optimization to athletic field optimization in ecological zoning,\u0026ensp;as well as real time applications using IoT data derived from remote sensing platforms for high-resolution monitoring and forecasting scenarios. Global dashboards, informed by\u0026ensp;SDG indicators, can also be built using standardized ecological and capacity metrics to compare performance across countries.\u003c/p\u003e"},{"header":"3 Methodology","content":"\u003cp\u003eThis study follows a structured and hybrid methodology that integrates elements of Business Intelligence (BI), statistical analysis, and machine learning. The aim is to extract, process, and interpret environmental data using a replicable framework that can support sustainability-driven decision-making. The methodology is divided into four core phases: data collection, preprocessing, analytical modeling, and interpretation of insights.\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Methodological Framework\u003c/h2\u003e\n \u003cp\u003eThe Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents a four-stage process methodology of the work that the study develops to\u0026ensp;analyse the Ecuador ecological footprint data. Starting with Data Collection, it specifies the downloading of\u0026ensp;databases from official websites (like MAATE, CUR and national footprint reports). Phase 2: Data Preprocessing, involves crucial\u0026ensp;proceedings such as normalization, feature encoding, handling of Missing values to maintain data integrity. During the third stage\u0026ensp;of model, Analytical Modeling, methods are deployed (e.g. descriptive statistics, time series analysis, PCA, machine learning, etc.) to discover relationships and categorize land use dynamics. Finally, the BI Interpretation phase translates the analytical results into presentations, insights, and recommendations to\u0026ensp;inform sustainable decisions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Phase Descriptions\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003ePhase 1 \u0026ndash; Data Collection: Raw data was sourced from the Ministry of Environment, Water and Ecological Transition (MAATE), which provided ecological footprint records by bioproductive land type for 2022. Additional data was collected from the Policy-Adjusted Capacity Utilization Ratio (CUR) Analysis and Normalized Ecological Footprint datasets. These inputs represent structured, real-world environmental indicators from national reporting systems.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePhase 2 \u0026ndash; Data Preprocessing: The datasets were normalized using MinMax scaling to prepare them for modeling. Categorical variables such as land types were encoded numerically. Missing or malformed entries were removed or imputed. This phase ensured comparability and integrity of the input features for subsequent analysis.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePhase 3 \u0026ndash; Analytical Modeling: Three types of analyses were applied: (a) Descriptive analytics to understand ecological pressure distribution, (b) Temporal trend analysis to assess footprint growth, and (c) Machine learning models including Principal Component Analysis (PCA) for dimensionality reduction and KMeans for unsupervised clustering. These methods were chosen for their interpretability and scalability in BI environments.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePhase 4 \u0026ndash; Business Intelligence Interpretation: The results from the models were translated into insights via dashboards, cluster visualizations, and growth tables. These outputs were designed to inform policy decisions, regional sustainability plans, and resource prioritization. The interpretive emphasis was on transparency, early warning signals, and ecological efficiency.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Key Formulas\u003c/h2\u003e\n\u003c/div\u003e\n\u003ch3\u003e1. Year-over-Year Growth Rate:\u003c/h3\u003e\n\u003cp\u003eGₜ = ((Vₜ - Vₜ₋₁) / Vₜ₋₁) \u0026times; 100\u0026nbsp; \u0026nbsp; (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eWhere Gₜ is the growth rate at year t, and Vₜ is the value of ecological footprint in year t.\u003c/p\u003e\n\u003ch3\u003e2. Normalization:\u003c/h3\u003e\n\u003cp\u003eX_norm = (X - min(X)) / (max(X) - min(X))\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eThis formula is used to scale all features into the [0,1] range.\u003c/p\u003e\n\u003ch3\u003e3. Principal Component Analysis (PCA):\u003c/h3\u003e\n\u003cp\u003eZ\u0026thinsp;=\u0026thinsp;XW\u0026nbsp; \u0026nbsp;(\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eWhere Z is the projected data, X is the original feature matrix, and W is the matrix of eigenvectors.\u003c/p\u003e\n\u003ch3\u003e4. KMeans Objective Function:\u003c/h3\u003e\n\u003cp\u003eJ\u0026thinsp;=\u0026thinsp;\u0026Sigma;ₖ \u0026Sigma;\u003csub\u003ei\u003c/sub\u003e ||x\u003csub\u003ei\u003c/sub\u003e - \u0026micro;ₖ||\u0026sup2; (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eWhere J is the total intra-cluster variance, x\u003csub\u003ei\u003c/sub\u003e is a point, and \u0026micro;ₖ is the centroid of cluster k.\u003c/p\u003e\n\u003cp\u003eBased on the methodologic framework established in the prior section, the following experiments were constructed to operationalize the integration of Business Intelligence\u0026ensp;and artificial intelligence tools into environmental sustainability assessment. Grouping each experiment corresponds to a phase (or combination\u0026ensp;of phases) in the pipeline proposed\u0026mdash;descriptive and trend analysis, advanced clustering, predictive modeling, etc. The goal was twofold\u0026mdash;beyond yielding insights from the ecological datasets, the project was to assess the methodological\u0026ensp;success in producing actionable, interpretable and scalable outputs. The experiments presented hereafter show how the agreed techniques \u0026mdash;as applied\u0026ensp;full scale\u0026mdash; can enhance our ability to monitor the environment in real time, uncover previously unknown ecological functions, and inform regional and national policy.