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China's paper industry development is rapid, but the recycling rate of China's waste paper has been low all the time. Meanwhile, material flow analysis can help determine the flow of waste paper, and life cycle assessment (LCA) is the methodological framework for quantifying greenhouse gas emissions. Therefore, present study integrates these two methods into the model construction of China's waste paper recycling decision system. Present study constructs a benchmark model of China's waste paper recycling decision system in 2017, focusing on the impact of nonstandard waste paper recycling on the economic and environmental benefits of China's domestic waste paper recycling system. This model construction is followed by sensitivity analysis of the relevant parameters affecting the efficiency of the waste paper recycling system. Finally, present study forecasts the system's economic benefits and greenhouse gas (GHG) emissions in the context of integrating and regulating nonstandard recycling vendors. The results show that the economic benefit of China's waste paper recycling in 2017 is approximately 458.3 yuan/t and that the GHG emissions are 901.1 kgCO 2 eq. The standard recovery rate and nonstandard recovery acceptance rate will both have a significant impact on the system's economic benefits and improve the GHG emissions structure. In the context of integrating nonstandard recycling enterprises and individual recycling vendors, the economic benefits will rise to 3312.5 yuan/t in 2030, while GHG emissions will rise to 942.9 kgCO 2 eq. Present study can play a certain guiding role for policy makers in formulating waste paper recycling industry specifications and formulating relevant policies., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Ozone pollution is the focus of current environmental governance in China and high-quality prediction of ozone concentration is the prerequisite to effective policymaking. The studied ozone pollution time series exhibits distinct seasonality and secular trends and is associated with various factors. This study developed an interpretable hybrid model by combining STL decomposition and the Transformer (STL-Transformer) with the prior information of ozone time series and global multi-source information as prediction basis. The STL decomposition decomposes ozone time series into trend, seasonal, and remainder components. Then, the three components, along with other air quality and meteorological data, are integrated into the input sequence of the Transformer. The experiment results show that the STL-Transformer outperforms the other five state-of-the-art models, including the standard Transformer. Specially, the univariate forecasting for ozone relies on mimicking the patterns and trends that have occurred in the past. In contrast, multivariate forecasting can effectively capture complex relationships and dependencies involving multiple variables. The method successfully grasps the prior and global multi-source information and simultaneously improves the interpretability of ozone prediction with high precision. This study provides new insights for air pollution forecasting and has reliable theoretical value and practical significance for environmental governance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Urban flood risk assessment is a complex task, as it requires extensive knowledge about hydrological features of the catchment, hydraulic characteristics of the drainage network and social characteristics of residential areas. How to accurately and efficiently quantify regional risk has always been a challenge in this field. To solve the problem, this study is developed to propose a novel integrated urban flood risk assessment approach based on one-two dimensional coupled hydrodynamic model and improved projection pursuit method. Two open source software like urban storm flood management model (SWMM) and TELEMAC-2D are introduced to build the one-two coupling hydrodynamic model through proprietary programming, which can accurately simulate urban inundation process. Based on the simulation results of hydrodynamic model and literature review, a set of urban flood risk assessment index system containing physical mechanism and statistical mechanism related index is established, including a total of 12 indicators covering three dimensions like hazard factor, exposure factor and vulnerability factor. Then an Improved Projection Pursuit (IPP) method coupling k-means clustering algorithm is proposed to determine the index weight. The novel integrated urban flood risk assessment approach is implemented in Suyu district, China. The results demonstrate that the accuracy and efficiency of evaluation urban flood risk assessment are greatly improved by the integrated approach. In conclusion, this research offers a novel methodology for urban flood risk assessment and contributes to decision-making in environmental management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Improving carbon emission efficiency of manufacturing is important for promoting low-carbon development and achieving harmonious coexistence between humans and nature. In this study, we use the Global-MinDS model to measure the carbon emission efficiency of China's provincial manufacturing from 2011 to 2020. To do so, we construct a mathematical model to analyze the mechanism of the interaction among internet development, market integration, and carbon emission efficiency of manufacturing. Then, we empirically examine the carbon emission reduction effect of internet development using the Bartik instrument variable method and verify the significance of market integration. Our results show that internet development can significantly improve the carbon emission efficiency of manufacturing, promoting manufacturing low-carbon transformation. Carbon emission reduction effect of internet development is characterized by heterogeneity in market integration, environmental regulation, and policy intensity. This means that the impact of internet development is more pronounced in lower integration regions, higher environmental regulation intensity and in big data comprehensive experimental zone. Intermediate mechanism tests show that internet development can promote market integration by reducing external transaction costs, so as to play a carbon emission reduction effect in the market mechanism. Our study provides empirical evidence for the carbon emission reduction effect of the digital economy and offers valuable references for effectively promoting the low-carbon transformation of manufacturing in China from the perspective of unified market., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Machine learning models are often viewed as black boxes in landslide susceptibility assessment, lacking an analysis of how input features predict outcomes. This makes it challenging to understand the mechanisms and key factors behind landslides. To enhance the interpretability of machine learning models in wide-area landslide susceptibility assessments, this study uses the Shapely method to explore the contributions of feature factors from local, global, and spatial perspectives. Landslide susceptibility assessments were conducted using random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) models, focusing on the geologically complex Sichuan-Tibet region. Initially, the study revealed the contributions of specific key feature factors to landslides from a local perspective. It then examines the overall impact of interactions among feature factors on landslide occurrence globally. Finally, it unveils the spatial distribution patterns of the contributions of various feature factors to landslide occurrence. The analysis indicates the following: (1) The XGBoost model excels in landslide susceptibility assessment, achieving accuracy, precision, recall, F1-score, and AUC values of 0.7815, 0.7858, 0.7962, 0.7910, and 0.86, respectively; (2) The Shapely method identifies the leading factors for landslides in the Sichuan-Tibet region as Elevation (3000-4000 m), PGA (1-2 g), NDVI (<0.5), and distance to rivers (<3 km); (3) Using the Shapely method, the study explains the contributions, interaction mechanisms, and spatial distribution patterns of landslide susceptibility feature factors across local, global, and spatial perspectives. These findings offer new avenues and methods for the in-depth exploration and scientific prediction of landslide risks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Evaluating the sustainable development level and obstacle factors of small towns is an important guarantee for implementing China's new-type urbanization and rural revitalization strategies, and is also a key path to promoting the United Nations Sustainable Development Goal 11 (SDG11). Traditional evaluation methods (such as Analytic Hierarchy Process, AHP, and Technique for Order Preference by Similarity to Ideal Solution, TOPSIS) mainly calculate the comprehensive score of each indicator through weighting. These methods have limitations in handling multidimensional data and system nonlinearity, and they cannot fully reveal the complex relationships and interactions within the sustainability systems of small towns. In contrast, the evaluation model combining Principal Component Analysis (PCA) and Catastrophe Progression Method (CPM) used in this study can better handle multidimensional data and system nonlinear relationships, reducing subjectivity in evaluation and improving the accuracy and reliability of the assessment results. The specific research process is as follows: First, based on the United Nations SDG11 framework, using multi-source big data, a theoretical framework and evaluation index system for the sustainable development of small towns suitable for the Chinese context were established. The impact of county-level factors on the sustainable development of small towns was also considered, and an entropy weight-grey correlation model was used to measure these impacts, resulting in a town-level dataset incorporating county-level influences. Secondly, the sustainability levels of 782 top small towns in China were evaluated using the comprehensive evaluation model based on PCA-CPM Model. Finally, an improved diagnostic model was used to identify obstacles influencing the sustainable development of small towns. The main findings include: 52.69% of the small towns have a sustainable development score exceeding 0.7255, indicating that the overall performance of small towns is at a medium to high development level. The development of small towns exhibits significant differences across regions and types, which are closely linked to county-level effects. Economic and social factors are the main obstacles to the sustainable development of small towns, and the impact of these obstacles intensifies from the eastern to the central, western, and northeastern regions. This study provides valuable insights for policymakers and scholars, promoting a deeper understanding of the sustainable development of small towns., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The carbon emissions trading (CET) policy