Ecological Indicators最新文献

{"title":"Study on the water quality evolution mechanism of typical shallow lake based on in-depth mining of water quality indicators","authors":"Wenqiang Zhang ,&nbsp;Dianwei Zhang ,&nbsp;Xin Jin ,&nbsp;Baoqing Shan","doi":"10.1016/j.ecolind.2025.113914","DOIUrl":"10.1016/j.ecolind.2025.113914","url":null,"abstract":"<div><div>The water quality (WQ) of Baiyangdian Lake (BYDL), known as the ‘kidney of North China’, declined continuously for about four decades from the 1980s because of socioeconomic development. Since the Xiong’an New Area was established in 2017, remediation efforts have been implemented and the WQ has improved. In this study, we analyzed six years of WQ data from BYDL using ordinary least squares regression, time series analysis, and the Mann-Kendall trend test. The results reveal a marked improvement in WQ since 2018, with Class III accounting for over 60 % of observations. There were significant decreasing trends in chemical oxygen demand, total nitrogen, total phosphorus, ammonia nitrogen, and Chlorophyll-a (<em>p</em> &lt; 0.05) and an overall increasing trend in dissolved oxygen (<em>p</em> &lt; 0.05). The WQ indicators varied seasonally. The WQ improvements generally aligned with the timing and patterns of the major engineering interventions but short-term fluctuations in the WQ indicators may have been caused by climatic variability. The findings highlight the complexity of the WQ pattens in BYDL and illustrate that the management strategy shifted from an initial phase that focused on pollution control to a second phase of ecological restoration integrated with environmental governance. This information can be used to refine future restoration strategies for BYDL and to guide sustainable management practices in similar shallow lake systems.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"178 ","pages":"Article 113914"},"PeriodicalIF":7.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The mortality impacts of implementing the 3-30-300 greenness rule in Taranto, Southern Italy","authors":"Orazio Valerio Giannico ,&nbsp;Francesco Addabbo ,&nbsp;Feliciana Catino ,&nbsp;Lucia Bisceglia ,&nbsp;Sante Minerba ,&nbsp;Antonia Mincuzzi ,&nbsp;Rodolfo Sardone","doi":"10.1016/j.ecolind.2025.113895","DOIUrl":"10.1016/j.ecolind.2025.113895","url":null,"abstract":"<div><div>The '3-30-300' rule advocates for 30 % tree canopy cover in neighborhoods, with no one living more than 300 m from green space. This health impact assessment study evaluated how implementing this policy could reduce adult mortality in Taranto, an industrialized area in Southern Italy. Population-weighted exposures were calculated using 2020 population data, and 2022 satellite data at 10-meter resolution within 300 m from homes. The study derived a normalized difference vegetation index (NDVI) target corresponding to a 30 % tree-cover, and to interim targets from 10 % to 25 %. The uncertainty analysis was based on 100,000 Monte-Carlo simulations. The results for 2022 estimated that achieving the 30 % tree-cover target, currently achieved by only 1.4 % of the population and corresponding to an estimated NDVI of 0.33 (95 % confidence interval: 0.32–0.33), could prevent 123 (93–155) deaths and 1,246 (929–1,563) years of life lost, reducing mortality by 5.3 % (3.9–6.6 %). Three-cover, shrubland, grassland and cropland were associated with higher NDVI, while built-up areas and deprivation were linked to lower greenness.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"178 ","pages":"Article 113895"},"PeriodicalIF":7.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of spatial and temporal characteristics and influence mechanisms of blue-green spaces in China’s, 2000–2020","authors":"Zhaoxu Zhang ,&nbsp;Sijia Du ,&nbsp;Lei Qian ,&nbsp;Guanyu Qian ,&nbsp;Zhenwei Shi ,&nbsp;Cong Yan","doi":"10.1016/j.ecolind.2025.113903","DOIUrl":"10.1016/j.ecolind.2025.113903","url":null,"abstract":"<div><div>Blue-green spaces are critical in mitigating climate challenges, preserving ecological equilibrium, and enhancing land quality. Previous research predominantly relied on traditional geographic detectors, focusing narrowly on the evolution of blue-green spaces within individual cities or provinces, while neglecting macro-scale investigations. This research employed a comprehensive approach to investigate the evolution patterns of China’s blue-green spaces from 2000 to 2020, utilizing the optimal-parameter geographic detector to elucidate the roles of its driving factors. Ultimately, three scenarios for the blue-green spatial distribution in 2030 were simulated. The results showed: (1) Based on land use data spanning