Porter effect vs cost effect: The impact of China's low carbon city pilot on carbon emissions and economic performance.

Building a low-carbon economy can help cities effectively mitigate climate risks, but it is challenging for developing countries. Using a difference-in-difference and event study model, we investigate the joint impact of China's Low-Carbon City Pilot (LCCP) on carbon emissions and economic performance. Our findings show that the LCCP significantly reduces carbon emissions and increases gross revenues, employee count, and fixed assets without compromising the net profit of manufacturing firms. The LCCP has a cumulative effect, with the positive joint impact increasing gradually over time. A heterogeneity analysis shows that the later pilot cities have not achieved better carbon emissions and economic performance than the early pilot cities. The reason for the positive joint effect of LCCP is that the Porter effect outweighs the cost effect. These findings contribute to knowledge about how developing countries can develop a low-carbon economy.

Integrated machine learning methods with oversampling technique for regional suitability prediction of waste-to-energy incineration projects.

China's tiered strategy to enhance county-level waste incineration for energy aligns with the sustainable development goals (SDGs), emphasizing the need for comprehensive assessments of waste-to-energy (WtE) plant suitability. Traditional assessment methodologies face challenges, particularly in suggesting innovative site alternatives, adapting to new data sets, and their dependence on strict assumptions. This study introduced enhancements in three pivotal dimensions. Methodologically, it leverages data-driven machine learning (ML) approaches to capture the complex relationships essential for site selection, reducing dependency on strict assumptions. In terms of predictive performance, the integration of oversampling with stacked ensemble models enhances the diversity and generalizability of ML models. The area under curve (AUC) scores from four ML models, enhanced by the oversampled dataset, demonstrated significant improvements compared to the original dataset. The stacking model excelled, achieving a score of 92%. It also led in overall Precision and Recall, reaching 85.2% and 85.08% respectively. Nevertheless, a noticeable discrepancy existed in Precision and Recall for positive classes. The stacking model topped Precision scores at 83.1%, followed by eXtreme Gradient Boosting (XGBoost) (82.61%). In terms of Recall, XGBoost recorded the lowest at 85.07%, while the other three classifiers all marked 88.06%. From an industry applicability standpoint, the stacking model provides innovative location alternatives and demonstrates adaptability in Hunan province, offering a reusable tool for WtE location. In conclusion, this study not only enhances the methodological aspects of WtE site selection but also provides practical and adaptable solutions, contributing positively to sustainable waste management practices.

Human umbilical cord mesenchymal stem cell-derived exosomal miR-214-3p regulates the progression of gallbladder cancer by regulating ACLY/GLUT1.

BACKGROUND: Human umbilical cord mesenchymal stem cell (hucMSC)-derived exosomes have been reported to be effective in the treatment of cancer. The miR-214-3p is a suppressor miRNA that has been extensively studied and has been proposed as a diagnostic and prognostic biomarker in some cancers. OBJECTIVES: The aim of this study was to investigate whether the regulatory mechanism of hucMSC-derived exosomal miR-214-3p with GLUT1 and ACLY affects the proliferation and apoptosis of gallbladder cancer (GBC) cells. MATERIAL AND METHODS: We found that the target genes of miR-214-3p on the TargetScan website contain GLUT1 and ACLY, and the targeting relationship was verified using luciferases. The GBC-SD cells overexpressing GLUT1 and ACLY were constructed to determine proliferation, apoptosis, migration, and other cellular activities. RESULTS: We identified hucMSCs and exosomes, and found that the exosomes contained miR-214-3p. Furthermore, TargetScan predicted that miR-214-3p had base interactions with ACLY. Dual luciferase assays showed that miR-214-3p could inhibit ACLY (p < 0.05). The results of quantitative reverse transcription polymerase chain reaction (RT-qPCR) and western blot showed that exosomal miR-214-3p could inhibit the expression of ACLY and GLUT1 (p < 0.05). Exosomal miR-214-3p can inhibit the proliferation, cloning and migration of GBC-SD cells (p < 0.05). The apoptosis of GBC-SD cells was increased (p < 0.05). The GBC-SD cells overexpressing ACLY and GLUT1 could reverse the efficacy of miR-214-3p. CONCLUSIONS: Exosomal miR-214-3p can inhibit the downstream expression of ACLY and GLUT1. The ACLY and GLUT1 could affect the proliferation and apoptosis of GBC-SD cells.

