Pub Date : 2024-09-13DOI: 10.3389/fenvs.2024.1459483
Yuting Duan
To gain a deeper understanding of the intrinsic dynamic relationship between energy consumption and economic growth in China. This study employs panel cointegration and causality models, utilizing the SYS-GMM technique to assess the factors influencing economic growth in China’s green finance sector from 2002 to 2022. The research explores the interactions among multiple variables related to the Chinese economic context, including economic growth, carbon dioxide emissions, total natural resource rents, energy consumption, and environmental impact. While considering key factors that may cause structural disturbances in the time series analysis. The findings indicate the existence of long-term cointegration relationships among these variables, with positive correlations between economic growth and total natural resource rents, energy consumption, energy quantity, and ecological footprint. Results also show a bidirectional causal relationship between carbon dioxide emissions and energy consumption and a unidirectional correlation between energy consumption and GDP growth. Additionally, energy intensity (EI) improvements supported by green finance are linked to a significant reduction in CO2 emissions, with a coefficient of −1.933 (p < 0.05), underscoring the role of technological innovation. Further evaluations suggest that investments in renewable energy can promote economic growth, create job opportunities, and reduce greenhouse gas emissions. Energy-saving measures and green finance-supported technological innovations play crucial roles in improving energy intensity and reducing CO2 emissions. The study also underscores the importance of economic diversification to reduce dependence on natural resources and enhance economic stability. Future research should further explore the economic feasibility and environmental benefits of emerging technologies such as Carbon Capture and Storage (CCS), providing deeper insights into sustainable energy practices.
为了深入了解中国能源消耗与经济增长之间的内在动态关系。本研究采用面板协整和因果关系模型，利用 SYS-GMM 技术，评估 2002 年至 2022 年中国绿色金融领域经济增长的影响因素。研究探讨了与中国经济背景相关的多个变量之间的相互作用，包括经济增长、二氧化碳排放、自然资源总租金、能源消耗和环境影响。在时间序列分析中，考虑了可能导致结构性扰动的关键因素。研究结果表明，这些变量之间存在长期协整关系，经济增长与自然资源租金总额、能源消耗、能源数量和生态足迹之间存在正相关关系。结果还显示，二氧化碳排放与能源消耗之间存在双向因果关系，能源消耗与 GDP 增长之间存在单向相关关系。此外，绿色金融支持的能源强度（EI）改善与二氧化碳排放量的显著减少有关，系数为-1.933（p &p;lt;0.05），凸显了技术创新的作用。进一步的评估表明，对可再生能源的投资可以促进经济增长、创造就业机会并减少温室气体排放。节能措施和绿色金融支持的技术创新在改善能源强度和减少二氧化碳排放方面发挥着至关重要的作用。本研究还强调了经济多样化对于减少对自然资源的依赖和提高经济稳定性的重要性。未来的研究应进一步探讨碳捕集与封存（CCS）等新兴技术的经济可行性和环境效益，为可持续能源实践提供更深入的见解。
{"title":"The interaction between China’s economic recovery and environmental governance: a comprehensive analysis of energy consumption, CO2 emissions, and resource management","authors":"Yuting Duan","doi":"10.3389/fenvs.2024.1459483","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1459483","url":null,"abstract":"To gain a deeper understanding of the intrinsic dynamic relationship between energy consumption and economic growth in China. This study employs panel cointegration and causality models, utilizing the SYS-GMM technique to assess the factors influencing economic growth in China’s green finance sector from 2002 to 2022. The research explores the interactions among multiple variables related to the Chinese economic context, including economic growth, carbon dioxide emissions, total natural resource rents, energy consumption, and environmental impact. While considering key factors that may cause structural disturbances in the time series analysis. The findings indicate the existence of long-term cointegration relationships among these variables, with positive correlations between economic growth and total natural resource rents, energy consumption, energy quantity, and ecological footprint. Results also show a bidirectional causal relationship between carbon dioxide emissions and energy consumption and a unidirectional correlation between energy consumption and GDP growth. Additionally, energy intensity (EI) improvements supported by green finance are linked to a significant reduction in CO<jats:sub>2</jats:sub> emissions, with a coefficient of −1.933 (<jats:italic>p</jats:italic> &lt; 0.05), underscoring the role of technological innovation. Further evaluations suggest that investments in renewable energy can promote economic growth, create job opportunities, and reduce greenhouse gas emissions. Energy-saving measures and green finance-supported technological innovations play crucial roles in improving energy intensity and reducing CO<jats:sub>2</jats:sub> emissions. The study also underscores the importance of economic diversification to reduce dependence on natural resources and enhance economic stability. Future research should further explore the economic feasibility and environmental benefits of emerging technologies such as Carbon Capture and Storage (CCS), providing deeper insights into sustainable energy practices.","PeriodicalId":12460,"journal":{"name":"Frontiers