Digital Economic Development Benefits Water Environmental Quality in the Yellow River Basin

Abstract

The digital economy, as an advanced economic form, exerts a profound yet unclear influence on water environmental quality within large-scale watersheds. Focusing on the Yellow River Basin (YRB), the second-largest river in China, this study investigates this complex relationship. We developed a novel dual-engine coupling model integrating Support Vector Machines (SVM) and Light Gradient Boosting Machines (LightGBM) to establish comprehensive multi-input, multi-output linkages between digital economy indicators and water quality parameters. Results show that (1) There are notable spatial disparities and synergies in the basin, regions with more developed digital economy generally have better water environmental quality. (2) The SVM model effectively captures the complex spatial relationship between digital economy inputs and water quality outputs, with an average training accuracy above 0.80 and average validation accuracy above 0.70, indicating that digital economy variables are sensitive to water quality changes. (3) The LightGBM model identifies key driving factors and contributions, revealing that digital industrialization has a more significant impact on water quality improvement than industrial digitization. Thus, digital industrialization is a crucial pathway for green transformation in large—scale catchments.

1. Introduction

Global environmental governance has entered a new era marked by green and low—carbon development [1,2]. As an inevitable result of the progress in information technology, networking, and intelligence, the digital economy, succeeding the agricultural and industrial economies, represents a more advanced stage of economic evolution. It reshapes production and consumption patterns and plays a crucial role in the global economy and high—quality development [3,4]. In 2024, it accounted for 60% of the global GDP, underscoring its rising significance [5]. The Yellow River Basin (YRB), with high sediment loads and significant governance challenges, faces severe water security threats such as scarcity, pollution, imbalanced water—sediment dynamics, and restricted socioeconomic development [6,7,8]. Resolving these issues is vital for China’s ecological protection and high—quality development in this region. Notably, in 2022, nearly 30,000 digital economy enterprises in the basin generated 1.2 trillion RMB (∼$168 billion), contributing over 30% to the regional GDP, which demonstrates its key role in regional transformation [9]. Consequently, elucidating digital economy-water quality interactions in the YRB is essential for both safeguarding water security and establishing replicable green development frameworks for global river basins.

The term “Digital Economy” initially introduced by Tapscott in the 1990 [10]. Since then, it has become a pivotal strategy for numerous countries and regions to expedite economic growth [11,12,13]. In ecological conservation, the digital economy is key in shifting economic structures towards low—carbon and sustainable models. It fosters green technology adoption, optimizes industries, curbs emissions and pollution, boosts water efficiency, and safeguards water ecosystems [14,15,16]. However, some scholars argue that the digital economy may also lead to increased energy consumption and potential pollution, threatening the resilience of ecosystems [17]. Therefore, the interaction between the digital economy and environmental protection is highly complex, involving numerous theories, methods, and heterogeneous data. Identifying the key driving factors influencing the water environment in large river basins remains a significant challenge.

Environmental economics research aims to balance ecological conservation with economic growth by developing methodologies to quantify their coordination and evaluate eco-economic benefits [18,19]. Established approaches include input–output models [20], econometric frameworks based on the Environmental Kuznets Curve (EKC) [21,22], comprehensive evaluation model [23,24,25], coordination degree model [26], and the ecological—economic integration mode [25]. Empirical applications demonstrate their effectiveness: fixed-effects, spatial Durbin, and mediation models have revealed linkages between digital economic expansion and urban pollution reduction across 285 Chinese cities [27]. EKC-based analyses further correlate chemical oxygen demand and ammonia nitrogen emissions with per capita GDP dynamics in Chinese watersheds [28], while EKC model and comprehensive evaluation model elucidate non-linear relationships between environmental quality and economic growth in the YRB [29].

The recognition of non-linear dynamics between socioeconomic shifts and environmental outcomes has driven the adoption of machine learning for big data analytics and multivariate relationship modeling in environmental economics [30,31,32], though most applications focus on atmospheric studies. In predictive and policy-relevant modeling, gray fuzzy techniques have been employed to forecast CO₂ emissions in several countries and assess the impacts of environmental policies on economic growth, industry, employment, and income—forming coupled economic-environmental policy models. For trend projection, machine learning clustering s and dimensionality reduction techniques have been applied to predict economic growth, energy consumption, and CO₂ emissions across multiple countries [32,33]. To attribute emissions to socioeconomic factors, decision tree inversion algorithms have been used to analyze relationships between provincial CO₂ emissions and nighttime light data over specific periods [34]. Furthermore, integrated approaches combining multiple regression with deep learning, along with optimization algorithms such as the Fruit Fly Optimization Algorithm (FOA), have been deployed to identify key drivers and underlying causes of changes in CO₂ emissions [35].

