Environmental and Sustainability Indicators最新文献_第3页

A multi-criteria decision analysis method for sustainability improvement of the hydropower megaproject system 大型水电工程系统可持续性改进的多准则决策分析方法

IF 5.6 Q1 ENVIRONMENTAL SCIENCES

Environmental and Sustainability Indicators

Pub Date : 2025-12-25 DOI: 10.1016/j.indic.2025.101106

Peiran Jing , Linlin Fan , Lidan Guo , Chenchen Wei , Jinbao Sheng , Kai Dong , Rui Zhu , Yong Liu , Qinyuan Li

The sustainable development of the hydropower megaproject (HM) is a vital component of watershed sustainable water resources management. Multiple measures should be taken to enhance the HM system's overall performance and sustainability when environmental changes disrupt it. Hence, making scientific and applicable decisions on various measures to improve the HM system's sustainability is a critical and complex problem. This study proposed a novel multi-criteria decision analysis method for sustainability improvement (MCDA-SI) of the HM system. The MCDA-SI method develops the multi-criteria evaluation indicator system, the Cloud-EM-AHP weight solution model, the comprehensive development index (CDI) model, and the coupling coordination degree (CCD) model. The Three Gorges Project (TGP) is selected as a case study, and the key measure to improve the TGP system's sustainability is determined. The results showed that from 2003 to 2023, the CDI of the TGP rose from 0.40 to 0.61, and the CCD indicated a fluctuating increasing trend with a mean value of 0.42, revealing that the TGP system's comprehensive development level and coupling coordination development degree are gradually improving. The sustainability assessment results indicate that the critical factors influencing the TGP system's sustainable coordinated development are flood control safety, resettlement compensation, biodiversity protection, and prevention of water pollution. The multi-criteria decision analysis results illustrated that optimizing the TGP's scheduling, operation, and management is the most effective measure for enhancing the system's sustainability, which aligns with the TGP's actual state. Compared to traditional MCDA technology for hydropower and reservoirs, this study integrates Cloud-EM-AHP, CDI, and CCD within a unified MCDA-SI model framework to enhance the applicability of the MCDA method in sustainable hydropower assessment and decision-making. Meanwhile, the novel MCDA-SI framework can serve as an evaluation index and provide policy insights for coordinating the protection of watershed ecological environments and sustainable hydropower development.

特大水电工程的可持续发展是流域水资源可持续管理的重要组成部分。当环境变化破坏HM系统时，应采取多种措施来提高HM系统的整体性能和可持续性。因此，对提高HM系统可持续性的各种措施做出科学、适用的决策是一个关键而复杂的问题。本文提出了一种新的多准则可持续发展决策分析方法（MCDA-SI）。MCDA-SI方法建立了多准则评价指标体系、Cloud-EM-AHP权解模型、综合发展指数（CDI）模型和耦合协调度（CCD）模型。以三峡工程为例，确定了提高三峡工程系统可持续性的关键措施。结果表明：2003 - 2023年，三峡库区CDI由0.40上升至0.61，CCD呈波动上升趋势，平均值为0.42，表明三峡库区系统综合发展水平和耦合协调发展程度逐步提高。可持续性评价结果表明，影响三峡工程系统可持续协调发展的关键因素是防洪安全、移民补偿、生物多样性保护和水污染防治。多准则决策分析结果表明，优化三峡工程调度、运行和管理是提高系统可持续性的最有效措施，符合三峡工程的实际情况。与传统的水电和水库MCDA技术相比，本研究将Cloud-EM-AHP、CDI和CCD集成在统一的MCDA- si模型框架内，增强了MCDA方法在水电可持续评价和决策中的适用性。同时，新的MCDA-SI框架可作为流域生态环境保护与水电可持续发展协调的评价指标和政策见解。

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引用次数: 0

Multi-perspective assessment of ecological civilization progress across cities in the Yangtze River Economic Belt 长江经济带城市生态文明建设多视角评价