in Environmental Science","volume":"18 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Indoor environments, considered sanctuaries from external pollutants, are increasingly recognized as reservoirs for microplastics (MP). This research employed a comprehensive approach, combining dust sampling from diverse indoor spaces, density separation method, and microscopic observation to quantify and characterize microplastic particles. This is the first initial study worldwide that incorporated MP identification in indoor dust from different indoor environments along with factor analysis, health, and ecological risk assessment. The average MP concentration in the indoor environment was 4333.18 ± 353.85 MP/g. The MP distribution pattern was in institutional areas < residential areas < industrial areas < and commercial areas. Black color, fiber, <0.5 mm size was the dominant color, morphology, and size, respectively, among the detected MP from the studied samples. In addition, the polymer types of the MP were detected by Fourier Transform-Infrared (FT-IR) spectroscopy, and ten types of polymers were detected while PET was in high abundance. Population number, architectural features of habitat, human activities, urban topography, and particle residence time were determined as responsible factors for MP abundance in indoor areas. The estimated daily intake (EDI) value via ingestion was higher than the inhalation of MP. Infants are highly susceptible to MP exposures. According to Polymer Hazard Index (PLI) and Polymer Hazard Index (PHI) values, the exposure risk was in the minor and extreme risk categories.
{"title":"Microplastics in indoor dust at Dhaka city: unveiling the unseen contaminants within our homes","authors":"Md. Rashedul Haque, Wahida Ahmed, Md. Rayhanul Islam Rayhan, Md. Mostafizur Rahman","doi":"10.3389/fenvs.2024.1437866","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1437866","url":null,"abstract":"Indoor environments, considered sanctuaries from external pollutants, are increasingly recognized as reservoirs for microplastics (MP). This research employed a comprehensive approach, combining dust sampling from diverse indoor spaces, density separation method, and microscopic observation to quantify and characterize microplastic particles. This is the first initial study worldwide that incorporated MP identification in indoor dust from different indoor environments along with factor analysis, health, and ecological risk assessment. The average MP concentration in the indoor environment was 4333.18 ± 353.85 MP/g. The MP distribution pattern was in institutional areas &lt; residential areas &lt; industrial areas &lt; and commercial areas. Black color, fiber, &lt;0.5 mm size was the dominant color, morphology, and size, respectively, among the detected MP from the studied samples. In addition, the polymer types of the MP were detected by Fourier Transform-Infrared (FT-IR) spectroscopy, and ten types of polymers were detected while PET was in high abundance. Population number, architectural features of habitat, human activities, urban topography, and particle residence time were determined as responsible factors for MP abundance in indoor areas. The estimated daily intake (EDI) value via ingestion was higher than the inhalation of MP. Infants are highly susceptible to MP exposures. According to Polymer Hazard Index (PLI) and Polymer Hazard Index (PHI) values, the exposure risk was in the minor and extreme risk categories.","PeriodicalId":12460,"journal":{"name":"Frontiers in Environmental Science","volume":"15 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2024-09-12DOI: 10.3389/fenvs.2024.1475497
Minglan Yuan, Zetai Shi, Decai Tang, Jie Zhu, Jiannan Li
IntroductionThe Yangtze River Economic Belt (YREB) is experiencing rapid economic development, while ecological and environmental problems are prominent. The development of green finance can help optimize the upgrade of regional industrial structure and promote the improvement of the ecological environment.MethodsThis study constructs an evaluation system for the development level of the YREB based on the panel data of 11 provinces (cities) in the YREB from 2010 to 2020. The entropy method is used to evaluate and analyze the current status of the ecosystem in the YREB, and a panel data model is used to conduct an in-depth investigation to explore the impact of green finance (GF) on the industrial structure upgrade (INS) of the YREB.ResultsThe results of the study show that from 2010 to 2020, the level of GF development in the YREB has increased, and the INS has further developed. In addition, the growth of GF injects a strong impetus to the improvement of INS in YREB, but there are regional differences, which are more obvious in the eastern region and not significant in other regions.DiscussionFinally, based on the research conclusions, relevant strategies and suggestions are proposed to assist the development of GF and INS in the YREB.