Currently, Research on the influence mechanism of the digital economy on aquatic environmental quality in large river basins remains limited. This study aims to address this gap in the YRB through three integrated objectives: (1) Construct an indicator system for the YRB to quantify the spatial pattern and distribution characteristics of digital economy and water quality. (2) Develop a dual-algorithm diagnostic framework (Support Vector Machines and Light Gradient Boosting Machine) to model multi-input/output relationships between digital economy drivers and aquatic systems. (3) Quantitatively identify and interpret digital economy impacts on basin-scale water quality. We expect to unravel the mechanism by which digital economy influences water quality and provide specific recommendations to assist in the ecological protection and high-quality development of the YRB.

2. Materials and Methods

2.1. Study Area and Data

The Yellow River, renowned as the fifth-longest river in the world and the second- longest in China, spans an impressive 5464 km and covers a basin area of approximately 752,443 km². It originates from the Bayan Har Mountains’ northern foothills on the Qinghai–Tibet Plateau and flows through nine provinces: Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shanxi, Shaanxi, Henan, and Shandong, before emptying into the Bohai Sea [9,36]. This study focuses comprehensively on the entire YRB, aiming to thoroughly analyze the socio-economic landscape and water environment quality throughout 2022 and their interrelated dynamics.

Prefecture-level cities were chosen as the core analytical unit due to their pivotal role in China’s administrative structure, wielding significant authority in economic and environmental planning—making them an ideal scale to examine interactions between socioeconomic drivers and water quality. Although the YRB contains diverse administrative unit types (e.g., autonomous prefectures with distinct policy frameworks), this heterogeneity reflects regional characteristics rather than confounds, thereby strengthening the analytical depth.

The research area includes 73 prefecture-level cities across eight provinces (autonomous regions) within the YRB (Table S1). Specifically, the scope covers eight prefecture-level cities in Qinghai Province (including the provincial capital Xining), eleven prefecture-level cities in Gansu Province (including the provincial capital Lanzhou), five prefecture-level cities in Ningxia Hui Autonomous Region (including the provincial capital Yinchuan), seven prefecture-level cities in Inner Mongolia Autonomous Region (including the provincial capital Hohhot), nine prefecture-level cities in Shaanxi Province (including the provincial capital Xi’an), eleven prefecture-level cities in Shanxi Province (including the provincial capital Taiyuan), eleven prefecture-level cities in Henan Province (including the provincial capital Zhengzhou), eleven prefecture-level cities in Shandong Province (including the provincial capital Jinan). The specific administrative regions and the precise boundaries of the YRB are clearly depicted in Figure 1.

Data for this study on the relationship between digital economy development and water environmental quality in the YRB were collected from authoritative sources. Primary indicators of the digital economy were sourced from the China City Statistical Yearbook (2022) and the Peking University Digital Inclusive Financial Index Report (2022). Water quality data were obtained from 53 National and provincial water quality stations (Figure S1).

2.2. Methods

The analyze methods include two parts in this study, (1) Construction of the Index System: selecting the representative indicators to characterize the development quality of the digital economy and the water environmental quality in the YRB, respectively. (2) Development of the Response Model: developing a Response Model Driven by a Dual-Engine Machine Learning Algorithm, then identifying the relationship between the digital economy and water quality in the YRB.

2.2.1. Construction of the Index System

The prefecture-level city was chosen as the key analytical unit due to its pivotal role in China’s administrative structure, wielding significant authority in economic and environmental planning. This scale is optimally suited for investigating interactions between socioeconomic drivers, such as the digital economy, and environmental outcomes like water quality.

To comprehensively assess the interplay between the digital economy (DE) and water environmental quality in the YRB, a composite index system was constructed for each domain (Figure 2). The construction process followed a structured approach: indicator selection, standardization, dimensionality reduction/integration, and index formulation, as detailed below.