IF 5.6 Q1 ENVIRONMENTAL SCIENCES

Environmental and Sustainability Indicators

Pub Date : 2025-12-25 DOI: 10.1016/j.indic.2025.101107

Li Ma, Huiyuan Zhang, Qing Lu

Ecological civilization, China's national strategy, promotes sustainable development through green growth, harmony between humans and nature, and improved well-being. The Yangtze River Economic Belt (YREB), with diverse urban trajectories, plays a key role in this goal. However, conventional static indices fail to capture dynamic changes, goal deviations, and multidimensional linkages. While the SDGs set ambitious targets, they lack explicit benchmarks, and current evaluation methods, relying on subjective or static data structures, limit goal-oriented assessments. Based on multi-criteria decision-making (MCDM) theory, this study integrates multi-source data to create a multidimensional indicator system for ecological environment (EE), ecological economy (EEC), ecological living (EL), and ecological framework (EF). An ecological civilization profile is developed using the mean-trend-fluctuation method. Dynamic weighting is achieved through K-means, hierarchical clustering, SOM, and machine learning models (RF, SVM, CatBoost). The goal-oriented S-P-G (Status-Progress-Gap) framework evaluates 108 YREB cities (2013–2022), identifies development stages, reveals spatial patterns via Getis-Ord Gi∗, and quantifies indicator contributions using RF-SHAP. Results show that the SI increased from 65 in 2013 to 70 in 2022, the PI rose rapidly (average annual growth >8 % in 2014–2017) before stabilizing with a −4.84 % drop in 2020, and the GI declined from 38.9 % to 27.0 %. The “east-high-west-low” pattern was observed, with provincial capitals catching up (PI > 55 by 2022). RF-SHAP analysis shows EEC drives SI, EL enhances PI and narrows GI, while EE is a bottleneck in advanced cities. Based on these findings, we offer differentiated policy recommendations tailored to cities at various development stages.

生态文明是中国的国家战略，旨在通过绿色发展、人与自然和谐发展、增进人民福祉，推动可持续发展。长江经济带以其多样化的城市发展轨迹，在实现这一目标方面发挥着关键作用。然而，传统的静态指标无法捕捉动态变化、目标偏差和多维联系。虽然可持续发展目标设定了雄心勃勃的目标，但它们缺乏明确的基准，目前的评估方法依赖于主观或静态数据结构，限制了以目标为导向的评估。本研究基于多准则决策（MCDM）理论，整合多源数据，构建了生态环境（EE）、生态经济（EEC）、生态生活（EL）和生态框架（EF）的多维指标体系。采用平均趋势波动法绘制了生态文明剖面图。动态加权是通过K-means、分层聚类、SOM和机器学习模型（RF、SVM、CatBoost）实现的。以目标为导向的S-P-G（现状-进展-差距）框架评估了108个YREB城市（2013-2022年），确定了发展阶段，通过Getis-Ord Gi∗揭示了空间格局，并使用RF-SHAP量化了指标贡献。结果表明，SI从2013年的65上升到2022年的70，PI快速上升（2014-2017年年均增长>； 8%），然后趋于稳定，到2020年下降- 4.84%，GI从38.9%下降到27.0%。观察到“东高西低”的模式，省会城市正在追赶（到2022年为55）。RF-SHAP分析表明，EEC驱动SI， EL提高PI并缩小GI，而EE是先进城市的瓶颈。基于这些发现，我们针对不同发展阶段的城市提出了差异化的政策建议。

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引用次数: 0

Assessing the spatio-temporal impacts of digital economy drivers on carbon emission intensity: An interactive geographically and temporally weighted regression framework 数字经济驱动因素对碳排放强度的时空影响评估：一个地理和时间加权的交互式回归框架

