Big data technology has become increasingly prevalent in facilitating the delivery of government services and public goods in China and beyond.This article seeks to examine how big data can be leveraged in poverty gov...Big data technology has become increasingly prevalent in facilitating the delivery of government services and public goods in China and beyond.This article seeks to examine how big data can be leveraged in poverty governance in rural China,and what the political implications are.This phenomenon of big data-driven welfare governance is particularly salient considering the broader context of governmental digital transformation,both within China and globally.This research sheds light on how big data usage functions in poverty alleviation,highlighting general motivations and developments in Chinese data-centric welfare governance.More importantly,this article introduces the novel concept of"digital cybernetic capacity"to examine public sector modernization as big data transforms the landscape of welfare delivery and governance.By delving into the intersection between technology and social welfare,we explicate how the use of big data in social welfare policies can go beyond merely improving information capacity to redefine state capacity.This article argues that such a new governance ecosystem,driven as it is by data technologies,not only provides insights about the transformative resilience of Chinese governance but also opens a new theoretical frontier for research into other countries.展开更多
面对复杂高风险的隧道场景,机器学习(machine learning,ML)为地质认知、施工优化与运营安全提供数据驱动的新范式。基于Web of Science核心期刊的1633篇文献,通过科学计量分析系统梳理ML在隧道工程中的研究热点与演化趋势,围绕围岩识别...面对复杂高风险的隧道场景,机器学习(machine learning,ML)为地质认知、施工优化与运营安全提供数据驱动的新范式。基于Web of Science核心期刊的1633篇文献,通过科学计量分析系统梳理ML在隧道工程中的研究热点与演化趋势,围绕围岩识别-掘进优化-健康监测全生命周期链条,综述现有研究所使用的ML算法与应用现状。在围岩识别方面,融合地质、随钻参数、图像和光谱特征与物探信息的集成学习与深度学习(deep learning,DL)显著提升岩性判别与异常体识别;在掘进优化方面,时序与多模态模型用于推进速度、能耗预测及参数自适应控制,增强对非线性耦合的刻画;在健康监测方面,衬砌缺陷检测正向高精度、实时化与轻量化演进。然后,对领域内存在的关键挑战进行分类和总结,提出未来需从数据标准化生态体系、开放数据平台、多模态数据融合框架、模型可解析性提升、不确定性量化、大语言模型深化应用、轻量化部署、模型优化、鲁棒性优化、跨学科协作、可持续发展11个方面推进,旨在推动ML在隧道工程中从理论研究向工程实用化转化。展开更多
【目的】本研究旨在系统解析多源大数据驱动的生态系统文化服务(cultural ecosystem service,CES)评估创新,明晰研究进展与未来方向。【方法】以“生态系统文化服务”和“价值评估”为关键词,检索Web of Science与CNKI数据库2000—2024...【目的】本研究旨在系统解析多源大数据驱动的生态系统文化服务(cultural ecosystem service,CES)评估创新,明晰研究进展与未来方向。【方法】以“生态系统文化服务”和“价值评估”为关键词,检索Web of Science与CNKI数据库2000—2024年的文献。从大数据类型、CES价值类型、评估对象与评估方法4个维度梳理研究成果,对当前研究机遇、挑战及未来趋势进行系统性评述,并系统性总结基于多源大数据的CES评估工作流。【结果】1)CES评估范式呈现从传统经济核算向智能评估转型的趋势。统计表明,约70%的研究通过多源数据的应用实现了范式革新,主要体现在CES价值类型维度拓展、评估对象类型细化、评估方法应用创新3个方面。2)大数据应用突破了传统信息获取瓶颈,形成政府公开数据(生态环境数据、人口经济数据等)与用户生成数据(社交媒体数据、地图与兴趣点数据、位置服务数据等)融合的多元化格局,显著提升了CES价值解析的精度、时空覆盖度及场景适用性。3)机器学习、深度学习等人工智能技术与大数据分析手段成为新兴的CES评估方法,能进行海量数据处理与深度信息挖掘,有效提升了评估效率与准确性。【结论】多源大数据的应用使得CES评估从传统经济核算转向智能感知分析,为CES研究提供了新依据。未来需推动评估框架的标准化,以提升研究结果的科学性和解释力。展开更多
Law enforcement remains to be the main strategy used to combat poaching and account for high budget share in protected area management. Studies on efficiency of wildlife law enforcement in the protected areas are limi...Law enforcement remains to be the main strategy used to combat poaching and account for high budget share in protected area management. Studies on efficiency of wildlife law enforcement in the protected areas are limited. This study analyzed economic efficiency of wildlife law enforcement in terms of resource