Regional Economic Development Level Research Articles

Does Low-Carbon Transition Promote Regional Sustainable Development? Evidence from the Huaihe River Ecological Economic Belt

The Chinese government has adopted a significant low-carbon transition strategy aimed at enhancing resource efficiency, advancing ecological conservation, and augmenting societal well-being. This research employs the super efficiency SBM model to evaluate the ecological well-being performance (EWP) within the Huaihe River Ecological Economic Belt. Subsequently, it employs the Sys-GMM model and conducts a threshold effect analysis to regressively examine the impact of the low-carbon transition on EWP. The key findings are as follows. The EWP in the Huaihe River Ecological Economic Belt is relatively low; cities with higher economic development levels tend to exhibit lower EWP scores. The impact of the low-carbon transition on EWP is contingent upon the level of regional economic development and exhibits a singular threshold effect predicated on the economic development level as the threshold variable. Specifically, when economic development is at a low level, an intensification of the low-carbon transition results in a decline in EWP within the Huaihe River Ecological Economic Belt. Conversely, when the economic development level surpasses the threshold, the low-carbon transition facilitates the enhancement of EWP. This study’s principal contribution lies in elucidating the intricate relationship between the low-carbon transition and sustainable development.

Estimation of Regional Electricity Consumption Using National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite Night-Time Light Data with Gradient Boosting Regression Trees

With the rapid development of society and economy, the growth of electricity consumption has become one of the important indicators to measure the level of regional economic development. This paper utilizes NPP-VIIRS nighttime light remote sensing data to model electricity consumption in parts of southern China. Four predictive models were initially selected for evaluation: LR, SVR, MLP, and GBRT. The accuracy of each model was assessed by comparing real power consumption with simulated values. Based on this evaluation, the GBRT model was identified as the most effective and was selected to establish a comprehensive model of electricity consumption. Using the GBRT model, this paper analyzes electricity consumption in the study area across different spatial scales from 2013 to 2022, demonstrating the distribution characteristics of electricity consumption from the pixel level to the city scale and revealing the close relationship between electricity consumption and regional economic development. Additionally, this paper examines trends in electricity consumption across various temporal scales, providing a scientific basis for the optimal allocation of energy and the effective distribution of power resources in the study area. This analysis is of great significance for promoting balanced economic development between regions and enhancing energy efficiency.

Digital Sharing of Social Public Resources and Its Influencing Factors: Insight from China

Digital sharing of social public resources (SPRs) is an approach of resource-allocation that can effectively compensate for the limitations of time, space and communication modes in accessing SPRs and achieve the balanced allocation of them. The purpose of this paper is to study the development level of digital sharing of SPRs and its influencing factors. The panel data of 31 Chinese provinces from 2015 to 2020 are collected. A comprehensive evaluation index system for digital sharing of SPRs is constructed to evaluate its development level using the Global Entropy Weight Method (GEWM). And the influencing factors of the sharing level are systematically identified based on the stepwise regression model. The results show that: the development level of digital sharing of SPRs varies significantly among Chinese provinces and cities, but the overall development level is gradually increasing. The government fiscal expenditure capacity, industrial structure, regional economic development level, scientific and technological innovation capacity, and urbanization level can promote the digital sharing of SPRs. The degree of external openness and regional population size have negative effects on the digital sharing of SPRs.

The Impact of Green Finance on Agricultural Non-Point Source Pollution: Analysis of the Role of Environmental Regulation and Rural Land Transfer

This study evaluates the impact of green finance on agricultural non-point source pollution control and emission reduction in 30 Chinese provinces from 2005 to 2022. Utilizing the entropy value method and the unit survey inventory method, the research measures the levels of green finance development and agricultural non-point source pollution. It employs a mediation effect model to empirically assess the pollution control efficacy of green finance and to elucidate the mechanisms underlying its influence. The findings indicate that green finance development significantly curtails agricultural non-point source pollution emissions. This conclusion is still valid after a series of robustness tests. The results of mechanism analysis show that environmental regulation and land transfer are important channels for green finance to reduce agricultural non-point source pollution. However, the slowing effect of green finance is stronger in provinces where the economic development level is still in the catch-up zone. Consequently, this study suggests strengthening green finance infrastructure in rural areas, coordinating green finance and environmental regulation policies, optimizing land transfer systems to promote scale management, and developing differentiated green finance policies based on regional economic development levels. These measures aim to augment the role of green finance in pollution treatment and emission reduction, thereby optimizing the green financial system, advancing environmental protection, and fostering sustainable development in China’s agricultural sector.