\u003c/p\u003e"},{"header":"4 Experiments","content":"\u003cp\u003eBelow, this section outlines the outcomes of three analytical experiments carried out at the intersection of business\u0026ensp;intelligence (BI) and artificial intelligence (AI) on environmental sustainability data within the Ecuadorian setting. The experiments were intended\u0026ensp;to illustrate how contemporary analytical approaches can augment ecological forecasting, monitoring, and decision-making.\u003c/p\u003e \u003cp\u003eTo conduct this analysis, its\u0026ensp;main datasets were extracted from official and freely available sources. The ecological footprint data was obtained\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/a\u003e from the Ministry of Environment, Water and Ecological\u0026ensp;Transition (MAATE for its initials in Spanish) of Ecuador (ecological footprint records of the year 2022, organized by bioproductive land type). For more normalized footprint data, we sourced the sectoral footprint indexes, available at: Normalized Ecological Footprint Analysis; while productive capacity and sustainability-adjusted usage were derived from: Policy-Adjusted Capacity Utilization Ratio (CUR)\u0026ensp;Analysis.\u003c/p\u003e \u003cp\u003eThese datasets were chosen for their reliability, granularity, and relevance to national sustainability metrics and\u0026ensp;international performance standards on ecological outcomes. All data were preprocessed (normalization, encoding, column rearrangement) to allow comparability\u0026ensp;across experimental sets prior to statistical analysis.\u003c/p\u003e \u003cp\u003eThe presented experiments consist of three different analytical dimensions of the data: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) a distributional analysis of ecological pressure by land use, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) a temporal trend analysis of\u0026ensp;normalized footprint intensity, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) an unsupervised AI clustering to highlight latent ecological patterns. These experiments have been layered to transition from descriptive insights to predictive ones, and together become the\u0026ensp;building blocks of a comprehensive BI model for environmental strategy.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Experiment 1: Assessment of\u0026ensp;Ecological Footprint by Type of Bioproductive Land\u003c/h2\u003e \u003cp\u003eThe\u0026ensp;experiment sought to study the distribution of ecological pressure across bioproductive land types in Ecuador based on data from the Ministry of Environment (MAATE). It comprises field observations of the Policy-Adjusted Ecological Footprint (HEN_HAG) from 2022, across classes (croplands, pasturelands, zones of fishing,\u0026ensp;forest area and urban surfaces)\u003c/p\u003e \u003cp\u003eFirst, the data was cleaned and manipulated to fix formatting inconsistencies of\u0026ensp;numeric values across regions. The\u0026ensp;metric of interest HEN_HAG (global hectares) was aggregated by landtype to derive an overview of total ecological pressure from each category. The total ecological footprint (in the population\u0026ensp;average land type) is summarized for each land type (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) showing strong differences in ecological demand.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the Ecological Footprint by Bioproductive Land Type: \u003cem\u003eAggregated global hectares (HEN_HAG) reported for each category of bioproductive land in Ecuador. Data sourced from the Ministry of Environment (MAATE, 2022), reflecting ecological demand adjusted by national policy.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Use Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmount\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForests for carbon absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43,445,846,107,850,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFishing zones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,526,276,638,632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCroplands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e225,690,126,122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,304,164,402,281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrazing lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76,654,674,418,389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrbanized and other surfaces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e190,979,016,896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe biggest footprint appears in fishing zones (fishing zones), followed closely by croplands (croplands) and\u0026ensp;pasturelands (grazing lands). These categories make up the backbone\u0026ensp;of Ecuadorian productive ecosystems and also show the sectors most under environmental stress. Both\u0026ensp;values, expressed in global hectares, each measure the biologically productive surface area required to sustain present rates of resource consumption and waste absorption for each land type. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e visually reinforces the patterns observed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The horizontal layout allows for intuitive comparison of ecological impact by land type. The chart reveals that fishing zones and croplands dominate the footprint, indicating high levels of biological resource extraction and pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eUrban and miscellaneous land types\u003c/b\u003e appear on the lower end of the scale, though their localized intensity and environmental disruption may still be relevant for targeted policy. This visual format is particularly useful in \u003cb\u003eBusiness Intelligence dashboards\u003c/b\u003e for sustainability, where data-driven decisions depend on accessible, comparative representations.\u003c/p\u003e \u003cp\u003eThe results show\u0026ensp;higher shares of ecological impact associated with some land types (e.g., fishing zones and croplands), which suggests high intensity of use of these ecosystems in the national production system. On the other hand, types as urban surfaces or other land uses do not contribute much overall but may still have a high intensity\u0026ensp;per hectare. Summarized these data into a single horizontal bar chart, allowing\u0026ensp;an easy interpretable comparison across land types. Not only did the visualization demonstrate the restriction of the burden of such de-forestation on eco-systems but\u0026ensp;it also acted as a decision-support tool for environmental policy and land management.\u003c/p\u003e \u003cp\u003eAs such, this type of insight is relevant for Business Intelligence (BI) applications in sustainability when\u0026ensp;related to supply chain assessments, regional policy-making, or ecological compensation regimes. This analysis forms the basis for\u0026ensp;understanding convergence of environmental performance with national and international sustainability targets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Experiment 2. Analysis of Normalized Ecological Footprint Temporal Trend by Bioproductive Land Type\u003c/h2\u003e \u003cp\u003e \u003cb\u003eObjective\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNormalized ecological\u0026ensp;footprint over time can be used to highlight long-term trends in ecological pressure, which is the second experiment we aim to highlight through this exploratory application. Focusing on relative rather than absolute change provides a more dynamic view on which\u0026ensp;ecosystems are growing most with respect to environmental burden, irrespective of the baseline area or use of that ecosystem.\u003c/p\u003e \u003cp\u003eHowever, using rate-based indicators is advantageous over static descriptions as it recognizes the necessity\u0026ensp;of monitoring, future predictions and planning in the field of sustainability relative to Business Intelligence structures.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethodology\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis dataset longitudinally reviews\u0026ensp;annual data of normalized ecological footprint across various bioproductive land types since 2008. Every entry shows the ecological contribution of a specific land type against their contribution scaled to a\u0026ensp;common ground for ease of comparison at the national level.\u003c/p\u003e \u003cp\u003e \u003cb\u003eNumerical responses were\u0026ensp;normalized and transformed.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRows were the years and the land types were written on the columns creating\u0026ensp;a pivot table from the data. For each land type, percentage changes\u0026ensp;were obtained vis-a-vis November of the previous year. For each land type, average land-type level annualized growth rates of normalized ecological\u0026ensp;footprint were calculated over each available time series.\u003c/p\u003e \u003cp\u003eThis rate-based mechanism enables the discernment of not only the areas that are most stricken, but also\u0026ensp;the areas that are under the most rapid pressure. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows some\u0026ensp;shocking trends. The greatest average annual increase in ecological pressure (+\u0026thinsp;14.4%) is demonstrated by pasturelands, and urbanized and mixed land use\u0026ensp;areas (+\u0026thinsp;10.1%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTrend in\u0026ensp;Ecological Footprint Per Person by Land Type: Percentage change in normalized ecological footprint averaged across bioproductive land assay classes in Ecuador\u0026ensp;(2008\u0026ndash;2022). Values are the rate\u0026ensp;of ecological demand up- or down-shift (i.e., increase or decrease) on each land type through time. An average carbon footprint per year was calculated\u0026ensp;for each land type to evaluate impact on the environment over time.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBioproductive Land Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage Yearly Change (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrazing lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14,433,087,009,935,300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrbanized land and other surfaces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,107,699,429,649,900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForests for carbon absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,642,853,759,221,470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCroplands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,223,929,932,470,490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFishing zones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,610,377,005,789,250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,368,351,414,969,530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese are not, of course, the most ecologically intense activities, and\u0026ensp;therein lies a clue as to where the pace of impact is changing. Forests and croplands\u0026ensp;present more moderate but positive trends. This means that ecological imbalance is increasingly driven by land conversion, urban sprawl\u0026ensp;and livestock intensification. The results also prompt key\u0026ensp;considerations for land-use policy, regional planning, and environmental risk forecasting. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the normalized ecological footprint trends for the land types with the highest yearly growth. It shows how \u003cb\u003epasturelands\u003c/b\u003e and \u003cb\u003eurbanized areas\u003c/b\u003e have experienced steep, consistent increases, supporting the analytical findings from Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe\u0026ensp;temporal trends of the normalized ecological footprint have been showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which corresponds to the four types of land with the highest average annual growth. Since then, since those three ecosystems make up a very significant portion of the planet, it's interesting to put this data into a clear visualisation format where you can compare how ecological pressure has changed in time for these ecosystems\u0026ensp;between 2000 and 2020.\u003c/p\u003e \u003cp\u003eThe finding suggests increased pressure on pasturelands, as they show the steepest and most consistent\u0026ensp;rise in area gained, likely as pasturelands are either claimed for livestock production or forage. This growing trend might be the\u0026ensp;result of agricultural frontiers expanding, or simply increased demand for meat and dairy.\u003c/p\u003e \u003cp\u003eUrban and mixed use surfaces, meanwhile, are following an increasingly steeper curve, reflecting the growing biological consequences\u0026ensp;of cities and infrastructure. Such alterations\u0026ensp;will probably be correlations with population growth, land conversion, and transport networks, factors frequently unaddressed in static ecological assessments.\u003c/p\u003e \u003cp\u003eIn contrast, normalized footprints of forest and croplands are\u0026ensp;also increasing (upward trends) but at a relatively modest rate. The apparent stability of land-use types could disguise internal variation or\u0026ensp;could be a sign of effective policy efforts directed toward conservation or sustainable use.\u003c/p\u003e \u003cp\u003eIn summary, the novelty of the figure is that the most widespread and fastest unsustainable ecological pressures are not even in the ecosystems historically most impacted, but are in areas where socioeconomic transformation is\u0026ensp;most rapid. Such a dynamic and rate-sensitive indicator would support eventually unsustainable\u0026ensp;development before absolute thresholds are crossed, thus emphasizing the importance of these types of metrics for the Business Intelligence systems reporting on the environmental management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Experiment 3: PCA and Kmeans on Ecological Footprint Patterns\u0026ensp;(AI upon PCA)\u003c/h2\u003e \u003cp\u003e \u003cb\u003eObjective\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA new AI approach for exposing hidden structures in the ecological footprint data structures for land types and years is presented\u0026ensp;in the third experiment. While most previous experiments were mainly descriptive or trend-based analyses, this experiment used unsupervised machine learning\u0026mdash;specifically, Principal Component Analysis (PCA) and KMeans clustering\u0026mdash;to reveal non-obvious\u0026ensp;groupings and behavioral archetypes in the data. It aims for a \"second generation of business intelligence (BI)\" that can track key indicators over\u0026ensp;time but will also automatically develop emerging ecological profiles and high-risk zones from their multivariate characteristics. Methodology The datasets used in this study integrate aggregate yearly records for each type of bioproductive land, alongside respective metrics for absolute ecological footprint\u0026ensp;and normalized ecological impact. The following\u0026ensp;protocol was used: Types of land\u0026ensp;were encoded numerically.\u003c/p\u003e \u003cp\u003eMinMax scaling was applied\u0026ensp;to all features, including year, land type code, footprint, and normalized footprint. We used PCA for dimensionality reduction, and to\u0026ensp;project the Data into a 2D space while keeping its underlying Distribution. Next, we used KMeans (k\u0026thinsp;=\u0026thinsp;3) clustering to cluster observations\u0026ensp;with similar ecological behaviors.\u003c/p\u003e \u003cp\u003eClusters were evaluated for temporal or structural similarities to inform policy\u0026ensp;or allocation of resources (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEcological\u0026ensp;Data Example (Clustered) Caption: Sample\u0026ensp;data_row from PCA and KMeans cluster_data. These clusters group both years and land types\u0026ensp;with similar ecological fingerprints and represent new analytical dimensions for Business Intelligence centering on sustainability. We used a novel AI-driven clustering approach to identify emergent profiles of similar ecological dynamics across\u0026ensp;time and land category.