(1) 

Digital Economy Index System

The digital economy is a multi-faceted concept, primarily encompassing digital industrialization and industrial digitalization [37]. To measure city-level digital economic development in the YRB, we drew upon well-established indicator frameworks from prior studies [37,38,39,40].

(a) Indicator Selection: Based on data availability and relevance to the YRB context, five representative indicators were selected:

Internet Broadband Access Users (IBAU): Reflects the penetration of fundamental digital infrastructure.

Number of Mobile Phone Subscribers (NMPS): Indicates the popularity of mobile digital terminals.

Employees in Computer Services and Software Industries (ECS): Represents the scale of the digital core industry workforce.

Total Telecommunications Business Revenue (TR): Measures the economic output of the digital sector.

Financial Inclusion (FI) Index: Serves as a proxy for industrial digitization, particularly the application of digital technologies in the financial sector [41].

The first four indicators collectively characterize digital industrialization, while the FI index reflects industrial digitalization.

(b) Data Standardization and Integration: Given the multi-dimensionality and potential correlation among these indicators, Principal Component Analysis (PCA) was employed to reduce dimensionality and avoid multicollinearity issues. The raw data for each indicator were first standardized to a mean of 0 and a standard deviation of 1 to eliminate unit differences. PCA was then performed on the standardized data. The number of principal components (PCs) retained was determined based on the Kaiser criterion (eigenvalue > 1) [42]. To enhance the interpretability and normalize the distribution, this preliminary score underwent a logarithmic transformation, resulting in the final Digital Economy Index (DE) for each city. A higher DE value indicates a higher level of digital economic development.

(2) 

Water Environmental Quality Index System

Water quality was evaluated using the well-established Water Quality Index (WQI) method [43], which integrates multiple physical-chemical parameters into a single score, facilitating spatial and temporal comparisons.

(a) Indicator Selection: Given that there is little difference in dissolved oxygen among water quality stations, four conventional and crucial water quality parameters were selected as they are key indicators of organic pollution and eutrophication, which are of particular concern in the YRB [38,44]: Total Phosphorus (TP), Total Nitrogen (TN), Ammonia Nitrogen (NH₃-N), Chemical Oxygen Demand (COD).

(b) WQI Calculation: The calculation followed the standard procedure outlined in [43]. Briefly, each parameter’s measured concentration was first assigned a sub-index (SI) value based on China’s Grade III surface water standards. The overall Water Quality Index (WQI) was then computed as the weighted sum of these sub-indices:

WQI = Σ (Weightᵢ × SIᵢ)

(1)

The weights for each parameter were determined based on their relative importance and perceived impact on overall water quality, as recommended in [43]. Consistent with previous applications [38,44], a higher WQI value signifies better water quality.

2.2.2. Machine Learning Model Coupled with SVM and LightGBM

This study investigates the complex interactions between the digital economy and water quality by leveraging machine learning techniques capable of capturing non-linear relationships among multifaceted variables. A novel dual-engine framework integrating Support Vector Machines (SVM) and Light Gradient Boosting Machines (LightGBM) is proposed to quantitatively assess these connections. SVM is employed to delineate spatial relationships between digital economy indicators and water quality under limited sample availability, while LightGBM identifies key driving factors and their contributions to changes in water quality. The model incorporates five digital economy input variables and four water quality output indicators. The overall technical workflow is illustrated in Figure 3.

The development of this multi-input-multi-output framework was motivated by the limitations of conventional indexing systems for this specific research problem. Traditional approaches often rely on pre-defined, subjective weightings to aggregate either economic or environmental variables into monolithic indices [12,39]. This aggregation obscures the individual contributions and non-linear pathways through which specific digital economy drivers (e.g., infrastructure) influence distinct water quality parameters. Our approach is therefore required to model the full suite of complex relationships without losing this critical granularity. It moves beyond predicting a single outcome to elucidating the specific sensitivity of each water quality response to each economic driver, a capability that earlier indexing systems lack.