{"title":"Synergistic relationship between green finance and industrial structure upgrade in the yangtze river economic belt","authors":"Minglan Yuan, Zetai Shi, Decai Tang, Jie Zhu, Jiannan Li","doi":"10.3389/fenvs.2024.1475497","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1475497","url":null,"abstract":"IntroductionThe Yangtze River Economic Belt (YREB) is experiencing rapid economic development, while ecological and environmental problems are prominent. The development of green finance can help optimize the upgrade of regional industrial structure and promote the improvement of the ecological environment.MethodsThis study constructs an evaluation system for the development level of the YREB based on the panel data of 11 provinces (cities) in the YREB from 2010 to 2020. The entropy method is used to evaluate and analyze the current status of the ecosystem in the YREB, and a panel data model is used to conduct an in-depth investigation to explore the impact of green finance (GF) on the industrial structure upgrade (INS) of the YREB.ResultsThe results of the study show that from 2010 to 2020, the level of GF development in the YREB has increased, and the INS has further developed. In addition, the growth of GF injects a strong impetus to the improvement of INS in YREB, but there are regional differences, which are more obvious in the eastern region and not significant in other regions.DiscussionFinally, based on the research conclusions, relevant strategies and suggestions are proposed to assist the development of GF and INS in the YREB.","PeriodicalId":12460,"journal":{"name":"Frontiers in Environmental Science","volume":"26 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2024-09-12DOI: 10.3389/fenvs.2024.1459764
Xinhao Zheng, Yuexin Chen
IntroductionGross Domestic Product (GDP) is the most well-known and widely used measure of a country’s economic health. However, GDP fails to account for the depletion of natural resources and the environmental damage that occurs in the pursuit of economic growth, leading to an incomplete and potentially misleading picture of a nation’s well-being. To address this shortcoming, Green GDP (GGDP) is proposed as a more comprehensive indicator that incorporates environmental factors into the economic assessment. This study builds on extensive literature reviews, internationally accepted GGDP accounting methods, and scholarly research to propose a new GGDP calculation model that better reflects a country’s sustainable development.MethodsThe proposed GGDP model is divided into two main components: natural resource loss and environmental pollution loss. Each component is further broken down into primary factors that are condensed into 13 sub-criteria reflecting a country’s capacity for sustainable development. Principal Component Analysis (PCA) is utilized to identify the most representative factors from these sub-criteria and to analyze the relationships among GGDP, these factors, and global mean temperature. Additionally, the Integrated Environmental Sustainability Index (IESI) is used to develop a global temperature mitigation prediction model, which considers the impacts of epidemics, sea and land temperatures, and variations in climate across different regions.ResultsThe analysis shows a 74% probability that positive GGDP growth correlates with temperature changes over a 50-year period, indicating that economic activities measured by GGDP are linked to climate change. The GGDP model reveals significant differences between global GDP and Green GDP, with the latter growing at a much slower rate. This slower growth of Green GDP is primarily due to the declining share of GDP from natural resource-dependent activities, which has fallen from 90% in the 1970s to 80% in 2020. This trend underscores the increasing gap between traditional economic growth and sustainable development, suggesting that as countries continue to rely on natural resources, their overall ecological efficiency declines, environmental pressures increase, and the potential for long-term sustainable development diminishes.DiscussionThe findings demonstrate that all factors within the GGDP model are proportional to global temperature, underscoring the significant impact that natural resource utilization and pollution emissions have on economic growth and climate change. The study further evaluates global sustainable development by considering both economic and environmental perspectives. Using Brazil as a case study, the model is applied to assess the values of each component within the GGDP framework, providing a comprehensive analysis of the country’s sustainable development challenges and potential solutions. This approach establishes a method for assessing sustainable development that
导言：国内生产总值（GDP）是衡量一个国家经济健康状况的最著名和最广泛使用的指标。然而，GDP 没有考虑到在追求经济增长的过程中对自然资源的损耗和对环境的破坏，导致对一个国家福祉的描述不全面，并可能产生误导。为了弥补这一缺陷，我们提出了绿色 GDP（GGDP）作为一个更全面的指标，将环境因素纳入经济评估。本研究在广泛的文献综述、国际公认的 GGDP 核算方法和学术研究的基础上，提出了一个新的 GGDP 计算模型，以更好地反映一个国家的可持续发展。每个组成部分又进一步细分为主要因素，并浓缩为 13 个次级标准，以反映一个国家的可持续发展能力。利用主成分分析法（PCA）从这些子标准中找出最具代表性的因素，并分析 GGDP、这些因素和全球平均气温之间的关系。此外，还利用综合环境可持续性指数 (IESI) 建立了全球气温减缓预测模型，该模型考虑了流行病、海陆温度以及不同地区气候差异的影响。结果分析表明，在 50 年的时间里，GGDP 的正增长与气温变化相关的概率为 74%，这表明以 GGDP 衡量的经济活动与气候变化有关。GGDP 模型揭示了全球 GDP 和绿色 GDP 之间的显著差异，后者的增长速度要慢得多。绿色 GDP 增长放缓的主要原因是依赖自然资源的活动在 GDP 中所占的份额不断下降，从 20 世纪 70 年代的 90%下降到 2020 年的 80%。这一趋势凸显了传统经济增长与可持续发展之间日益加大的差距，表明随着各国对自然资源的持续依赖，其整体生态效率下降，环境压力增大，长期可持续发展的潜力减弱。 讨论研究结果表明，GGDP 模型中的所有因素都与全球气温成正比，强调了自然资源利用和污染排放对经济增长和气候变化的重大影响。本研究从经济和环境两个角度对全球可持续发展进行了进一步评估。该模型以巴西为案例，用于评估全球可持续发展总值框架内各组成部分的价值，对该国的可持续发展挑战和潜在解决方案进行了全面分析。这种方法确立了一种评估可持续发展的方法，可适用于其他国家，为将环境因素纳入经济政策提供了前进的道路。