internalises the cost of carbon emission reductions borne by companies, which will affect the companies' investment and management decisions. From a micro perspective, this paper analyzes the impact on company investment expenditure and its transmission mechanism by implementing the CET policy. Based on panel data of China's A-share listed companies from eight carbon-intensive industries spanning 2010 to 2020, the time-varying difference-in-difference model and its extended model are used to evaluate the impact of the policy in the pilot areas. The results show that: first, based on the cost effect and legality theories, CET policy can reduce the investment expenditure of the companies by 71.95%. Second, CET policy reduces corporate investment expenditures by increasing corporate debt financing costs. When debt financing costs increase by 120.25%, the investment expenditures will reduce by 2.56% indirectly while the intermediary effect of equity financing costs is not significant. Finally, with the implementation of CET policy, the inhibitory effect on corporate investment expenditures has gradually increased. CET policy has a more significant inhibitory effect on investment expenditures of nonstate-owned companies and small-scale companies. The results have passed the robustness test and provide evidence for the policy-maker to balance microeconomic entity development and carbon reduction, and for companies to make optimization investment and financing decisions in response to policy shocks effectively., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The research and development (R&D) of renewable energy (RE) is crucial for cost reduction in electricity generation and enhancing power system stability. Compared to traditional fossil fuels, it demands more financial support. To investigate Chinese residents' willingness to pay (WTP) for the R&D of RE and its influencing factors, we conducted a large-scale online survey in four first-tier cities in China in 2023. The research findings indicate that (1) Chinese residents are willing to pay approximately 31.20 yuan (4.34 USD) per month for the R&D of RE. (2) WTP is higher under a mandatory payment model than a voluntary one. (3) Electricity consumption, environmental concern, environmental behavior, willingness to participate, satisfaction with government RE policies, and trust in the government's environmental governance capability significantly influence WTP. (4) Younger, male, and larger household residents exhibit higher WTP. Based on these findings, targeted policy recommendations were proposed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Rare earth elements have garnered increasing attention due to their strategic properties and chronic toxicity to humans. To better understand the content, migration, and ecological risk of rare earth elements in a 180 cm depth sediment profile downstream of a decommissioned uranium hydrometallurgical site in South China, X-ray powder diffraction (XRD) and High-resolution transmission electron microscope (HRTEM) were additionally used to quantify and clarify the mineral composition features. The results showed a high enrichment level of total rare earth elements in the sediment depth profile (range: 129.6-1264.3 mg/kg); the concentration variation of light rare earth elements was more dependent on depth than heavy rare earth elements. Overall, there was an obvious enrichment trend of light rare earth elements relative to heavy rare earth elements and negative anomalies of Ce and Eu. The fractionation and anomaly of rare earth elements in sediments were closely related to the formation and weathering of iron-bearing minerals and clay minerals, as confirmed by the correlation analysis of rare earth elements with Fe (r 2  = 0.77-0.90) and Al (r 2  = 0.50-0.71). The mineralogical composition of sediments mainly consisted of quartz, feldspar, magnetite, goethite, and hematite. Pollution assessment based on the potential ecological risk index, pollution load index (PLI), enrichment factor, and geological accumulation index (I geo ) showed that almost all the sediments had varying degrees of pollution and a high level of ecological risk. This study implied that continued environmental supervision and management are needed to secure the ecological health in terms of rare earth elements enrichment around a decommissioned uranium hydrometallurgical site. The findings may provide valuable insights for other uranium mining and hydrometallurgical areas globally., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Increasingly frequent intense rainfall events have caused flood disasters of metro systems worldwide. Flood management based on flood resilience is a novel strategy for dealing with floods. Nevertheless, limited research has been conducted on the correlation between metro systems and flood resilience. In this study, an index system-based framework is proposed to evaluate the flood resilience of metro station entrances, the selected indices are multidimensional, emcompassing external environment, entrance structure, socio-economic, and post-disaster response factors. The flood resilience assessment of metro station entrances is carried out via a case study of the Donghaochong Basin, Guangzhou, China. The pressure-state-response (PSR) model is used to establish the index system for the assessment of the flood resilience. Indices of inundation at the entrance sites are simulated and extracted using the InfoWorks ICM model. Two rainfall scenarios are chosen to simulate inundation, and the resilience results of metro station entrances in each scenario are compared. The results suggest that in the 200-year (200a) return period scenario, 70% of the entrances are at the high and highest resilience levels, whereas in the Zhengzhou "720" scenario, the proportion of that is 43.33%. The resilience of metro station entrances is significantly reduced under the higher rainfall scenario. However, factors unrelated to rainfall are found to mitigate the extent to which total resilience is reduced. Before and during a flood disaster, the indices indicate the resistance and adaptability of metro entrances to the disaster. After a flood disaster, they indicate the ability of entrances to recover to regular functioning. Specific steps should be implemented before and after a disaster to effectively enhance the flood resilience of metro entrances. This study provides valuable insights into enhancing the pertinence and effectiveness of flood disaster management at metro station entrances, and reducing flood damage to metro systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Improving water quality to provide freshwater is an urgent requirement for regional and even global social development. More accurate simulation of non-point sources pollution, monitored mainly by total nitrogen (TN) and total phosphorus (TP), has always been a challenge for InVEST water purification model, particularly in agricultural areas. This can be attributed to the fact that there is no reference data for TN and TP to rectify the outcomes modelled by this model. This paper provided these data to rectify simulation results of TN and TP to ensure their accuracy. The Huai River watershed (HRW) is an important grain production area with slow economic development, and non-point source pollution has exceeded point-source pollution. There is an urgent need for water management authorities to obtain complete spatio-temporal data on TN and TP loads and their exports to improve water quality. The reference data onloads and exports of TN and TP were estimated for the entire watershed and its sub-watersheds through an investigation-evaluation technique during 1980-2018. TN and TP loads generated from the agricultural sector were the major pollution sources in the HRW and had similar time trends during the same period. The spatial distribution of TN and TP exports was modelled byusingthe InVEST water purification model, and it was found that the temporal trends for the final exports of TN and TP into river systems were similar to those for TN and TP loads in the HRW for 1980-2018. Key driving factors were detected using the Geo-detector method to quantify the contribution rates of factors to the spatiotemporal exports of TN and TP. Our results showed that individual factors, such as precipitation and land use/cover, were the most important factors driving spatio-temporal variations in TN and TP exports in the HRW from 1980 to 2018. Meanwhile, the contribution rates of interactions between land use/cover and other factors were consistently highest in this watershed during the same period. In this study, we estimated the loads and exports of TN and TP, and modelled their spatial patterns in this watershed from 1980 to 2018, providing important information on TN and TP for water-related management authorities. We also provide a method for other river systems to calibrate the parameters in the biophysical table of InVEST water purification model based on final exports of TN and TP., Competing Interests: Declaration of competing interest The authors in this paper declare that they have no known competing financial interests or personal relationships that could have appeared to influnence the work done here., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Increased ecological land (IEL) such as forests and grasslands can greatly enhance ecosystem carbon sinks. Understanding the mechanisms for the magnitude of IEL-induced ecosystem carbon sinks is crucial for achieving carbon neutrality. We estimated the impact of IEL, specifically the increase in forests and grasslands, as well as global changes including atmospheric CO 2 concentration, nitrogen deposition, and climate change on net ecosystem productivity (NEP) in National Key Ecological Function Zones (NKEFZs) in China using a calibrated ecological process model. The NEP in NKEFZs in China was calculated to be 119.4 Tg C yr -1 , showing an increase of 42.6 Tg C yr -1 from 2001 to 2021. Compared to the slight contributions of climate change (-8.0%), nitrogen deposition (11.5%), and reduction in ecological land (-3.5%), the increase in NEP was primarily attributed to CO 2 (66.5%) and IEL (33.5%). Moreover, the effect of IEL (14.8 Tg C yr -1 ) surpassed that of global change (13.1 Tg C yr -1 ) in the land use change zone. The IEL-induced NEP is significantly associated with CO 2 fertilization, regulated by precipitation and nitrogen deposition. The high values of IEL-induced NEP occurred in areas with precipitation exceeding 800 mm and nitrogen deposition exceeding 25 kg N ha -1 yr -1 . We recommend prioritizing the expansion of ecological land in areas with sufficient water and nutrients to enhance CO 2 fertilization, while avoiding increasing ecological land in regions facing unfavorable climate change conditions. This study serves as a foundation for comprehending the NEP response to ecological restoration and global change., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Given the increasing threat of economic downturns and global warming, the governments of developing countries urgently need effective measures that can both stimulate