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Population and Environment
Erschienen in: Population and Environment 4/2015

01.06.2015 | Original Paper

Exploring relationship between social inequality and adaptations to climate change: evidence from urban household surveys in the Yangtze River delta, China

verfasst von: Yan Tan, Xuchun Liu, Graeme Hugo

Erschienen in: Population and Environment | Ausgabe 4/2015

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Abstract

This research enhances our understanding of the complex relationship between climate change, social inequality, and adaption, in urban areas. It is novel, being the first research in this area to be based on a conceptual econometric framework within which multiple stages are explicitly developed, and for which empirical evidence is gathered. We use this approach to examine the role of material, social status, and power inequality in influencing spontaneous adaptation choices in urban settings of China’s Yangtze River delta. This framework differentiates two vital stages in adaptation decision making at the household level which allows us to examine, first, how social inequality shapes the severity of climate impact and, second, how social inequality interacts with this experience to influence responses to these impacts. We pilot this approach in selected metropolitan areas of Shanghai and Nanjing. Our results show that all dimensions of social inequality are significantly associated with experiences of climate change and adaptation choice. Application of our conceptual framework provides policymakers and planners with a new and useful tool that can be used to formulate better policy measures that either enable the disadvantaged to adapt in situ or provide these groups with real opportunities and capacities to migrate.
Vorheriger Artikel Conservation implications of the diffusion of Christian religious ideals in rural Africa
Nächster Artikel The influence of environmental factors on childhood fever during the rainy season in an African city: a multilevel approach in Dakar, Senegal

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Fußnoten
1
In situ adaptation can take the following forms: adopting new crop(s) to suit the altered growing conditions, improving water management, adopting new agricultural techniques, returning farmland to forestry in rural areas, and insuring assets against climate change-related damages, changing personal lifestyle and habits, improving building structure, and purchasing climate change protection devices in urban areas.
 
2
The YRD encompasses two provinces (Jiangsu, Zhejiang) and one municipality (Shanghai). The region supports 8.1 % of China’s population (1.34 billion) and contains the nation’s largest urban cluster—one that comprises 16 major cities. According to China’s New National Urbanisation Plan (2014–2020), which places urban policy at the heart of Chinese decision-making, the YRD, together with another two largest urban clusters that involve Beijing–Tianjin–Hebei and Pearl River Delta in east coast of the country, will continue to gain momentum in the process of urbanization and will play pivotal roles in the new era of urbanization (State Council 2014).
 
3
A residents’ committee is a grassroots organization of self-governance by residents in cities on the basis of residence. It is essential in direct grass-roots democracy, crucial pillars of grass-roots agencies of state power, and links between the government and residents. It plays major roles in: handling family planning, social service, public affairs, and public welfare services of the residents in the local residential area; reporting local residents’ opinions and demands to the local government; and publicizing the principles, policies, and statutes of the Communist Party and the government.
 
4
We used publicly available sources to construct a household roster. Such information was obtained from the administrative sheets maintained by local residents’ committees, which recorded the names, basic demographic characteristics (e.g., age, sex), occupation, and residential address of people who have registered their household status with the local residents’ committees as either permanent urban residents or temporary residents (the transient migrant or “floating” population). Using these frames, we systematically drew random samples of households in each of the 50 selected local residents’ communities. The sample was proportionately distributed to each local community to be surveyed in terms of population size.
 
5
The household head was asked to respond to the questionnaire. Where the household head was unavailable, his or her spouse, or an informant who was most knowledgeable about the household situations, answered the survey questions.
 
6
There are varied push and pull factors for migration, including crop failure, displacement, and resettlement due to urbanization or other infrastructure projects. To clarify for our research, we specifically asked respondents if climate change was the primary reason, or not, for their migration.
 
7
The power inequality variable collected information for the 5-year period from 2007 to 2011. It was also used as a proxy measurement for 2007 in the first-stage model. We argue that power inequality is almost unlikely to change over the 5-year period and likely to be highly correlated across years.
 
8
The factor of climate change is simply assessed by assessing respondents’ answers to the question: “Did climatic events or climate-induced disasters in the Delta (or origin areas of migrants) occur frequently in the past 5 years (2007–2011), compared to the preceding decades?” Answers to this question were coded as 5 levels at Likert scale ranging from 1 (“never happened”) to 5 (“happened very frequently”). This variable of “frequency of climate-related events since 2007” is only used in the second-stage regression, but not in the first-stage model, as the 5-year period covered means it would not be appropriate to use it to predict the climate change impact in 2007 in the first-stage regression.
 
9
The MProbit model is used to estimate several correlated binary outcomes jointly (Greene 2008: 827).
 
10
The MLogit model is one of the most frequently used regression models in situations of unordered multiple choice dependent variables (Greene 2008: 843–845).
 
11
We only present the results of the MLogit modeling with respect to to the impacts of climate change on the economic situation and living environment of surveyed households, as this model has a slightly better fit in terms of BIC than the modeling of impacts on health conditions and living environment.
 
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Metadaten
Titel
Exploring relationship between social inequality and adaptations to climate change: evidence from urban household surveys in the Yangtze River delta, China
verfasst von
Yan Tan
Xuchun Liu
Graeme Hugo
Publikationsdatum
01.06.2015
Verlag
Springer Netherlands
Erschienen in
Population and Environment / Ausgabe 4/2015
Print ISSN: 0199-0039
Elektronische ISSN: 1573-7810
DOI
https://doi.org/10.1007/s11111-014-0223-2

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