Review of calibration strategies for discrete element model in quasi-static elastic deformation.

This study first reviewed theories of the mechanical response of structures under loading, and the discrete element method provides a route for studying mechanical response including elastic deformation and structure failure. However, the direct acquisition of the microscopic parameters from the governing equations of the discrete element method via experiments encounters challenges. One possible strategy to obtain these microscopic parameters is parameter calibration that are widely used by researchers. Secondly, the governing equations and failure criterion of the discrete element method are summarized, and the microscopic parameters that would be calibrated are pinpointed. Next, the principles of classical calibration methods of discrete element method are explicated in detail, alongside the validation and discussion of their properties. Lastly, this study examined the applicability of calibrated parameters and points out that the size ratio, porosity, maximum radius, and minimum radius of particles should be identical in both the geometric calibration model and that for applications.

Probabilistic carbon price prediction with quantile temporal convolutional network considering uncertain factors.

Accurate carbon price projections can serve as valuable investment guides and risk warnings for carbon trading participants. However, the escalation of uncertain factors has brought numerous new hurdles to existing carbon price forecast methods. In this paper, we develop a novel probabilistic forecast model called quantile temporal convolutional network (QTCN) that can precisely describe the uncertain fluctuation of carbon prices. We also investigate the impact of external factors on carbon market prices, including energy prices, economic status, international carbon markets, environmental conditions, public concerns, and especially uncertain factors. Taking China's Hubei carbon emissions exchange as a study case, we verify that our QTCN outperforms other classical benchmark models in terms of prediction errors and actual trading returns. Our findings suggest that coal prices and EU carbon prices have the most significant effect on Hubei carbon price forecasting, while air quality index appears to be the least important. Besides, we demonstrate the great contribution of geopolitical risk and economic policy uncertainty to carbon price projections. The effect of these uncertainties is more pronounced when the carbon price is at a high quantile level. This research can offer valuable guidelines for carbon market risk management and provide new insight into carbon price formation mechanisms in the era of global conflict.

Big data and artificial intelligence application in energy field: a bibliometric analysis.

This paper uses bibliometrics to characterize the knowledge systems of big data, artificial intelligence (AI), and energy based on the Science Citation Index Extension (SCI-E) and Social Science Citation Index (SSCI) of the Web of Science from 2001 to 2020. Results show that China is the country with the highest number of publications (1115), accounting for 29% of the total; however, the most influential country in the field is the USA, with an h-index of 75. The Chinese Academy of Sciences publishes the largest number of papers (104) and plays a vital role in the collaboration network. The study also reveals that the IEEE Access is the most productive journal (195) in terms of the number of publications, and engineering is the most popular discipline (1526). The key theoretical foundation includes deep learning (293), big data (105), energy consumption (79), and reinforcement learning (40). The application of big data and AI in the field of energy focuses on smart grid, energy consumption, and renewable energy. Early research frontiers involve optimization and prediction of energy-related problems using the genetic algorithm and neural networks. Since 2013, energy big data have gained prominence. At present, machine learning, deep learning, and fog computing are frequently combined with energy saving. In the future, big data and AI will be utilized to promote the application of renewable energy and energy-saving renovation of buildings. These findings can help researchers understand the developmental trends and correctly grasp the research direction and method of the emerging interdisciplinary field.

Predicting the loss to follow-up (LTFU) of HIV/AIDS patients in China using a recency-frequency (RF) model.

BACKGROUND: We constructed a recency-frequency (RF) model for predicting the loss to follow-up (LTFU) in HIV/AIDS patients in China. METHODS: Data on HIV/AIDS outpatients in the research unit from 1 August 2009 to 30 September 2020 and from 1 October to 31 December 2020 were exported as the observation and prediction datasets, respectively. The classic recency-frequency-monetary (RFM) model was expanded into RFm, RF, RFL and RFmL models. In the observation dataset, the best predictive model was obtained using k-means clustering and C5.0 verification. Then, two rounds of k-means modelling were performed on the best model: data with R ≤ 6 months were retained, randomly divided into a training set (70%) and a testing set (30%) and used to perform the second round of modelling to subdivide the churn and non-churn patients. Next, an ANN algorithm was used to predict LTFU, and the confusion matrix with prediction datasets was constructed. RESULTS: The observation and prediction datasets included 16 949 and 10 748 samples, respectively. The RF model with three clusters and a quality of 0.82 was the best predictive model. From the observation set, 13 799 samples were retained, and the model accuracy was 100% on the training and testing sets. These 13 799 samples were subdivided into 1563 samples of churn patients and 12 216 samples of non-churn patients. The accuracy of ANN prediction was 99.89%. The accuracy and precision of the confusion matrix were 85.41% and 99.76%, respectively. CONCLUSION: The RF model is effective in predicting the LTFU of HIV/AIDS patients in China and preventing its occurrence.

