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GCRF: Dynamics of Health & Environmental Inequalities in Hebei Province, China

GCRF：健康动态

基本信息

批准号：
ES/P003567/1
负责人：
Gwilym Pryce
金额：
$ 20.54万
依托单位：
University of Sheffield
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FP003567%2F1
关键词：
GCRF Dynamics Health Environmental Inequalities

项目摘要

Hebei province has some of the most polluted cities in the world. A key priority for the 74 million people who live there is to find ways of reducing air pollution and to address the associated social and health inequalities. It is crucial that the true cost of pollution is included in economic planning. The first step is to develop the necessary data infrastructure needed to provide the government a clear picture of how pollution, health and social deprivation are related and how they have changed over time. Our proposal offers an outstanding opportunity to make a step change in the quality and richness of evidence available to decision makers in Hebei Province. The potential for impact is exceptional due to: (a) the scale and importance of the problem we are seeking to address, and (b) access to senior policy makers made possible through collaboration with the Hebei Institute of Statistical Sciences (HISS). HISS plays a vital role in generating data and developing decision support tools for the Hebei government. Over the past year we have been forging a collaboration agreement with HISS. This is fortuitous in that it sets out a long-term vision for data access and research that will inform industrial restructuring and social policy in Hebei Province during a crucial phase in its development. The imperative for change has become all the more pressing since 2014 when the Premier of China declared "war" on air pollution. Because of Hebei's proximity to Beijing (Hebei essentially surrounds the capital), it has become a target for drastic measures to reduce air pollution. There is an urgent imperative, therefore, to provide an evidence base that will inform these decisions to make them as efficient and socially just as possible, maximising the benefits for the socially vulnerable. So, our proposed project is both timely, and highly relevant to the development challenges facing Hebei. The proposed programme of research will provide the Hebei government with robust estimates of the spatial dynamics of poverty, pollution and health. Ours will be the first attempt to construct deprivation indices for Hebei province and the first estimates anywhere in China of how the geography of deprivation has changed between the 2000 and 2010 Censuses. We will also provide a range of nuanced measures that capture how the spatial structure of poverty and segregation has evolved over time, revealing, for example, whether poverty has become less centralised in Hebei's key cities (an important trend in many Western conurbations but, as yet, an unexplored issue in China). We also want to help the Hebei government understand how pollution, health and deprivation are related by developing robust statistical models. This is vital if the true costs of pollution are to be included in economic and social policy decisions in a rational and systematic way. These research plans are made possible by the unprecedented opportunities for data access afforded through the collaboration between SMI and HISS. Together with the pressing policy issues noted above the newly available data also opens up an opportunity for world-leading methodological innovation. Our research team has pioneered statistical techniques for incorporating the effects of both spatial proximity and hierarchical structuring (e.g. individuals nested within neighbourhoods) in geographical data. The health variable we plan to model has particular features (bounded between 0 and 100). This motivates a novel extension of our methodology that will yield more reliable models of the relationship between health and pollution exposure. Our project will also establish a platform for exciting ambitious research opportunities in future, paving the way for further data linkage and potentially leading to a world-class longitudinal dataset that links individuals from multiple Censuses over time.

河北省有一些世界上污染最严重的城市。对于居住在那里的7400万人来说，一个关键的优先事项是找到减少空气污染的方法，并解决相关的社会和健康不平等问题。将污染的真实成本纳入经济规划至关重要。第一步是开发必要的数据基础设施，使政府能够清楚地了解污染、健康和社会贫困之间的关系，以及它们如何随着时间的推移而变化。我们的建议提供了一个绝佳的机会，使河北省决策者可以获得的证据的质量和丰富性发生重大变化。由于以下原因，产生影响的可能性非常大：（a）我们寻求解决的问题的规模和重要性，以及（B）通过与河北省统计科学研究所的合作，能够接触到高级决策者。HISS在为河北省政府生成数据和开发决策支持工具方面发挥着重要作用。在过去的一年里，我们一直在与HISS达成合作协议。这是一个偶然的机会，因为它为数据访问和研究提出了一个长期愿景，将在河北省发展的关键阶段为产业结构调整和社会政策提供信息。自2014年中国总理向空气污染宣战以来，改变的必要性变得更加紧迫。由于河北靠近北京（河北基本上环绕着首都），它已成为采取严厉措施减少空气污染的目标。因此，迫切需要提供一个证据基础，为这些决定提供信息，使其尽可能有效和社会公正，最大限度地为社会弱势群体带来利益。因此，我们提出的项目既及时，又与河北面临的发展挑战高度相关。拟议的研究计划将为河北省政府提供有关贫困、污染和健康的空间动态的可靠估计。我们将首次尝试构建河北省的贫困指数，也是中国第一次估计2000年至2010年人口普查期间贫困地理的变化。我们还将提供一系列细致入微的衡量标准，以捕捉贫困和隔离的空间结构如何随着时间的推移而演变，例如，揭示河北省主要城市的贫困是否变得不那么集中（这是许多西方大都市的一个重要趋势，但在中国尚未探索的问题）。我们还希望通过开发强大的统计模型，帮助河北省政府了解污染、健康和贫困之间的关系。如果要以合理和系统的方式将污染的真正代价纳入经济和社会政策决定，这一点至关重要。通过SMI和HISS之间的合作提供前所未有的数据访问机会，这些研究计划成为可能。与上述紧迫的政策问题一起，新获得的数据也为世界领先的方法创新提供了机会。我们的研究团队开创了统计技术，将空间邻近性和层次结构（例如，嵌套在街区内的个人）的影响纳入地理数据。我们计划建模的健康变量具有特定的特征（介于0和100之间）。这促使我们的方法，将产生更可靠的健康和污染暴露之间的关系模型的一种新的扩展。我们的项目还将为未来令人兴奋的雄心勃勃的研究机会建立一个平台，为进一步的数据链接铺平道路，并可能导致一个世界级的纵向数据集，随着时间的推移将多个人口普查中的个人联系起来。