Search Results (32)

Urbanization and mixed-land-use development significantly impact water quality dynamics in watersheds, necessitating continuous monitoring and advanced analytical techniques for sustainable water management. This study employs continuous wavelet analysis to investigate the temporal variability and correlations of real-time water quality parameters in the Credit River watershed, Ontario, Canada. The Integrated Watershed Monitoring Program (IWMP), initiated by the Credit Valley Conservation (CVC) Authority, has facilitated long-term real-time water quality monitoring since 2010. Fundamental and exploratory statistical analyses were conducted to identify patterns, trends, and anomalies in key water quality parameters, including pH, specific conductivity, turbidity, dissolved oxygen (DO), chloride, water temperature ($T_{H_{2}O}^{°}$), air temperature ($T_{air}^{°}$), streamflow, and water level. Continuous wavelet transform and wavelet coherence techniques revealed significant temporal variations, with “1-day” periodicities for DO, pH, ($T_{H_{2}O}^{°}$), and ($T_{air}^{°}$) showing high power at a 95% confidence level against red noise, particularly from late spring to early fall, rather than throughout the entire year. These findings underscore the seasonal influence on water quality and highlight the need for adaptive watershed management strategies. The study demonstrates the potential of wavelet analysis in detecting temporal patterns and informing decision-making for sustainable water resource management in rapidly urbanizing mixed-land-use watersheds. Full article

Elevated chloride (Cl⁻) concentrations in surface water and groundwater are an increasing concern in cold region urban environments, largely due to long-term road salt application. This study investigates the Cl⁻ distribution across southern Ontario, Canada, using geospatial methods to identify contamination hot spots and assess groundwater vulnerability at both regional and watershed scales. Chloride data from 2001 to 2010 and 2011 to 2020 were compiled from public sources and interpolated using inverse distance weighting. A regional-scale vulnerability index was developed using slope (SL), surficial geology (SG), and land use (LU) (SL-SG-LU), and compared it to a more detailed DRASTIC-LU index within the Credit River watershed. Results show that Cl⁻ hot spots are concentrated in urbanized areas, including the Greater Toronto Area and Golden Horseshoe, with some rural zones also exhibiting elevated concentrations. Vulnerability mapping corresponded well with the observed Cl⁻ patterns and highlighted areas at risk for groundwater discharge to surface waters. While the DRASTIC-LU method offered finer resolution, the simplified SL-SG-LU index effectively captured broad vulnerability trends and is suitable for data-limited regions. This work provides a transferable framework for identifying Cl⁻ risk areas and supports long-term monitoring and management strategies in cold climate watersheds. Full article

Amid the global drive to enhance public sector performance in the digital economy era, improving government spending efficiency has become a critical governance objective. This study investigates the impact of digital inclusive finance on government spending efficiency from a digital finance systems perspective using county-level panel data in China’s Yangtze River Delta for the period 2014–2022 and constructing the fixed-effects model and instrumental variable method to estimate the effect of digital inclusive finance and explore its underlying mechanisms. Heterogeneity across regions with varying economic development levels is analyzed, and fiscal pressure is examined as a potential mediating factor. The results indicate that (1) digital inclusive finance significantly enhances government spending efficiency, primarily through broad service coverage and deep usage of digital financial services such as mobile payments, digital credit, and insurance; (2) the positive effect is more pronounced in counties with lower government spending efficiency and economic development; and (3) fiscal pressure acts as a key transmission channel, with broader digital inclusive finance coverage helping to alleviate fiscal stress and improve government spending efficiency. These findings offer empirical insights into the role of digital finance in promoting effective and adaptive public financial governance. Full article