economic development and promote low-carbon transitions. As a widely accepted method for stimulating economic growth, the improvement of the business environment and its effect on economic activity have been fully discussed. However, little is known about whether and how the business environment affects the low-carbon transition. By leveraging city-level data from China, this paper exploits whether and through which channels a favorable business environment promotes a low-carbon transition. We find that improving the business environment has a positive impact on carbon efficiency. This relationship is verified to be robust through various alternative tests. Additionally, it is demonstrated that enhancing the business environment can increase carbon efficiency by promoting green technology progress and stimulating new firm entry. Heterogeneity analysis indicates that the positive effect of the business environment is pronounced for cities with intensive environmental regulation, sufficient environmentally targeted fiscal expenditure, and loose economic growth requirements. This paper reveals the unintentional environmental gains of business environment optimization and provides important insights into the low-carbon transition for developing economies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The ecological security pattern can harmonize the relationship between natural environmental protection and socio-economic development. This study proposes a regional ecological security pattern optimization framework by integrating theory and practice with landslide sensitivity and landscape structure. Using Yan'an City as an example, this study optimizes the landscape layout of preliminary ecological sources. The landslide sensitivity index is generated using the information value model and then used to adjust the ecological resistance surface. The Minimum Cumulative Resistance (MCR) approach is used to extract ecological corridors, locate ecological nodes utilizing circuit theory, and outline crucial ecological control areas. The results demonstrate: (1) the ecological sources are primarily composed of forestlands, with a total area of 2,352.2400 km 2 , concentrated in the southwest, central, and southeast regions. The optimal landscape granularity for the source patches is 600 m. (2) Yan'an is divided into four landslide sensitivity level zones: extremely high, high, medium, and low, with the overall landslide sensitivity of the region being high. (3) The highest ecological resistance is observed in built-up land and the lowest in forestland. The total number of ecological corridors is 26, avoiding most of the highly sensitive areas of landslides. (4) The number of ecological pinch points is 61, while the ecological barrier points amounted to 54. The critical ecological control areas consist mainly of cropland, forestland, and grassland, and differentiated restoration strategies are proposed to address their unique characteristics. The findings of the research can offer scientific guidance for the practice of ecological security protection in geohazard-prone areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The development of nuclear power plants is progressing rapidly worldwide. However, there is currently a lack of dynamic monitoring of the thermal discharge temperature rise from these plants, making it unclear to governments where their nuclear power thermal discharges stand globally. We hypothesize that between 2013 and 2022, there are significant temporal and spatial differences in the thermal discharge temperature rise from nuclear power plants globally. Temporal differences are expected to reflect a country's nuclear power installed capacity and thermal discharge treatment capabilities, while spatial differences are related to the type of water bodies where nuclear power plants are located. To test these hypotheses, we utilized Landsat data to get the distribution range of thermal discharge and temperature rise levels ranging from 1 °C to 4 °C, and compared the temporal and spatial characteristics of temperature rise in different countries. The results indicate that: (1) Currently, China, the United States, and Canada rank among the top three globally in terms of the area experiencing temperature rise due to thermal discharge, which correlates with the total installed capacity of nuclear power in these countries. (2) Countries such as Russia, Finland, and Mexico exhibit larger areas with a 4 °C temperature rise level per unit installed capacity, with their thermal rise area per unit installed capacity (TRAUIC) exceeding the global average by more than 1.5 times. (3) The spatial dispersion trends of thermal discharges from nuclear power plants vary across different types of water bodies. For nuclear power plants located in bays, thermal discharges primarily disperse along the coast, while in open sea and lakes, thermal discharges tend to spread in a fan-shaped pattern. The findings of this study are crucial for understanding the efficiency of thermal discharge from nuclear power plants across different countries globally, assessing potential environmental risks during the operation of these plants, and promoting the safe and orderly development of nuclear power plants worldwide., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Promoting the green development of the logistics industry has become a focal point of attention in China. This study combines an improved gravity model and social network analysis method, focusing on the nineteen provinces of the Yangtze Economic Belt and Yellow River Basin. It constructs a spatial correlation network of carbon emissions in the logistics industry from 2010 to 2021, exploring its formation mechanism and spatial evolution characteristics. The study utilizes a quadratic assignment procedure model to investigate internal driving factors. Building upon this, the study employs an improved STIRPAT model to predict the emission reduction path of the future logistics industry. Results are as follows: (1) Carbon consumption is most pronounced in Shandong, Jiangsu, and Shanghai. The carbon emission shows the characteristics of larger downstream; (2) The bridges of carbon emission-related networks gradually shift from Shandong to Shanxi, Anhui, and Sichuan. Carbon emissions in each sector and spatial spillover effects exhibit dynamic correlations and interactive influences; (3) Energy intensity, freight volume, and the spatial correlation network of the logistics industry are highly correlated; (4) The overall carbon emissions from the logistics industry show a decreasing trend in the future. Anhui and Shaanxi provinces will have high carbon emissions in 2035. The conclusions aim to provide policy suggestions for the region's low-carbon transformation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The continuous expansion of modern cities not only leads to ecological degradation but also seriously threatens regional ecological security and sustainable development. The construction of ecological security patterns (ESPs) has emerged as a significant approach to alleviate or even solve the conflict between regional development and ecological protection. The Chengdu-Chongqing Economic Circle (CCEC) represents the core area of regional economic development strategy in western China, characterized by rapid economic growth from 2000 to 2020. This study integrates assessments of ecosystem services importance, eco-environmental sensitivity and landscape connectivity; uses circuit theory and hydrological analysis to establish a research framework for the spatiotemporal evolution of regional ESP; and develops an optimized ESP combined with the Major Function Oriented Zone. The results indicate that urban expansion significantly impacted the ESP of the CCEC between 2000 and 2020. The fragmentation and merging of ecological sources occurred simultaneously, the number of patches reduced by 28.13% from 64 to 46. The early ecological security network was compromised, leading to the disappearance or elongation of some ecological corridors. The number of ecological corridors decreased by 36.03% from 136 to 87; the total length was reduced by 29.92% from 7500.57 km to 5256.28 km. Urgent optimization of the ESP is needed, reducing the number of key ecological protection areas by 50% from 106 to 53 while increasing priority restoration areas by 13.51% from 37 to 42. The study also reveals the insufficiency of the current Major Function Oriented Zone in protecting linear corridors, necessitating focused attention on the protection and restoration of ecological sources and surrounding corridors in important development zones. Additionally, a spatial optimization strategy of "one shelter, two cores, and three regions" is proposed to enhance regional ecosystem stability and connectivity. The aim was to strike a balance between ecological protection and food security by recommending an ecological corridor width range of 30∼100 m. These research findings offer scientific guidance for ecological space protection and restoration in the CCEC, contributing to the enhancement of both scientific and rational ecological planning in rapidly urbanizing areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Identifying streamwater-groundwater interactions (SGI) is crucial for effective water resource management, especially in arid and semi-arid regions. Despite the effectiveness of tracers in detecting these interactions, their large-scale application is challenged by the variability in tracer characteristics and natural conditions. This study addresses these challenges through extensive research across seven watersheds (7636-60,916 km 2 ) in China's Loess Plateau (CLP). We utilized multiple physicochemical and stable isotope tracers (δ 2 H and δ 18 O) to elucidate the spatiotemporal variations and controlling factors of SGI, and to estimate uncertainties in quantifying SGI using various indicators during unidirectional water exchange periods. Our findings indicated that groundwater discharge into streamwater dominates SGI in the CLP, with mean discharge ratios (the percentage of river flow that originates from groundwater discharge) varying from 10% to 57%. Significant spatial variability was observed both across and within watersheds. The central watersheds exhibited lower discharge ratios (23 ± 11%) compared to the northern (29 ± 12%) and southern (25 ± 13%) watersheds. The upper reaches showed higher discharge ratios (28 ± 12%) compared to the middle and lower reaches (22 ± 8%). Loess thickness and vegetation primarily limit groundwater discharge by affecting groundwater storage and water flow velocity. The utilization of individual isotopic or hydrochemical indicators introduces large uncertainties in quantifying groundwater discharge ratios due to isotope fractionation or water-rock interaction, while the combination of these two indicators can reduce uncertainties in quantifying SGI. This study provides valuable insights for selecting environmental tracers to quantify SGI, contributing to sustainable water resource management in arid and semi-arid regions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Climate