钱浩祺
The Perspective of Application of Environmental Big Data
Qian Haoqi
摘要：社会科学研究中如何更好地利用环境数据开展定量研究，是帮助我国提升环境治理能力的重要一环。本文基于大数据5V模型提出了环境大数据体系的概念，并基于该数据体系构建了针对环境大数据应用的分析框架，从宏观环境统计数据、微观环境数据、环境监测数据、卫星数据以及异构数据五个类别分析环境数据在我国社会科学研究中的应用现状与不足。研究发现，在环境大数据体系中，不同类别的数据各有其优势与劣势，相比传统环境统计数据，新形式的环境数据虽然在时间频度和数据粒度上得到了巨大的提升，但是其提供的环境信息种类较少，且数据质量参差不齐，目前主要适用于相对有限的环境问题研究，但是在未来有着较大的提升潜力。为了进一步拓展和深化环境大数据的应用，则需要从提升数据质量、引入新研究方法以及加强协同合作三个方面来进行改进。
关键词：环境大数据；微观环境数据；监测数据；卫星数据；异构数据
Abstract：Conducting quantitative researches by using high quality environmental big data in social science fields is one of the key aspects in enhancing China's environmental governance capacity. This paper defines the environmental big data system based on 5V model of big data, and establishes an analytical framework for analyzing the application of environmental big data. The analyses consist of reviewing researches in social science which involve five different environmental data types such as macro-level environmental statistics, micro-level environmental data, environmental monitoring data, satellite data and miscellaneous data. This paper finds each type of environmental data has its own advantages and disadvantages. When compared to the traditional statistical data, although environment data in new forms typically have higher time and spatial resolutions, they provide less information on pollution types and typically have low data qualities. As a result, the latter ones are currently applied to study some specific environmental problems, but have great potentials in the future. In order to expand and deepen the applications of environmental big data, more efforts should be made in three aspects such as improving data qualities, adopting new analytical techniques and enhancing collaborations.
Keywords: Environmental Big Data; Micro-level Data; Monitoring Data; Satellite Data; Miscellaneous Data
基金项目：国家重点研发计划资助课题“应对气候变化科学数据与知识集成共享平台建设”（2018YFC1509007）、国家自然科学基金青年科学基金项目“碳排放峰值约束下的中国绿色电力转型研究——基于电力大数据与中国多区域CGE 模型”（71703027）、国家杰出青年科学基金项目“能源环境经济与政策分析”（71925010）、国家社会科学基金重大项目“基于大数据的宏观经济现时预测理论与方法研究”（15ZDB148）