Understanding the complex and multidimensional nature of poverty is essential for designing effective and targeted policy interventions in rural Ethiopia. This study examined the determinants of multidimensional poverty in Bilate River Basin in South Ethiopia, employing cross-sectional household survey data collected in 2024. A total of 359 households were selected using a multistage sampling technique, ensuring representation across agro-ecological and socio-economic zones. The analysis applied the Generalized Ordered Logit (GOLOGIT) model to categorize households into four mutually exclusive poverty statuses: non-poor, vulnerable, poor, and extremely poor. The results reveal that age, dependency ratio, education level, livestock and ox ownership, access to information and credit, health status, and grazing land access significantly influence poverty status. Higher dependency ratios and poor health substantially increase the likelihood of extreme poverty, while livestock ownership and access to grazing land reduce it. Notably, credit use and access to information typically considered poverty reducing were associated with increased extreme poverty risks, likely due to poor financial literacy and exposure to misinformation. These findings underscored the multidimensional and dynamic nature of poverty, driven by both structural and behavioral factors. Policy implications point to the importance of integrated interventions that promote education, health, financial literacy, and access to productive assets to ensure sustainable poverty reduction and improved rural livelihoods in Ethiopia. Full article

The Colorado and Mississippi River basins are crucial for water supply, agriculture, and ecological stability in the U.S., yet climate change, water management practices, and energy sector demands pose significant challenges to their sustainability. This paper highlights the potential of leveraging the Voluntary Carbon Market (VCM) to address these challenges by creating new revenue streams and incentivizing sustainable water management practices. It provides high-level estimates by extrapolating from existing literature. The paper finds that water projects in these basins could generate over 45 million carbon credits annually, potentially attracting around USD 4.5 billion in investments over the next decade. However, challenges such as high costs, complex regulations, and stakeholder coordination must be addressed. The paper also identifies opportunities for advancing water resiliency projects, including increasing public awareness, engaging corporations, and utilizing innovative financing mechanisms. Recommendations include promoting the VCM–water relationship, encouraging methodology innovation, developing pilot programs, investing in digital monitoring technologies, and conducting localized analysis to optimize carbon credit potential in water management. In conclusion, this paper quantifies the potential of water projects to generate carbon credits and indicates that integrating carbon markets with water management strategies can significantly contribute to global climate goals and improve water resilience in these critical regions. Full article

Ecological environment restoration has become an important strategy for the high-quality development of the Yangtze River Economic Belt, and green finance is indispensable to supporting industrial transformation and green innovation. It is of great importance to clarify the internal relationship between green finance and ecological resilience construction. This paper introduces the concept of resilience into the field of ecological construction and constructs an ecological resilience index system from three dimensions of “resistance-adaptability-resilience”. On this basis, it focuses on the different aspects of green finance, such as green credit, green securities, green investment, green insurance, etc., and examines the role of green financial development on the ecological resilience of the Yangtze River Economic Belt. The results of the study showed that (1) during the study period, the overall ecological resilience level of the Yangtze River Economic Belt improved significantly and there were significant differences in the ecological resilience of the economic belts but such spatial differences are converging; (2) green insurance has a significant positive influence on ecological resilience, while green credit, green securities, and green investment have a significant negative influence on ecological resilience; (3) green credit and green securities have a significant positive effect on the resistance to ecological resilience, green credit and green investment inhibit the adaptability of ecological resilience, and green insurance significantly improves the resilience of ecological resilience. Green financial policies should be further optimized, and innovative all-round and multi-level products and services should be provided. It is necessary to leverage social capital to promote green transformation and technological innovation in high-pollution industries. By combining resource endowment and location advantages, we can explore the benign interaction between green finance and ecological civilization construction. Full article