change and intensified human activities are exacerbating the frequency and severity of extreme precipitation events, necessitating more precise and timely flood risk assessments. Traditional models often fail to dynamically and accurately assess flood risks due to their static nature and limited handling of spatiotemporal variations. This study confronts these challenges head-on by developing a novel coupled hydrological-hydrodynamic model integrated with a Block-wise use of the TOPMODEL (BTOP) and the Rainfall-Runoff-Inundation (RRI) model. This integrated approach enables the rapid acquisition of high-precision flood inundation simulation results across large-scale basins, addressing a significant gap in dynamic flood risk assessment and zoning. A critical original achievement of this research lies in developing and implementing a comprehensive vertical-horizontal combined weighting method that incorporates spatiotemporal information for dynamic evaluation indicators, significantly enhancing the accuracy and rationality of flood risk assessments. This innovative method successfully addresses the challenges posed by objective and subjective weighting methods, presenting a balanced and robust framework for flood risk evaluation. The findings from the Min River Basin in China, as a case study, demonstrate the effectiveness of the BTOP-RRI model in capturing the complex variations in runoff and the detailed simulations of flood processes. The model accurately identifies the timing of these peaks, offering insights into the dynamic evolution of flood risks and providing a more precise and timely assessment tool for policymakers and disaster management authorities. The flood risk assessment results demonstrate good consistency with the actual regional conditions. In particular, high-risk areas exhibit distinct characteristics along the river channel, with the distribution area significantly increasing with a sudden surge in runoff. Intense precipitation events expand areas classified as moderate and high risk, gradually shrinking as precipitation levels decrease. This study significantly advances flood risk assessment methodologies by integrating cutting-edge modeling techniques with comprehensive weighting strategies. This is essential for improving the scientific foundation and decision-making processes in regional flood control efforts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Microplastic (MP) pollution has become a pressing concern in global freshwater ecosystems because rivers serve as essential channels for the transport of terrestrial debris to the ocean. The current researches mostly focus on the large catchments, but the impact on the small catchments remains underexplored. In this study, we employed Strahler's stream order classification to delineate the catchment structure of the Beijiang River in South China. The distribution pattern of MP contamination and the factors influencing the distribution pattern, were assessed across the streams at different orders. We found that the Beijiang River was moderately polluted compare to other rivers in China, with an average MP abundance of 2.15 ± 1.65 items/L. MP abundance ranged from 3.17 to 1.45 items/L in the streams at different orders, and significantly decreased with increasing stream order (R 2  = 0.93). This highlights the key role of small rivers as the channels for the transport of MPs from watersheds to main streams. The high abundance of PP and PE fibers, the high correlation between the stream order and the resin proportion (R 2  = 0.89), and the significant correlation between MP abundance and proximity to urban centers (P = 0.02), indicated that MP pollution across the streams at different orders was predominantly influenced by anthropogenic activities, rather than natural environmental factors. By integrating MP data with hydrographic information, the annual MP loads for the streams at Orders 1 to Order 5 were estimated to be 4.63, 39.38, 204.63, 503.06, and 1137.88 tons/yr, respectively. Additionally, an ecological risk assessment indicates that MP pollution led to a low risk in the Beijiang River. Our findings deepen the understanding of MP pollution within freshwater river networks, and emphasize the crucial role of tributary systems in transporting MPs to main river channels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The dune ecosystem plays a significant role in the global carbon cycle. The Horqin Sandy Land is a typical semi-arid fragile ecosystem in northern China. Understanding the magnitudes and dynamics of carbon dioxide fluxes within this region is essential for understanding the carbon balance. Used 6 years (2013-2018) measurements from an eddy-covariance system, we analyzed the dynamic patterns of net ecosystem carbon exchange (NEE), gross primary production (GPP), and ecosystem respiration (Reco) of the dune ecosystem in Horqin Sandy Land and examined their responses to climate factors with a focus on the precipitation. The results showed that the NEE of the dune ecosystem fluctuated from -166 to 100 gCO 2 ·m -2 ·year -1 across the 6 growing seasons, with an average of -56 gCO 2 ·m -2 ·year -1 . The precipitation was not a key factor influencing the carbon flux variability. During the mid-growth stage, GPP was primarily affected by the effective precipitation frequency (R 2 ranging from 0.65 to 0.85, P < 0.05), followed by fractional vegetation cover (R 2 ranging from 0.65 to 0.68, P < 0.05). However, in the early and late growth stages, temperature predominantly drove the carbon flux (R 2  = 0.75, P < 0.01). The interannual variability of carbon flux can be predominantly elucidated by phenological indicators such as CO 2 uptake (CU start ), end of CO 2 uptake (CU end ), CO 2 uptake period (CUP), and Spring lag. The results demonstrated the dune ecosystem is a weak carbon sink in semi-arid ecosystems. Furthermore, we emphasized the significance of effective precipitation frequency in regulating carbon fluxes. Our results provide a foundational understanding of the carbon balance in semi-arid ecosystems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Effectively managing drought in the China-Pakistan Economic Corridor (CPEC) region requires a precise understanding of the three-dimensional characteristics of meteorological drought (MD) and agricultural drought (AD), as well as the factors that trigger their propagation. This study employed non-stationary drought indices (NSPEI and SSMI) to develop a cutting-edge 3-dimensional drought identification model. This model was used to detect MD and AD patterns from 1981 to 2022 in the CPEC region and was integrated with binomial logistic regression to identify the critical factors that drive drought propagation. This study's key findings include: 1) Between 1981 and 2022, droughts in Xinjiang, China, exhibited a discernible southward migration trend, while in Pakistan, droughts showed a northward migration pattern. Drought frequency and extent have increased over time, with affected regions becoming more widespread in CPEC. Notably, drought events with higher preceding drought contagion indices (DCI) were more likely to evolve into extreme, long-term droughts. 2) Drought area emerged as a significant positive triggering factor for drought propagation in the CPEC region. Conversely, snowmelt in Xinjiang and the leaf area index for low vegetation in Pakistan acted as triggering elements affecting negatively. 3) Various factors played a pivotal role during drought propagation process, including geographical coordinates of drought centroids, DCI, and temperature variations. Additionally, snowmelt and snow evaporation significantly impacted drought propagation in Xinjiang, while vegetation cover in Pakistan played a crucial role during the drought propagation process. By utilizing four regression models and conducting comprehensive attribution analysis, this study sheds light on the characteristics of drought propagation and the factors influencing it. These findings are valuable for enhancing early warning systems and implementing effective drought mitigation strategies in the CPEC region., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Businesses embracing green innovation can encourage high-quality green economic development in addition to reducing emissions. In this paper, we use the Difference-in-Differences (DID) to investigate the influence of green investor behavior on the green innovation of companies, using the first-ever green investor investment in a company as a quasi-natural experiment. According to research, green investors have the power to accelerate corporate green innovation greatly. Three key strategies that green investors can use to do this include raising institutional investment levels, enhancing the green perception of executives, and bringing in top talent. Heterogeneity analysis shows that non-high-polluting, big, and state-owned enterprises (SOEs) are more likely to benefit from green investors' green innovation effects. Further analysis reveals that (ⅰ) green investors' influence on an enterprise's level of green innovation can help it improve its ESG ratings; (ii) green investors can encourage green innovation in source control but have little effect on green innovation in end-of-pipe treatment; (ⅲ) green investors can support both non-green and green innovation in enterprises, but have a greater influence on green innovation. This study strengthens the micro relationship between green investors and corporate green innovation. It also supports the theoretical underpinnings of corporate green innovation, which is significant for advancing green innovation, environmental protection, and high-quality economic development in emerging economies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Northwest China has abundant solar energy resources and extensive land, making it a pivotal site for solar energy development. However, restrictions on site selection and severe weather conditions have hindered the establishment and operation of photovoltaic (PV) power stations. Previous studies have not considered meteorological factors when evaluating site suitability, leading to research gaps in identifying suitable areas and establishing indicator systems. We aimed to address these gaps by considering seven factors constraining the construction of centralized PV power stations (CPPS) and developing an indicator system based on terrain, climate, soil, and economic factors. Furthermore, we conducted analyses to quantify the solar energy generation potential (SEGP), carbon emissions reduction benefits, and land utilization potential at different sites. The findings indicate that areas rated as very suitable and extremely suitable comprised the largest proportion (62.35%) of site suitability. The correlation between site suitability and electricity consumption was largely non-significant, highlighting the need for enhanced coordination. Additionally, we forecast the electricity consumption in Xinjiang, Gansu, Inner Mongolia, Qinghai, Ningxia, and Shaanxi for 2030 to be 56.62, 19.86, 54.54, 13.59, 15.96, and 33.34 ( × 10 11 kWh), respectively, with corresponding carbon