Spatio-Temporal Diversification of per Capita Carbon Emissions in China: 2000–2020

Exploring the low-carbon transition in China can offer profound guidance for governments to develop relevant environmental policies and regulations within the context of the 2060 carbon neutrality target. Previous studies have extensively explored the promotion of low-carbon development in China, yet no studies have completely explained the mechanisms of the low-carbon transition in China from the perspective of per capita carbon emissions (PCEs). Based on the statistics and carbon emissions data of 367 prefecture level cities in China from 2000 to 2020, this study employed markov chain, kernel density analysis, hotspots analysis, and spatial regression models to reveal the spatiotemporal distribution patterns, future trends, and driving factors of PCEs in China. The results showed that China’s PCEs in 2000, 2010, and 2020 were 0.72 ton/persons, 1.72 ton/persons, and 1.91 ton/persons, respectively, exhibiting a continuous upward trend, with evident regional heterogeneity. PCEs in northern China and the eastern coastal region were higher than those of southern China and the central and southwestern regions. The PCEs in China showed obvious spatial clustering, with hot spots mainly concentrated in Inner Mongolia and Xinjiang, while cold spots were mainly in some provinces in southern China. The transition of PCEs in China exhibited a strong stability and a ‘club convergence’ phenomenon. A regression analysis revealed that the urbanization level and latitude had negative effects on PCEs, while the regional economic development level, average elevation, average slope, and longitude showed positive effects on PCEs. These findings have important implications for the promotion of the low-carbon transition and the effective achievement of the “dual carbon” goal.

Path to Net Zero: Understanding the Building Energy Efficiency in Different Climates across Various Building Types

This study analyses the determinants of building energy efficiency in different climate zones and user types. The energy consumption of buildings in different climate zones can be affected by well-known determinants in different ways. So do the buildings with different user types. The primary aim of this study is to investigate how building energy efficiency is determined in five major climate zones and four main property types. This study uses the global building data (Points Achieved dataset) from the Leadership in Energy and Environmental Design (LEED) rating system 2010 to conduct three cross-sectional tests with logit regression models. The results confirm that the determinants of building energy efficiency are the location of the building, adoption of Building Energy Codes (BECs), climate zones, building types, regional economic development level (namely Gross National Income—GNI, Purchasing Power Parity—PPP) and population density. However, the impact of the determinants varies considerably in different climate zones or for different building types. This is the first empirical study exploring building energy efficiency and how it is determined in different climate conditions and user types. The findings are helpful for the stakeholders, such as policymakers, developers, and local authorities, when they hope to implement measures to improve building energy efficiency and the policy/regulation to boost it. Each building requires specific measures that suit its different climate zones or building types to enhance energy efficiency.

Market‐incentivized environmental regulation policy and company green transformation: An analytical perspective based on the cost transfer capability of companies

AbstractBased on the carbon emissions trading pilot policy in China, this paper takes Chinese A‐share listed companies from 2012 to 2020 as a sample, and analyzes the role of company's cost transfer capability (ECTC) in the mechanism of market‐incentivized environmental regulation policy (MERP) influencing company green transformation. This paper finds the following four primary results. First, ECTC significantly strengthens the green transformation driving effect of MERP in general. However, results based on the division of ECTC by percentiles show that MERP‐induced green transformation is more pronounced in the top 25% of companies, which distinguishes them from companies in the top 25%–50%, whose cost transfer capacity hampers MERP's contribution to the green transformation. Second, the level of regional economic development, emission regulation, and business reputation have heterogeneous influences on the moderating effect of ECTC. Third, the results based on the moderated mediation model show that ECTC can influence the green transition driving effect of MERP by moderating the two paths of risk‐taking ability and financing constraints of companies. Finally, there is a diffusion effect on the green transformation driven by MERP through cost transfer among companies in non‐pilot industries and non‐pilot regions.