Dissolved oxygen (DO) is a key indicator of water quality and the health of an aquatic ecosystem. Aspiring to reach a more accurate forecasting approach for DO levels of natural streams, the present work proposes new graph-based and transformer-based deep learning models. The models were trained and validated using a network of real-time hydrometric and water quality monitoring stations for the Credit River Watershed, Ontario, Canada, and the results were compared with both benchmarking and state-of-the-art approaches. The proposed new Graph Neural Network Sample and Aggregate (GNN-SAGE) model was the best-performing approach, reaching coefficient of determination (R²) and root mean squared error (RMSE) values of 97% and 0.34 mg/L, respectively, when compared with benchmarking models. The findings from the Shapley additive explanations (SHAP) indicated that the GNN-SAGE benefited from spatiotemporal information from the surrounding stations, improving the model’s results. Furthermore, temperature has been found to be a major input attribute for determining future DO levels. The results established that the proposed GNN-SAGE model outperforms the accuracy of existing models for DO forecasting, with great potential for real-time water quality management in urban watersheds. Full article

The pressure of nutrient pollution derived from wastewater treatment works and agricultural runoff is a reason for the decline in the ecological health of aquatic habitats. Projected residential development in catchments creates further nutrient loading that can be offset by nutrient management solutions that maintain ‘nutrient neutrality’ either onsite or elsewhere within the same catchment. This study developed an export coefficient model in conjunction with detailed farm business data to explore a nature-based solution to nutrient neutrality involving seven scenarios of crop conversion to mixed woodland or grazing grass in an area of intensive arable cultivation in the groundwater-fed Blackwater sub-catchment of the River Wensum, UK. When compared with the monitored riverine export of nutrients, the calculated nitrogen (N) and phosphorus (P) inputs under current land use showed that subsurface denitrification is removing 48–78% of the leached N and that P is accumulating in the field soils. The addition of 235 residential homes planned for 2018–2038 in the Blackwater will generate an additional nutrient load of 190 kg N a⁻¹ and 4.9 kg P a⁻¹. In six of the seven scenarios, the modelled fractions of crop conversion (0.02–0.21) resulted in the required reduction in P loading and more than sufficient reduction in N loading (196–1874 kg a⁻¹ for mixed woodland and 287–2103 kg a⁻¹ for grazing grass), with the additional reduction in N load above the requirement for nutrient neutrality potentially contributing to further improvement in water quality. The cost of land conversion is modelled in terms of crop gross margins and nutrient credits generated in the form of 0.1 kg units of N or P. For the range of scenarios considered, the annual cost per credit ranged from GBP 0.78–11.50 for N for mixed woodland (GBP 0.74–7.85 for N for grazing grass) and from GBP 160–782 for P for both scenarios. It is concluded that crop conversion is a viable option to achieve nutrient neutrality in arable catchments in eastern England when considered together with other nutrient management solutions. Full article

Successful aquatic ecosystem conversation strategies depend on high-quality data from monitoring studies and improved habitat requirement knowledge. Remote Underwater Video (RUV) is a non-extractive alternative to capture-based techniques for studying and monitoring fish and is increasingly used in smaller channels. This study uses field observations made with waterproof Sony HDR-AS100V action cameras positioned in stream channels to determine species and population during various flow conditions across three sites within the Credit River Watershed, Ontario, Canada. Six fish species were identified, and individual fish lengths were estimated using the inverse square law to proportionally adjust size scales to fish positions relative to the camera. Successful identification and measurements were limited by turbidity, with camera placements in >6 NTU conditions (18% of all placements) resulting in at least one fish observed in the frame. With over 24 h of video recordings with 94 individual video clips, the optimal filming duration was determined to be 20–25 min. RUV surveys provide managers with useful monitoring data regarding fish present in an environment in a cost-effective and efficient manner. Additionally, as the method is largely non-invasive, RUV surveys are especially useful for studying fish behaviour, sensitive or endangered species, and working in difficult-to-access channels (e.g., shallow, faster flow). Full article

