Soil and water matching in a land basin is important for securing land demand,alleviating human-land conflicts,and promoting sustainable development in the region.The Tarim River Basin(TRB)is the largest inland river ...Soil and water matching in a land basin is important for securing land demand,alleviating human-land conflicts,and promoting sustainable development in the region.The Tarim River Basin(TRB)is the largest inland river basin in China and primarily sustains an agricultural economy centered around oases.This study employs the Patch-generating Land-Use Simulation(PLUS)model to forecast the changing patterns of land use across various future scenarios.The connection between land development and the ecological environment is examined through the lens of relative ecological value and ecological impact.The results indicate that:(1)From 1992 to 2020,the ecology of the basin showed an improving trend,with the area of new cropland increasing by 18,850.51 km^(2)at a growth rate of 56.13%.Grassland area increased by 10,235.29 km^(2)and barren land area decreased by 20,597.29 km^(2).(2)Under the four tested scenarios of Natural Development,Cropland Conservation,Ecological Protection,and Urban Expansion(scenarios Ⅰ-Ⅳ,respectively),the PLUS results for the year 2050 show an increase in cropland area of 12.69% under Scenario Ⅱ,an increase in grassland area of 20,374.82 km^(2)under Scenario Ⅳ,and an increase in built-up land area of 1,105.57 km^(2)under Scenario Ⅲ.(3)A simulation of the basin's ecology in 2050 shows a significant improvement trend under Scenario Ⅳ.Specifically,the development of a large amount of barren land into grassland and woodland has significant ecological benefits,with a contribution rate of 61.88%to 70.18%.This study provides a strong scientific foundation for future land management and ecological sustainable development in the TRB.展开更多
Quantitative assessment of development sustainability could be a challenge to regional management and planning, especially for areas facing great risks of water shortage. Surface-water decline and groundwater over-pum...Quantitative assessment of development sustainability could be a challenge to regional management and planning, especially for areas facing great risks of water shortage. Surface-water decline and groundwater over-pumping have caused serious environmental problems and limited economic development in many regions all around the world. In this paper, a framework for quantitatively evaluating development sustainability was established with water-related eco-environmental carrying capacity （EECC） as the core measure. As a case study, the developed approach was applied to data of the Haihe River Basin, China, during 1998 through 2007. The overall sustainable development degree （SDD） is determined to be 0.39, suggesting that this rate of development is not sustainable. Results of scenario analysis revealed that overshoot, or resource over- exploitation, of the Basin＇s EECC is about 20% for both population and economy. Based on conditions in the study area in 2007, in order to achieve sustainable development, i.e., SDD〉0.70 in this study, the EECC could support a population of 108 million and gross domestic product （GDP） of 2.72 trillion CNY. The newly developed approach in quantifying ecoenvironmental carrying capacity is anticipated to facilitate sustainable development oriented resource management in waterdeficient areas.展开更多
Offshore carbon dioxide(CO_(2)) geological storage(OCGS) represents a significant strategy for addressing climate change by curtailing greenhouse gas emissions. Nonetheless, the risk of CO_(2) leakage poses a substant...Offshore carbon dioxide(CO_(2)) geological storage(OCGS) represents a significant strategy for addressing climate change by curtailing greenhouse gas emissions. Nonetheless, the risk of CO_(2) leakage poses a substantial concern associated with this technology. This study introduces an innovative approach for establishing OCGS leakage scenarios, involving four pivotal stages, namely, interactive matrix establishment, risk matrix evaluation, cause–effect analysis, and scenario development, which has been implemented in the Pearl River Estuary Basin in China. The initial phase encompassed the establishment of an interaction matrix for OCGS systems based on features, events, and processes. Subsequent risk matrix evaluation and cause–effect analysis identified key system components, specifically CO_(2) injection and faults/features. Building upon this analysis, two leakage risk scenarios were successfully developed, accompanied by the corresponding mitigation measures. In addition, this study introduces the application of scenario development to risk assessment, including scenario numerical simulation and quantitative assessment. Overall, this research positively contributes to the sustainable development and safe operation of OCGS projects and holds potential for further refinement and broader application to diverse geographical environments and project requirements. This comprehensive study provides valuable insights into the establishment of OCGS leakage scenarios and demonstrates their practical application to risk assessment, laying the foundation for promoting the sustainable development and safe operation of ocean CO_(2) geological storage projects while proposing possibilities for future improvements and broader applications to different contexts.展开更多