IF 5.6 Q1 ENVIRONMENTAL SCIENCES

Environmental and Sustainability Indicators

Pub Date : 2025-12-24 DOI: 10.1016/j.indic.2025.101104

Huiqing Dai , Luanyun Hu , Xiaoye Zhu , Panyue Zhang , Yuwei Wang , Xiaoling Guo

This study aims to precisely quantify the spatio-temporal impacts of specific digital economy (DE) drivers and their interactions on carbon emission intensity (CEI) across China. Utilizing a panel dataset from 30 Chinese provinces (2011–2022), this study first constructs a comprehensive composite digital economy index. The core of the methodology is a novel interactive geographically and temporally weighted regression model, which integrates interaction factors identified by the optimal parameter geographical detector into the geographically and temporally weighted regression model. The results indicate that (1) Among DE driving factors, software business revenue and enterprise R&D expenditure intensity exert the strongest explanatory power on CEI. (2) Interactions between DE driving factors augment their individual effects, with the interaction between proportion of digital industry employees and per capita telecommunication business volume exhibiting the strongest explanatory power. (3) The impacts of DE driving factors and their interactions on CEI exhibit pronounced spatial heterogeneity. Interactions between factors induce nonlinear changes in their individual effects, mainly reflected in the direction, intensity, and spatial scope of their impacts on CEI. These findings provide critical and actionable insights for designing regionally differentiated policies, enabling policymakers to harness the synergistic potential of the digital economy for targeted carbon mitigation, thereby supporting the achievement of China's “Dual Carbon” goals.

本研究旨在精确量化中国特定数字经济驱动因素及其相互作用对碳排放强度的时空影响。利用2011-2022年中国30个省份的面板数据，本文首先构建了综合数字经济指数。该方法的核心是一种新的地理与时间加权交互回归模型，该模型将最优参数地理检测器识别的交互因素整合到地理与时间加权回归模型中。结果表明：(1)在企业创新能力驱动因素中，软件业务收入和企业研发支出强度对企业创新能力的解释力最强。(2) DE驱动因素之间的交互作用增强了其个体效应，其中数字产业员工比例与人均电信业务量之间的交互作用解释力最强。(3) DE驱动因子及其相互作用对CEI的影响具有明显的空间异质性。因子间的相互作用导致个体效应的非线性变化，主要体现在影响CEI的方向、强度和空间范围上。这些研究结果为制定区域差异化政策提供了关键和可操作的见解，使决策者能够利用数字经济的协同潜力进行有针对性的碳减排，从而支持中国实现“双碳”目标。

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引用次数: 0

Cross-sensitivity of ecosystem services to land use change in China's poverty-stricken areas 中国贫困地区生态系统服务功能对土地利用变化的交叉敏感性

IF 5.6 Q1 ENVIRONMENTAL SCIENCES

Environmental and Sustainability Indicators

Pub Date : 2025-12-23 DOI: 10.1016/j.indic.2025.101105

Jiale Liang , Sipei Pan , Nan Xia , Wanxu Chen , Manchun Li

Contiguous poverty-stricken areas commonly face dual pressures of ecological conservation and economic development. Land use change may lead to ecological degradation, which in turn may exacerbate poverty. Understanding how ecosystem services respond to land use change is crucial to mitigate the vicious cycle between poverty and ecological fragility. Therefore, this study focuses on 680 counties within China's contiguous poverty-stricken areas. Using remote sensing and statistical data, we employed a cross-sensitivity method to quantify county-level ecosystem services response to land use change from 1980 to 2020. Results revealed that the most dramatic change in China's contiguous poverty-stricken areas was the interconversion between agricultural production land (APL) and pasture ecological land (PEL). The transition from PEL to APL inhibited the growth of ecosystem services value (ESV). Overall, ESV initially declined before increasing, resulting in a net gain of US$11.04 billion. Cross-sensitivity analysis showed that sensitivity coefficients in 2000–2020 were generally lower than those in 1980–2000, especially for transitions among ecological land types. ESV was more sensitive to the transition from APL to ecological land and to transitions occurring within ecological land. Changes in APL had the greatest impact on ESV. Transitions from industrial and mining land (IML) to other land types tended to enhance ESV, whereas transition from forest ecological land (FEL) to other land types resulted in substantial ESV losses. Spatial zoning further revealed strong clustering patterns of ecological sensitivity, with highly sensitive areas concentrated east of the Hu Line and extensive no-net change areas on the Qinghai-Tibet Plateau. These findings provide a scientific basis for improving land use management and enhancing ecosystem functions in poverty-stricken areas.