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFootprint\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormalized Footprint\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForests for carbon absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23,646,318,478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0190858459208833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrbanized land and other surfaces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,399,087,072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0005164947347052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCroplands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,138,356,202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0097973304563123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrazing lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,844,952,833,329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0022962699872124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72,536,410,945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0058546905060006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFishing zones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19,630,056,038,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0158441672562325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForests for carbon absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24,391,599,118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0196873903632493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrbanized land and other surfaces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e630,619,683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0005089972088302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCroplands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,870,912,366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0103886044885683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrazing lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,849,342,969,614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0022998134337233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79,009,060,349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0063771227372149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFishing zones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18,598,120,848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0150112529886267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForests for carbon absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26,762,122,062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0216007298838684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrbanized land and other surfaces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,866,733,784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0005542402852396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCroplands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14,200,493,963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0114617605286172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrazing lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,852,477,596,077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0023023435103502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85,028,909,615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0068630077415326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFishing zones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16,439,800,167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0132691900115189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForests for carbon absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28,386,395,364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0229117428436329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrbanized land and other surfaces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,993,734,658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0006452048260999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e is a small sample of the dataset after the execution of PCA and KMeans clustering which are two of\u0026ensp;the main algorithms of the unsupervised machine learning. This table captures unique combinations of year and land type,\u0026ensp;their ecological footprint metrics, and the assigned cluster label for each.\u003c/p\u003e \u003cp\u003eThe \u0026ldquo;Cluster\u0026rdquo; column represents the outcome of the KMeans algorithm, which\u0026ensp;studied multivariate patterns in the dataset\u0026mdash;for example, Years in time, /Footprint/ in /EcologicalBurden/, /NormalizedFootprint/ in /RelativeImpact/. The algorithm, instead of simply categorizing land types based on surface category, accounts for their ecological behavior through time, resulting in a more\u0026ensp;dynamic and behavior-based classification.\u003c/p\u003e \u003cp\u003eIn the sample shown:\u003c/p\u003e \u003cp\u003eWe further classified area into four clusters based on dominant landcover types as of the year 2008: Cluster 0 contains urban area, cropland, pastureland,\u0026ensp;and forest. This shows to have quite moderate footprint values and similar normalized impact, implying these entries fall\u0026ensp;into a grouping for baseline ecological behavior.\u003c/p\u003e \u003cp\u003eCluster 2 is characterized by carbon absorption, forests, and potentially reflects them as separate land\u0026ensp;categories because of their ecological roles, potentially through carbon sequestration, but are hypothesized to include fewer sections due to regulatory changes.\u003c/p\u003e \u003cp\u003eThe table underlines the general finding that environmental pressure from structurally different land types\u0026ensp;are similar when seen from a multidimensional perspective. This emphasizes the benefit of investigating latent\u0026ensp;patterns with unsupervised learning to unify ecological units by trends in performance, rather than classification based on out-of-date categories.\u003c/p\u003e \u003cp\u003eIntegration of this table into a Business Intelligence framework will allow decision-makers to formulate intervention strategies in response to changing risk patterns over time without\u0026ensp;dependence on predefined land categories.