In cold-climate regions, road salt is used as a deicer for winter road maintenance. The applied road salt melts ice and snow on roads and can be washed off through storm sewer systems into nearby urban streams, harming the freshwater ecosystem. Therefore, aiming to develop a precise and accurate model to determine future chloride concentration in the Credit River in Ontario, Canada, the present work makes use of a “Graph Neural Network”–“Sample and Aggregate” (GNN-SAGE). The proposed GNN-SAGE is compared to other models, including a Deep Neural Network-based transformer (DNN-Transformer) and a benchmarking persistence model for a 6 h forecasting horizon. The proposed GNN-SAGE surpassed both the benchmarking persistence model and the DNN-Transformer model, achieving RMSE and R² values of 51.16 ppb and 0.88, respectively. Additionally, a SHAP analysis provides insight into the variables that influence the model’s forecasting, showing the impact of the spatiotemporal neighboring data from the network and the seasonality variables on the model’s result. The GNN-SAGE model shows potential for use in the real-time forecasting of water quality in urban streams, aiding in the development of regulatory policies to protect vulnerable freshwater ecosystems in urban areas. Full article

Green credit is a vital instrument for promoting low-carbon transition. However, designing a reasonable development pattern and efficiently allocating limited resources has become a challenge for developing countries. The Yellow River Basin, a critical component of the low-carbon transition in China, is still in the early stages of green credit development. Most cities in this region lack green credit development plans that suit their economic conditions. This study examined the impact of green credit on carbon emission intensity and utilized a k-means clustering algorithm to categorize the green credit development patterns of 98 prefecture-level cities in the Yellow River Basin based on four static indicators and four dynamic indicators. Regression results based on city-level panel data from 2006 to 2020 demonstrated that the development of green credit in the Yellow River Basin can effectively reduce local carbon emission intensity and promote low-carbon transition. We classified the development patterns of green credit in the Yellow River Basin into five types: mechanism construction, product innovation, consumer business expansion, rapid growth, and stable growth. Moreover, we have put forward specific policy suggestions for cities with different development patterns. The design process of this green credit development patterns is characterized by its ability to achieve meaningful outcomes while relying on fewer numbers of indicators. Furthermore, this approach boasts a significant degree of explanatory power, which may assist policy makers in comprehending the underlying mechanisms of regional low-carbon governance. Our findings provide a new perspective for the study of sustainable finance. Full article

An attention U-Net was proposed to reconstruct the missing chlorophyll-a concentration (C_chla) data. The U-Net is a lightweight full convolution neural network architecture consisting of an enccoder-decoder (i.e., down-sampling and up-sampling). The attention gates (AGs) were integrated into the U-Net. Training the U-Net with AGs could implicitly teach it to suppress irrelevant areas and highlight the salient features in the missing data areas, which would increase the network sensitivity and reconstruction accuracy. The neural network uses the satellite-derived C_chla anomalies and its variance as the input, and the reconstructed fields along with their variances as outputs. The trained network was applied to long-term daily MODIS/Aqua C_chla products in the Pearl River estuary (PRE) and adjacent continental shelf area. The model performance was evaluated by using an independent test dataset from both satellite-derived and in-situ measurements. The results showed that the proposed neural network not only had good performance in the reconstruction of valid pixels, but also provided a more reasonable reconstruction compared to the standard U-Net without AGs. This study provided a feasible method for the reconstruction task in the field of ocean color, which should be helpful in producing a creditable dataset to study the ecological effects of extreme weather conditions such as typhoons on the upper ocean in the PRE waters. Based on the reconstructed C_chla products, the footprints of the typhoons were studied. An increase in surface C_chla near the typhoons’ track and a decrease in estuary were found. The composite results illustrated that the C_chla increases occurred for almost the entire area within a radius of 100 km. The time series analysis showed that the C_chla peak appeared on the fifth day after the typhoon’s passage. Full article