used and output generated using three different protected areas (PAs) of Serengeti ecosystem namely Serengeti National Park (SENAPA), Ikorongo/Grumeti Game Reserves (IGGR) and Ikona Wildlife Management Area (IWMA). Three years (2010-2012) monthly data on wildlife law enforcement inputs and outputs were collected from respective PAs authorities and supplemented with key informant interviews and secondary data. Questionnaire surveys were conducted to wildlife law enforcement staff. Shadow prices for non-marketed inputs were estimated, and market prices for marketed inputs. Data Envelopment Analysis (DEA) was used to estimate economic efficiency using Variable Return to Scale (VRS) and Constant Return to Scale (CCR) assumptions. Results revealed that wildlife law enforcement in all PAs was economically inefficient, with less inefficiency observed in IWMA. The less inefficiency in IWMA is likely attributed to existing sense of ownership and responsibility created through community-based conservation which resulted in to decrease in law enforcement costs. A slacks evaluation revealed a potential to reduce fuel consumption, number of patrol vehicles, ration and prosecution efforts at different magnitudes between studied protected areas. There is equal potential to recruit more rangers while maintaining the resting time. These finding forms the bases for monitoring and evaluation with respect to resource usage to enhance efficiency. It is further recommended to enhance community participation in conservation in SENAPA and IGGR to lower law enforcement costs. Collaboration between protected area, police and judiciary is fundamental to enhance enforcement efficiency. Despite old dataset, these findings are relevant since neither conservation policy nor institution framework has changed substantially in the last decade.展开更多
基金the youth project"Research on the Practical Challenges of Government Digital Transformation and Their Mitigation Mechanisms from the Perspective of Algorithmic Governance"supported by the National Social Science Fund of China(No.21CZZ039).
文摘Big data technology has become increasingly prevalent in facilitating the delivery of government services and public goods in China and beyond.This article seeks to examine how big data can be leveraged in poverty governance in rural China,and what the political implications are.This phenomenon of big data-driven welfare governance is particularly salient considering the broader context of governmental digital transformation,both within China and globally.This research sheds light on how big data usage functions in poverty alleviation,highlighting general motivations and developments in Chinese data-centric welfare governance.More importantly,this article introduces the novel concept of"digital cybernetic capacity"to examine public sector modernization as big data transforms the landscape of welfare delivery and governance.By delving into the intersection between technology and social welfare,we explicate how the use of big data in social welfare policies can go beyond merely improving information capacity to redefine state capacity.This article argues that such a new governance ecosystem,driven as it is by data technologies,not only provides insights about the transformative resilience of Chinese governance but also opens a new theoretical frontier for research into other countries.
