Data Needs for Accelerating Progress in Corporate Sustainability Workshop to be held in Washington, DC during January 2010.

加速企业可持续发展进程所需的数据研讨会将于 2010 年 1 月在华盛顿特区举行。

• 批准号: 0957988
• 负责人: Michael Lenox
• 金额: $ 5万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2010-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0957988&HistoricalAwards=false
• 关键词: Data; Needs; Accelerating; Progress; Corporate

This workshop will be a gathering of approximately 30 academics and individuals from both the public and private sector. The discussion will focus on data needs to guide effective policies and firm strategies related to sustainability. Without such a workshop, this type of collaboration between academic researchers on corporate sustainability, government officials and corporate leaders would likely not take place.The event will likely occur in January, 2010. An assessment of existing corporate sustainability-related data sets will be completed by a researcher and communicated to attendees prior to the workshop. A primary goal of the workshop will be to develop an action plan for collecting the data determined by the attendees to be essential. Subsequent to the event, the researcher will engage with partners to begin the data collectionprocess.The goals of the Data Needs workshop are to:-Foster collaboration between academics and both and public and private organizations regarding data needs for rigorous academic research on corporate sustainability issues;-Identify specific data needs and the resources necessary to collect them;-Reduce redundancy and identify ways to maximize use of data collection resources.

本次研讨会将聚集来自公共和私营部门的约 30 名学者和个人。讨论将重点关注指导与可持续发展相关的有效政策和公司战略的数据需求。如果没有这样的研讨会，企业可持续发展学术研究人员、政府官员和企业领导人之间的这种类型的合作可能不会发生。该活动可能会在 2010 年 1 月举行。研究人员将完成对现有企业可持续发展相关数据集的评估，并在研讨会之前传达给与会者。研讨会的主要目标是制定一项行动计划，以收集与会者认为重要的数据。活动结束后，研究人员将与合作伙伴一起开始数据收集过程。数据需求研讨会的目标是：-促进学术界、公共和私人组织之间就企业可持续发展问题进行严格学术研究的数据需求方面的合作；-确定具体的数据需求以及收集这些数据所需的资源；-减少冗余并确定最大限度地利用数据收集资源的方法。