连片特困地区普遍面临生态保护和经济发展的双重压力。土地利用变化可能导致生态退化，而生态退化又可能加剧贫困。了解生态系统服务如何响应土地利用变化，对于缓解贫困与生态脆弱性之间的恶性循环至关重要。因此，本研究以中国连片特困地区的680个县为研究对象。利用遥感和统计数据，采用交叉敏感性方法定量分析了1980—2020年县域生态系统服务对土地利用变化的响应。结果表明，中国连片特困地区变化最剧烈的是农业生产用地（APL）与牧场生态用地（PEL）的相互转化。从PEL到APL的转变抑制了生态系统服务价值（ESV）的增长。总体而言，ESV最初下降，然后增加，导致净收益110.4亿美元。交叉敏感性分析表明，2000-2020年的敏感性系数普遍低于1980-2000年，特别是生态土地类型之间的转换。ESV对从APL到生态地的过渡以及生态地内部的过渡更为敏感。APL的变化对ESV的影响最大。工矿用地向其他土地类型的过渡倾向于增强生态环境价值，而森林生态用地向其他土地类型的过渡则导致生态环境价值的大量损失。空间分区进一步显示出较强的生态敏感性集聚格局，高敏感区集中在胡线以东，无净变化区分布广泛。研究结果为改善贫困地区土地利用管理和增强生态系统功能提供了科学依据。

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引用次数: 0

Evaluating the impacts of wetland degradation on watershed hydrology: Implications for Eco-hydrological Restoration of Gojeb River sub basin, Ethiopia 湿地退化对流域水文的影响评价：对埃塞俄比亚Gojeb河流域生态水文恢复的启示

IF 5.6 Q1 ENVIRONMENTAL SCIENCES

Environmental and Sustainability Indicators

Pub Date : 2025-12-23 DOI: 10.1016/j.indic.2025.101103

Wakjira Takala Dibaba , Eyasu Tafese Mekuria , Bereket Abera Bedada , Bikila Takala Dibaba , Wakene Negassa

Combined human and climate pressures have led to significant changes in the spatial distribution and extent of wetlands, disrupting associated ecosystem services in Ethiopia. Nonetheless, the hydrological effects of wetland loss are still poorly understood across most of Ethiopia's river basins. This study aimed to analyse the long-term hydrological impacts of wetland loss in the Gojeb River sub-basin and evaluate wetland management interventions in restoring key eco-hydrological processes using the Soil and Water Assessment Tool (SWAT). Following the calibration and validation of historical streamflow records, the SWAT model was used to examine three primary scenarios: land use/land cover (LU/LC), wetland loss, and wetland restoration scenarios. The decline in wetland area between 2000 and 2024, led to a 2.3 % decline of groundwater recharge, while surface runoff and sediment yield increased by 8.5 % and 27.7 %, respectively. An extreme flow study revealed a flashier regime with greater peak flows at the same exceedance probability, implying a higher flood risk. Wetland degradation represented a significant trade-off, sacrificing short-term land gain for long-term vulnerability to flooding, erosion, and groundwater depletion. In contrast, wetland restoration has been shown to restore hydrological systems while decreasing sediment output. Wetland restoration, therefore, is an important strategy not only for ecological conservation but also for long-term water resource management, climate change adaptation, and local livelihood protection. The results highlight the importance of wetlands in maintaining hydrological stability, reducing the danger of erosion and floods, and supporting several other ecosystem services.

人类和气候的双重压力导致了湿地空间分布和范围的显著变化，破坏了埃塞俄比亚相关的生态系统服务。尽管如此，人们对埃塞俄比亚大部分河流流域湿地损失的水文影响仍然知之甚少。本研究旨在分析Gojeb河流域湿地损失的长期水文影响，并利用水土评估工具（SWAT）评估湿地管理干预措施在恢复关键生态水文过程中的作用。在对历史流量记录进行校准和验证之后，利用SWAT模型研究了三种主要情景：土地利用/土地覆盖（LU/LC）、湿地损失和湿地恢复情景。2000 - 2024年湿地面积减少，地下水补给减少2.3%，地表径流和产沙分别增加8.5%和27.7%。一项极端流量研究表明，在相同的超过概率下，峰值流量更大的闪光状态意味着更高的洪水风险。湿地退化是一种重要的权衡，以牺牲短期土地收益换取长期易受洪水、侵蚀和地下水枯竭影响的脆弱性。相比之下，湿地恢复已被证明在减少沉积物输出的同时恢复水文系统。因此，湿地恢复不仅是生态保护的重要战略，也是长期水资源管理、气候变化适应和当地生计保护的重要战略。研究结果强调了湿地在维持水文稳定、减少侵蚀和洪水危险以及支持其他几种生态系统服务方面的重要性。