emissions reduction potentials of 20.2, 7.1, 19.4, 4.8, 5.7, and 11.9 ( × 10 9  kg). Consequently, PV carbon reduction and land utilization potential are substantial., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Although it is a key measure to control energy consumption and promote the improvement of industrial structure, energy market allocation reform has rarely been concerned with its impact on employment, an important livelihood issue. To fill this gap, this paper takes the Energy-Consumption Rights Trading System (ECRTS) enacted by China in 2016 as a research background and adopts the difference-in-difference approach to explore the effects and mechanisms of the ECRTS on enterprise labor demand. The results show that the ECRTS significantly reduces firms' labor demand, particularly for low-skilled workers, through both production scale effects and technological upgrading effects. Specifically, the ECRTS has led to a decrease in sales revenues and an increase in labor productivity, thereby reducing firms' labor demand. Heterogeneity tests indicate that the ECRTS has a greater impact on employment in firms with lower energy-consumption intensity, domestic capital injections, weaker innovation capacity, and lower market power. The paper also explores the welfare consequences of the policy, finding that while the ECRTS does not improve the environmental performance of firms it does not pass on the compliance costs of regulations to incumbent workers. The overall impact is neutral. This paper extends the study of the economic consequences of the ECRTS and has implications for other developing countries in reconciling energy regulation and employment., Competing Interests: Declaration of competing interest I declare that I have no financial and personal relationships with other people or organizations that can inappropriately influence this work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Corporate Environmental, Social, and Governance (ESG) performance reflects the alignment of firms with national strategies and macroeconomic development principles, and serves as a crucial foundation for achieving sustainable development. Investigating the factors driving corporate ESG performance has significant theoretical and practical implications. This study utilizes data from Shanghai and Shenzhen A-share listed companies in China from 2010 to 2022 and theoretically analyzes and empirically tests the effects and mechanisms of executives' green experience on overall ESG performance and performance on each element of ESG. The results indicate that executives' green experience enhances the efficiency of corporate green patent innovation, thereby improving environmental performance. In addition, green experiences foster environmental investment and charitable donations, leading to better social performance. Executives with green experience also attract green investors and analysts' attention, thereby improving corporate governance and ultimately positively affecting overall corporate ESG performance. The robustness of these findings is confirmed through alternative variable measurements, Propensity Score Matching(PSM) Tests, Entropy Balancing Tests, Instrumental Variable Tests, and controlling for the impact of significant public health events. Further analysis revealed that the positive effects of executives' green experiences on corporate ESG performance are more pronounced in non-heavily polluting corporations, non-state-owned corporations, and firms where executives have higher power. This study enriches research on the consequences of executives' green experiences and the determinants of corporate ESG performance, extending the boundaries of how executive characteristics influence micro-level corporate outcomes. It also provides valuable references for regulatory bodies promoting ESG practices and decision-making by capital market participants., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Many studies have confirmed that climate change leads to frequent urban flooding, which can lead to significant socioeconomic repercussions. However, most existing studies have not evaluated the impacts of climate change on urban flood from both event-scale and annual-scale dimensions. In addition, there are only few studies that simultaneously consider scenario and model uncertainties of climate change, and combine flood risk assessment and uncertainty analysis results to provide practical suggestions for urban drainage system management. This study uses the statistical downscaling method to calculate the design rainfall under ten rainfall return periods of four climate models and three climate change scenarios in 2040s, 2060s, and 2080s in various prefecture-level cities in China. The four climate models are HadGEM2- ES, MPI-ESM-MR, NorESM1-M and FGOALS-g2 models and the three climate change scenarios are constructed by different representative concentration pathways (RCP), namely RCP2.6, RCP4.5 and RCP8.5. On this basis, relying on the generated drainage systems using geographical information and other data, event-scale and annual-scale precipitation are combined to calculate the change ratio of annual flood volume expectation in prefecture-level cities in each future year compared with the current situation. Furthermore, the study evaluates scenario and model uncertainties of climate change, and then comprehensively integrates the flood risk and its uncertainties to provides suggestions for urban drainage system management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The limited availability of carbon sources in low carbon source wastewater has always hindered nitrogen removal efficiency. The residual slurry liquid after anaerobic digestion has the potential to be used as a carbon source. This study investigated the optimal parameters of dissolved oxygen (DO) for enhancing the treatment of low carbon source wastewater using slurry, and revealed the characteristics of carbon metabolism gene enrichment and carbon fixation potential driven by DO. The results indicated that treating wastewater under high DO concentrations (3-4 mg/L) conditions could meet the emission standards set by wastewater treatment plants in China. However, the lower-cost DO concentration of 3 mg/L is considered a more cost-effective parameter, effectively removing 85.68% of chemical oxygen demand and 91.56% of total nitrogen. Mechanistic analysis suggested that reducing DO concentration increased the diversity of microbial communities. Regulating DO concentration reshaped the co-metabolic network of microorganisms with different DO sensitivities by influencing Hydrogenophaga and Chlorobium. This ultimately led to the reconstruction of heterotrophic microbial communities dominated by Sphaerotilus and Acidovorax under high DO conditions, and heterotrophic-autotrophic co-enriched microbial communities dominated by Chlorobium under low DO conditions (1-2 mg/L). Additionally, under high DO conditions, high microbial mass transfer efficiency and the enrichment of functional genes were crucial for achieving high nitrogen removal performance. Further, the microbial carbon fixation potential was relatively high under the DO 3 mg/L condition, helping to reduce the consumption of additional carbon sources. This study provided innovative ideas for the sustainable and low-carbon development of wastewater treatment technology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Megacities face significant pollution challenges, particularly the elevated levels of heavy metals (HMs) in particulate matter (PM). Despite the advent of interdisciplinary and advanced methods for HM source analysis, integrating and applying these approaches to identify HM sources in PM remains a hurdle. This study employs a year-long daily sampling dataset for PM 1 and PM 1-10 to examine the patterns of HM concentrations under hazy, clean, and rainy conditions in Hangzhou City, aiming to pinpoint the primary sources of HMs in PM. Contrary to other HMs that remained within acceptable limits, the annual average concentrations of Cd and Ni were found to be 20.6 ± 13.6 and 46.9 ± 34.8 ng/m³, respectively, surpassing the World Health Organization's limits by 4.1 and 1.9 times. Remarkably, Cd levels decreased on hazy days, whereas Ni levels were observed to rise on rainy days. Using principal component analysis (PCA), enrichment factor (EF), and backward trajectory analysis, Fe, Mn, Cu, and Zn were determined to be primarily derived from traffic emissions, and there was an interaction between remote migration and local emissions in haze weather. Isotope analysis reveals that Pb concentrations in the Hangzhou region were primarily influenced by emissions from unleaded gasoline, coal combustion, and municipal solid waste incineration, with additional impact from long-range transport; it also highlights nuanced differences between PM 1 and PM 1-10 . Pb isotope and PCA analyses indicate that Ni primarily stemmed from waste incineration emissions. This explanation accounts for the observed higher Ni concentrations on rainy days. Backward trajectory cluster analysis revealed that southern airflows were the primary source of high Cd concentrations on clean days in Hangzhou City. This study employs a multifaceted approach and cross-validation to successfully delineate the sources of HMs in Hangzhou's PM. It offers a methodology for the precise and reliable analysis of complex HM sources in megacity PM., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Improving the resilience of wastewater treatment facilities (WWTFs) has never been more important with rising risks of disasters under climate change. Beyond physical damages, non-physical shocks induced by disasters warrant attention. Human mobility is a vital mediator in transferring the stresses from extreme events into tangible challenges for urban sewage systems by reshaping influent characteristics. However, the impact path remains inadequately explored. Leveraging the stay-at-home orders during the COVID-19 pandemic as a natural experiment, this study aims to quantify and interpret the heterogeneous impacts of mobility reduction on the influent characteristics of WWTFs with different socio-economic, infrastructural, and climatic conditions. To achieve this goal, we developed a research framework integrating causal inference and interpretable machine learning techniques. Based on the empirical data from China, we find that 79.1% of the studied WWTFs, typically located in cities with well-developed drainage infrastructures and low per capita water usage, exhibited resilience against drastic mobility reduction. In contrast, 20.9% of the studied WWTFs displayed significant variations in influent characteristics. Large-capacity WWTFs in subtropical regions encountered challenges with low-load operations, and small-capacity facilities in suburban areas grappled with nutrient imbalances. This study provides valuable insights to equip WWTFs in anticipating and adapting potential variations in influent characteristics triggered by mobility reduction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Green energy transition directly contributes to Sustainable