The SVM, a powerful machine learning algorithm introduced by Vapnik in 1995 [45], for predictive tasks owing to its broad applicability across scientific fields [46,47,48]. The essence of SVM regression is mapping n-dimensional sample vectors from their original space to a high-dimensional feature space F using a non-linear mapping function Φ. In this space, the algorithm seeks a linear hyperplane that minimizes the total deviation ɛ (ɛ ≥ 0) between all sample points and the hyperplane, represented by:

F(x) = ω^T·Φ(x) + b

(2)

However, the SVM model struggles to fully capture the intricate relationships and contributions among multiple factors [49]. To overcome this limitation, LightGBM is adopted as a complementary learning algorithm. LightGBM builds complex non—linear models by integrating multiple weak learners, usually decision trees. By learning from the residuals of previous iterations to refine the model, it allows for the assessment of feature importance and variable interactions. Through a multi—iteration optimization process, LightGBM excels at capturing non—linear relationships and identifying key predictive factors, making it ideal for discerning global driver sensitivities [50,51]. Compared with traditional GBM, LightGBM has distinct traits. It uses a leaf—wise strategy for faster convergence and efficiency, a histogram—based algorithm to reduce memory and handle large datasets, supports parallel training, and integrates optimizations to cut memory use and boost performance [52,53,54].

Consequently, the LightGBM model is predominantly employed to evaluate the effects of multiple input-output factors on the water quality of the YRB. The aim is to identify the main factors driving changes in the water environment. This dual—engine approach, which combines the SVM and LightGBM models, offers a powerful framework for in—depth and comprehensive analysis. The architecture of this coupled machine—learning model is depicted in Figure 4.

In this study, five digital economy indicators served as input variables, while four water quality indicators were selected as outputs. This configuration enables the construction of a multi-input, multi-output SVM model to analyze the relationship between digital economic activity and water quality in the YRB. The model’s training and predictive performance were evaluated using multiple metrics—including accuracy, precision, and recall—tailored to its objective function. Hyperparameter optimization was conducted within a defined search space, with a maximum of 200 iterations (Max_evals) and a train-test split ratio of 0.3 (test_size). The SVM was configured with a kernel parameter of 1, while the regularization parameter C and kernel coefficient gamma were set to 22.447 and 0.517, respectively. After training the SVM with these optimal parameters, we computed a suite of performance metrics to evaluate overall, training, and validation accuracy. These included the Nash–Sutcliffe Efficiency (NSE), Coefficient of Determination (R²), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and a general coefficient term (Coef).

3. Results

3.1. Spatial Distribution Analysis of Digital Economy and Water Quality in the YRB

WQI assessment revealed a distinct longitudinal pattern of water environmental quality across the YRB in 2022: upstream regions exhibited the highest quality (WQI = 47.27), followed by downstream areas (WQI = 41.54), with midstream sections demonstrating relatively inferior conditions (WQI = 38.86). At the provincial-level, the median WQI analysis showed that the water quality in the upstream provinces of Qinghai and Gansu was relatively good, with median WQI values of 53.89 and 51.05, respectively. The water quality in four provinces was at a medium level, namely Ningxia, Henan, Inner Mongolia and Shandong, the median WQI in Ningxia is 47.89, while that in the other regions was 42.22 (Figure 5a). The water quality in Shanxi and Shaanxi provinces was relatively poor, with the median WQI in both provinces being less than 40. Among prefecture-level cities within the province, Shanxi, Inner Mongolia and Shandong provinces exhibited greater variability in water quality across their respective regions, while other provinces showed comparatively less variability (Figure 6a). Prefecture-level cities with lower WQI were primarily located in Jinzhong, Shuozhou of Shanxi Province, Yan’an, Yulin, Weinan of Shaanxi Province, Bayannur, Wuhai of Inner Mongolia, Guyuan of Ningxia, Qingyang of Gansu Province.

In 2022, the spatial distribution of the DE in the YRB showed a gradually decreasing trend from east to west. The downstream reaches had the highest level of digital economic development (DE = 11,006.49), followed by the midstream reaches(DE = 9889.92), and the upper reaches had a relatively lower level (DE = 3987.05), highlighting significant disparities in development across the regions (Figure 5b). At the provincial-level, the median DE analysis showed that the digital economy development in the Shanxi, Henan, Shandong and Shannxi was relatively good, with median DE values of 12,618, 10,423, 6012 and 5470, respectively, especially in provincial capital cities such as Xi’an, Jinan, Zhengzhou, Yinchuan, and Taiyuan (Figure 6b). Conversely, relatively less developed regions were distributed across cities including Tianshui and Qingyang in Gansu Province, Bayannur, Wuhai in Inner Mongolia, Jinzhong, Shuozhouin Shanxi Provinceas, well as Guyuan and Zhongwei in southern Ningxia. Among prefecture-level cities within the province, significant disparities in digital economy development levels were observed among cities within the middle and lower reach provinces, including Shandong, Henan, and Shaanxi. In contrast, Qinghai, Inner Mongolia, and Ningxia demonstrated comparatively smaller differences across their prefecture-level cities.

