Effects of transport infrastructures and climate change on ecosystem services in the integrated transport corridor region of the Qinghai-Tibet Plateau

Yang, Siqi; Zhang, Lixiao; Zhu, Gaoru

doi:10.1016/j.scitotenv.2023.163961

Cited by 25 publications

(3 citation statements)

References 46 publications

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“…Climatic factors, including precipitation and temperature, show positive effects on RESV, and their synergistic effects contribute to RESV growth. Mechanistically, temperature enhances carbon sequestration in the region by enhancing the assimilation process of photosynthesis (Yang et al, 2023), resulting in a higher value of CSOR. At the same time, precipitation also increases air relative humidity and regulates the water vapor pressure difference between air and plant leaves (Zhao et al, 2023), which promotes transpiration and leads to an increase in climate regulation value.…”

Section: Factors Driving Changes In Resv and Their Influence Pathwaymentioning

confidence: 99%

Spatio-temporal evolution and driving factors of regulating ecosystem service value: a case study of Poyang Lake Area, China

Liu,

Huang,

Liu

et al. 2024

Front. Ecol. Evol.

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Clarifying the driving mechanisms of spatial and temporal changes in the regulating ecosystem service value (RESV) is an important part of realizing the goal of sustainable development. Existing studies have focused on specific factors, ignoring the complex interactions between factors and their regional differences. In this regard, the spatial and temporal changes of RESV and its driving mechanisms in the different zones (core area, fringe area, and peripheral area) were explored in the Poyang Lake Area, China. The results showed that RESV spatially showed the distribution characteristics of fringe area > core area > peripheral area, while the lakes influenced the provision of regulating ecosystem services, showing that RESV per unit area was higher in the core area, and gradually declined with the increase of distance from the lakes, presenting the decreasing trend of fringe area > peripheral area. From 2000 to 2020, the study area lost 70.5988 billion CNY for RESV, in which the core area was the most affected. Further analysis of the driving mechanism of RESV in different areas found that there are regional differences in the paths of the driving factors: Population density mainly affects the core area, precipitation mainly affects the fringe area, and GDP per land mainly affects the peripheral area.

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Section: Factors Driving Changes In Resv and Their Influence Pathwaymentioning

confidence: 99%

Spatio-temporal evolution and driving factors of regulating ecosystem service value: a case study of Poyang Lake Area, China

Liu,

Huang,

Liu

et al. 2024

Front. Ecol. Evol.

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“…Currently, research on trade-offs/synergies of ecosystem service mainly focuses on two categories of issues: first, assessing the trade-offs/synergies among ESs, analyzing their spatiotemporal characteristics, identifying bundles of ESs, and forming ecological management zones [18]; second, exploring the driving mechanisms behind the tradeoffs/synergies [19][20][21][22]. For example, the driving forces include land use [23,24], climate change impacts [25], and grazing exclusion effects [26], among others. Regarding the research techniques for studying the trade-offs and synergies between paired ESs, there are mainly static and dynamic spatial correlation methods [27].…”

Section: Introductionmentioning

confidence: 99%

Assessment and Management Zoning of Ecosystem Service Trade-Off/Synergy Based on the Social–Ecological Balance: A Case of the Chang-Zhu-Tan Metropolitan Area