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引用次数: 0

Relationship between GDP, FDI, renewable energy, trade openness, innovation, and CO2 in Slovakia: New insights from ARDL methodology 斯洛伐克国内生产总值、外国直接投资、可再生能源、贸易开放、创新和二氧化碳的关系：来自ARDL方法的新见解

IF 5.6 Q1 ENVIRONMENTAL SCIENCES

Environmental and Sustainability Indicators

Pub Date : 2025-12-22 DOI: 10.1016/j.indic.2025.101102

Thi Lan Anh Nguyen , Huong Giang Luong , Vu Ngoc Xuan

This paper examines the dynamic and long-term relationships between real gross domestic product (GDP), foreign direct investment (FDI), renewable energy consumption (RE), trade openness (TO), innovation (INN), and carbon dioxide emissions (CO₂) in Slovakia. Using the Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration and error correction modelling, we explore whether clean energy and innovation can decouple growth from emissions in a small open European economy integrated into global value chains. Annual data are modelled with careful attention to lag selection, structural breaks, persistence, and endogeneity. We complement baseline ARDL with robustness checks (dynamic ARDL simulations, FMOLS/DOLS, and Toda–Yamamoto causality). The results template indicates: (i) a cointegrating relationship among the variables; (ii) in the long run, RE and INN are associated with lower CO₂ intensity, while TO and FDI exert mixed effects depending on composition and technological spillovers; and (iii) short-run dynamics are dominated by adjustment toward equilibrium with moderate speed of correction. We discuss the policy implications for Slovakia's green transition in light of its EU climate targets.

本文研究了斯洛伐克实际国内生产总值（GDP）、外国直接投资（FDI）、可再生能源消费（RE）、贸易开放（TO）、创新（INN）和二氧化碳排放（CO2）之间的动态和长期关系。利用协整和误差修正模型的自回归分布滞后（ARDL）边界检验方法，我们探讨了清洁能源和创新是否可以将融入全球价值链的小型开放欧洲经济体的增长与排放脱钩。对年度数据进行建模时要注意滞后选择、结构断裂、持续性和内生性。我们用鲁棒性检查（动态ARDL模拟、FMOLS/DOLS和Toda-Yamamoto因果关系）来补充基线ARDL。结果模板表明：(1)各变量之间存在协整关系；(2)从长期来看，可再生能源和新兴产业与较低的二氧化碳强度相关，而外商直接投资和外商直接投资的影响则因其构成和技术溢出而不同；(3)短期动态以中等修正速度的均衡调整为主。我们根据欧盟气候目标讨论斯洛伐克绿色转型的政策影响。

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引用次数: 0

Beyond rainfall and fertilizer: Autoregressive distributed lag insights on population, greenhouse gas emissions, and wheat sustainability in Bangladesh 超越降雨和肥料：孟加拉国人口、温室气体排放和小麦可持续性的自回归分布滞后洞察

IF 5.6 Q1 ENVIRONMENTAL SCIENCES

Environmental and Sustainability Indicators

Pub Date : 2025-12-20 DOI: 10.1016/j.indic.2025.101098

Abu Hayat Md Saiful Islam , Md. Monirul Islam , Arifa Jannat , Md Shishir Ahamed , S.M. Shahriar