Optimizing Resource Allocation for Regional Employment Governance:A Dynamic Fuzzy-Set QCA Analysis of Low-Carbon Pilot Cities in China

<p style="text-align:justify"><span style="font-size:10.5pt"><span style="line-height:115%"><span style="font-family:等线"><a name="_Hlk171796579"><span lang="EN-US" style="font-family:"Times New Roman",serif">This study employs a dynamic fuzzy-set qualitative comparative analysis (fsQCA) approach, utilizing panel data from 121 low-carbon pilot cities in China from 2007 to 2019. Grounded in complex systems theory and the triple bottom line framework (Economy-Society-Environment), the research aims to optimize resource allocation to enhance regional employment governance performance. The key findings include that the initial implementation of low-carbon policies resulted in a short-term decline in employment levels, with minimal long-term impact on overall employment figures but a significant effect on high-level urban wages. Significant disparities in employment levels were observed among pilot cities, driven by regional population sizes and economic development levels. Four development models for low-carbon cities were identified: human resources-driven, energy transition-driven, industrial cluster-driven, and comprehensive factor-driven models. These models provide strategic pathways for promoting low-carbon urban development and enhancing employment. The findings offer valuable insights into governance strategies for China’s low-carbon pilot cities, facilitating the context-specific promotion of sustainable urban development and improved employment opportunities.</span></a></span></span></span></p>

Has the smart city construction reduced energy consumption? Evidence from China

PurposeThis study investigates the impact of smart city construction (SCC) on urban energy consumption.Design/methodology/approachThe focus is on a panel of 285 prefecture level cities in China from 2010 to 2021. The empirical evidence is based on the difference-in-difference (DID) method. We uses per capita coal consumption as a proxy variable to measure urban energy consumption energy consumption. We set the SCC as a policy dummy variable, with pilot cities set to 1 and non-pilot cities set to 0. We also selected a series of control variables that affect urban energy consumption, such as urbanization rate, labor force, road density, number of college students per 10000 people, regional economic development level and so on.Findings(1) SCC significantly reduces urban energy consumption, and the conclusion still holds after conducting robustness testing; (2) SCC reduces urban energy consumption is mainly effective in those cities with larger scale, stronger human capital, larger financial services and better information infrastructure construction; (3) The technological innovation and industrial structure upgrading are the main mechanisms for the SSC policy to reduce urban energy consumption.Originality/valueThe results in this study provide evidence for achieving an environmentally friendly society.

Developing a method for diagnosing the level of the Kirov Region social and economic development within the Volga Federal District of the Russian Federation

The authors have set the task to assess the socio-economic development level of the Kirov Region. The Volga Federal District has been chosen as the base of comparison. The key problems of the Russian constituent entities territorial development have been analyzed, and the authors’ methods of characterizing the regional social and economic development level published in early scientific works studied. The order of calculating normative values for selected indicators used to assess the Volga Federal District regions has been given. The diagnostic procedure has been carried out to characterize the Kirov Region social and economic development level in comparison with other district entities in the context of the economic development pace and the regional development overall level. Based on the analysis, the initial hypothesis of the study has been confirmed, and conclusions drawn about the general lag of the region from the federal district, while higher rates of its development in comparison with other regions of the Volga Federal District noted. The study results application scope consists in its further use within the framework of scientific works on asses–sing economic development of other district entities, as well as the used methodology application for other entities. The conclusion formulates further direction for improving the diagnostics of the Kirov Region social and economic development level and names specific steps to detail and clarify the future study results.

ECONOMIC ASPECTS OF USING THE POTENTIAL OF BIOENERGY CROPS FOR BIOGAS PRODUCTION AND ADVANCED TECHNOLOGIES FOR DIGESTATE APPLICATION