two decades (2000–2020), the area of blue-green spaces had steadily diminished, contracting from 7,320,700 km² to 7,270,200 km². (2) 99.35 % of areas exhibited no significant change in blue-green spaces, while a significant upward trend (0.40 %) was predominantly observed in the eastern region and a significant downward trend (0.25 %) was in the northern and western regions. (3) Both natural and anthropogenic factors contributed to the evolution of blue-green spaces. During the study period, the influence of human activities had intensified, and the combined effects of any two factors were greater than their individual contributions. (4) Across three developmental scenarios, the area of blue-green spaces continued inexorable decline, with areas projected at 7,163,800 km² (natural development), 7,180,700 km² (ecological protection), and 7,093,800 km² (urban development) respectively. This study not only unravels the intricate processes and pivotal influencing factors governing the evolution of blue-green spaces but also furnishes critical quantitative evidence for safeguarding and planning these essential spaces.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"178 ","pages":"Article 113903"},"PeriodicalIF":7.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining spectral and texture indexes predicts plant diversity in temperate riverine wetlands of the lower Yellow River, China","authors":"Honglei Zhu ,&nbsp;Yanwei Huang ,&nbsp;Yanqi Wang ,&nbsp;Kun Chen ,&nbsp;Weifeng Qian ,&nbsp;Guo Zhang ,&nbsp;Yifan Hou ,&nbsp;Yifan Wang ,&nbsp;Jingjing Lyu ,&nbsp;Aobo Zhang ,&nbsp;Chan Zhang ,&nbsp;Cuicui Hou","doi":"10.1016/j.ecolind.2025.113899","DOIUrl":"10.1016/j.ecolind.2025.113899","url":null,"abstract":"<div><div>Remote sensing technology offers a cost-effective and efficient solution for the large-scale monitoring of wetland plant diversity. This study introduces a methodological framework that integrates spectral and texture indexes for predicting plant diversity by systematically quantifying the impacts of direction and step size on texture-diversity relationships. Firstly, we evaluated the correlation between diversity and spectral indexes derived from UAV RGB and GF-1D multispectral data. Then, GLCM texture indexes for 4 directions (0°, 45°, 90° and 135°) and different step sizes (1, 3, 5, 7, 9, 11 and 15 pixels for UAV RGB image; 1, 3 and 5 pixels for GF-1D image) were computed, and the influence of direction and step size on the correlation between texture and diversity indexes was examined. After index screening, we investigated the performance of the combination of spectral and texture indexes in predicting plant diversity at 2-m and 10-m quadrat scales. Results showed that: 1) Most texture indexes exhibited sensitivity to both direction and step size. Their correlation with the diversity indexes varied due to differing directional influences, or it may show a gradual increase or decrease in correlation as step size increases. In some cases, a negative correlation can even transition into a positive one. 2) Compared to using spectral indexes alone, the integration of spectral and texture indexes improved prediction R² by an average of 0.06 (2-m quadrat scale with UAV RGB imagery), 0.21 (10-m quadrat scale with UAV RGB imagery), and 0.25 (10-m quadrat scale with GF-1D multispectral imagery). 3) The <em>Erigeron canadensis</em> community and <em>Tamarix chinensis</em> + <em>Phragmites australis</em> community exhibited divergent responsiveness to spectral and texture indexes. The combined spectral-texture model demonstrated higher prediction accuracy for plant diversity indexes in single-type communities (e.g., mean R² = 0.64 for the <em>Erigeron canadensis</em> community and 0.58 for the <em>Tamarix chinensis</em> + <em>Phragmites australis</em> using GF-1D data) compared to the merged community (e.g., mean R² = 0.33 using GF-1D data). 4) The linear fitting of UAV RGB spectral-texture integration demonstrated progressive improvement as the quadrat scale expanded from 2 m to 10 m.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"178 ","pages":"Article 113899"},"PeriodicalIF":7.