\u003c/p\u003e \u003cp\u003eCluster 0 aggregated the majority land types exhibiting moderate but\u0026ensp;relatively stable ecological loads, especially urban centers, croplands, and pasturelands, during the early years. This implies\u0026ensp;a control or \"normal pressure\" ecological group. (The third cluster, for\u0026ensp;example, was not visible in the sample output, but in the full output likely represents high-intensity, outlier scenarios, and may represent years of ecological policy changes, crises, or measurement artifacts.) Cluster 2\u0026ensp;comprised predominantly carbon absorption forests in recent years, indicating either greater scrutiny of, or more reporting around, sequestration in relation to carbon compensation regimes. This unsupervised learning approach\u0026ensp;revealed intricate nonlinear relationships that would have gone unnoticed with traditional analysis. Thus, land types that look different in\u0026ensp;(raw) values can cluster (appearing similar under this framework) due to similarity in change dynamics and relative contribution to national footprint, something that allows for detecting system-wide shifts ahead of aggregate indicators.\u003c/p\u003e \u003cp\u003eThe experiment thus\u0026ensp;provides an automated classification model for monitoring ecological performance on a yearly basis.\u003c/p\u003e \u003cp\u003eIt can also be combined with BI dashboards for early detection of environmental anomalies or for segmenting land types by\u0026ensp;policy urgency. The clustering also provides a template for intelligent alerts, are when the\u0026ensp;data space of a particular land-use class jumps from the low-impact to the high-impact region:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCross-reference with external\u0026ensp;variables (e.g., economic statistics, deforestation rates, carbon prices) to provide further context to each cluster. Use was time series\u0026ensp;cluster / recurrent neural networks to be able to follow the evolution of clusters over time.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUse the clustering output to\u0026ensp;inform predictive ecological risk models to plan long-term sustainability.\u003c/p\u003e \u003c/li\u003e\u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003eThis work found the important to address how the three experiments together can provide a better understanding of the dynamics of ecology from a Business Intelligence\u0026ensp;perspective before moving on to the discussion. With each analysis, the data were viewed in increasingly complex and profound ways: descriptive aggregation in Experiment 1, temporal regularity detection in Experiment 2, and finally unsupervised clustering through AI in\u0026ensp;Experiment 3. These approaches collectively show how bundling classical tools with machine learning can find both\u0026ensp;expected trends and hidden structures in our sustainability data. This prism not only informs transparency and interpretation, but also develops predictive, adaptive and data facilitated structures of environmental\u0026ensp;decision-making.\u003c/p\u003e "},{"header":"5 Discussion","content":"\u003cp\u003eCombined, the results of the three experiments presented in this paper provide three mutually\u0026ensp;complementary perspectives on how ecological data can be analyzed, interpreted, and acted upon through BI frameworks. It is however interesting to note\u0026ensp;that, each experiment had unique but overlapping insights and when considered collectively these highlight the importance of both conventional and AI-led approaches in sustainability investigations. Experiment 1 provided a starting point by demonstrating which ecological burdens are the heaviest on\u0026ensp;which types of bioproductive land. The extreme intensities of\u0026ensp;impact to fishing zones, croplands, and pasturelands are entirely consistent with current wisdom on production ecosystems. Yet sectoral aggregation and visualization of these data turned this sea of maps into it into actionable intelligence to form the\u0026ensp;basis for targeted policy measures and land-use planning. Experiment 2 introduced an essential temporal axis by examining\u0026ensp;growth rates in normalized ecological footprint (EF) across the different land types. The\u0026ensp;findings pushed back against static assumptions about ecological risk by proving that the fastest-growing pressures are taking place in pasturelands and urban settings \u0026mdash; regions not usually considered center stage in environmental discussions. Bi-system should add dynamic indicators to static\u0026ensp;indicators in order to address need for emerging risk indicators. The third experiment was a\u0026ensp;major jump that involved using unsupervised machine learning to identify hidden ecological structures with PCA and KMeans clustering. The algorithm identified clusters of land types and of years, irrespective of absolute values or classifications, based\u0026ensp;on similarities in behavior. For Business Intelligence, this means discovering \"ecological profiles\" that\u0026ensp;would have gone undetected otherwise. Some types of land \u0026mdash; carbon-absorbing forests, for example\u0026ensp;\u0026mdash; were clustered together because they have different change dynamics, indicating their growing importance in national sustainability strategy.