Development Goals 7 and 13, which are among the 17 development goals determined by the United Nations. However, what are the determinants of this green energy transition? Researchers' answers to this question will assist in formulating policy prescriptions that will enable concrete steps toward achieving these goals. This study investigates how the development of the banking sector, human rights, economic complexity, and economic freedom affect the green energy transition in China, which is the biggest emitter of greenhouse gases globally but is also one of the leading countries in renewable energy production. The study covers the years 1995-2022. Advanced time series analysis methods were employed, and robust results were reported. According to the findings, Economic Freedom, Human Rights, and Economic Complexity have increased the transition to green energy in China. No effect of banking sector development was found. The country should improve economic freedom and human rights and increase knowledge/innovation-based production to accelerate China's transition to green energy and contribute to environmental sustainability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Identifying the response characteristics of ecosystem service value (ESV) to changes in spatial scales, known as spatial scale effects, is crucial in guiding the development of corresponding management strategies. This paper examines ESV in China's terrestrial area during the year 2020, revealing the spatial aggregation characteristics of ESV and the trade-off and synergistic relationships of ecosystem services at different spatial scales, ranging from 1 km × 1 km-10 km × 10 km, with a gradient of 1 km. The results indicate: 1) The distribution pattern of ESV in China's terrestrial area is "high in the southeast and low in the northwest." 2) The spatial characteristics of ESV in China's terrestrial area undergo a distinct transition at the 3 km × 3 km scale. In detail, the spatial clustering features show a trend of first rising and then falling with the increase in spatial scale, while the synergistic relationships between different ecosystem services strengthen and the trade-off relationships weaken with the increase of the spatial scale. These findings can inform the formulation of differentiated ecological protection compensation policies and enable cross-area trading of ecological values in China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Grazing exclusion (GE), as an effective strategy for revitalizing degraded grasslands, possesses the potential to increase ecosystem respiration (R e ) and significantly influence the capacity of grassland soils to sequester carbon. However, our current grasp of R e dynamics in response to varying durations of GE, particularly in the context of precipitation fluctuations, remains incomplete. To fill this knowledge gap, we conducted a monitoring of R e over a 40-year GE chronosequence within Inner Mongolia temperate typical steppe across two distinct hydrologically years. Overall, R e exhibited a gradual saturation curve and an increasing trend with the duration of GE in the wet year of 2021 and the normal precipitation year of 2022, respectively. The variance primarily stemmed from relatively higher microbial biomass carbon observed in the short-term GE during 2022 in contrast to 2021. Moreover, the impacts of GE on the sensitivities of R e to moisture and temperature were intricately tied to precipitation patterns. increasing significantly with prolonged GE duration in 2022 but not in 2021. Our study highlights the intricate interplay between GE duration, precipitation variability, and R e dynamics. This deeper understanding enhances our ability to predict and manage carbon cycling within typical steppe in Inner Mongolia, offering invaluable insights for effective restoration strategies and climate change mitigation., Competing Interests: Declaration of competing interest We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Based on city-level panel data spanning 2008 to 2021, this study investigates the impact of government environmental attention (GEA) on green total factor productivity (GTFP). The findings suggest that increased GEA substantially enhances the growth of GTFP. After conducting robustness and endogeneity tests, the study's results consistently show reliability and robustness. Further analysis elucidates several mechanisms through which GEA influences GTFP, including fostering green technology innovation, optimizing resource allocation, and promoting upgrades in industrial structure. Heterogeneity analyses reveal that the impact of GEA on GTFP is notably pronounced in eastern cities, as well as in cities characterized by low resource dependency, mature industrial development, and high market competition. Conversely, the influence of GEA on GTFP is less discernible in cities prioritizing economic development goals, possibly due to differing policy orientations and resource allocation strategies. This study offers a novel perspective on understanding how GEA shape green development and provides empirical support for policy formulation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The use of agrochemical inputs has significantly enhanced agricultural yields in China; however, their excessive utilization has also caused a range of environmental issues. This paper examines the costs associated with reducing agrochemicals by employing shadow prices, which represent the value of the marginal product of agrochemicals, to further develop cost-effective environmental policy measures for reducing their usage. To this end, the shadow prices of agrochemicals have been assessed by adopting a newly developed convex expectile regression approach and using statistical data from 31 provinces in China spanning from 2005 to 2020. Furthermore, the present study investigates the disparities between shadow prices and market prices for different agrochemicals across various regions in China. The findings suggest that the costs of reducing chemical fertilizers are higher than those of reducing pesticides and plastic films. Moreover, the results indicate that central China exhibits relatively high potential for decreasing agrochemical usage. Finally, these findings can inform the Chinese government's restructuring of producer support and environmental policy in a cost-effective way to mitigate agrochemicals use in the future. Additionally, the research method employed in this study holds potential for extension to other agrochemicals-dependent countries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

During rapid urbanization in developing countries, changes in land use and land cover (LULC) can significantly alter urban land surface temperatures (LST), exacerbating the urban heat island (UHI) effect and degrading the outdoor environment. In this study, taking Guangzhou, China, as an example, we used Landsat series satellite data from 1992 to 2022, classified the LULC of the study area by the Support Vector Machine (SVM) method, estimated the LST of the area by the mono-window algorithm, and classified the LST of the study area into five UHI intensity classes based on the normalized values of the LST, and explored the influence of the LULC on the distribution of the UHI intensity. The CA-ANN (cellular automata-artificial neural network) model in QGIS software was employed to forecast the distribution of LULC and UHI intensity in Guangzhou for 2032. The findings reveal a strong correlation between UHI intensity and LULC, with water bodies and vegetation primarily exhibiting low and sub-low temperatures, while urban areas exhibit sub-high and high temperatures. The prediction results show that, according to the current development trend, compared with 1992, the water body and vegetation cover in 2032 will decrease by 46.97% and 34.24%, the building land will increase by 263.71%, and the sub-high and high temperature areas will increase by 127.76% and 375.92%. By analysing the spatial and temporal changes in LULC and its relationship with the distribution of UHI intensity during urbanization, this study assists government administrations and urban planners in devising sensible urban development strategies and implementing effective measures to plan LULC rationally. This approach aims to mitigate the impacts of the urban heat island and foster sustainable urbanization., Competing Interests: Declaration of competing interest To the best of our knowledge and belief, neither the entire paper nor any part of its content has been published or has been accepted elsewhere. It is not being submitted to any other journal. All authors have seen the manuscript and approved to submit to your journal. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, no conflict of interest exists in the submission of this manuscript. Thank you very much for your attention and consideration., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Nutrient loads in lakes are spatially heterogeneous, but current spatial analysis method are mainly zonal, making them subjective and uncertain. This study proposes a high-resolution model for assessing spatial differences in nutrient loads based on the lattice Boltzmann method. The model was applied to Dongping Lake in China. Firstly, the contribution rates of four influencing factors, including water transfer, inflow, wind, and internal load, were calculated at different locations in the lake. Then, their proportionate contributions during different intervals to the whole lake area were calculated. Finally, the cumulative load could be calculated for any location within the lake. The validation showed that the model simulated hydrodynamics and water quality well, with relative errors between the simulated and measured water quality data smaller than 0.45. Wind increased the nutrient loads in most parts of the lake. The loads tended to accumulate in the east central area where high-frequency circulation patterns were present. Overall, the proposed water quality model based on the lattice Boltzmann method was able to simulate seven indexes. Therefore, this model represents a useful tool for thoroughly assessing nutrient load distributions in large shallow lakes and could help refine lake restoration management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Combatting land damage has become a global priority, and China has adopted a series of ecological engineering measures, especially in the agro-pastoral area with fragile ecological environment. The effectiveness of ecological engineering construction (EEC), from a comprehensive recognition encompassing its quality, quantity, and function, has remained largely unknown. To this end, Zhangbei County, a typical agro-pastoral ecotone of northern China, was chosen as our focal area. After summarizing the timelines, aims and results of the EEC during various periods in Zhangbei, the linear spectral mixture analysis was employed to process Landsat 5 TM images in 2000 and 2010, as well as Landsat 8 OLI images in 2020. Then, a comprehensive evaluation framework of EEC was established from the perspective of "quantity-quality-function", and the ecological