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DDWQI: A novel water quality index based on data-driven approaches","authors":"Chao Liu ,&nbsp;Xiuhe Yuan ,&nbsp;Han Li ,&nbsp;Yansu Qi ,&nbsp;Xiang Shen ,&nbsp;Sheng Miao ,&nbsp;Weijun Gao","doi":"10.1016/j.ecolind.2025.113850","DOIUrl":"10.1016/j.ecolind.2025.113850","url":null,"abstract":"<div><div>Faced with the ongoing deterioration of global water quality, scientific assessment of water quality is crucial for water resource classification management and pollution source tracing. To improve the adaptability and accuracy of water quality evaluation methods, this study builds on the traditional water quality index, proposes a system for constructing water quality index based on a data-driven mechanism, achieves scientific optimization in terms of indicator selection, weight allocation, and aggregation calculation, and constructs a novel water quality index. In the indicator selection stage, machine learning, SHapley Additive exPlanations (SHAP), and recursive feature elimination are combined to quantify and select water quality indicators. In terms of weight allocation, a SHAP-ROC method based on water quality indicator contribution was proposed to enhance the objectivity and stability of weight calculation. In aggregation calculations, the Normalized Weighted Harmonic Mean (NWHM) function is applied to reduce indicator eclipsing effect and improve accuracy. To validate the practical application of this method, an empirical analysis was conducted using a reservoir in a city in eastern China as a case study. The results indicate that this method can effectively identify key water quality indicators. The coefficient of variation of the constructed weighting system is 0.85, and the weighting fit R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> is 0.86, significantly enhancing stability. In addition, the eclipsing effect of NWHM is 24.30%, better than traditional aggregation functions. The overall evaluation results demonstrate that the proposed method exhibits high reliability and applicability, offering an efficient and practical tool for water quality assessment in complex environments.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"178 ","pages":"Article 113850"},"PeriodicalIF":7.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping the ecological worth of glaciers of the Indian Himalayan region","authors":"A.R. Arya,&nbsp;Harini Santhanam","doi":"10.1016/j.ecolind.2025.113917","DOIUrl":"10.1016/j.ecolind.2025.113917","url":null,"abstract":"<div><div>Glacial ecosystem services for the Indian Himalayan Region (IHR) provide critical climatic and environmental information to plan appropriate Natural Climatic Solutions (NCS). We assessed the research, knowledge and interventional gaps in the context of glacier conservation, specific to IHR through a systematic review of literature. Beginning with a cognitive database of 3813 studies, we analysed 122 specific studies published in the last two decades on glacier ecosystem mapping, ecosystem service assessment (ESA), particularly for Western Himalayas. We found that the methodological gaps in ESA diminish the focus towards natural glacier conservation efforts. Based on our analyses, we hypothesise that a holistic approach to glacier ES research should ideally consider interventional models of provisioning, regulatory, cultural and supportive ES proportionate to the 3 proximate causes: sensitivity of seasonal water mass balance, resources availability, and end-usages scenarios existing in the IHR and more specifically, the Indus basins. Our research also illustrated the need to value the system interconnectedness in ESA as a critical parameter to devise NCS facilitating glacier regeneration to advance SDG13 goals.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"178 ","pages":"Article 113917"},"PeriodicalIF":7.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a rapid and cost-effective groundwater quality assessment model based on hybrid ensemble learning","authors":"Xiaoyun Wang,&nbsp;Jing Su,&nbsp;Yue Liu,&nbsp;Yao Ji,&nbsp;Qiuling Dang,&nbsp;Yuanyuan Sun,&nbsp;Quanli Liu","doi":"10.1016/j.ecolind.2025.113894","DOIUrl":"10.1016/j.ecolind.2025.113894","url":null,"abstract":"<div><div>Assessing groundwater quality and health risks using machine learning is receiving widespread concern. However, assessment accuracy and cost-effectiveness are key factors in determining the model implementation. Therefore, the main purpose of this study is to develop a convenient, low-cost, and accurate hybrid ensemble model to predict water quality index (WQI) and hazard index (HI). Firstly, Pearson correlation matrix and ‘SHAP’ value were compared to select the Optimum feature combination. Secondly, base learners were selected from 12 different machine learning candidates. And then select eXtreme Gradient Boosting (XGB) as meta learner to construct stacking and blending ensemble model. The prediction results of the base learners are averaged to obtain the prediction results of averaging ensemble model. Finally, evaluation matrix (R² and RMSE), <em>t</em>-test and probabilistic forecasting were integrated to assess models’ performance. The