Carbon-economic inequality in global ICT trade.

The expansion of information and communications technology (ICT) trade has contributed to rising trade imbalances and international tensions. A detailed assessment of the potential carbon and economic impacts of ICT trade is pertinent. We assess to what extent and how the carbon costs and economic benefits embodied in ICT trade were unevenly distributed among global regions in the period 2000-2018 using multiregional input-output models. We show that in 2018, emerging economies received 82% of the CO2 emissions while developed economies gained 42% of the value-added in ICT exports. This carbon-economic inequality (CEI) decreased (i.e., improved) by 16% from 2000 to 2018, arising from global production fragmentation, with developed economies retaining downstream high value-added ICT marketing but outsourcing upper- and middle-stream carbon-intensive material extraction and manufacturing to emerging economies. This study provides insights for enhancing negotiations and cooperation among global regions to light a path toward sustainable ICT trade.

COVID-19 and China commodity price jump behavior: An information spillover and wavelet coherency analysis.

Jumps in commodity prices can make asset risk management challenging. This study explores the influence feature of the COVID-19 epidemic on China's commodity price jumps, using 5-min intraday high-frequency futures data of three China's commodity markets (energy, chemical, and metal) from January 23, 2020 to June 10, 2022. We find that firstly the information spillover from the COVID-19 spread situation to China's energy price jumps is relatively weak, and the COVID-19 epidemic shows the most substantial jump information spillover pattern to China's chemical price. The information spillover pattern is time-varying across the COVID-19 spread situation phase. Secondly, there are co-movement patterns between China's commodity price and China/global COVID-19 confirmed cases. This co-movement feature mainly occurs at the medium- or long-run time scales, and varies across commodities. Thirdly, the demand elasticity for China's commodities and its dependence on imports and exports are the main factors influencing the sensitivity of its price jumps to the COVID-19 outbreak.

China's energy stock market jumps: To what extent does the COVID-19 pandemic play a part?

The price jump behavior may bring tremendous challenges on risk management and asset pricing. This paper uses the BN-S test, the wavelet coherence method, and applies high-frequency data to explore whether and to what extent the COVID-19 pandemic impacts China's energy stock market jumps and its characteristics. The empirical results uncover the significant and heterogeneous interactions between the COVID-19 pandemic and China's energy stock market jumps across market specifications, investment horizons, and China/global pandemic tolls at different time scales. First, the oil stock market jumps were the most correlated with the pandemic, especially during the peak and re-deterioration phases. The pandemic played a positive and leading role in the short term (1-4 days length period) and long term (over 32 days length period). Second, the coal stock market jumps have similar characteristics to those of oil, but mainly show a negative correlation with the pandemic. Third, renewable energy stock market jumps were the least correlated, mainly showing a positive correlation in the short term and a negative correlation in the long term. In addition, the interaction characteristics of systemic co-jumps in different China's energy stock markets are also significant.

Does air pollution inhibit manufacturing productivity in Yangtze River Delta, China? Moderating effects of temperature.

China has been experiencing serious and recurrent incidences of air pollution in recent years. The frequency and timespans of such incidences are uncertain because of variable urban weather conditions, especially temperature, that inhibit the productivity of manufacturing companies. Matching data about listed manufacturing companies in China's Yangtze River Delta urban cluster from 2003 to 2018 with data on urban air pollution and weather, we explored the effects of air pollution on corporate productivity and the moderating role of temperature. We found that air pollution significantly inhibited the productivity of these companies, which decreased by about 0.1% for 1% increase in the concentration of PM2.5. Regarding industry heterogeneity, high-energy-consumption and low-technology manufacturing industries were more sensitive to the negative effects of air pollution. Furthermore, we concluded that low temperatures played an important role in causing significant increases in the negative effects of air pollution. In the fall and winter (October to January), the lower the temperatures resulted in stronger inhibitory effects of air pollution on corporate productivity. When the average daily temperature is 0°C-3°C, the moderating effects of temperature are even more obvious. To minimize the inhibitory effects of air pollution on productivity, governments and companies should implement positive adaptions to simultaneously confront air pollution and temperature change.