Overall, cities in the YRB with more developed digital economies tend to exhibit relatively better water environment quality. The advancement of the digital economy typically drives technological innovation, enhances information infrastructure, and fosters the growth of high-tech industries. These developments play a crucial role in enabling scientifically informed, integrated approaches for precision management and restoration of regional water environment [3,4].

3.2. Sensitivity Analysis of Digital Economy and Water Quality Factors

To ensure model precision and prevent insensitivity or overfitting, correlation analysis preceded development of the machine learning model linking digital economy and water quality. This step strengthened the mode’s robustness and resilience to diverse data sources. The analysis was essential for characterizing interactions between independent and dependent variables, and its results directly informed prediction model refinement [52,53,54].

Pearson correlation analysis revealed positive associations between the WQI and five digital economy indicators, including DE, TR, ECS, NMPS and IBAU, the correlation coefficients were 0.26, 0.39, 0.17, 0.15 and 0.24, respectively (Figure 7). These positive correlations indicated a significant influence of digital industrialization factors on water environment quality. Notably, DE, as a comprehensive indicator of the digital economy, showed a strong correlation with the water quality index, underscoring a broad association between these variables.

Conversely, FI exhibited a negative correlation with WQI, the correlation coefficient was −0.3. This suggests that in some contexts, advancing financial inclusion may inversely relate to water quality. This finding highlights a potentially complex, nuanced relationship between digital economy dimensions and environmental quality.

3.3. Results of the Coupled Machine Learning Model for Digital Economy and Water Quality

Figure 8 and

Table 1. Provincial DE and WQI in the YRB.

Provinces	WQI		DE
	Average	Medium	Average	Medium
Qinghai	53.22	53.89	487	494
Gansu	47.60	50	4991	3529
Ningxia	43.82	42.22	6456	3972
Inner Mongolia	46.51	47.78	2303	1617
Shaanxi	37.74	34.44	10,262	5470
Shanxi	38.57	37.78	12,087	12,618
Henan	41.57	42.22	9203	10,423
Shandong	43.02	42.22	11,510	6012

summarize the training and validation results of the SVM model, demonstrating its simulation accuracy. The NSE values for the entire period, training period, and validation period were 0.858, 0.891, and 0.710, respectively. These NSE values indicated strong model performance across periods. Similarly, the R² values for these periods were 0.853, 0.809 and 0.767. These R² values reinforced the model’s accuracy, confirming its effectiveness in predicting water quality based on digital economy variables. The RMSE values were 0.622, 0.493, and 0.848 for the entire period, training period, and validation period, respectively. These values suggested reasonable performance across periods [32,33,34]. The MAPE values were 0.171, 0.170, and 0.172 for the respective periods. Consistent MAPE values indicated that the model maintained similar accuracy across analysis phases. The Coef values of the model for the entire period, training period, and validation period were 0.930, 0.956, and 0.868, demonstrating robustness and reliability over time (

Table 2. The objective function results of SVM model.

	NSE	R2	RMSE%	MAPE	Coef
ALL (the entire dataset period)	0.858	0.853	0.622	0.171	0.930
TRA (the training period)	0.891	0.809	0.493	0.170	0.956
VAL (the validation period)	0.710	0.767	0.848	0.172	0.868

). Collectively, these results showed that the SVM model achieved average fitting coefficients exceeding 0.85 for the entire period, 0.80 for the training period, and 0.70 for the validation period. The minimal relative error between predicted and observed values confirmed a mathematical relationship between digital economy indicators and water quality parameters [33,34,35].

The results suggest that the five digital economy indicators (TR, IBAU, NMPS, ECS, and FI) used as inputs have significant predictive power regarding the trends in water environmental quality in the YRB. These indicators were identified as key drivers of water quality changes within the basin.