The Lower Mekong River basin (LMB) covers the lower part of the Mekong river basin, including Laos, Thailand, Cambodia and Vietnam. Due to numerous pressures from high population growth and intensive hydropower develo...The Lower Mekong River basin (LMB) covers the lower part of the Mekong river basin, including Laos, Thailand, Cambodia and Vietnam. Due to numerous pressures from high population growth and intensive hydropower development, the LMB has been facing significant challenges concerning its biodiversity and ecosystem. In 2017, Mekong River Commission (MRC), an intergovernmental organisation founded in 1995 among LMB countries, established the Council Study, which analysed the impacts of water development scenarios concerning the environmental, socioeconomic aspects of the LMB. This paper explores the nature of risks to the LMB water development and subsequently evaluates LMB’s water development scenarios described in the Council Study by using a multi-criteria decision analysis (MCDA) method. MCDA method has been widely applied in the field of water resource management in order to assist the decision-making process by systematically evaluating a certain number of alternatives against well-selected criteria through a preference rating scheme. By implementing a risk-based comprehensive assessment of the LMB transboundary water, this study provides insights into the impacts of the increasing risks to the ecosystem and human beings on the water development of the basin over time, which assists to change the awareness and the perspective toward humans’ risks and transboundary river ecosystem of decision-makers. This paper provides valuable recommendations for MRC to improve their policy concerning benefit-sharing scheme, water planning and risk mitigation strategies.展开更多
To effectively fight against traffic accidents,it is of great importance to analyse and understand the conditions that are linked with accidents.Such an analysis can serve as the basis to(i)develop reactive measures b...To effectively fight against traffic accidents,it is of great importance to analyse and understand the conditions that are linked with accidents.Such an analysis can serve as the basis to(i)develop reactive measures by finding the links between the pre-accident conditions(ii)devise proactive strategies that will prevent the occurrence of accidents by making the vehicles safer.This paper contributes to advancement of both approaches.For(i),one needs to identify the patterns in accidents.For(ii),introduction of Connected and Automated Vehicles(CAVs)is a promising solution.However CAVs need to be tested under numerous traffic scenarios to prove their safety before their deployment on public roads.This necessitates a great demand for high quality test scenarios for CAVs.This paper achieves two goals.First,it analyses the past traffic accidents(UK’s STATS19 database)to identify trends in the heterogeneous accident data and unravel the relationships between pre-accident conditions.This is done using a clustering algorithm(ROCK).Seven distinct large clusters emerge as a result.Each of these clusters are then further analysed for their meaning using the frequency analysis and geometric analysis.Secondly the paper underpins the proactive route(ii)by systematically developing,using the information in each cluster,test-case scenarios for CAVs which reflect the risk-prone conditions of the respective clusters.This is done using a data mining method(Market Basket algorithm)and further geometric interpretation of clusters.This way explicit scenarios are developed carrying the characteristics of the clusters that they come from.展开更多
Autonomous vehicles with self-evolution capabilities are expected to improve their performance through learning algorithms,to automatically adapt to the external environment.However,due to the infinity,complexity,and ...Autonomous vehicles with self-evolution capabilities are expected to improve their performance through learning algorithms,to automatically adapt to the external environment.However,due to the infinity,complexity,and variability of the actual traffic environment,it is necessary to develop quantitative representation indicators of scenario difficulty and generate targeted scenarios to ensure the evolution gradually,so as to quickly approach the performance limit of the algorithm.Therefore,this paper proposes a data-driven quantitative representation method of scenario difficulty.Specifically,the concept of environment agent is proposed,and a reinforcement learning method combined with mechanism knowledge is constructed for policy search to obtain an agent with an adversarial behavior.The model parameters of the environment agent at different stages in the training process are extracted to construct a policy group,and then agents with different adversarial intensities are obtained,which are used to realize data generation in different difficulty scenarios through the simulation environment.Finally,a data-driven scenario difficulty quantitative representation model is constructed,which is used to output the environment agent policy under different difficulties.Experimental results show the effectiveness of the proposed method.The result analysis shows that the proposed algorithm can generate reasonable and interpretable scenarios with high discrimination and can provide quantifiable difficulty representation without any expert logic rule design.Compared with the rule-based discrete scenario difficulty representation method,the proposed algorithm can achieve continuous difficulty representation.The video link is https://www.youtube.com/watch?v=GceGdqAm9Ys.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.W2412135)the Tianshan Yingcai Program of Xinjiang Uygur Autonomous Region(Grant No.2022TSYCCX0038)the International Cooperation Program of Chinese Academy of Sciences(Grant No.131965KYSB20210045)。