{"title":"A better strategy: using green GDP to measure economic health","authors":"Xinhao Zheng, Yuexin Chen","doi":"10.3389/fenvs.2024.1459764","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1459764","url":null,"abstract":"IntroductionGross Domestic Product (GDP) is the most well-known and widely used measure of a country’s economic health. However, GDP fails to account for the depletion of natural resources and the environmental damage that occurs in the pursuit of economic growth, leading to an incomplete and potentially misleading picture of a nation’s well-being. To address this shortcoming, Green GDP (GGDP) is proposed as a more comprehensive indicator that incorporates environmental factors into the economic assessment. This study builds on extensive literature reviews, internationally accepted GGDP accounting methods, and scholarly research to propose a new GGDP calculation model that better reflects a country’s sustainable development.MethodsThe proposed GGDP model is divided into two main components: natural resource loss and environmental pollution loss. Each component is further broken down into primary factors that are condensed into 13 sub-criteria reflecting a country’s capacity for sustainable development. Principal Component Analysis (PCA) is utilized to identify the most representative factors from these sub-criteria and to analyze the relationships among GGDP, these factors, and global mean temperature. Additionally, the Integrated Environmental Sustainability Index (IESI) is used to develop a global temperature mitigation prediction model, which considers the impacts of epidemics, sea and land temperatures, and variations in climate across different regions.ResultsThe analysis shows a 74% probability that positive GGDP growth correlates with temperature changes over a 50-year period, indicating that economic activities measured by GGDP are linked to climate change. The GGDP model reveals significant differences between global GDP and Green GDP, with the latter growing at a much slower rate. This slower growth of Green GDP is primarily due to the declining share of GDP from natural resource-dependent activities, which has fallen from 90% in the 1970s to 80% in 2020. This trend underscores the increasing gap between traditional economic growth and sustainable development, suggesting that as countries continue to rely on natural resources, their overall ecological efficiency declines, environmental pressures increase, and the potential for long-term sustainable development diminishes.DiscussionThe findings demonstrate that all factors within the GGDP model are proportional to global temperature, underscoring the significant impact that natural resource utilization and pollution emissions have on economic growth and climate change. The study further evaluates global sustainable development by considering both economic and environmental perspectives. Using Brazil as a case study, the model is applied to assess the values of each component within the GGDP framework, providing a comprehensive analysis of the country’s sustainable development challenges and potential solutions. This approach establishes a method for assessing sustainable development that","PeriodicalId":12460,"journal":{"name":"Frontiers in Environmental Science","volume":"261 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2024-09-12DOI: 10.3389/fenvs.2024.1340565
Luke Quill, Diogo Ferreira, Brian Joyce, Gabriel Coleman, Carla Harper, Marta Martins, Trevor Hodkinson, Daniel Trimble, Laurence Gill, David W. O’Connell
Non-point source pollution and water eutrophication from agricultural runoff present global challenges that impact ground and surface waters. The search for a feasible and sustainable mitigation strategy to combat this issue remains ongoing. This scoping review aims to explore one potential solution by examining relevant literature on agricultural practices of the past and recent edge-of-field measures, designed to ameliorate the impacts of agricultural runoff on soil and water quality. The study focuses on integrating findings from diverse research fields into a novel myco-phytoremediation approach, which involves the synergistic relationship of plants, arbuscular mycorrhizal fungi, and plant beneficial bacteria within vegetative buffer strips. The implementation of these augmented buffer strips enhances nutrient retention in the soil, reduces runoff volume, promotes biodiversity, and increases plant biomass. This biomass can be converted into biochar, an effective sorbent that can be used to filter dissolved and particulate nutrients from surface waterways. The resulting nutrient-rich biochar can be repurposed as a form of bio-fertiliser, optimizing fertiliser consumption and subsequently reducing the depletion rate of phosphorus, a limited resource. This paper investigates a circular model of abatement of agricultural runoff via maximal nutrient retention and subsequent recycling of nitrogen and phosphorus back into the agricultural system. The key impact lies in its contribution to addressing the issue of non-point source pollution and eutrophication by encouraging multidisciplinary research aimed at solving these complex environmental issues.