3.4. Impact of the Digital Economy on Water Quality in the YRB

Based on the response relationship established by the SVM model, the LightGBM model was further used to quantitatively analyze the primary driving factors and their contributions to changes in water environmental quality of the YRB [52,53,54]. The results of the final GBM model are shown in Figure 9a, all the selected digital economy indicators contribute more than 30% to the water environmental quality of the YRB. Among them, TR, FI, and ECS were the key factors. TR was the factor with the highest contribution to the prediction of the WQI. That is, the total telecommunication service revenue was the most sensitive in the prediction model, with a contribution degree of 0.54 (approximately 54%). In addition, the contributions of the other two groups of variables, FI and ECS, to the water quality of the YRB were 0.411 (approximately 41%) and 0.403 (approximately 40%), respectively, second only to that of TR, indicating that the comprehensive impact of the digital economy also made a relatively significant contribution. The contributions of NMPS and IBAU to the water environmental quality were relatively small, with contribution coefficients of 0.348 (approximately 35%) and 0.298 (approximately 30%), respectively.

Figure 9b further illustrates the differences in the contributions of five digital economy changes. The boxplot revealed consistently high impact contributions for TR across all distribution metrics (upper extreme, upper quartile, median, and lower quartile), coupled with substantial dispersion, confirming its dominant influence on water quality. In contrast, FI exhibited the lowest median contribution and minimal dispersion extremes, aligning with its established negative correlation to water quality. ECS displayed near-symmetrical distribution with a high upper extreme, though its median fell slightly below NMPS. Despite this median difference, ECS maintained greater overall impact magnitude than NMPS. IBAU demonstrated the narrowest dispersion range with low central tendency metrics, indicating marginal influence. Collectively, these distributions quantified the differential environmental impacts of digital economy indicators in the YRB, establishing a statistical basis for their relative contributions.

4. Discussion

4.1. Spatial Difference in Digital Economy and Water Quality

Spatial analysis of the digital economy and water quality in the YRB revealed significant regional disparities and correlations. Water quality displays superior conditions in upstream and downstream segments but degradation in midstream sections. This spatial pattern—characterized by severe pollution in the midstream region—aligns with the findings of previous studies [7,8,55,56,57], which attribute it to the concentration of energy-intensive and polluting industries (e.g., coal chemistry) in this part of the basin. This spatial heterogeneity arises from distinct regional drivers. The upstream region benefits from low population density, minimal industrial activity, and robust water conservation capacity, particularly above Lanzhou where 60% of basin runoff originates [55]. Downstream areas exhibit enhanced ecological flows and environmental carrying capacity through integrated water management enabled by diversion projects [58]. Conversely, the midstream region concentrates China’s energy infrastructure, hosting 80% of national coal chemical enterprises. Industrial point-source pollution dominates this zone, with wastewater discharge representing approximately 8% of basin runoff. Critical carrying capacity constraints emerge here: main channel segments receive over 91% of the basin’s pollutant load while possessing only 37% assimilation capacity [59,60]. This imbalance highlights the urgent need for circular economy approaches in industrial pollution management.

Our findings demonstrate an increasing longitudinal intensification of the digital economy from upper to lower reaches [61], with economically advanced cities maintaining stronger digital development and water quality. Provincial capitals such as Xi’an, Jinan, and Zhengzhou lead in digital metrics through early development initiatives and industrial foundations in emerging technologies [61,62]. This pattern mirrors global trends where digital advancement correlates with environmental management capabilities yet also raises concerns about the material footprint of digital technologies, as highlighted in the UNCTAD Digital Economy Report 2024 [63].

In contrast, mid- and upper-basin regions face dual challenges of limited digital economy development and compromised water quality. Non-capital cities in these areas lag in industrial transformation due to geographical constraints and heavy reliance on traditional sectors such as energy, chemicals, and agriculture [8,64]. The midstream basin in particular concentrates China’s core energy infrastructure, hosting dense clusters of coal chemical enterprises—which account for over three-quarters of local industries in some areas [59,65]. Critically, many of these facilities are located within key watershed zones, contributing to chronically poor water quality in tributaries across Inner Mongolia, Shanxi, and Shaanxi [66].

The spatial differentiation suggests opportunities for implementing circular economy principles through “digital eco-compensation” mechanisms, whereby downstream beneficiaries support upstream conservation. This approach addresses both regional disparities and the global challenge of electronic waste, which grew by 30% from 2010 to 2022 with only 24% recycling rate for digital equipment [63]. Policy measures should therefore promote digital industrialization that enhances environmental monitoring while ensuring industrial digitalization incorporates strong environmental safeguards.