The development of China’s digital finance provides new ideas for solving the financial constraints faced by some collaborative innovation activities in the Yangtze River Delta (YRD). Therefore, based on the panel data of 41 cities in the YRD from 2011 to 2020, this study empirically tests the impact and transmission mechanisms of digital finance on collaborative innovation through GMM and a dynamic mediation model. The results show that in the YRD region, digital finance significantly stimulates collaborative innovation, but the effect of the decomposition index varies. The effect of depth of use is the strongest, followed by breadth of coverage and degree of digitization. In terms of the transmission mechanism, digital finance can increase the scale of credit, social consumption, and industrial upgrading to form a positive local effect. It can also improve the development of collaborative innovation, and lead to a spillover effect through the flow of R&D capital and R&D personnel. The conclusion indicates that it is necessary to stimulate the digital transformation in the financial field, giving full play to liquidity, facilitating the upgrading of credits, consumption, and industries. This study enriches the theoretical framework of digital finance and collaborative innovation. Moreover, the empirical test provides data and evidence for the construction of a world-class science and technology innovation center in the YRD. The paper also presents limitations, including the influence of factors such as urban heterogeneity and financial supervision, worthy of further research. Full article

A more scientific green economy efficiency indicator is constructed based on OH (2010), and a multiperiod spatial DID model is used to examine the impact of national credit demonstration policies on urban green economy efficiency in a sample of cities above the prefecture level in the Yangtze River Delta. The study confirms the following: (1) The national credit demonstration policy makes a significant contribution to the green economic efficiency of cities, and it is conducive to strengthening awareness of the rule of law in the market to regulate market order. (2) The demand for credit regulation in coastal areas has increased under the new development pattern, and the national credit demonstration policy has effectively enhanced green economy efficiency through institutional supply. (3) Under the national credit demonstration policy, the subprovincial level and above can mobilise more resources for policy refinement and support, reducing transaction costs and improving the efficiency of the green economy. (4) The impact of the national credit demonstration policy on the efficiency of Zhejiang’s green economy is more obvious; but, under the overall framework of the Yangtze River Delta, the policy has a more prominent role in promoting green economy efficiency in other provinces. Policy insights are as follows: (1) Different cities have different degrees of impact on the efficiency of the green economy from the national credit demonstration policy, and they should implement differentiated measures based on regional heterogeneity; (2) regulating the use of administrative resources and avoiding undue administrative intervention are important prerequisites for promoting regional integration to enhance the efficiency of the green economy; and (3) strengthening interprovincial credit policy synergies can help to alleviate administrative distortions of policy implementation and enhance the efficiency of the regional green economy. Full article

In the current scenario of anthropogenic climate change, carbon credit security is becoming increasingly important worldwide. Topsoil is the terrestrial ecosystem component with the largest carbon sequestration capacity. Since soil organic matter (SOM), which is mostly composed of organic carbon, and can be affected by rainfall, cultivation, and pollutant inflow, predicting SOM content through regular monitoring is necessary to secure a stable carbon sink. In addition, topsoil in the Republic of Korea is vulnerable to erosion due to climate, topography, and natural and anthropogenic causes, which is also a serious issue worldwide. To mitigate topsoil erosion, establish an efficient topsoil management system, and maximize topsoil utilization, it is necessary to construct a database or gather data for the construction of a database of topsoil environmental factors and topsoil composition. Spectroscopic techniques have been used in recent studies to rapidly measure topsoil composition. In this study, we investigated the spectral characteristics of the topsoil from four major rivers in the Republic of Korea and developed a machine learning-based SOM content prediction model using spectroscopic techniques. A total of 138 topsoil samples were collected from the waterfront area and drinking water protection zone of each river. The reflection spectrum was measured under the condition of an exposure time of 136 ms using a spectroradiometer (Fieldspec4, ASD Inc., Alpharetta, GA, USA). The reflection spectrum was measured three times in wavelengths ranging from 350 to 2500 nm. To predict the SOM content, partial least squares regression and support vector regression were used. The performance of each model was evaluated through the coefficient of determination (R²) and root mean square error. The result of the SOM content prediction model for the total topsoil was R² = 0.706. Our findings identified the important wavelength of SOM in topsoil using spectroscopic technology and confirmed the predictability of the SOM content. These results could be used for the construction of a national topsoil database. Full article