In China,historic blocks1 are undergoing extensive regeneration and facing challenges of touristification and commercialization,with regard to which the impacts of B&Bs are not yet thoroughly discussed.Based on a ...In China,historic blocks1 are undergoing extensive regeneration and facing challenges of touristification and commercialization,with regard to which the impacts of B&Bs are not yet thoroughly discussed.Based on a field survey conducted in the Pingjiang Historic Block in Suzhou,this study examines the participation of the local government,residents,B&B operators,and tourists in shaping B&B spaces.The results show that B&B operators take a dominant position in this process,while the local government acts as both regulator and developer.In contrast,local residents remain largely passive with limited participation.While leading to the touristification of the historic block,the development of B&Bs has contributed to the conservation and adaptive reuse of historic and cultural resources.Finally,the study concludes that ensuring the sustainable development of B&Bs in historic blocks requires both more effective regulation of the local government and more active participation of native residents.展开更多
Finding equitable policy solutions is critical for developing sustainable energy use. This paper presents a system-of-systems （SOS） formalism for addressing the equity issue in multi-actor policymaking. In a SoS, th...Finding equitable policy solutions is critical for developing sustainable energy use. This paper presents a system-of-systems （SOS） formalism for addressing the equity issue in multi-actor policymaking. In a SoS, the control of the overall system performance is shared among a network of actors. In contrast to a single optimal solution that aggregates objectives of actors, the solution conCept of iso-performance is formulated and employed to illuminate multiple solutions and hence the ＇space＇ for actors to compromise. By specifically accounting for the equity issue, the level of sacrifice each actor makes for each iso-performance solution is computed. To demonstrate the approach, a case study is presented about policymaking to reduce fuel life cycle aviation emissions in the United States based on the year 2020 reduction target, involving government, airlines, jet fuel refinery companies, and aircraft and engine manufacturers. A resource allocation mixed integer programming model is employed to calculate carbon emissions resulting from airlines＇ deployment of aircraft fleet to meet changing air transport demand. The paper discusses three iso-performance solutions; each of them requires a different level of sacrifice from each actor. Such an insight can inform policymaking in determining the magnitude of compensation required when a particular solution is pursued.展开更多
Data is not only a key production factor but also an important foundation and strategic resource that drives economic growth and social progress in the era of digital economy. Data sharing and innovative utilization i...Data is not only a key production factor but also an important foundation and strategic resource that drives economic growth and social progress in the era of digital economy. Data sharing and innovative utilization in an ethical and responsible manner is a focus of the current studies on smart city construction. Taking Shenzhen as an example, this paper analyzes the three typical cases of data legislation, data sharing and utilization,and data-based anti-epidemic action in its smart city construction and explores the respective role of the four actors of the government, enterprises,research institutes, and the public in innovating data utilization to serve the public interests through data sharing. By studying Shenzhen’s multi-actor interaction mechanism of smart city construction, the paper tries to provide a useful experience for the construction of smart cities in China from the perspectives of data management, data sharing, and innovative data utilization.展开更多
针对深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法在一些大状态空间任务中存在学习效果不佳及波动较大等问题,提出一种基于渐近式k-means聚类算法的多行动者深度确定性策略梯度(multi-actor deep deterministic po...针对深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法在一些大状态空间任务中存在学习效果不佳及波动较大等问题,提出一种基于渐近式k-means聚类算法的多行动者深度确定性策略梯度(multi-actor deep deterministic policy gradient based on progressive k-means clustering,MDDPG-PK-Means)算法.在训练过程中,对每一时间步下的状态进行动作选择时,根据k-means算法判别结果辅佐行动者网络的决策,同时随训练时间步的增加,逐渐增加k-means算法类簇中心的个数.将MDDPG-PK-Means算法应用于MuJoCo仿真平台上,实验结果表明,与DDPG等算法相比,MDDPG-PK-Means算法在大多数连续任务中都具有更好的效果.展开更多
文摘In China,historic blocks1 are undergoing extensive regeneration and facing challenges of touristification and commercialization,with regard to which the impacts of B&Bs are not yet thoroughly discussed.Based on a field survey conducted in the Pingjiang Historic Block in Suzhou,this study examines the participation of the local government,residents,B&B operators,and tourists in shaping B&B spaces.The results show that B&B operators take a dominant position in this process,while the local government acts as both regulator and developer.In contrast,local residents remain largely passive with limited participation.While leading to the touristification of the historic block,the development of B&Bs has contributed to the conservation and adaptive reuse of historic and cultural resources.Finally,the study concludes that ensuring the sustainable development of B&Bs in historic blocks requires both more effective regulation of the local government and more active participation of native residents.
基金supported through a Cooperative Agreement with the NASA Glenn Research Center (NNX07013A)
文摘Finding equitable policy solutions is critical for developing sustainable energy use. This paper presents a system-of-systems （SOS） formalism for addressing the equity issue in multi-actor policymaking. In a SoS, the control of the overall system performance is shared among a network of actors. In contrast to a single optimal solution that aggregates objectives of actors, the solution conCept of iso-performance is formulated and employed to illuminate multiple solutions and hence the ＇space＇ for actors to compromise. By specifically accounting for the equity issue, the level of sacrifice each actor makes for each iso-performance solution is computed. To demonstrate the approach, a case study is presented about policymaking to reduce fuel life cycle aviation emissions in the United States based on the year 2020 reduction target, involving government, airlines, jet fuel refinery companies, and aircraft and engine manufacturers. A resource allocation mixed integer programming model is employed to calculate carbon emissions resulting from airlines＇ deployment of aircraft fleet to meet changing air transport demand. The paper discusses three iso-performance solutions; each of them requires a different level of sacrifice from each actor. Such an insight can inform policymaking in determining the magnitude of compensation required when a particular solution is pursued.
基金supported by the National Natural Science Foundation of China(Project No.52078197)。
文摘Data is not only a key production factor but also an important foundation and strategic resource that drives economic growth and social progress in the era of digital economy. Data sharing and innovative utilization in an ethical and responsible manner is a focus of the current studies on smart city construction. Taking Shenzhen as an example, this paper analyzes the three typical cases of data legislation, data sharing and utilization,and data-based anti-epidemic action in its smart city construction and explores the respective role of the four actors of the government, enterprises,research institutes, and the public in innovating data utilization to serve the public interests through data sharing. By studying Shenzhen’s multi-actor interaction mechanism of smart city construction, the paper tries to provide a useful experience for the construction of smart cities in China from the perspectives of data management, data sharing, and innovative data utilization.
文摘针对深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法在一些大状态空间任务中存在学习效果不佳及波动较大等问题,提出一种基于渐近式k-means聚类算法的多行动者深度确定性策略梯度(multi-actor deep deterministic policy gradient based on progressive k-means clustering,MDDPG-PK-Means)算法.在训练过程中,对每一时间步下的状态进行动作选择时,根据k-means算法判别结果辅佐行动者网络的决策,同时随训练时间步的增加,逐渐增加k-means算法类簇中心的个数.将MDDPG-PK-Means算法应用于MuJoCo仿真平台上,实验结果表明,与DDPG等算法相比,MDDPG-PK-Means算法在大多数连续任务中都具有更好的效果.