文摘Soil and water matching in a land basin is important for securing land demand,alleviating human-land conflicts,and promoting sustainable development in the region.The Tarim River Basin(TRB)is the largest inland river basin in China and primarily sustains an agricultural economy centered around oases.This study employs the Patch-generating Land-Use Simulation(PLUS)model to forecast the changing patterns of land use across various future scenarios.The connection between land development and the ecological environment is examined through the lens of relative ecological value and ecological impact.The results indicate that:(1)From 1992 to 2020,the ecology of the basin showed an improving trend,with the area of new cropland increasing by 18,850.51 km^(2)at a growth rate of 56.13%.Grassland area increased by 10,235.29 km^(2)and barren land area decreased by 20,597.29 km^(2).(2)Under the four tested scenarios of Natural Development,Cropland Conservation,Ecological Protection,and Urban Expansion(scenarios Ⅰ-Ⅳ,respectively),the PLUS results for the year 2050 show an increase in cropland area of 12.69% under Scenario Ⅱ,an increase in grassland area of 20,374.82 km^(2)under Scenario Ⅳ,and an increase in built-up land area of 1,105.57 km^(2)under Scenario Ⅲ.(3)A simulation of the basin's ecology in 2050 shows a significant improvement trend under Scenario Ⅳ.Specifically,the development of a large amount of barren land into grassland and woodland has significant ecological benefits,with a contribution rate of 61.88%to 70.18%.This study provides a strong scientific foundation for future land management and ecological sustainable development in the TRB.
基金funding support from the Key Knowledge Innovation Project of the Chinese Academy of Sciences(Kzcx2-yw-126)the Key Technology R&D Program of China(2006BAB14B07)the National Natural Sciences Foundation of China(40730632,40701027)
文摘Quantitative assessment of development sustainability could be a challenge to regional management and planning, especially for areas facing great risks of water shortage. Surface-water decline and groundwater over-pumping have caused serious environmental problems and limited economic development in many regions all around the world. In this paper, a framework for quantitatively evaluating development sustainability was established with water-related eco-environmental carrying capacity （EECC） as the core measure. As a case study, the developed approach was applied to data of the Haihe River Basin, China, during 1998 through 2007. The overall sustainable development degree （SDD） is determined to be 0.39, suggesting that this rate of development is not sustainable. Results of scenario analysis revealed that overshoot, or resource over- exploitation, of the Basin＇s EECC is about 20% for both population and economy. Based on conditions in the study area in 2007, in order to achieve sustainable development, i.e., SDD〉0.70 in this study, the EECC could support a population of 108 million and gross domestic product （GDP） of 2.72 trillion CNY. The newly developed approach in quantifying ecoenvironmental carrying capacity is anticipated to facilitate sustainable development oriented resource management in waterdeficient areas.
文摘Offshore carbon dioxide(CO_(2)) geological storage(OCGS) represents a significant strategy for addressing climate change by curtailing greenhouse gas emissions. Nonetheless, the risk of CO_(2) leakage poses a substantial concern associated with this technology. This study introduces an innovative approach for establishing OCGS leakage scenarios, involving four pivotal stages, namely, interactive matrix establishment, risk matrix evaluation, cause–effect analysis, and scenario development, which has been implemented in the Pearl River Estuary Basin in China. The initial phase encompassed the establishment of an interaction matrix for OCGS systems based on features, events, and processes. Subsequent risk matrix evaluation and cause–effect analysis identified key system components, specifically CO_(2) injection and faults/features. Building upon this analysis, two leakage risk scenarios were successfully developed, accompanied by the corresponding mitigation measures. In addition, this study introduces the application of scenario development to risk assessment, including scenario numerical simulation and quantitative assessment. Overall, this research positively contributes to the sustainable development and safe operation of OCGS projects and holds potential for further refinement and broader application to diverse geographical environments and project requirements. This comprehensive study provides valuable insights into the establishment of OCGS leakage scenarios and demonstrates their practical application to risk assessment, laying the foundation for promoting the sustainable development and safe operation of ocean CO_(2) geological storage projects while proposing possibilities for future improvements and broader applications to different contexts.