4.2. Advantages of Dual—Engine Coupling Model of SVM and LightGBM

Our dual-engine coupling model addresses critical limitations in previous research by capturing non-linear relationships between digital economy development and water quality. Prior research has predominantly centered on the influence of socio-economic factors on water system [67,68,69]. our model enables sophisticated analysis of the complex interactions that characterize sustainability challenges. Compared to artificial neural network (ANN), SVM may produce higher prediction accuracy and show higher generalization ability by an upper bound on the generalization error [70,71]. This balance is crucial for implementing the precautionary principle in environmental management and for designing evidence-based policies that support both economic development and environmental protection [71,72].

The SVM-LightGBM framework demonstrates particular value for sustainable water management in several aspects. First, its ability to handle multiple input and output variables aligns with the integrated approach needed for circular economy implementation [73]. Second, the model’s performance (average fitting coefficient > 0.85) surpasses traditional methods in predicting water quality trends, providing reliable support for policy decisions [74,75]. Third, the model’s interpretability features help identify key leverage points for intervention, which is essential for designing targeted sustainability policies. LightGBM estimates the influence of each predictor variable on the final prediction result by analyzing the contribution indicators of the loss function during the training process, enabling accurate capture of the non-linear relationships and contributions between predictor variables and influencing factors [76,77].

Our machine learning approach aligns with global trends in applying advanced computational methods to address complex environmental challenges. The integration of multiple indicators in our model resonates with comprehensive water assessment frameworks developed for other major river systems. For instance, research on the Yangtze River Economic Belt has similarly incorporated both quantity (ESDRws) and quality (ESDRdq) aspects to evaluate water supply-demand imbalances [78], though our methodology provides enhanced capability for capturing non-linear relationships through the SVM-LightGBM coupling.

The superior performance of our dual-engine model in handling the YRB’s complex socio-hydrorological system mirrors international efforts to develop sophisticated assessment tools for water sustainability. Similarly to studies that have integrated hydrological models with socioeconomic data to evaluate strategies for water sustainability in the YRB [65,68], our approach offers a complementary data-driven perspective focused specifically on digital economy-water quality interactions. This methodological innovation contributes to the growing arsenal of analytical tools supporting the achievement of SDG 6 (clean water and sanitation) targets.

4.3. Driving Forces of Digital Economy Development on Water Quality

Based on the SVM-LightGBM model results, all five digital economy indicators contribute significantly to water quality variations, with four digital industrialization indicators (TR, NMPS, IBAU, ECS) exhibiting positive correlations. Regions with advanced digital infrastructure show improved water quality through enhanced monitoring capabilities enabled by the Internet of Things (IoT) technologies and communication networks [79,80]. This mutually reinforcing relationship between digital advancement and environmental quality reflects successful international examples, such as digital water monitoring systems in Bangladesh benefiting approximately 250,000 residents [81]. Additionally, previous studies have indicated that improved environmental conditions can enhance regional attractiveness, potentially leading to increased tourism and residential mobility. This phenomenon may subsequently stimulate demand for telecommunications services and support sector growth [82,83]. Consequently, TR and NMPS form a mutually reinforcing relationship [84].

In addition, IBAU and ECS positively influence water quality, as these factors typically reflect a region’s efficiency in intelligent management and optimal control. Specifically for water environments, this translates to more effective water quality prediction, pollution source tracking, and control [85]. For instance, digital industrialization has enhanced the accuracy of water environment prediction and early warning systems in the YRB, as well as improved intelligent management and control [86,87]. Digital technologies enable precise linkage of pollution sources to critical areas, facilitating early warning systems and informed management decisions [41,88,89]. These benefits align with global demonstrations of digital technologies enabling environmental improvements when appropriately designed and implemented.

However, the significant negative correlation between financial inclusion (FI) and water quality highlights the dualistic nature of digital development. While industrial digitalization may drive technological transformation, increased financial inclusiveness can stimulate socioeconomic activities that strain water resources [42,90,91]. This finding echoes research on other economic development initiatives where singular focus on economic growth exacerbated environmental issues [92], emphasizing the need for balanced approaches.