{"title":"An integrated mitigation approach to diffuse agricultural water pollution–a scoping review","authors":"Luke Quill, Diogo Ferreira, Brian Joyce, Gabriel Coleman, Carla Harper, Marta Martins, Trevor Hodkinson, Daniel Trimble, Laurence Gill, David W. O’Connell","doi":"10.3389/fenvs.2024.1340565","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1340565","url":null,"abstract":"Non-point source pollution and water eutrophication from agricultural runoff present global challenges that impact ground and surface waters. The search for a feasible and sustainable mitigation strategy to combat this issue remains ongoing. This scoping review aims to explore one potential solution by examining relevant literature on agricultural practices of the past and recent edge-of-field measures, designed to ameliorate the impacts of agricultural runoff on soil and water quality. The study focuses on integrating findings from diverse research fields into a novel myco-phytoremediation approach, which involves the synergistic relationship of plants, arbuscular mycorrhizal fungi, and plant beneficial bacteria within vegetative buffer strips. The implementation of these augmented buffer strips enhances nutrient retention in the soil, reduces runoff volume, promotes biodiversity, and increases plant biomass. This biomass can be converted into biochar, an effective sorbent that can be used to filter dissolved and particulate nutrients from surface waterways. The resulting nutrient-rich biochar can be repurposed as a form of bio-fertiliser, optimizing fertiliser consumption and subsequently reducing the depletion rate of phosphorus, a limited resource. This paper investigates a circular model of abatement of agricultural runoff via maximal nutrient retention and subsequent recycling of nitrogen and phosphorus back into the agricultural system. The key impact lies in its contribution to addressing the issue of non-point source pollution and eutrophication by encouraging multidisciplinary research aimed at solving these complex environmental issues.","PeriodicalId":12460,"journal":{"name":"Frontiers in Environmental Science","volume":"9 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2024-09-12DOI: 10.3389/fenvs.2024.1416269
Tingyu Tao, Hao Zhang, Zikun Hu
With urbanization acceleration, ensuring urban water use security and sustainable water resource management has become a major global challenge. As a populous country, China faces increasingly severe challenges. Comprehensive and systematic urban wastewater treatment efficiency (UWTE) assessments constitute a prerequisite for addressing this problem. Based on 2011–2021 panel data of 30 Chinese provinces, the superefficiency SBM model was employed for UWTE measurement from national and regional perspectives. ArcGIS software and the Tobit model were adopted to analyse the spatial-temporal patterns and factors influencing UWTE. UWTE in most provinces generally exhibited a fluctuating upward trend, with an uneven east-high and west-low spatial distribution pattern. The decomposition results showed that the low UWTE in the eastern region was mainly constrained by scale efficiency, while in the central region, pure technical efficiency was the primary constraint. The shunt pipeline construction level, load rate, and wastewater treatment scale significantly positively impacted UWTE, while economic scale yielded a negative impact. It is recommended that the Chinese government adjust the outdated construction-without-operation model and implement differentiated wastewater treatment policies. It is necessary to vigorously promote rainwater and wastewater diversion pipeline construction, optimize and upgrade sewer networks and wastewater treatment facilities, and fully utilize scale effects. These findings provide insights for China and countries similar to China to facilitate efficient wastewater management practices.