results show TDS, HCO₃^–, Mg²⁺, SO₄^2- is the best feature combination for WQI prediction, and Na⁺, Ca²⁺, Mg²⁺, HCO₃^– is the best feature combination for HI prediction. SHAP value perform better than Pearson correlation matrix in reducing the number of input variables and improving model accuracy. The accuracy of stacking ensemble model on test/validation sets (average R² = 0.966/0.921 and 0.835/0.714 for WQI and HI respectively) significantly (p &lt; 0.05) higher than the other models. The Stacking ensemble model developed in this study provides supports for governments to assess groundwater quality and formulate rational policies. Meanwhile, the integration of evaluation metrics and statistical analysis also offers new ideas for model evaluation in the environmental field.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"178 ","pages":"Article 113894"},"PeriodicalIF":7.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation and spatiotemporal dynamic analysis of air-mediated ecosystem service flows: A case study of Guanzhong Plain urban agglomeration","authors":"Yan Wang ,&nbsp;Zhongxue Zhou ,&nbsp;Rukeya Reheman ,&nbsp;Bingjie Song ,&nbsp;Enle Qiao ,&nbsp;Haoran Huang","doi":"10.1016/j.ecolind.2025.113892","DOIUrl":"10.1016/j.ecolind.2025.113892","url":null,"abstract":"<div><div>Analyzing the relationship between supply and demand of ecosystem services (ESs) from a spatiotemporal flow perspective is crucial for in-depth insight into flow process of ESs, and ensuring regional ecological security, optimizing resource allocation, and promoting sustainable development. However, previous studies have primarily focused on the flow of ecosystem service mediated by tangible substances (e.g., water, soil, animals etc.) with specific physical channels, the flow of ecosystem services mediated by intangible carriers (especially by air) remain rare and are lack of methods. Thus, taking the Guanzhong Plain urban agglomeration (GPUA) as a case, we proposed a flow analysis framework for air-mediated ecosystem service flows (AMESFs), quantified the supply–demand relationship of five ESs, simulated the air-mediated ESs flow field, determined the flow direction, volumes and paths, and analyzed their spatiotemporal dynamics. The results showed significant spatial mismatches of ESs in GPUA. High surplus areas were primarily distributed in forest-covered mountainous regions, while high deficit areas were concentrated in the cultivated regions of the Fenwei Plain and parts of the Loess Plateau, particularly in urban and adjacent areas. The flows of above ESs exhibited a similar pattern with minimal interannual variation during 2000–2020. Overall, five primary flow pathways were identified, extending from the northeastern and southeastern parts of GPUA toward the west, northwest, and southwest. Although the total supply of services was greater than the total demand, most of surplus service flowed out of the study area and the demand was not satisfied locally. The AMESFs were predominantly driven by the local prevailing wind rather than the supply–demand relationships. The framework is crucial for revealing the spatial flow characteristics and dynamic processes of ESs mediated by air and applicable to other regions. That also provides an assessment method for regional air pollution prevention and control, and green infrastructure development.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"178 ","pages":"Article 113892"},"PeriodicalIF":7.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ecotourism suitability at county scale in China: Spatial pattern, obstacle factors, and driving factors","authors":"Wei Shen ,&nbsp;Yanli Chen ,&nbsp;Peijun Rong ,&nbsp;Weiwei Cao ,&nbsp;Ruyi Yu ,&nbsp;Pengfei Wang ,&nbsp;Jinlong Cheng","doi":"10.1016/j.ecolind.2025.113911","DOIUrl":"10.1016/j.ecolind.2025.113911","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The comprehensive evaluation of the suitability of county-level ecotourism and the analysis of influencing factors can provide detailed guidance for the planning and development of ecotourism, which is of great significance for promoting the high-quality and sustainable development of ecotourism. However, few studies have focused on in-depth analyses of the influencing factors and its mechanisms of the county ecotourism suitability in China, which severely restricts the comprehensive development and sustainable management of county-level ecotourism in China. Based on this, this study conducts a comprehensive assessment of the county ecotourism suitability level in China and systematically explains the law of its spatial distribution. Secondly, the obstacle degree model and the