文摘面对复杂高风险的隧道场景,机器学习(machine learning,ML)为地质认知、施工优化与运营安全提供数据驱动的新范式。基于Web of Science核心期刊的1633篇文献,通过科学计量分析系统梳理ML在隧道工程中的研究热点与演化趋势,围绕围岩识别-掘进优化-健康监测全生命周期链条,综述现有研究所使用的ML算法与应用现状。在围岩识别方面,融合地质、随钻参数、图像和光谱特征与物探信息的集成学习与深度学习(deep learning,DL)显著提升岩性判别与异常体识别;在掘进优化方面,时序与多模态模型用于推进速度、能耗预测及参数自适应控制,增强对非线性耦合的刻画;在健康监测方面,衬砌缺陷检测正向高精度、实时化与轻量化演进。然后,对领域内存在的关键挑战进行分类和总结,提出未来需从数据标准化生态体系、开放数据平台、多模态数据融合框架、模型可解析性提升、不确定性量化、大语言模型深化应用、轻量化部署、模型优化、鲁棒性优化、跨学科协作、可持续发展11个方面推进,旨在推动ML在隧道工程中从理论研究向工程实用化转化。
文摘【目的】本研究旨在系统解析多源大数据驱动的生态系统文化服务(cultural ecosystem service,CES)评估创新,明晰研究进展与未来方向。【方法】以“生态系统文化服务”和“价值评估”为关键词,检索Web of Science与CNKI数据库2000—2024年的文献。从大数据类型、CES价值类型、评估对象与评估方法4个维度梳理研究成果,对当前研究机遇、挑战及未来趋势进行系统性评述,并系统性总结基于多源大数据的CES评估工作流。【结果】1)CES评估范式呈现从传统经济核算向智能评估转型的趋势。统计表明,约70%的研究通过多源数据的应用实现了范式革新,主要体现在CES价值类型维度拓展、评估对象类型细化、评估方法应用创新3个方面。2)大数据应用突破了传统信息获取瓶颈,形成政府公开数据(生态环境数据、人口经济数据等)与用户生成数据(社交媒体数据、地图与兴趣点数据、位置服务数据等)融合的多元化格局,显著提升了CES价值解析的精度、时空覆盖度及场景适用性。3)机器学习、深度学习等人工智能技术与大数据分析手段成为新兴的CES评估方法,能进行海量数据处理与深度信息挖掘,有效提升了评估效率与准确性。【结论】多源大数据的应用使得CES评估从传统经济核算转向智能感知分析,为CES研究提供了新依据。未来需推动评估框架的标准化,以提升研究结果的科学性和解释力。
文摘Law enforcement remains to be the main strategy used to combat poaching and account for high budget share in protected area management. Studies on efficiency of wildlife law enforcement in the protected areas are limited. This study analyzed economic efficiency of wildlife law enforcement in terms of resource used and output generated using three different protected areas (PAs) of Serengeti ecosystem namely Serengeti National Park (SENAPA), Ikorongo/Grumeti Game Reserves (IGGR) and Ikona Wildlife Management Area (IWMA). Three years (2010-2012) monthly data on wildlife law enforcement inputs and outputs were collected from respective PAs authorities and supplemented with key informant interviews and secondary data. Questionnaire surveys were conducted to wildlife law enforcement staff. Shadow prices for non-marketed inputs were estimated, and market prices for marketed inputs. Data Envelopment Analysis (DEA) was used to estimate economic efficiency using Variable Return to Scale (VRS) and Constant Return to Scale (CCR) assumptions. Results revealed that wildlife law enforcement in all PAs was economically inefficient, with less inefficiency observed in IWMA. The less inefficiency in IWMA is likely attributed to existing sense of ownership and responsibility created through community-based conservation which resulted in to decrease in law enforcement costs. A slacks evaluation revealed a potential to reduce fuel consumption, number of patrol vehicles, ration and prosecution efforts at different magnitudes between studied protected areas. There is equal potential to recruit more rangers while maintaining the resting time. These finding forms the bases for monitoring and evaluation with respect to resource usage to enhance efficiency. It is further recommended to enhance community participation in conservation in SENAPA and IGGR to lower law enforcement costs. Collaboration between protected area, police and judiciary is fundamental to enhance enforcement efficiency. Despite old dataset, these findings are relevant since neither conservation policy nor institution framework has changed substantially in the last decade.