{"title":"A study on the measurement and influencing factors of the urban wastewater treatment efficiency in China based on the superefficiency SBM-Tobit model","authors":"Tingyu Tao, Hao Zhang, Zikun Hu","doi":"10.3389/fenvs.2024.1416269","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1416269","url":null,"abstract":"With urbanization acceleration, ensuring urban water use security and sustainable water resource management has become a major global challenge. As a populous country, China faces increasingly severe challenges. Comprehensive and systematic urban wastewater treatment efficiency (UWTE) assessments constitute a prerequisite for addressing this problem. Based on 2011–2021 panel data of 30 Chinese provinces, the superefficiency SBM model was employed for UWTE measurement from national and regional perspectives. ArcGIS software and the Tobit model were adopted to analyse the spatial-temporal patterns and factors influencing UWTE. UWTE in most provinces generally exhibited a fluctuating upward trend, with an uneven east-high and west-low spatial distribution pattern. The decomposition results showed that the low UWTE in the eastern region was mainly constrained by scale efficiency, while in the central region, pure technical efficiency was the primary constraint. The shunt pipeline construction level, load rate, and wastewater treatment scale significantly positively impacted UWTE, while economic scale yielded a negative impact. It is recommended that the Chinese government adjust the outdated construction-without-operation model and implement differentiated wastewater treatment policies. It is necessary to vigorously promote rainwater and wastewater diversion pipeline construction, optimize and upgrade sewer networks and wastewater treatment facilities, and fully utilize scale effects. These findings provide insights for China and countries similar to China to facilitate efficient wastewater management practices.","PeriodicalId":12460,"journal":{"name":"Frontiers in Environmental Science","volume":"25 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Citizen science projects have garnered attention for their potential to engage the public in scientific research and address societal challenges. However, assessing their impacts has often been overlooked or approached with overly simplistic methods. Aiming to fill this gap, this article draws on existing literature to propose an evaluation framework to critically examine how citizen science initiatives influence science, society and the participants themselves. This framework is tested on four citizen sciences projects in the field of radon research through content analysis of project reports and deductive analysis of 11 semi-structured interviews with citizen scientists and coordinators of the projects. The study demonstrates the feasibility of measuring the impacts of citizen science projects across scientific, participant, societal and researcher dimensions at the outcome level but also process evaluation at the process level. Our findings indicate that the proposed framework provides a comprehensive evaluation tool for citizen science projects, particularly in the field of radon research, and underscore the significant potential for improving participants’ knowledge on radon and risk mitigation strategies, as well as positive shifts in behaviour towards testing and mitigation and influencing public health policies.
{"title":"Evaluating citizen science projects: insights from radon research","authors":"Mabel Akosua Hoedoafia, Meritxell Martell, Tanja Perko","doi":"10.3389/fenvs.2024.1436283","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1436283","url":null,"abstract":"Citizen science projects have garnered attention for their potential to engage the public in scientific research and address societal challenges. However, assessing their impacts has often been overlooked or approached with overly simplistic methods. Aiming to fill this gap, this article draws on existing literature to propose an evaluation framework to critically examine how citizen science initiatives influence science, society and the participants themselves. This framework is tested on four citizen sciences projects in the field of radon research through content analysis of project reports and deductive analysis of 11 semi-structured interviews with citizen scientists and coordinators of the projects. The study demonstrates the feasibility of measuring the impacts of citizen science projects across scientific, participant, societal and researcher dimensions at the outcome level but also process evaluation at the process level. Our findings indicate that the proposed framework provides a comprehensive evaluation tool for citizen science projects, particularly in the field of radon research, and underscore the significant potential for improving participants’ knowledge on radon and risk mitigation strategies, as well as positive shifts in behaviour towards testing and mitigation and influencing public health policies.","PeriodicalId":12460,"journal":{"name":"Frontiers in Environmental Science","volume":"10 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2024-09-12DOI: 10.3389/fenvs.2024.1431645
Bwalya Mutale, Neel Chaminda Withanage, Prabuddh Kumar Mishra, Jingwei Shen, Kamal Abdelrahman, Mohammed S. Fnais
Reliable information plays a pivotal role in sustainable urban planning. With advancements in computer technology, geoinformatics tools enable accurate identification of land use and land cover (LULC) in both spatial and temporal dimensions. Given the need for precise information to enhance decision-making, it is imperative to assess the performance and reliability of classification algorithms in detecting LULC changes. While research on the application of machine learning algorithms in LULC evaluation is widespread in many countries, it remains limited in Zambia and Sri Lanka. Hence, we aimed to assess the reliability and performance of support vector machine (SVM), random forest (RF), and artificial neural network (ANN) algorithms for detecting changes in land use and land cover taking Lusaka and Colombo City as the study area from 1995 to 2023 using Landsat Thematic Mapper (TM), and Operational Land Imager (OLI). The results reveal that the RF and ANN models exhibited superior performance, both achieving Mean Overall Accuracy (MOA) of 96% for Colombo and 96% and 94% for Lusaka, respectively. Meanwhile, the SVM model yielded Overall Accuracy (OA) ranging between 77% and 94% for the years 1995 and 2023. Further, RF algorithm notably produced slightly higher OA and kappa coefficients, ranging between 0.92 and 0.97, when compared to both the ANN and SVM models, across both study areas. A predominant land use change was observed as the expansion of vegetation by 11,990 ha (60.4%), primarily through the conversion of 1,926 ha of bare lands into vegetation in Lusaka during 1995–2005. However, a noteworthy shift was observed as built-up areas experienced significant growth from 2005 to 2023, with a total increase of 25,110 ha (71%). However, despite the conversion of vegetation to built-up areas during the entire period from 1995 to 2023, there was still a net gain of over 11,000 ha (53.4%) in vegetation cover. In case of Colombo, built-up areas expanded by 1,779 ha (81.5%), while vegetation land decreased by 1,519 ha (62.3%) during concerned period. LULC simulation also indicated a 160-ha expansion of built-up areas during the 2023–2035 period in Lusaka. Likewise, Colombo saw a rise in built-up areas by 337 ha within the same period. Overall, the RF algorithm outperformed the ANN and SVM algorithms. Additionally, the prediction and simulation results indicate an upward trend in built-up areas in both scenarios. The resultant land cover maps provide a crucial baseline that will be invaluable for urban planning and policy development agencies in both countries.