Climate change, coupled with rapid population growth, poses substantial and escalating risks to agricultural productivity, particularly in highly vulnerable regions like Bangladesh. This study investigates the impacts of climatic and non-climatic factors on wheat production in Bangladesh over the period 1972–2021, using 50 years of annual data and employing an autoregressive distributed lag approach in conjunction with an error correction model. Long-run cointegration results indicate that a 1 % increase in cultivated area, fertilizer application, and rainfall leads to increases in wheat production of approximately 3.19 %, 1.51 %, and 0.18 %, respectively. In contrast, a 1 % rise in greenhouse gas (GHG) emissions and population is associated with reductions in wheat production of about 2.05 % and 6.89 %, respectively. In the short run, a 1 % increase in cultivated area, fertilizer use, gross domestic product, GHG emissions, and rainfall results in corresponding increases in wheat production of approximately 1.75 %, 1.28 %, 4.60 %, 0.44 %, and 0.05 %, respectively. Conversely, a 1 % increase in the total ecological footprint reduces wheat yield by 2.41 %. These findings underscore the importance of policies that expand access to land (e.g. khas land, land rental market), provide subsidized fertilizers, and curb GHG emissions to enhance wheat production and climate resilience. Furthermore, prioritizing population management is essential to mitigating adverse impacts on wheat production and ensuring long-run food security and sustainable agricultural development in Bangladesh.

气候变化加上人口快速增长，对农业生产力构成了巨大且不断升级的风险，特别是在孟加拉国等高度脆弱的地区。本研究利用50年的年度数据，采用自回归分布滞后方法结合误差修正模型，研究了1972-2021年期间气候和非气候因素对孟加拉国小麦生产的影响。长期协整结果表明，耕地面积、施肥量和降雨量每增加1%，小麦产量分别增加约3.19%、1.51%和0.18%。相比之下，温室气体（GHG）排放量和人口每增加1%，小麦产量分别减少约2.05%和6.89%。在短期内，耕地面积、肥料使用量、国内生产总值、温室气体排放和降雨量每增加1%，小麦产量分别增加约1.75%、1.28%、4.60%、0.44%和0.05%。相反，总生态足迹每增加1%，小麦产量就会减少2.41%。这些发现强调了扩大土地获取渠道（如khas土地、土地租赁市场）、提供补贴肥料和遏制温室气体排放等政策对提高小麦产量和气候适应能力的重要性。此外，优先考虑人口管理对于减轻对小麦生产的不利影响以及确保孟加拉国的长期粮食安全和可持续农业发展至关重要。

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引用次数: 0

Variations and drivers of biomass and soil carbon stocks in planted forests across India 印度人工林生物量和土壤碳储量的变化和驱动因素

IF 5.6 Q1 ENVIRONMENTAL SCIENCES

Environmental and Sustainability Indicators

Pub Date : 2025-12-20 DOI: 10.1016/j.indic.2025.101101

Jintu Kumar Bania , Amitabha Nath , Gudeta W. Sileshi , Arnab Paul , Tara Kumari , Ashesh Kumar Das , Arun Jyoti Nath

Plantation forestry in India is aims to sustain forest cover, rehabilitation of degraded land and support the forestry industry in providing timber, fuelwood and other products. However, the effect of soil and climatic factors on aboveground and belowground biomass carbon (AGB and BGB) as well as soil organic carbon (SOC) dynamics under plantation management remains poorly understood. Therefore, the objectives of the present study are to: (i) evaluate differences in root to shoot ratios, and tree biomass and biomass carbon stocks, (ii) measure SOC stocks among different plantation types and stand age, (iii) quantify variation of SOC stock, and (iv) identify key bioclimatic drivers of carbon storage. Published data on biomass, biomass carbon and soil carbon stock were collected from 53 peer-reviewed studies encompassing 205 different plantation sites across India, covering stand ages of 1–50 years. Bioclimatic variables were obtained from WorldClim 2.1 based on geo-locations of the plantations. Biomass carbon and SOC stocks significantly differed among soil types. The lowest mean aboveground biomass carbon stock (21.8 Mg C ha⁻¹) was recorded on Fluvisols and the highest (93.8 Mg C ha⁻¹) on Acrisols. Similarly, the lowest mean belowground biomass carbon stock (5 Mg C ha⁻¹) was found on Fluvisols and the highest (25.8 Mg C ha⁻¹) on Acrisols. The mean SOC stock was lowest (24.7 Mg C ha⁻¹) on Xerosols and highest (96.5 Mg C ha⁻¹) on Regosols. Ensemble machine learning models identified precipitation and temperature as key factors influencing the soil organic carbon (SOC); however, the models failed to identify the most influential predictor for AGBC and BGBC. The XGBoost model provided the best prediction for SOC stocks (R² = 0.85). The results show that soil type and precipitation strongly influence carbon accumulation, underscoring the need to integrate soil–climate interactions into plantation-based carbon management strategies.