effectiveness of EEC was evaluated from 2000 to 2020 in Zhangbei. Results revealed that EEC played a critical role in enhancing quantity, quality and function, in spite of that, there were still numerous regions showing varying degrees of degradation in terms of these aspects. Then, by extending the three-dimensional cube as the theoretical basis for the zoning management of EEC, we merged four zones according to the space matching relationship among quantity, quality and function of EEC, namely, Ecological conservation area, Ecological improvement area, Ecological restoration area and Ecological remodeling zone. More targeted ecological measures were required for specific matching relationship among quantity, quality and function of EEC. This study is expected to present an empirical case for assessing the ecological effectiveness of EEC in areas or countries with similar restoration demand and support regional management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study provides first evidence on the effects of Chinese FDI (Foreign Direct Investment) outflows on host country environments. The study draws upon a comprehensive dataset covering aggregate Chinese FDI outflows and sector specific data into 65 host nations over the 2007-2019 period. Employing a STIRPAT (Stochastic Regression on Population, Affluence and Technology) model and several different techniques including DID (Difference-in-Difference), pooled OLS (Ordinary Least Squares), quantile regression, IV (Instrumental Variable) estimation, threshold and Tobit regression, the findings suggest that Chinese FDI leads to an increase in host country CO2 (Carbon Dioxide) emissions, aligning with the pollution haven hypothesis at the aggregate level. A closer investigation at the development regime and sectoral levels indicates that in the low development regime, FDI inflows into the financial and real estate sector increase emissions. Conversely in the high-income regime, Chinese FDI into the entertainment sector is associated with an increase in carbon emissions. Chinese FDI is further found to lead to an increase in emissions in countries with a per capita GDP (Gross Domestic Product) of below USD72041.7. However, as per capita income rises above USD72041.7, FDI leads to a fall in carbon emissions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The distribution of China's energy resources is concentrated in the central and western regions, whereas the energy demand is predominantly centered in the eastern regions. Under the ambitious "carbon neutrality" goal, how to effectively quantify and respond to the impact of this energy "endowment-demand" distortion (EEDD) on economy and environment has become a key issue. We introduce an EEDD index that quantifies the distortion characteristics of China's energy endowment and demand. Based on the EEDD index, a panel vector auto regression-generalized method of moments (PVAR-GMM) model is adopted to assess the interactive effects between regional EEDD and sustainable development variables. The obtained results indicate that the energy endowment-demand distortions are progressively worsening across most provinces. Interestingly, we discover that the EEDD has significant beneficial effects on regional sustainable development. Moreover, the EEDD is found to be significantly influenced by the sustainability-related variables. These impacts between EEDD and sustainable development variables demonstrate significant regional heterogeneity. This study provides crucial empirical evidence for advancing the comprehensive and sustainable development of regional energy, environment, and economy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The continuous excessive application of phosphorus (P) fertilizers in intensive agricultural production leads to a large accumulation of P in surface soils, increasing the risk of soil P loss by runoff and leaching. However, there are few studies on the accumulation and loss of P from surface soil to deep soil profiles driven by shallow groundwater table (SGT) fluctuations. This study used the intensive cropland around 7 plateau lakes in Yunnan Province as an example and conducted in situ monitoring of P storage in the soil profile and SGT during the rainy season (RS) and dry season (DS) as well as simulation experiments on soil P loss. The aim was to study the spatiotemporal variation in P accumulation in the soil profile of cropland driven by SGT fluctuations in the RS and DS and estimate the P loss in the soil profile driven by SGT fluctuations. The results showed that fluctuations in the SGT promoted P accumulation from the surface soil to deeper soil. The proportions of P stored in various forms in the 30-60 cm and 60-100 cm soil layers in the RS were greater than those in the DS, while the average proportion in the 0-30 cm soil layer in the DS was as high as 48%. Compared with those in the DS, the maximum decreases in the proportion of P stored as TP and Olsen-P in the 0-100 cm soil layer in the RS were 16% and 58%, respectively, due to the rise in the SGT (SGT <30 cm), while the soil TP storage decreased by only 1% when the SGT was maintained at 60-100 cm. The critical thresholds for soil Olsen-P and TP gradually decreased with increasing soil depth, and the risk of P loss in deeper soil increased. The loss of soil P was increased by fluctuations in the SGT. Based on the cropland area around the 7 plateau lakes, P storage, and SGT fluctuations, the average loss intensity and loss amount of TP in the 0-100 cm soil layer around the 7 plateau lakes were estimated to be 25 kg/ha and 56 t, respectively. Therefore, reducing exogenous P inputs, improving soil endogenous P utilization efficiency and maintaining deep soil P retention are the basic strategies for preventing and controlling P accumulation and loss in deep soil caused by SGT fluctuations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Regions can meet their development demands through trade, with the attendant environmental costs being shifted to other regions, and carbon emissions emitted from different industries could be transferred over long distances through the increasingly diversified trade network. However, it remains unclear how regional trade leads to the tele-connection and transfer of embodied carbon emissions form industries, and what is the structure and characteristics of the transfer. Thus, multiregional input‒output models and complex network analysis are employed to reveal the tele-connection of carbon emissions from industries in China. The results show that embodied carbon emissions from trade increased by 869.47 million tons during in five years, with North China being the largest outflow area, while the coastal regions being the inflow areas. Moreover, the secondary industry is the highest source of embodied carbon emissions, accounting for 96.68 % of the volume, and the transfer of carbon emissions mainly occurs in North and East China. In carbon emissions networks, North China holds a controlling position, as analysed by degree and strength. The first 23.3%-30% of nodes carry about 62.6%-72.4% of the entire carbon emissions flow, and the network conforms to scale-free features. Centrality further reveals that northern and coastal areas occupy core positions, with interregional carbon flows dominating the critical pathways in the network. The number of clusters evolved from three to four communities during 2012-2017 in the network, demonstrating that the carbon flow network is developing towards multipolarity and modularity. This study underscores the urgency of mitigating carbon emissions in industrial trade by identifying key nodes and cluster structures in emission networks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

In China, over 65% of human activities are concentrated in cities, resulting in a conflict between the supply and demand of ecosystem services (ESs). To alleviate this problem, many cities have adopted eco-friendly development modes, however, the effectiveness of these models in reducing ESs supply-demand conflicts has not been comprehensively reviewed, and the human and natural drivers behind these relationship shifts remain unclear. To bridge this gap, this study analyzed the shifts in the relationships between supply and demand of ESs across China from 2010 to 2020 at a city level, as well as identified the human and natural drivers behind them. Firstly, the InVEST models were integrated with socioeconomic data to evaluate the supply and demand distribution for three pivotal ESs: water yield (WY), habitat quality (HQ), and soil retention (SR). Then, a four-quadrant diagram approach was proposed to enhance the analysis of their spatiotemporal relationships. Furthermore, random forest models were employed to examine the drivers of the shifts in these relationships. The results showed that WY and SR services witnessed growth until 2015, and then receded, while HQ saw a modest decline from 2010 to 2020. Spatial synergies in the supply and demand of ESs were primarily observed in the southern cities, with a significant northward extension by 2020. From a temporal perspective, the percentage of cities achieving coordination in WY and SR services increased from 32.6% to 57.3%, respectively, in the 2010-2015 period to 42.4% and 63.3% between 2015 and 2020, meanwhile, HQ service conflicts diminished from 58.7% to 53.5%. The changes in socioeconomic and land use factors contributed to 64.3%, 36.1%, and 33.3% of the shifts in the supply-demand relationship for HQ, WY, and SR services, respectively. Our analysis highlights the potential of human-driven ecological management to enhance the balance of this relationship. It can support the design of city-specific policies that foster a balance between ecological processes and socio-economic development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