The research examines the prospects for using the potential of the agro-industrial complex of Ukraine to study green technology innovations during economic development under martial law. Based on the study, it is proved that technological innovations in the agricultural sector contribute to economic development and improve the quality of agricultural land. The article analyses the successful implementation of the case of LLC Yuzhef-Mykolayiv Biogas Company, which has successfully restructured the production of a sugar plant and produces biogas and digestate with further processing into electricity and fertiliser. The modern capacities of the studied enterprise are highlighted, which allow to produce 2800 m3 of biogas and ensure the production of 5200 kWh of electricity, while consumption does not exceed 150 kW, at a load of 80%. The volume of digestate produced per year is 100,000 tonnes. In the regional context, innovative environmental technologies aimed at improving the quality of agricultural land have been introduced. An effective mechanism of spatial synergy with adaptation of this practice at most sugar factories through restructuring of production - creation of biogas production facilities on the basis of sugar factories - is proposed. As a long-term development perspective, the paper presents further creation of alcohol production facilities to use excess heat energy from mini-thermal power plants for the production of alcohol and bioethanol. Furthermore, this study confirms the non-linear effect of economic development; a significant result shows that the impact of green technology innovations on improving the quality of agricultural land (soil humus content, amount of mineral and organic fertilisers, pesticides, area of land under organic production, etc.) contributes to the increase in the level of regional economic development of the agricultural sector. The article clarifies the intermediary role of economic development of the agro-industrial complex. The results of the study implement the tasks of sustainable development, such as improving the environment through the innovation of green technologies and achieving regional synergy in green development.

Research on multiscale OpenStreetMap in China: data quality assessment with EWM-TOPSIS and GDP modeling

ABSTRACT OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in economic modeling at different spatial scales in China was explored by using a geographic information system (GIS) analysis method and a geographically and temporally weighted regression (GTWR) model. Four machine learning models, SVM, RF, XGBoost and CatBoost, were used to simulate the grid-scale GDP, and the effectiveness of these simulations was discussed. The results showed that (1) the weights of OSM data quality indicator factors vary across different spatial scales. (2) From 2014 to 2020, the quality of national-scale OSM data first increased, then decreased and then gradually stabilized. In addition, the quality of OSM data at the provincial and municipal scales is significantly different, and the distribution is affected by the population and geographical environment. (3) Over time, the spatial clustering characteristics of OSM data quality at different spatial scales in China has continuously strengthened. In addition, the quality of Chinese OSM data displays obvious local spatial autocorrelation characteristics, which are dominated by H-H clustering and L-L clustering. (4) The GTWR model performs well in predicting and revealing the spatiotemporal correlation characteristics between GDP and OSM data quality indicators at the provincial and municipal scales in China. The correlations increase with decreasing spatial scale (provincial to municipal). Moreover, the GDP modeling ability is better in economically underdeveloped Northwest China and economically developed East China. (5) Four machine learning models coupled with road network length completeness, relative linear density, road name attribute completeness, POI name attribute completeness, road network accuracy, road network update frequency, POI update frequency, topological consistency and directional similarity yielded the best grid-scale GDP simulation values. Notably, the CatBoost model provided the best accuracy, and the results further verified that it is feasible to use the proposed OSM data quality index system to predict the regional economic development level in China.

Does Environmental Regulation Affect Circular Economy Performance? Evidence from China

Environmental regulation is an important means to solve the environmental pollution challenges faced by economic development. Under the predicament of economic growth facing enormous environmental challenges, this paper aims to explore whether environmental regulation has a promoting effect on the circular economy performance, construct the mechanism through which environmental regulation impacts circular economy performance, as well as test regional heterogeneity on the impact of environmental regulation on circular economy performance under different levels of economic development. Exploring these factors is of great significance for meeting people’s demand for better living environments and promoting green and sustainable economic development. This paper takes interprovincial panel data of China from 2011 to 2022 as the sample and adopts structural equation modeling to explore the influence mechanism of environmental regulation on circular economy performance by constructing a measurement system of environmental regulation and circular economy performance. The results indicate the following: environmental regulation has a significant role in promoting circular economy performance, and there is a partial mediating effect of industrial structure upgrading within it; heterogeneity analysis shows that the promoting effect of environmental regulation on circular economy performance varies according to the different levels of regional economic development. Therefore, environmental regulation policies should be moderately adjusted to promote balanced economic and environmental development; environmental regulation policies should be enriched to promote industrial structure transformation and upgrading; regional synergistic governance should be improved to facilitate the promotion of the circular economy.