The two-stage factors driving changes in China's industrial SO2 emission intensity: A production-theoretical decomposition analysis.

Exploring the efficiency and technology related driving factors of China's industrial sulfur dioxide (SO2) emission intensity change from a staged perspective is significant for reducing emissions in an efficient way. This study shows how efficiency and technology related factors at two stages, cleaner production and end-of-pipe treatment, influence changes in China's industrial SO2 emission intensity, by implementing a two-stage production-theoretical decomposition analysis (PDA) approach. The empirical research was conducted by decomposing changes in China's industrial SO2 emission intensity during 2011-2015. The results show that potential pollution intensity and treatment technological change substantially benefited the industrial SO2 emission intensity reduction, while changes in the treatment technical efficiency largely inhibited decreases in the industrial SO2 emission intensity. At the regional level, the decrease in industrial SO2 emission intensity created by production technological change occurred in the eastern and north-eastern regions, while this factor increased industrial SO2 emission intensity in the western and central regions. This study also found that changes in treatment technological change reduced industrial SO2 emission intensity in all four regions. Based on the decomposition results, this paper makes targeted policy recommendations for different levels of governments.

Does Fear of the New Coronavirus Lead to Low-Carbon Behaviors: The Moderating Effect of Outcome Framing.

PURPOSE: Air pollution has been found to aggravate the infection and mortality of COVID-19, leading to increasing attention on pro-environmental behaviors. Considering individuals' psychological distance from COVID-19, this research aims to examine the relationship between fear of COVID-19, air pollution concern, and low-carbon behaviors. METHODS: Two survey-based studies were conducted in this research. Study 1 consisted of 323 participants and examined the relationships between psychological distance (PD) from COVID-19, fear of COVID-19, air pollution concern, and low-carbon behaviors. Study 2 identified the moderating effect of outcome framing using an intergroup experiment in which 304 participants were randomly assigned to two groups (gain framing vs loss framing). RESULTS: The results of Study 1 showed that the closer the PD was, the higher the fear was. Fear of COVID-19 and low-carbon behaviors were positively associated. Additionally, air pollution concern acted as a mediator in their relationship. The results of the moderating effect test in Study 2 showed that fear and air pollution concern led to higher low-carbon behavioral intention within gain framing than within loss framing. CONCLUSION: This research revealed that personal fear of public health emergencies will lead to environmental pollution concern and pro-environmental behaviors, and information from the outside plays a moderating role. The results provide implications for policy advocacy of the health and environmental sectors and for guiding people's low-carbon behaviors.

A bibliometric study about energy, environment, and climate change.

Using the extended science citation index database (SCI) and social science citation index (SSCI) databases, this paper analyzed the characteristics of publications, research foundations, research hotspots, and the evolutionary tracks of studies in the field of energy, environment, and climate change from 1990 to 2019 using a bibliometric method. This method is useful because it involves the quantitative analysis of large amounts of literature, using mathematical and statistical method. The results showed that the United States (US), the United Kingdom (UK), and China were the countries with the most published papers in the field. The US plays a key role in the cooperation between international institutions. An assessment conducted by the Intergovernmental Panel on Climate Change (IPCC) created the standard scientific reference for the research on climate change and its consequences. From 2006 to 2016, a large number of co-cited papers laid a solid foundation for research in the field. During this period, the research focused on the impact of climate change on the ecological environment, began to propose different countermeasures, and formed a set of mature research methods. From 2017 to 2019, there was an acceleration in the growth rate of the number of published articles. Strategies to address climate change, including renewable energy and energy transition, were the focus during this phase. Future studies are expected to focus on climate change mitigation strategies and energy policies. The findings provide a reference for researchers and can help policy makers balance economic development with environmental protection.

Psychological Distance Toward Air Pollution and Purchase Intention for New Energy Vehicles: An Investigation in China.