印度的种植园林业旨在维持森林覆盖，恢复退化的土地，并支持林业提供木材、薪材和其他产品。然而，在人工林管理下，土壤和气候因子对地上和地下生物量碳（AGB和BGB）以及土壤有机碳（SOC）动态的影响尚不清楚。因此，本研究的目标是：(i)评估根冠比、树木生物量和生物量碳储量的差异；（ii）测量不同人工林类型和林龄之间的有机碳储量；（iii）量化有机碳储量的变化；（iv）确定碳储量的关键生物气候驱动因素。已发表的关于生物量、生物量碳和土壤碳储量的数据来自53项同行评审的研究，涵盖了印度205个不同的人工林，覆盖了1-50年的林龄。基于人工林地理位置，利用WorldClim 2.1获取生物气候变量。不同土壤类型间生物量碳和有机碳储量差异显著。地上生物量碳储量平均最低的是Fluvisols (21.8 Mg C ha - 1)，最高的是Acrisols （93.8 Mg C ha - 1）。同样，地下生物量碳储量平均最低的是Fluvisols (5 Mg C ha - 1)，最高的是Acrisols （25.8 Mg C ha - 1）。平均碳储量在干溶胶上最低（24.7 Mg C ha−1），在雷果溶胶上最高（96.5 Mg C ha−1）。集成机器学习模型识别降水和温度是影响土壤有机碳（SOC）的关键因素；然而，该模型未能确定AGBC和BGBC最具影响力的预测因子。XGBoost模型对SOC库存的预测效果最好（R2 = 0.85）。结果表明，土壤类型和降水强烈影响碳积累，强调需要将土壤-气候相互作用纳入基于人工林的碳管理策略。

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引用次数: 0

Identifying key parameters to design sustainable projects in the field of aeronautics, in France 在法国航空领域确定设计可持续项目的关键参数

IF 5.6 Q1 ENVIRONMENTAL SCIENCES

Environmental and Sustainability Indicators

Pub Date : 2025-12-20 DOI: 10.1016/j.indic.2025.101097

Alexis Lalevée , Anne-Laure Capomaccio , Claudine Gillot

Due to social, environmental and technical evolutions, we need to change the way we design and manage systems. Traditional project management practices must evolve to incorporate new sustainability objectives. The aeronautics sector is highly strategic and accounts for 7 % of France's greenhouse gas emissions. It is beginning to attract the attention of researchers and manufacturers, with life cycle inventory development being a key topic. However, the literature on the parameters for successfully managing sustainability-oriented design projects in this field is lacking. The proposed paper aims to identify these key parameters. The research methodology involves analysing literature to compare the specificities and characteristics of the management of traditional design projects with those of sustainability-oriented design projects. The intersection of these two domains highlights a list of key parameters. Finally, these parameters are presented to practitioners through semi-structured interviews. These tests demonstrate practitioners' interest in these parameters and the need to position each project according to them.

由于社会、环境和技术的发展，我们需要改变设计和管理系统的方式。传统的项目管理实践必须发展，以纳入新的可持续性目标。航空航天业具有高度的战略意义，占法国温室气体排放量的7%。生命周期库存开发已开始引起研究人员和生产厂家的重视。然而，关于在这一领域成功管理以可持续为导向的设计项目的参数的文献是缺乏的。本文旨在确定这些关键参数。研究方法包括分析文献，比较传统设计项目与可持续发展设计项目管理的特殊性和特点。这两个域的交集突出显示了一系列关键参数。最后，通过半结构化访谈将这些参数呈现给从业者。这些测试显示了从业者对这些参数的兴趣，以及根据这些参数定位每个项目的需要。