Liang,

Yang,

Zhang

et al. 2024

Land

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Clarifying the trade-offs/synergies of ecosystem services is crucial for achieving a win-win situation in economic development and ecological conservation. Past studies have lacked research on ecosystem service functional management zones that integrate socio-economic factors and ecological conservation, particularly based on predictive scenarios. Based on the above, this study innovatively established a multi scenario simulation model and framework (EST-EMZ) for the study of ecosystem service (ES) trade-off/synergy and ecological management zoning, combining remote sensing and socio-economic data from 2000 to 2020 in the Chang-Zhu-Tan Metropolitan Area (CZTMA). The model evaluates the dynamic trade-offs/synergies among different ecosystem services under various scenarios, aiming to seek the optimal management approach for enhancing the functionality and optimizing the structure of ESs in the future of the CZTMA. The results indicate the following: (1) From 2000 to 2020, the Ecosystem Service Value (ESV) of the CZTMA gradually declined from 601.57 billion yuan to 584.65 billion yuan. Under the three future scenarios, the ESV also decreased, with the Ecological Conservation Scenario (ECS) experiencing the most minor decline, and the Economic Priority Scenario (EPS) witnessing the most substantial decrease. (2) In the historical period and the 2030 predicted scenarios, there is a predominant synergy among paired ESs in the CZTMA. Throughout the study period, the region’s dominant ecosystem service bundle (ESB) is the high-service ecological regulation bundle, primarily located in the northeastern, western, and southern areas dominated by forests. (3) Based on ESV and urbanization intensity (UI), five different ecosystem management zones were identified: water balance zone (WBZ), coordinated improvement zone (CIZ), ecologically weak zone (EWZ), ecological conservation zone (ECZ), and ecological derivative zone (EDZ). Corresponding management and protection strategies for ecosystem services were proposed. The research findings offer potential solutions for optimizing land use and managing the trade-offs of ESs in metropolitan areas.

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“…Guided Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles by the national grand strategy, transportation construction is progressively advancing into high-altitude and high-latitude cold regions [3][4][5] . Typical projects in these cold regions include major national construction projects such as the Sichuan Tibet Railway, Yunnan Tibet Railway, and New Tibet Railway [6][7][8] , which are the pillars of human activities in remote areas [9][10][11] . However, in high-altitude and cold regions, rock masses often contain a large number of initial defects, such as joints and cracks [12,13] .…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles

Lv,

Zhang,

Zhang

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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The combined impact of freeze-thaw cycles and dynamic loads significantly influences the long-term durability of rock engineering in high-cold regions. Consequently, investigating the dynamic compressive strength (DCS) of rocks subjected to freeze-thaw cycles has emerged as a crucial area of scientific research to advance rock engineering construction in cold regions. Presently, the determination of the DCS of rocks under freeze-thaw cycles primarily relies on indoor experiments. However, this approach has faced criticism due to its drawbacks, including prolonged duration, high costs, and reliance on rock samples. To address these limitations, the exploration of using artificial intelligence technology to develop more accurate and convenient DCS prediction models for rocks under freeze-thaw cycles is a promising attempt. In this context, this paper introduces a DCS prediction model for rocks under freeze-thaw cycles, which integrates the Sparrow Search Algorithm (SSA) with Random Forest (RF). Firstly, employing a dataset of 216 samples, Principal Component Analysis (PCA) is utilized to reduce the dimensionality of ten influential factors. Subsequently, five optimization algorithms are employed to optimize the hyperparameters of both the BP and RF algorithms. Finally, a comprehensive evaluation and comparative analysis are carried out to assess the predictive performance of the optimized model, using evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).The research findings demonstrate that the SSA-RF model exhibits the best predictive performance, surpassing the other nine models in terms of generalization. The prediction model proposed in this study has good applicability for predicting DCS of freeze-thaw rock in cold regions, and also provides new ideas for the combination of machine learning and rock mass engineering in cold regions.

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Effects of transport infrastructures and climate change on ecosystem services in the integrated transport corridor region of the Qinghai-Tibet Plateau

Cited by 25 publications

References 46 publications

Spatio-temporal evolution and driving factors of regulating ecosystem service value: a case study of Poyang Lake Area, China

Spatio-temporal evolution and driving factors of regulating ecosystem service value: a case study of Poyang Lake Area, China

Assessment and Management Zoning of Ecosystem Service Trade-Off/Synergy Based on the Social–Ecological Balance: A Case of the Chang-Zhu-Tan Metropolitan Area

Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles

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