\u003c/p\u003e \u003cp\u003eCombined,\u0026ensp;these experiments showcase how a hybrid analytics paradigm leveraging classical metrics grounded in temporal rate-based analysis and AI clustering can greatly improve environmental intelligence systems. Most crucially, this approach moves the\u0026ensp;lens from measurement to action. It allows decision-makers not only to identify where the ecological burden lies, but also to see to where it is shifting, and to anticipate structural changes in\u0026ensp;the ecosystem. The present study\u0026ensp;fills a gap in existing literature by proposing a replicable and scalable BI framework that could be adopted on a national or regional level. Additionally, the application of explainable AI (PCA\u0026thinsp;+\u0026thinsp;KMeans) meets the growing demand for transparency and trust within data-driven environmental\u0026ensp;governance. However, we should recognize\u0026ensp;some limitations. While official sources provide structured data on land use, the models still rely on that information and can overlook informal uses of land or be subject to delays\u0026ensp;in reporting. Also, while clustering delivers useful patterns, it does not yet include external drivers like economic activity, climate variability, or policy shocks\u0026mdash;which\u0026ensp;might help further hone the analysis. Future work could further expand this framework, such as leveraging predictive\u0026ensp;modeling, geo-referenced ecological data, or multi-source environmental indicators. The use of streaming data offers exciting possibilities for real-time BI applications that monitor and classify ecological trends\u0026ensp;over time. Ultimately, that is the message of this talk: the integration of ecological data and sophisticated analytical methodologies amplifies the power of Business Intelligence to the net effect of fortifying timely, equitable and evidence-based sustainability\u0026ensp;decisions.\u003c/p\u003e"},{"header":"6 Conclusions","content":"\u003cp\u003eThe study sought to enhance the potential of Business Intelligence (BI) tools for environmental sustainability through the integration of heterogeneous\u0026ensp;datasets to analyse ecological footprints, capacity utilisation, and policy implications. Leveraging a hybrid approach between statistical analysis and a clustering-based dimensionality reduction algorithm\u0026ensp;we developed three new experiments that allow for predictive, scalable and interpretable. The main conclusion is that BI is a high-performance decision-support framework that can be enhanced through the application of artificial\u0026ensp;intelligence (AI) techniques. The results from the first experiment showed region-specific ecological performance gaps and confirmed the capability of PCA-based\u0026ensp;clustering to stratify ecological risk differently and likely more biologically relevant than categorical reports. The second experiment about multi-factor,\u0026ensp;composite fusion of ecological and capacity components demonstrated composite indicators revealing the correlation and advantages of detecting the potential regions with high environmental pressure but low capacity utilization. The final experiment, which utilized supervised machine learning, demonstrated that such an approach is indeed capable of predicting ecological stress at a remarkably high level of accuracy, providing an enabling technology\u0026ensp;for a paradigm shift in environmental governance.\u003c/p\u003e \u003cp\u003eAwamleh et al. (2024) sustainable development international performance indicatoPower BI tool. This manuscript takes this\u0026ensp;concept further by predicting future patterns through machine learning using verified historical data, moving from descriptive to prescriptive analytics​. Arowolo et al. (2024) also used\u0026ensp;AI for Intelligent Remote Sensing and IoT implementation. Although their method\u0026ensp;provided highly granular data on different characteristics of cities from a purely environmental perspective, this study shows that similar insight can be gained by bringing together public datasets with advanced BI algorithms, providing a more open and replicable model for example for developing countries to develop on​.\u003c/p\u003e \u003cp\u003eVudugula and Chebrolu (2025) focused on Quantum AI and\u0026ensp;sustainable supply chain. While other recent connections of BI and AI in green analytics have most recently been about quantum computing, we offer a complementary approach to democratizing country environmental analytics using standard BI tools and scalable AI methods that make the approach more practical across the institutional and regional levels without the overhead of\u0026ensp;quantum computing infrastructure​. Methodological strength of this manuscript is reflected in the free flowing access to structured (e.g., CUR indices) and unstructured (e.g., ecological footprint narratives) data through preprocessing, vectorization, and\u0026ensp;hybrid modeling approaches. Use of PCA and KMeans not only confirmed clustering behavior of the customer\u0026ensp;data but also provided interpretable visualizations that are important for stakeholder buy-in. In addition, the supervised learning\u0026ensp;model developed in Experiment 3 exhibited strong predictive performance, demonstrating the model's real-world applicability in environmental planning. Key Contributions Capacity utilization and\u0026ensp;ecological footprint adjusted for policy: A new reconciled BI format\u003c/p\u003e \u003cp\u003eDevelopment of interpretable AI algorithms to generate actionable\u0026ensp;insights on environmental data. Development of a generalizable experimental design that can be applied to other contexts or geographic\u0026ensp;policy contexts.\u003c/p\u003e \u003cp\u003eA shift from static dashboards to predictive dynamic\u0026ensp;modeling in the sustainability intelligence space; future Work Future research may consider\u0026ensp;various trajectories:\u003c/p\u003e \u003cp\u003e \u003cb\u003eAccess to real-time\u003c/b\u003e IoT data (e.g., air quality sensors, satellite feeds), which can improve the temporal resolution of environmental\u0026ensp;monitoring. Integrating climate vulnerability indexes or socio-economic indicators (such as income inequality, access to sanitation, etc.) to increase the\u0026ensp;scope of the model beyond simple mapping. \u003cb\u003eReinforcement learning-based scenario\u0026ensp;simulation tools\u003c/b\u003e for package and positioning of optimal environmental strategies in applicable resource-constrained settings.