From the perspective of innovation motivation, low-carbon innovation is divided into substantive low-carbon innovation and strategic low-carbon innovation. On this basis, this study empirically investigates the impact and mechanism of digital transformation on enterprise low-carbon innovation using data from China's A-share listed enterprises from 2005 to 2019. The results show that digital transformation promotes the overall level of enterprise low-carbon innovation, substantial low-carbon innovation, and strategic low-carbon innovation, with a more significant impact on strategic low-carbon innovation. For enterprises with large-scale, belonging to high-monopoly industries and located in high-marketization regions, the promotion of digital transformation is more significant and facilitates strategic low-carbon innovation more than substantive low-carbon innovation. Based on the internal capability perspective, enhancing management capability, R&D capability, and environmental information disclosure capability are three important channels of digital transformation affecting low-carbon innovation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Suspended particulate matter (SPM) plays a crucial role in assessing the health status of coastal ecosystems. Satellite remote sensing offers an effective approach to investigate the variations and distribution patterns of SPM, with the performance of various satellite retrieval models exhibiting significant spatial heterogeneity. However, there is still limited information on precise remote sensing retrieval algorithms specifically designed for estimating SPM in tropical areas, hindering our ability to monitor the health status of valuable tropical ecological resources. A relatively accurate empirical algorithm (root mean square error = 2.241 mg L -1 , mean absolute percentage error = 42.527%) was first developed for the coastal SPM of Hainan Island based on MODIS images and over a decade of field SPM data, which conducted comprehensive comparisons among empirical models, semi-analytical models, and machine learning models. Long-term monitoring from 2003 to 2022 revealed that the average SPM concentration along the coastal wetlands of Hainan Island was 6.848 mg L -1 , which displayed a decreasing trend due to government environmental protection regulations (average rate of change of -0.009 mg L -1 /year). The seasonal variations in coastal SPM were primarily influenced by sea surface temperature (SST). Spatially, the concentrations of SPM along the southwest coast of Hainan Island were higher in comparison to other waters, which was attributable to sediment types and ocean currents. Further, anthropogenic pressure (e.g., agricultural waste input, vegetation cover) was the main influence on the long-term changes of coastal SPM in Hainan Island, particularly evident in typical tropical ecosystems affected by aquaculture, coastal engineering, and changes in coastal green vegetation. Compared to other typical ecosystems around the globe, the overall health status of SPM along the coast wetlands of Hainan is considered satisfactory. These findings not only establish a robust remote sensing model for long-term SPM monitoring along the coast of Hainan Island, but also provide comprehensive insights into SPM dynamics, thereby contributing to the formulation of future coastal zone management policies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The imperative to enhance corporate environmental performance is not only pivotal for a company's growth but also crucial for fulfilling societal responsibilities and protecting global environmental interests. Recognizing the inadequacies of standalone environmental policies, our study delves into the synergistic effects of incentive-based and regulatory approaches on the environmental performance of listed firms in China. We meticulously examine the interplay between environmental punishment and subsidies over the period of 2015-2019. Our analysis reveals that a strategic combination of punishment and subsidies can substantially improve firms' environmental performance. This effect intensifies with the increasing amounts of fines and subsidies. Additionally, we explore the dynamic effects of policy implementation. Our results indicate that subsidies implemented either a year before or after the imposition of punishment might diminish the effectiveness of standalone environmental penalty policies. Furthermore, our findings suggest that diverse regulatory policies enhance firm environmental performance by promoting investments in environmental protection and fostering green innovation. This discovery highlights the need for a nuanced understanding of policy mixes and their implications for corporate environmental strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In the era of climate change and carbon neutrality, China is actively coping with its carbon lock-in dilemma. In this context, the development of the digital economy is considered a possible path to carbon unlocking. This study contributes to the literature by providing a comprehensive analysis of how the digital economy can be leveraged to address carbon lock-in, highlighting the importance of formal environmental regulation and informal environmental regulation in enhancing this effect. Accordingly, following findings are highlighted. (1) Our primary findings provide strong evidence on the negative effect of the digital economy on carbon lock-in; by implication, improving the digital economy is an efficient measure for eradicating carbon lock-in. (2) The digital economy shows the greatest marginal impact on industrial lock-in, while its inhibiting effect on behavior lock-in is the least pronounced. Moreover, the digital economy plays a prominent role in curbing carbon lock-in in provinces with a higher level of physical, human, and social capital. And the asymmetric impacts of the digital economy on carbon lock-in are significant at most quantiles. (3) Environmental regulation is a significant moderator. Put differently, the synergy of formal environmental regulation and the digital economy, as well as informal environmental regulation and the digital economy, are important means to break carbon lock-in. (4) The carbon lock-in mitigation effect of the digital economy is caused mainly by increasing technical market turnover and the efficiency of energy consumption., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Implementing a Carbon Peak Action Plan at the regional level requires comprehensive consideration of the developmental heterogeneity among different provinces, which is an effective pathway for China to realize the goal of carbon peak by 2030. However, there is currently no clear provincial roadmap for carbon peak, and existing studies on carbon peak pathways inadequately address provincial heterogeneity. Therefore, this paper employs the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to decompose assess 8 factors influencing carbon emissions of 30 provinces. According to scenario analysis, the paper explores the differentiated pathways for provincial carbon peaks based on policy expectation indicators (including population, economy, and urbanization rate) and comprises policy control indicators (including the energy structure, energy efficiency, industrial structure, transportation structure, and innovation input). The results indicate that population, per capita GDP, urbanization rate, and innovation input are the primary factors for influencing (negatively) the growth of carbon emissions. In contrast, the optimization and upgrading of the industrial structure, energy intensity, energy structure, and transportation structure have mitigating effects on carbon emissions, especially for the first two factors. The forecasting results reveal that robust regulations of the energy and industry can effectively accelerate carbon peak at a reduced magnitude. If developed at BAU, China cannot achieve carbon peak by 2030, continuing an upward trend. However, by maximizing the adjustment strength of energy and industrial transformation within the scope of provincial capabilities, China could achieve carbon peak as early as 2025, with a peak of 12.069 billion tons. In this scenario, 24 provinces could achieve carbon peak before 2030. Overall, this study suggests the feasibility of differentiated pathway to achieve carbon peaks in China, exploring the carbon peak potential and paths of 30 provinces, and identifying provinces where carbon peak is more challenging. It also provides a reference for the design of carbon peak roadmaps at both provincial and national levels and offers targeted recommendations for the implementation of differentiated policy strategies for the government., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Priority ecological reserves (PER) aim to protect areas with significant ecological value and crucial ecological functions, optimizing resource allocation to maximize the benefits of ecological conservation. However, most previous studies have considered only ecosystem services (ESs) in delineating PER, neglecting eco-environmental quality (EEQ). This study used the Remote Sensing-based Ecological Index (RSEI) to represent EEQ and combined it with ESs to delineate PER at the county scale in the Yellow River Basin (YRB). Additionally, it employed Multiscale Geographically Weighted Regression to identify the driving factors influencing the ESs and EEQ of PER. The results showed that: (1) From 2000 to 2020, both RSEI and the Comprehensive ESs (CES) in the YRB exhibited a fluctuating upward trend; (2) Three types of PER were extracted, with ESs reserve mainly distributed in the upstream region, EEQ reserve primarily in the middle and lower reaches, and integrated ecological reserve mainly in the midstream region, all dominated by vegetation land-use types; (3) Within the extracted PER, RSEI was mainly influenced by soil, aspect, population (pop), PM2.5, temperature (tmp), and potential evapotranspiration (pet), while CES was affected by soil, pop, PM2.5, slope, tmp, precipitation, and pet. To enhance the EEQ and ESs of the YRB, it was recommended to incorporate at least 105,379 km 2 into the existing protected areas in the YRB. These areas should be subdivided based on their ecological status, with specific management measures for different types of PER. This study provides recommendations for environmental protection and land planning in the YRB, actively responding to current policies on high-quality development and ecological environmental protection in the YRB., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Urban areas contribute 85% of China's CO 2 emissions. Green finance is an important means to support green energy development and achieve the low-carbon transformation of high-energy-consuming industries. The motivation of this article is to investigate the impact and mechanism of green finance on urban carbon intensity. Most existing literature uses linear models to investigate urban carbon intensity, ignoring the nonlinear relationships between economic variables. The nonparametric models can fill the inherent shortcomings of linear models and effectively simulate the nonlinear nexus between economic variables. Based on the 2011-2021 panel data of 237 cities in China, this paper applies the nonparametric additive model to survey the influence of green finance on urban carbon intensity. Empirical findings exhibit that green finance exerts an inverted U-shaped effect on urban carbon intensity, indicating that the carbon reduction effect of green finance has gradually shifted from inconspicuous in the early stages to prominent in the later stages. Then, from the perspectives of region, city size, and carbon intensity, this article conducts heterogeneity analysis. The results show that the impact of green finance on various carbon intensities all exhibits obvious nonlinear feature. Furthermore, this article employs a mediation effect model to conduct mechanism analysis. The results display that technological progress and industrial structure are two important mediating variables, both of which produce an inverted U-shaped nonlinear impact on urban carbon intensity., Competing Interests: Declaration of competing interest We hope this article will be considered for publication in “Journal of Environmental Management”. No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed., (Copyright © 2024 Elsevier Ltd. All rights reserved.)