optimal parameter geographic detector model were adopted to diagnose the internal obstacle factors and external driving factors of the county ecotourism suitability respectively. Finally, based on the theoretical analysis framework and the quantitative analysis results, the influencing mechanism of the county ecotourism suitability in China was systematically analyzed. The results show that the spatial distribution of ecotourism suitability of counties in China is extremely unbalanced, with large differences between east and west and north and south. The unsuitable areas and the less suitable areas were mainly concentrated in the plateau areas and arid areas in Western China. Moderately suitable areas and highly suitable areas were mainly concentrated in the mountainous areas of north and central China, the transitional zone of mountain plain, the mountainous areas of southwest China and the coastal areas of southeast China. There are obvious high-value and low-value agglomeration areas in the local space, and it shows an obvious “core-edge” structure. The low-value heterogeneous areas have emerged in the peripheral regions of important ecological source areas. In terms of internal obstacle factors, factors such as ecotourism resources, market development potential, ecological environment quality and service facilities conditions are the main obstacles to ecotourism suitability in northwest China. The main obstacle factors of hilly county, mountain county and plateau county are the same, while the main obstacle factors and their order of plain county are different from the first three. In terms of external driving factors, economic urbanization, population urbanization, land urbanization, the level of scientific and technological innovation, the level of low-carbon economic development, and regional environmental policy factors all have significant impacts on the county ecotourism suitability in China. However, the main driving factors in different regions and counties with different terrains show differences. The interaction intensity of among the main driving factors of ecotourism suitability in different river basin areas and counties with different terrains sh","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"178 ","pages":"Article 113911"},"PeriodicalIF":7.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating ecosystem services: a new indicator for evaluating net carbon sink efficiency of urban green spaces and its influencing factors","authors":"Haoyang Song ,&nbsp;Min Wang","doi":"10.1016/j.ecolind.2025.113901","DOIUrl":"10.1016/j.ecolind.2025.113901","url":null,"abstract":"<div><div>Amid the global climate crisis, urban green spaces (UGS)—as composite ecosystems with both carbon sink and social service functions—have become vital strategic assets for reducing urban carbon emissions and enhancing residents’ well-being. In the context of increasingly constrained resources, this study systematically investigates the role of UGS in advancing carbon neutrality from an eco-social perspective, aiming to improve the efficiency of greening allocation. To this end, a novel net carbon sink efficiency (NCSE) indicator integrating ecosystem services was developed, forming a comprehensive evaluation framework that balances carbon sequestration enhancement, ecosystem service optimization, and resource input minimization. Using 102 subdistricts in downtown Shanghai as the study area, the super-slack-based measure (Super-SBM) model with undesired output was applied to assess NCSE, while the Tobit regression model identified key influencing factors, including built environment characteristics and spatial patterns of UGS. Key findings include: (1) Periphery subdistricts exhibited higher overall UGS output capacity, but dispersed small green spaces in the center showed superior per-unit efficiency. (2) Only 14 subdistricts (13.73%) have achieved effective NCSE, while most show low efficiency and need to be enhanced. (3) Based on NCSE and proximity to the city center, subdistricts can be classified into four types: center-efficient, peripheral-efficient, center-inefficient, and peripheral-inefficient. (4) Factors such as functional land-use mix, road network density, watershed area proportion, and greenway and pocket park development significantly improved NCSE. This integrated assessment framework links macro-level low-carbon policy with micro-level urban well-being, offering a transferable model for sustainable green infrastructure planning in global megacities.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"178 ","pages":"Article 113901"},"PeriodicalIF":7.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}