The impact of urbanization on food security in China

Against the background of urbanization's rapid development in China from 1984 to 2020, this research evaluates the mechanisms of its impact and food security by constructing a comprehensive evaluation index system for regional food security and verifying urbanization's spatial spillover effect on such security. We present the following findings. (1) A greater level of urbanization has a clear spatial effect on improving the level of food security in China. (2) Urbanization development plays a vital role in advancing local food security and at the same time has a greater negative impact on expanding surrounding food security. (3) The spatial spillover effects of urbanization on food security vary greatly, often according to different levels of regional economic development and natural geographic settings. This paper offers recommendations for controlling regional urbanization development, improving the radiation-driven role of urbanization, and implementing differentiated regional policies.

Research of multimodal transport potential of major cities in China based on the satellite remote sensing

Abstract. By two or more modes of transportation to connect with each other and complete the transport process, multimodal transport could significantly improve transport efficiency, reduce costs, while promote energy conservation and emission reduction, compared with the traditional single mode of transport. Therefore, the multimodal transport development reflects the level of national or regional economic development to some extent. In this study, multimodal transport potential index was built to evaluate the possibility of multimodal transport. Remote sensing images from satellites were used to extract roads, railway stations and airports. A method was proposed to derive the road-rail transport potential index and road-air transport potential index, and then for the analysis of multimodal transport potential in 36 cities. Results show that there is a big gap among 36 cities on the development potential of road-rail and road-air transport. Compared with the road-rail transport potential index, the distribution of road-air transport potential index is more consistent among and within the seven regions. Index values of megacity and super cities are much higher than type I and II big cities. Moreover, in the 36 cities, there is a positive correlation between the multimodal transport potential index and GDP. The research results could be used to quantitatively evaluate the development of transport infrastructure, consequently provide more solid and reliable basic information for the planning, design and scheme adjustment of multimodal transport, thus comprehensively improve the efficiency and quality of multimodal transport, serve the territorial spatial planning.

How Social Networks Affect Farmers’ Willingness to Withdraw from Homesteads: Evidence from Jiangsu Province, China

The orderly withdrawal from rural homesteads is an important path for the smooth promotion of rural revitalization and new urbanization. This study aims to explore the influence and mechanism of social networks on the willingness of farm households to withdraw from homesteads. The study is based on a sample of 1971 farmer households in Jiangsu Province and analyzes the data using the logit model and mediation effect model. The results show that the social network has a significant positive effect on farmers’ willingness to withdraw from homesteads. As the social network expands, the probability for forming the willingness to withdraw from the homestead is higher for farmers with weaker social networks. In addition, the effect of social networks is different according to the level of regional economic development. The mechanism analysis suggests that social networks can indirectly increase the willingness to withdraw from homesteads by improving farmers’ risk resilience. Based on these findings, this study puts forward targeted policy recommendations: focusing on the cultivation and enhancement of farmers’ social networks, strengthening farmers’ risk resilience, and designing differentiated homestead exit policies.

The heterogeneous air pollution response to shrunk socio-economic activities in 28 major northern cities of China

Heterogeneity in air quality response to implemented measures is apparent in many studies, yet the role of regional socio-economic features in the response mechanism remains unclear. This study uses empirical method to identify the direct impact of socio-economic factors on the response to interventions of economy. We constructed a counterfactual model using machine learning methods to quantitatively assess dynamic changes in air pollution based on multiple socio-economic drivers. We find that economy interventions have indeed had an apparent but temporary impact on air quality in northern China. NO2 concentrations decreased by 45.98%, 22.81%, and 0.2%, respectively, at different policy stages, while PM2.5 decreased by 38.66%, 21.63%, and 3.62%, respectively, on average. In contrast, O3 increased by 18.62% and then returned to normal levels. We also find the heterogeneous effect of air pollutants on human activity restrictions in cities with hierarchical socio-economic backgrounds. Highly industrialized cities face greater difficulties in reducing emissions, and cities with a higher urbanization rate may have a more urgent need to take action on O3 governance. Factors such as regional economic development level, industrial structure and urbanization should be considered as important indicators in the development of hierarchical environmental control policies. Our results shed light on the dynamic changes in air pollution due to reduced socio-economic activities, and the heterogeneity between cities should be taken into account when formulating future policies in response to major events and climate change.