Air pollution in China has been drawing considerable attention in recent years. The emergence of new energy vehicles (NEVs) provides hope to reduce air pollutant emission. However, consumers' recognition and acceptance of NEVs remain at the early stage. This research aims to explore how consumers' environmental concern influences their NEV purchase intention. Specifically, this research conducted an online survey and an experiment to address the following issues: (1) how consumers' psychological distance (PD) toward air pollution influences their purchase intention for NEVs, and does their risk perception of the consequences of air pollution mediate this influence; (2) whether consumers' perceived price level of NEVs plays a moderating role in the relationship between risk perception and purchase intention; and (3) whether the construal level of stimulus can be manipulated to influence consumers' PD toward air pollution to increase their purchase intention for NEVs. The results of study 1, based on a total of 293 valid samples, show that consumers' PD toward air pollution significantly affects their purchase intention for NEVs, and risk perception of the consequences of air pollution plays a considerable mediating role. Meanwhile, consumers' perceived price level of NEVs has a significant negative moderating effect on the relationship between risk perception and purchase intention. The results of study 2, based on an online experiment, show that the construal level of stimulus can influence consumers' PD toward air pollution, which in turn affects their purchase intention for NEVs. The findings of this research have implications for businesses' promotional strategies and governments' policies. For instance, low-construal-level promotional materials can be developed to arouse consumers' environmental concern, thereby facilitating their eco-friendly consumption behavior. Governmental financial assistance and other policies can also increase consumers' willingness to purchase NEVs.

Carbon emission reduction characteristics for China's manufacturing firms: Implications for formulating carbon policies.

The rapid development of China's manufacturing industry since China's accession to WTO in 2001 has dramatically increased China's carbon emissions. To inform the carbon policy development of China's manufacturing industry, this study constructed a DEA-GS (data envelopment analysis and grid search) model from a cost perspective to understand the their emission reduction characteristics. Using a large sample of manufacturing firms from 2008 to 2011, the carbon pricing and reduction potential of China's manufacturing firms was explored by analyzing the firms' marginal abatement costs. The results showed that: (a) with increasing marginal abatement costs, the growth rates of both cumulative emission reduction activities and emission reduction of these firms gradually slowed down. When the marginal abatement cost exceeds 200 Yuan/ton, neither the number of reduction activities nor the amount of reduced emissions increase. (b) The impact of marginal abatement costs on the numbers of reduction activities and firms in each sub-sector is heterogeneous. (c) The emission reduction behaviors of manufacturting firms, determined by carbon pricing, are mostly concentrated in developed areas or around large cities. In contrast, areas with substantial emission reductions are more scattered. The results suggest that The emission reduction characteristics of sub-sectors should be fully considered when formulating carbon policies for China's manufacturing industry. The carbon price for the China's manufacturing industry should not exceed 200 Yuan/ton. Furthermore, the carbon policy of China's manufacturing industry should have broader coverage, rather than merely covering developed areas.

Adherence predictor variables in AIDS patients: an empirical study using the data mining-based RFM model.

BACKGROUND: Highly active antiretroviral therapy (ART) is still the only effective method to stop the disease progression in acquired immunodeficiency syndrome (AIDS) patients. However, poor adherence to the therapy makes it ineffective. In this work, we construct an adherence prediction model of AIDS patients using the classical recency, frequency and monetary value (RFM) model in the data mining-based customer relationship management model to obtain adherence predictor variables. METHODS: We cleaned 257,305 diagnostic data elements of AIDS outpatients in Shanghai from August 2009 to December 2019 to obtain 16,440 elements. We tested the RFM and RFm (R: recent consultation month, F: consultation frequency, M/m: total/average medical costs per visit) models, three clustering methods (K-means, Kohonen and two-step clustering) and four decision algorithms (C5.0, the classification and regression tree, Chi-square Automatic Interaction Detector and Quick, Unbiased, Efficient, Statistical Tree) to select the optimal combination. The optimal model and clustering analysis were used to divide the patients into two groups (good and poor adherence), then the optimal decision algorithm was used to construct the prediction model of adherence and obtain its predictor variables. RESULTS: The results revealed that the RFm model, K-means clustering analysis and C5.0 algorithm were optimal. After three rounds of k-means clustering analysis, the optimal RFm clustering model quality was 0.8, 10,614 elements were obtained, including 9803 and 811 from patients with good or poor adherence, respectively, and five types of patients were identified. The prediction model had an accuracy of 100% with the recent consultation month as an important adherence predictor variable. CONCLUSIONS: This work presented a prediction model for medication adherence in AIDS patients at the designated AIDS center in Shanghai, using the RFm model and the k-means and C5.0 algorithms. The model can be expanded to include patients from other centers in China and worldwide.