{"title":"A performance evaluation of random forest, artificial neural network, and support vector machine learning algorithms to predict spatio-temporal land use-land cover dynamics: a case from lusaka and colombo","authors":"Bwalya Mutale, Neel Chaminda Withanage, Prabuddh Kumar Mishra, Jingwei Shen, Kamal Abdelrahman, Mohammed S. Fnais","doi":"10.3389/fenvs.2024.1431645","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1431645","url":null,"abstract":"Reliable information plays a pivotal role in sustainable urban planning. With advancements in computer technology, geoinformatics tools enable accurate identification of land use and land cover (LULC) in both spatial and temporal dimensions. Given the need for precise information to enhance decision-making, it is imperative to assess the performance and reliability of classification algorithms in detecting LULC changes. While research on the application of machine learning algorithms in LULC evaluation is widespread in many countries, it remains limited in Zambia and Sri Lanka. Hence, we aimed to assess the reliability and performance of support vector machine (SVM), random forest (RF), and artificial neural network (ANN) algorithms for detecting changes in land use and land cover taking Lusaka and Colombo City as the study area from 1995 to 2023 using Landsat Thematic Mapper (TM), and Operational Land Imager (OLI). The results reveal that the RF and ANN models exhibited superior performance, both achieving Mean Overall Accuracy (MOA) of 96% for Colombo and 96% and 94% for Lusaka, respectively. Meanwhile, the SVM model yielded Overall Accuracy (OA) ranging between 77% and 94% for the years 1995 and 2023. Further, RF algorithm notably produced slightly higher OA and kappa coefficients, ranging between 0.92 and 0.97, when compared to both the ANN and SVM models, across both study areas. A predominant land use change was observed as the expansion of vegetation by 11,990 ha (60.4%), primarily through the conversion of 1,926 ha of bare lands into vegetation in Lusaka during 1995–2005. However, a noteworthy shift was observed as built-up areas experienced significant growth from 2005 to 2023, with a total increase of 25,110 ha (71%). However, despite the conversion of vegetation to built-up areas during the entire period from 1995 to 2023, there was still a net gain of over 11,000 ha (53.4%) in vegetation cover. In case of Colombo, built-up areas expanded by 1,779 ha (81.5%), while vegetation land decreased by 1,519 ha (62.3%) during concerned period. LULC simulation also indicated a 160-ha expansion of built-up areas during the 2023–2035 period in Lusaka. Likewise, Colombo saw a rise in built-up areas by 337 ha within the same period. Overall, the RF algorithm outperformed the ANN and SVM algorithms. Additionally, the prediction and simulation results indicate an upward trend in built-up areas in both scenarios. The resultant land cover maps provide a crucial baseline that will be invaluable for urban planning and policy development agencies in both countries.","PeriodicalId":12460,"journal":{"name":"Frontiers in Environmental Science","volume":"17 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2024-09-11DOI: 10.3389/fenvs.2024.1430818
Willibroad Buma, Andrei Abelev, Trina Merrick
Grassland ecosystems play a critical role in global carbon cycling and environmental health. Understanding the intricate link between grassland vegetation traits and underlying soil properties is crucial for effective ecosystem monitoring and management. This review paper examines advancements in utilizing Radiative Transfer Models (RTMs) and hyperspectral remote sensing to bridge this knowledge gap. We explore the potential of vegetation spectra as an integrated measure of soil characteristics, acknowledging the value of other remote sensing sources. Our focus is on studies leveraging hyperspectral data from proximal and airborne sensors, while discussing the impact of spatial scale on trait retrieval accuracy. Finally, we explore how advancements in global satellite remote sensing contribute to vegetation trait detection. This review concludes by identifying current challenges, outlining future research directions, and highlighting opportunities for improved understanding of the vegetation-soil property interaction.