Correlation Analysis Between Regional Economic Development Level and Residents' Quality of Life: A Case Study Based on Economic Data

Correlation Analysis Between Regional Economic Development Level and Residents' Quality of Life: A Case Study Based on Economic Data

Coupling and Coordination Relationship of Economic–Social–Natural Composite Ecosystem in Central Yunnan Urban Agglomeration

Exploring the spatio-temporal differentiation characteristics and coupling coordination relationship of the composite ecosystem of urban agglomerations is of great significance for promoting the synergistic sustainable development and integration construction of urban agglomerations. This paper is based on LUCC (Land Use and Land Cover Change) data, a DEM (Digital Elevation Model), and temperature, precipitation, and other multi-source data, using the central Yunnan urban agglomeration as an example and constructing a multi-dimensional evaluation index system highlighting development quality and efficiency. The entropy weight method was first used to determine the comprehensive weights to evaluate the regional economic and social development level. Then, the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model was used to quantitatively calculate and analyze the spatial and temporal evolution characteristics of the four key ecosystem services, evaluating the spatio-temporal evolution characteristics of the natural subsystem. The coupling coordination model was used to quantitatively analyze the evolution of the coupling coordination of the composite ecosystems in the central Yunnan urban agglomeration during the period of 2010–2020, and to reveal its development law. The results show the following: ➀ During the study period, the socio-economic subsystems in the central Yunnan urban agglomeration demonstrated an outward radiative growth from Kunming City, marked by underdeveloped sub-centers and prevalent low-level areas. ② Trends in ecosystem services varied, with water and soil conservation showing fluctuating increases, carbon sequestration remaining stable, and habitat quality declining. The critically important integrated ecosystem services zone of the natural subsystem is mainly located in the northeastern region and southwestern edge of the study area. ③ In 2020, the coupling coordination degree of the composite ecosystem was 0.9492. This showed that economic, social, and ecological subsystems are highly coupled, with consistent overall development trends and strong interactions. ④ The increase in the degree of harmonization amounted to 7.82%, with lagging subsystems varying in the degree of harmonization in subregions. This study can provide a scientific foundation for the development of policies for the Central Yunnan urban agglomeration, promoting regional sustainable development and optimizing the spatial pattern of the national territory.

Contamination Characteristics and Ecological Risk Assessment of Pharmaceuticals and Personal Care Products in Drains Flowing into the Yellow River of Ningxia

Pharmaceuticals and personal care products (PPCPs) are a group of emerging contaminants causing detrimental effects on aquatic living organisms even at low doses. To investigate the contamination characteristics and ecological risks of PPCPs in drains flowing into the Yellow River of Ningxia, 21 PPCPs were detected and analyzed using solid phase extraction and ultra-high performance liquid chromatography-mass spectrometry in this study. All 21 targeted compounds were detected in the drains, with total concentrations ranging from 47.52 to 1 700.96 ng·L-1. Ciprofloxacin, acetaminophen, benzophenone-3, and diethyltoluamide were the more commonly detected compounds, with detection frequencies exceeding 80%. The five highest-concentration PPCPs were acetaminophen, diethyltoluamide, caffeine, benzophenone-3, and levofloxacin, with the maximum concentrations of 597.21, 563.23, 559.00, 477.28, and 473.07 ng·L-1, respectively. Spatial analysis showed that the pollution levels of PPCPs in the drains of the four cities were different, with average concentrations of ∑PPCPs in the order of Yinchuan>Shizuishan>Wuzhong>Zhongwei. The total concentration of PPCPs before flowing into the Yellow River ranged from 124.82 to 1 046.61 ng·L-1. Source analysis showed that livestock and poultry breeding wastewater was the primary source for sulfadiazine and oxytetracycline, whereas medical wastewater was the primary source for levofloxacin and ciprofloxacin. The primary sources of triclocarban and triclosan were domestic sewage and industrial wastewater, whereas the primary source of caffeine and diethyltoluamide was domestic sewage. The pollution of diciofenac, cimetidine, triclocarban, and triclosan in the drains was positively correlated with the regional population and economic development level. The ecological risk assessment indicated that levofloxacin, diclofenac, gemfibrozil, benzophenone-3, and triclocarban posed high risks to aquatic organisms in drains flowing into the Yellow River. It is worthwhile to consider the mixture risk of the PPCPs that exhibited high risk at most sampling sites.