文摘The Lower Mekong River basin (LMB) covers the lower part of the Mekong river basin, including Laos, Thailand, Cambodia and Vietnam. Due to numerous pressures from high population growth and intensive hydropower development, the LMB has been facing significant challenges concerning its biodiversity and ecosystem. In 2017, Mekong River Commission (MRC), an intergovernmental organisation founded in 1995 among LMB countries, established the Council Study, which analysed the impacts of water development scenarios concerning the environmental, socioeconomic aspects of the LMB. This paper explores the nature of risks to the LMB water development and subsequently evaluates LMB’s water development scenarios described in the Council Study by using a multi-criteria decision analysis (MCDA) method. MCDA method has been widely applied in the field of water resource management in order to assist the decision-making process by systematically evaluating a certain number of alternatives against well-selected criteria through a preference rating scheme. By implementing a risk-based comprehensive assessment of the LMB transboundary water, this study provides insights into the impacts of the increasing risks to the ecosystem and human beings on the water development of the basin over time, which assists to change the awareness and the perspective toward humans’ risks and transboundary river ecosystem of decision-makers. This paper provides valuable recommendations for MRC to improve their policy concerning benefit-sharing scheme, water planning and risk mitigation strategies.
文摘To effectively fight against traffic accidents,it is of great importance to analyse and understand the conditions that are linked with accidents.Such an analysis can serve as the basis to(i)develop reactive measures by finding the links between the pre-accident conditions(ii)devise proactive strategies that will prevent the occurrence of accidents by making the vehicles safer.This paper contributes to advancement of both approaches.For(i),one needs to identify the patterns in accidents.For(ii),introduction of Connected and Automated Vehicles(CAVs)is a promising solution.However CAVs need to be tested under numerous traffic scenarios to prove their safety before their deployment on public roads.This necessitates a great demand for high quality test scenarios for CAVs.This paper achieves two goals.First,it analyses the past traffic accidents(UK’s STATS19 database)to identify trends in the heterogeneous accident data and unravel the relationships between pre-accident conditions.This is done using a clustering algorithm(ROCK).Seven distinct large clusters emerge as a result.Each of these clusters are then further analysed for their meaning using the frequency analysis and geometric analysis.Secondly the paper underpins the proactive route(ii)by systematically developing,using the information in each cluster,test-case scenarios for CAVs which reflect the risk-prone conditions of the respective clusters.This is done using a data mining method(Market Basket algorithm)and further geometric interpretation of clusters.This way explicit scenarios are developed carrying the characteristics of the clusters that they come from.
基金the National Key R&D Program of China under Grant No.2022YFB2502900the National Natural Science Foundation of China(Grant Number:U23B2061)+1 种基金the Fundamental Research Funds for the Central Universities of Chinathe Xiaomi Young Talent Program。
文摘Autonomous vehicles with self-evolution capabilities are expected to improve their performance through learning algorithms,to automatically adapt to the external environment.However,due to the infinity,complexity,and variability of the actual traffic environment,it is necessary to develop quantitative representation indicators of scenario difficulty and generate targeted scenarios to ensure the evolution gradually,so as to quickly approach the performance limit of the algorithm.Therefore,this paper proposes a data-driven quantitative representation method of scenario difficulty.Specifically,the concept of environment agent is proposed,and a reinforcement learning method combined with mechanism knowledge is constructed for policy search to obtain an agent with an adversarial behavior.The model parameters of the environment agent at different stages in the training process are extracted to construct a policy group,and then agents with different adversarial intensities are obtained,which are used to realize data generation in different difficulty scenarios through the simulation environment.Finally,a data-driven scenario difficulty quantitative representation model is constructed,which is used to output the environment agent policy under different difficulties.Experimental results show the effectiveness of the proposed method.The result analysis shows that the proposed algorithm can generate reasonable and interpretable scenarios with high discrimination and can provide quantifiable difficulty representation without any expert logic rule design.Compared with the rule-based discrete scenario difficulty representation method,the proposed algorithm can achieve continuous difficulty representation.The video link is https://www.youtube.com/watch?v=GceGdqAm9Ys.