{"title":"Identifying key parameters to design sustainable projects in the field of aeronautics, in France","authors":"Alexis Lalevée , Anne-Laure Capomaccio , Claudine Gillot","doi":"10.1016/j.indic.2025.101097","DOIUrl":"10.1016/j.indic.2025.101097","url":null,"abstract":"<div><div>Due to social, environmental and technical evolutions, we need to change the way we design and manage systems. Traditional project management practices must evolve to incorporate new sustainability objectives. The aeronautics sector is highly strategic and accounts for 7 % of France's greenhouse gas emissions. It is beginning to attract the attention of researchers and manufacturers, with life cycle inventory development being a key topic. However, the literature on the parameters for successfully managing sustainability-oriented design projects in this field is lacking. The proposed paper aims to identify these key parameters. The research methodology involves analysing literature to compare the specificities and characteristics of the management of traditional design projects with those of sustainability-oriented design projects. The intersection of these two domains highlights a list of key parameters. Finally, these parameters are presented to practitioners through semi-structured interviews. These tests demonstrate practitioners' interest in these parameters and the need to position each project according to them.</div></div>","PeriodicalId":36171,"journal":{"name":"Environmental and Sustainability Indicators","volume":"29 ","pages":"Article 101097"},"PeriodicalIF":5.6,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Influencing factors and forecasting of European union allowance price: A comparative study using machine learning models 欧盟补贴价格的影响因素及预测：基于机器学习模型的比较研究

IF 5.6 Q1 ENVIRONMENTAL SCIENCES

Environmental and Sustainability Indicators

Pub Date : 2025-12-19 DOI: 10.1016/j.indic.2025.101100

Yanan Wang , Xiaochen Chen , Yang Yang , Rui Li , Meng Li , Wei Chen , Zengming Liu

The European Union Allowance (EUA) is acknowledged as a significant mechanism to motivate EU enterprises to decrease emissions and fulfill the United Nations Sustainable Development Goals (SDGs). Investigating the influencing factors and forecasting trajectory of the EUA prices is hindered by the nonlinearity and non-stationarity characteristics of the EUA prices. This study dissects the intricate relationships of energy prices, financial market variables, and climate factors influencing the EUA prices by the use of Vector Autoregression (VAR), Granger causality tests, Impulse response function(IRF), and Newey-West OLS model. The forecasting results of EUA prices are compared using machine learning models involving BP Neural Networks (BPNN), Random Forests (RF), and Support Vector Machines (SVM) in this study. The results show that, first, energy prices, financial market variables, and climate are influencing factors for EUA prices. However, the interplay among these factors is intricate. Second, Machine learning models that incorporate BPNN, RF, and SVM offer substantial advantages in forecasting of EUA prices. Third, it has been determined that the RF model exhibits superior accuracy and stability in forecasting the EUA prices when compared to the performance of the three machine learning models. These findings underscore the capability of machine learning models to handle intricate and dynamic carbon market data, offering critical insights for lowering carbon emissions and promoting sustainable development.

欧盟碳排放配额（EUA）被公认为是激励欧盟企业减少排放、实现联合国可持续发展目标（sdg）的重要机制。EUA价格的非线性和非平稳性阻碍了对EUA价格影响因素和预测轨迹的研究。本文运用向量自回归（VAR）、格兰杰因果检验、脉冲响应函数（IRF）和纽西OLS模型分析了能源价格、金融市场变量和气候因素对EUA价格的复杂影响关系。本文采用BP神经网络（BPNN）、随机森林（RF）和支持向量机（SVM）的机器学习模型对EUA价格的预测结果进行了比较。结果表明：第一，能源价格、金融市场变量和气候是EUA价格的影响因素；然而，这些因素之间的相互作用是复杂的。其次，结合BPNN、RF和SVM的机器学习模型在预测EUA价格方面具有实质性优势。第三，与三种机器学习模型的性能相比，已经确定RF模型在预测EUA价格方面表现出优越的准确性和稳定性。这些发现强调了机器学习模型处理复杂和动态碳市场数据的能力，为降低碳排放和促进可持续发展提供了重要见解。

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引用次数: 0