Leveraging the Google Cloud to Estimate Individual Level CO2 Emissions Linked to the School Commute

利用 Google Cloud 估算与学校通勤相关的个人二氧化碳排放量

基本信息

批准号：
ES/K007459/1
负责人：
Alex Singleton
金额：
$ 12.18万
依托单位：
University of Liverpool
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2013
资助国家：
英国
起止时间：
2013 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FK007459%2F1
关键词：
Leveraging Google Cloud Estimate Individual

项目摘要

Internationally, the rates of active transport (e.g. cycling or walking) to school are in decline and the corollary switch to less sustainable modes of travel are linked with negative effects on the environment in terms of increased emissions, increasing traffic congestion around schools and negative health impacts related to lower physical activity levels or pollutant exposure. In a UK context, schools account for 15% of total public sector emissions (DCFS, 2010), which in England is estimated to be the equivalent of around 9.4 million tonnes of CO2 per year (SDC, 2006). 7% (658k tonnes) of this total is associated with the pupil-school commute, and as such, there are significant environmental benefits of pupils adopting more sustainable travel behaviours.This research project creates a national coverage and geographically sensitive model of CO2 emissions linked with the school commute. This involves the integration of a variety of public sector "big data", including the origin destination and mode choices for around 7.5 million pupils, and small area estimates of the emission characteristics of cars registered within very small geographic areas. These data are integrated to create a geographically sensitive estimate measure of an individual pupils contribution of CO2 related to their journey. The computational burden of processing such large data, and especially in estimating routes to school at a transport network level (road, rail etc) are great. The Google cloud environment is utilised in this research to reduce this computational burden.Given the spatial diversity of population characteristics and circumstance, alongside differences in local infrastructure and policy; a 'one size fits all' approach to tacking the issue of emissions linked to the school commute is unlikely to be as fruitful as interventions tailored to local context. With the increasing availability of cloud computing in an era of public sector "big data", localised and geographically intelligent modelling approaches are increasingly accessible to the social sciences. However, technical challenges aside, there are also critical ethical concerns that need to be addressed related to data disclosure and privacy. As such this project establishes both technical procedures and also makes recommendations about the ethical use of cloud technology within the context sensitive individual level data.For the first time, the ambitious spatial modelling techniques presented in this research integrate geographically localised input parameters and control for geographical context in the calibration of emissions linked to the school commute, enabling outputs to be explored down to the level of an individual. This research will map the geography of mode choice and emissions, also measure how influences on these patterns vary spatially.

在国际上，积极运输的速度（例如骑自行车或步行）正在下降，而推论转向较低的可持续性旅行模式与对环境的负面影响有关，这在增加的排放量增加，学校周围的交通量增加以及与身体活动较低或污染物暴露水平下的负面影响有关。在英国的情况下，学校占公共部门总排放量的15％（DCFS，2010年），估计在英格兰，相当于每年约940万吨二氧化碳（SDC，2006年）。该总数中有7％（65.8万吨）与学生学校通勤有关，因此，学生采用更可持续的旅行行为有很大的环境利益。这项研究项目创建了与学校通勤联系的国家覆盖范围和二氧化碳排放模型的国家覆盖范围和地理敏感模型。这涉及整合各种公共部门的“大数据”，包括约750万学生的原始目的地和模式选择，以及对非常小的地理区域内注册的汽车排放特性的小面积估算。这些数据被整合在一起以创建对二氧化碳与旅程相关的各个学生贡献的地理敏感估计量度。处理如此大的数据的计算负担，尤其是在运输网络级别（道路，铁路等）估算上学路线时。在这项研究中利用了Google云环境来减轻这种计算负担。赋予人口特征和情况的空间多样性，以及本地基础设施和政策的差异；解决与学校通勤相关的排放问题的“一种适合所有尺寸”的方法不太可能像针对当地环境量身定制的干预措施富有成果。随着公共部门“大数据”时代云计算的可用性的增加，社会科学越来越容易获得本地化和地理上智能的建模方法。但是，除了技术挑战之外，还需要解决与数据披露和隐私有关的关键道德问题。这样，该项目就建立了技术程序，也提出了有关在上下文敏感的个人级别数据中对云技术的道德使用的建议。首次，这项研究中提出的雄心勃勃的空间建模技术将与学校通勤的校准相关的校准的地理上下文中的地理位置上的地理局部输入参数和控制级别的级别的成果，从而使成果降低了一个级别的级别，从而使降低了一个级别的水平。这项研究将绘制模式选择和排放的地理位置，还衡量对这些模式的影响如何在空间上变化。