Customer relationship management analysis of outpatients in a Chinese infectious disease hospital using drug-proportion recency-frequency-monetary model.

BACKGROUND: Identifying the patient types with different economic values can be useful for hospital development. OBJECTIVE: This work uses the theory of customer relationship management (CRM) to analyze the outpatients in the hospital for infectious diseases in Shanghai, China. METHODS: A total of 2,271,020 data elements of outpatients in the research unit between August 2009 and December 2019 were extracted, analyzed and cleaned to obtain 171,107 valid data elements (1 element per person). The main diseases were viral hepatitis B (VHB) and acquired immunodeficiency syndrome (AIDS), and the average percentage of drug expenditure was 80.39 %. We innovatively expanded the classic RFM (R: recency, F: frequency, M: monetary) model in CRM to the dRFM (d: percentage of drug expenditure) model. We selected the best clustering algorithm from the K-means, Kohonen and two-step clustering methods to find the optimal model to distinguish the types of patients with different economic values and the best decision-making algorithm from the C5.0, CART classification regression tree, CHAID and QUEST algorithms to verify the model. RESULTS: After performing two rounds of K-means clustering analysis on three models: RFM, RFM + dRFM and dRFM, and 97,855 data elements were retained. The RFM + dRFM model was the optimal model, clustering the patients into 3 types: potential patients (24.2 %) to be retained, with a high drug expenditure and the last visit in more than 19.06 months, high-value patients (24.5 %) to be attracted, with the last visit in about 6.66 months; basal patients (51.3 %) to be kept, with the last visit in about 3.7 months. The model was then verified using the C5.0 decision tree algorithm with an accuracy rate of 99.97 %. CONCLUSION: This objective CRM analysis of the patients in the hospital for infectious diseases using the dRFM model accurately identified different types of patients, providing an objective and effective basis for hospital management.

Efficiency assessment of industrial solid waste generation and treatment processes with carry-over in China.

Industrial solid waste (ISW) harms the eco-environment as well as human health; thus, both the generation and treatment processes of ISW are important aspects of a reduction of discharged ISW. Currently, only part of the generated ISW is treated, while the remainder is stored for later treatment, which is referred to as carry-over between two adjacent periods. According to such a two-stage process (with carry-over), a network slacks-based measure model is proposed to measure the overall and divisional efficiencies of 30 Chinese regions during 2011-2015. The main findings are summarized in the following: firstly, failing to consider carry-over underestimates both overall and divisional efficiencies. Secondly, the average annual generation efficiency exceeded the average annual treatment efficiency during the study period; however, this gap increasingly narrowed. Thirdly, the overall efficiency is more closely related to the treatment efficiency than to the generation efficiency. Moreover, synergies were identified between generation efficiency and treatment efficiency for each region. Fourthly, significant regional differences affect both overall and divisional efficiencies, and significant stage efficiency differences also affect both coastal and inland areas. To increase the overall efficiencies of different regions of China, different policies are suggested.

Investigating the spatiotemporal differences and influencing factors of green water use efficiency of Yangtze River Economic Belt in China.

Combining freshwater consumption and wastewater emissions into a unified analysis framework and utilizing the epsilon-based measure (EBM) model with the characteristics of radial model and non-radial model, this paper evaluates green water use efficiency (GWUE) of 11 provincial-regions in the Yangtze River Economic Belt (YREB) and investigates its spatiotemporal differences during the period 2005-2014, on basis of which the contribution rate of each input-specific green water use inefficiency in the overall green water use efficiency and the potential of freshwater-saving and wastewater emissions reduction are also calculated. The Theil index is used to explore the sources of the provincial gap of green water use inefficiency, and a random-effect panel Tobit model is applied to test the impact of the influencing factors of green water use inefficiency in the YREB. It is found that green water use inefficiency of the YREB is relatively low and regional differences is significant during the sample period, indicating a large potential of water-saving and water pollution reduction, and narrowing BGAP and WGAP of the Upstream is the key for improving green water use inefficiency in the YREB. The panel Tobit regression results show that economic development, technological innovation, water use structure, water resources endowment, environmental regulation and regional differences all play positive/negative effects on green water use inefficiency in the YREB, while these factors' influencing direction, degree and significance are significantly different. The conclusions of our study can provide considerably valuable information for the YREB to reserve water resources and reduce wastewater emissions.