Globally, the environmental footprint of digital technologies presents similar challenges, with each smartphone requiring approximately 70 kg of raw materials and e-waste generation varying significantly between developed (3.25 kg/capita) and developing countries (<1 kg/capita) [63,89]. These international patterns underscore the importance of adopting circular economy principles in the YRB’s digital development, transitioning from traditional linear models to approaches featuring durable design, improved recycling systems, and sustainable business models [89].

The differential impacts of digital economy components emphasize the need for policy frameworks that promote sustainable digital transformation. International cooperation should foster digitally inclusive and sustainable development models that protect planetary health while narrowing digital divides [63,89]. This global imperative aligns with our study’s emphasis on balancing digital industrialization with environmentally responsible industrial digitalization to maximize benefits while minimizing drawbacks across the YRB.

4.4. Recommendations and Future Applications

Based on the findings of this study, we propose a series of recommendations for policymakers and outline directions for future research to enhance the application of the SVM-LightGBM system in water environmental management.

Policy Recommendations:

(1)

Differentiated Regional Strategies: Policy interventions should be tailored to the specific digital-economic conditions of each river segment. For the upstream region, focus should be on developing green data centers and promoting digital technologies for ecological conservation and eco-tourism, minimizing high-pollution industrial digitalization. In the midstream region, policies must compel the digital transformation of traditional energy and chemical industries, using IoT and AI for real-time pollution monitoring and smart regulation to reduce point-source pollution. For the downstream region, encouragement should be given to innovate and export digital solutions for water management, serving as a model for the entire basin.

(2)

Promoting Circular Digital Economy: Government incentives should support the development of a circular digital economy. This includes investing in sustainable e-waste recycling infrastructure, especially in midstream cities where digital expansion is rapid, and offering tax breaks or subsidies for companies that design durable, repairable digital devices and adopt green cloud computing technologies.

(3)

Balancing Digital Development: Our results caution against the unregulated expansion of industrial digitalization (as proxied by FI). Policy should decouple financial inclusion from environmental degradation by integrating green finance principles. Loans and investments for digital projects in sensitive watersheds should be contingent on stringent environmental impact assessments and the adoption of best available technologies.

Future Applications and Research Directions:

(1)

System Integration and Real-time Forecasting: The primary future application of this SVM-LightGBM system is its integration into a digital twin platform for the YRB. Future work will focus on developing a real-time data pipeline that feeds continuous monitoring data into the model, enabling dynamic forecasting of water quality trends under different digital economic development scenarios. This would transform the model from an analytical tool into a proactive decision-support system.

(2)

Expanding the Model’s Scope: Future research should aim to incorporate more granular data, such as enterprise-level digital transformation metrics and high-frequency water quality sensor data, to enhance the model’s resolution and accuracy. Furthermore, testing the applicability of this coupled model in other major river basins in China (e.g., the Yangtze River Basin, Pearl River Basin) would verify its robustness and generalizability.

5. Conclusions

This study was initiated to address the significant knowledge gap in understanding the complex, non-linear relationships between digital economy development and water quality in large river basins under data scarcity constraints. By constructing an innovative SVM-LightGBM dual-engine coupled model, successfully quantified these complex interactions in the Yellow River Basin. Our findings reveal distinct spatial patterns: while water quality deteriorates in the midstream region, the digital economy exhibits a decreasing trend from east to west. The model demonstrated superior performance in capturing non-linear responses, identifying that digital economy indicators collectively explain over 40% of water quality variation, with Telecommunication Revenue (TR), Financial Inclusion (FI), and E-Commerce Sales (ECS) as primary drivers. Crucially, this paper uncovered the dualistic nature of digital impacts—digital industrialization shows strong positive effects, while industrial digitization may produce negative environmental consequences.

These findings carry profound implications for sustainable basin management within a global context. The spatial heterogeneity of digital economy effects necessitates region-specific policies: promoting digital industrialization in upstream areas while implementing stringent environmental safeguards for industrial digitization in midstream regions. Our study transcends theoretical contributions by offering a practical framework for balancing digital transformation with ecological conservation. The proposed integrated approach, emphasizing circular economy principles and differentiated regional strategies, provides a replicable model for other major river basins worldwide facing similar development-environment challenges, ultimately contributing to the achievement of sustainable development goals through scientifically informed digital transformation policies.