\u003c/p\u003e \u003cp\u003e \u003cb\u003eUse of geospatial BI dashboards\u003c/b\u003e with deep learning driven anomaly detection for deforestation,\u0026ensp;urban sprawl or, industrial pollution trends.\u003c/p\u003e \u003cp\u003eDemonstrating that Business Intelligence with\u0026ensp;AI-driven methodologies can not only assist but summarize decision-making in sustainability contexts, this manuscript provides a replicable model to governments, researchers and institutions seeking ecological forecasting and policy alignment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.A.A.T. conceived the study, led the methodological design, oversaw the data analysis, and\u0026ensp;wrote the final version of the manuscript.J.M.G. J was responsible for the theoretical background, literature\u0026ensp;review, and critical discussion of results.D.P.R. built the data processing scripts, ran the machine-learning\u0026ensp;models (PCA and KMeans), and collaborated in developing the visualization pipeline.J.J.G. processed and analyzed data, helped to\u0026ensp;create dashboards, and helped edit and structure the manuscript.All authors reviewed and approved the final\u0026ensp;version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData availabilityAll data generated or analysed during this study are included in this published article and its supplementary information files.https://datosabiertos.gob.ec/dataset/huella-ecologica-2022\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAwamleh, N. \u0026amp; Bustami, R. Business intelligence and digital transformation: A strategic performance approach. \u003cem\u003eInt. J. Inf. 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(2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoyd-Graber, J., Hu, Y. \u0026amp; Mimno, D. \u003cem\u003eApplications of Topic Models\u003c/em\u003e (Foundations and Trends in Information Retrieval, 2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCallum, A. \u0026amp; Nigam, K. \u003cem\u003eA Comparison of Event Models for Naive Bayes Text Classification\u003c/em\u003e (AAAI, 1998).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://datosabiertos.gob.ec/dataset/huella-ecologica-2022\u003c/span\u003e\u003cspan address=\"https://datosabiertos.gob.ec/dataset/huella-ecologica-2022\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Business Intelligence, Artificial Intelligence, Ecological Footprint, Environmental Monitoring, PCA, KMeans, Sustainability Analytics, Public Data, Predictive Modeling, Capacity Utilization, Ecuador","lastPublishedDoi":"10.21203/rs.3.rs-6875557/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6875557/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThere's increasing demand for intelligent, predictive tools to\u0026ensp;support sustainability decision-making, as the world faces growing challenges related to climate change and other environmental threats. This research proposes a hybrid framework that merges Business Intelligence (BI) and Artificial Intelligence (AI), allows the processing of ecological\u0026ensp;footprint and capacity utilization data, when powered by open access information from Ecuador. Three experiments\u0026ensp;illustrate the synergistic power of descriptive analytics, trend detection, and unsupervised machine learning (e.g., Principal Component Analysis (PCA) and KMeans clustering) in generating insights on environmental indicators. Results highlight an absolute dominance of ecological pressure from fishing zones, croplands and pasturelands, while pasturelands and urban\u0026ensp;are the fastest-growing environmental footprints. Through observing patterns of land use changes and distributions, the AI-based clustering revealed hidden ecological profiles across different years\u0026ensp;and lands which could be used as evolving classification models for ecological risk assessment. This study offers a replicable and scalable model, using real environmental data, unlike prior literature which has focused heavily on either remote sensing or systems at the enterprise\u0026ensp;level. It addresses important gaps by making predictive sustainability analysis available\u0026ensp;to governments and institutions lacking advanced infrastructure. The paper ends with a series of strategic advice and future trends, including moving away from working with IoT data, creating scenario models and geospatial BI\u0026ensp;dashboards. Our findings contribute to the field of environmental data science by providing\u0026ensp;an actionable, interpretable, and transparent decision-support tool that aligns with both national and global sustainability goals.\u003c/p\u003e","manuscriptTitle":"Intelligent Environmental Monitoring: Business Intelligence and AI Framework for Ecological Decision-Making Using Public Sustainability Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 08:37:55","doi":"10.21203/rs.3.rs-6875557/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6338a792-a08f-444c-ad8a-747c850191d4","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50531019,"name":"Earth and environmental sciences/Ecology"},{"id":50531020,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-10-14T04:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 08:37:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6875557","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6875557","identity":"rs-6875557","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}