{"title":"Vegetation spectra as an integrated measure to explain underlying soil characteristics: a review of recent advances","authors":"Willibroad Buma, Andrei Abelev, Trina Merrick","doi":"10.3389/fenvs.2024.1430818","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1430818","url":null,"abstract":"Grassland ecosystems play a critical role in global carbon cycling and environmental health. Understanding the intricate link between grassland vegetation traits and underlying soil properties is crucial for effective ecosystem monitoring and management. This review paper examines advancements in utilizing Radiative Transfer Models (RTMs) and hyperspectral remote sensing to bridge this knowledge gap. We explore the potential of vegetation spectra as an integrated measure of soil characteristics, acknowledging the value of other remote sensing sources. Our focus is on studies leveraging hyperspectral data from proximal and airborne sensors, while discussing the impact of spatial scale on trait retrieval accuracy. Finally, we explore how advancements in global satellite remote sensing contribute to vegetation trait detection. This review concludes by identifying current challenges, outlining future research directions, and highlighting opportunities for improved understanding of the vegetation-soil property interaction.","PeriodicalId":12460,"journal":{"name":"Frontiers in Environmental Science","volume":"71 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Water resource asset (WRA) accounting holds great importance in ecological civilization construction. Existing WRA accounting methods heavily rely on statistical data, resulting in issues such as missing and inaccessible data. Moreover, they only consider the value brought by the physical resources, such as water quantity and quality, while neglecting the value brought by the ecological functions. Therefore, by fully exploiting the rapid, objective, and efficient advantages of remote sensing (RS) in monitoring surface objects, this article develops a surface WRA (SWRA) accounting method based on multi-source RS data. First, a representation model is innovatively proposed, with full consideration of the ecological service functions offered by water resources. Specifically, the SWRAs are represented by two parts: tangible and intangible assets. The tangible asset refers to the quantifiable stock of water resources. Surface water volume is adopted as the indicator for tangible assets in this article. The intangible asset, which primarily embodies the ecological service functions provided by water resources, encompasses five major categories: flood regulation, carbon fixation, oxygen release, water purification, and water conservation. Furthermore, due to different units, the total amounts cannot be summed or compared directly. Therefore, this article utilizes price tools to convert SWRAs into price value, ultimately achieving SWRA accounting. The established method was tested in Miyun, Beijing, China, from 2013 to 2023. The findings demonstrate that the SWRA value reached its peak in 2023, amounting to 56,9368.6×104 yuan, while it had its lowest point in 2014, standing at 14,7402.7×104 yuan. The experimental results indicate that the proposed method can quickly provide the SWRA values for many years, offering a methodological foundation for SWRA asset auditing and enhancing the timeliness of the auditing work.
{"title":"A surface water resource asset accounting method based on multi-source remote sensing data","authors":"Hui Kang, Wenzhang Dou, Li Chen, Lingyi Han, Xinxin Sui, Ziyue Ding","doi":"10.3389/fenvs.2024.1473419","DOIUrl":"https://doi.org/10.3389/fenvs.2024.1473419","url":null,"abstract":"Water resource asset (WRA) accounting holds great importance in ecological civilization construction. Existing WRA accounting methods heavily rely on statistical data, resulting in issues such as missing and inaccessible data. Moreover, they only consider the value brought by the physical resources, such as water quantity and quality, while neglecting the value brought by the ecological functions. Therefore, by fully exploiting the rapid, objective, and efficient advantages of remote sensing (RS) in monitoring surface objects, this article develops a surface WRA (SWRA) accounting method based on multi-source RS data. First, a representation model is innovatively proposed, with full consideration of the ecological service functions offered by water resources. Specifically, the SWRAs are represented by two parts: tangible and intangible assets. The tangible asset refers to the quantifiable stock of water resources. Surface water volume is adopted as the indicator for tangible assets in this article. The intangible asset, which primarily embodies the ecological service functions provided by water resources, encompasses five major categories: flood regulation, carbon fixation, oxygen release, water purification, and water conservation. Furthermore, due to different units, the total amounts cannot be summed or compared directly. Therefore, this article utilizes price tools to convert SWRAs into price value, ultimately achieving SWRA accounting. The established method was tested in Miyun, Beijing, China, from 2013 to 2023. The findings demonstrate that the SWRA value reached its peak in 2023, amounting to <jats:inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"><mml:mn>56,9368.6</mml:mn><mml:mo>×</mml:mo><mml:mn>1</mml:mn><mml:msup><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msup></mml:math></jats:inline-formula> yuan, while it had its lowest point in 2014, standing at <jats:inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"><mml:mn>14,7402.7</mml:mn><mml:mo>×</mml:mo><mml:mn>1</mml:mn><mml:msup><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msup></mml:math></jats:inline-formula> yuan. The experimental results indicate that the proposed method can quickly provide the SWRA values for many years, offering a methodological foundation for SWRA asset auditing and enhancing the timeliness of the auditing work.","PeriodicalId":12460,"journal":{"name":"Frontiers in Environmental Science","volume":"14 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142209700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
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