Li, Qian (2016) Coal mining and human wellbeing: a case-study in Shanxi, China. PhD thesis, James Cook University.

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Abstract

Coal has been an essential source of energy that has fuelled economic growth and development throughout modern history. Its use has delivered astonishing developments in human living standards and wellbeing. Coal is the most affordable and widely available source of energy. It is particularly essential for developing countries, such as China, as it helps deliver economical and stable electricity to underpin economic growth and poverty alleviation. That said, environmental problems associated with coal, such as human-enhanced greenhouse effects generated when coal is burned are of concern.

For host (mining) communities, coal mining seems to also be a two-edged sword. Coal resource development, for example, can bring numerous jobs, can increase household incomes and can generate revenue for governments, which is significant for regional development. But numerous negative impacts have been documented; coal mining can adversely affect natural capital (environment), human capital, social capital, institutional capital and the economy (e.g. through inflation). These 'capitals' all contribute to human wellbeing; so the impacts of mining on human wellbeing are complex and multifaceted. Some impacts, such as mining revenues, are tangible, likely positive, and can be easily observed and quantified from market transactions. In contrast, other impacts, on the environment, culture, and society, are often intangible are thus much less easily quantified or observed.

Existing mining impact assessment processes, such as environmental/social/economic impact assessment, and assessments of eco-compensation for mining, struggle to quantify the numerous non-market impacts of mining; they thus struggle to provide data to defensibly assess trade-offs between the benefits and costs of mining that takes account of all tangible and intangible impacts on host communities. This difficulty results, partly, from the fact that currently available methods (mostly traditional economic nonmarket valuation techniques) for assessing intangible impacts are on occasion inadequate – particularly when assessing numerous simultaneous and inter-related impacts. The life satisfaction (LS) approach, shows advantages over other non-market valuation methods, and has been successfully used to assess a range of non-market goods, but it has not yet been used to measure the impacts of mining.

This study aims to assess the impacts of coal mining on LS – associated with the more general term 'human wellbeing', which includes objective and subjectice dimensions. Specifically, it uses insights from the wellbeing literature and from the LS approach to quantify and compare multiple impacts of coal mining; and it assesses the trade-off between benefits (mostly monetary – e.g. through income) and costs (numerous, often intangible) of coal mining on host communities. In doing so, it offers insights into how coal mining affects a range of wellbeing factors or life domains. It also provides insights about the net impacts of mining, about who benefits most/least from coal mining and about how one might target policy to compensate those who do not perceive a net benefit. It thus identifies policy priorities to help mitigate the negative and enhance the positive impacts of coal mining on human wellbeing.

This study focusses on 3 major research questions, each of which is directly linked to an identified research gap:

1. How does coal mining affect people's subjective perceptions of different wellbeing factors? This includes the importance attached to each wellbeing factor, satisfaction with each factor, and people's perception about the impacts of coal mining on these factors.

2. Does information about the impacts of coal mining derived from subjective assessments of wellbeing convey the same message about the impacts of coal mining as 'objective' measures of wellbeing?

3. Is it possible to quantify the net impacts of coal mining (on broad 'domains' of life and on the overall wellbeing of host communities) and to determine how much should, in principle, be paid 'in compensation' to those who are, overall, impacted negatively?

Shanxi province, the most important coal producer in China, was selected as the casestudy region. Within Shanxi, 5 types of case-study areas, including rural areas with coal mining (Rural With), rural areas close to coal mining (Rural Close), urban areas close to coal mining (Urban Close), urban areas far from coal mining (Urban Far) and rural areas far from coal mining (Rural Far) were selected for focus – providing insights from a crosssection of people with differential exposure to coal mining.

A comprehensive set of data on wellbeing was not available in the case-study areas. Therefore, questionnaire surveys were used to collect data on 29 different factors known (from the literature) to affect wellbeing. Residents were asked to indicate how important they thought each factor was to their overall wellbeing, how satisfied they were with each, and how they thought coal mining was (or could) impact those factors. They were also asked about their satisfaction with life overall, and to provide some basic sociodemographic information. 'Objective' indicators of air quality were collected at each location. A total of 542 valid questionnaires were collected.

Responses to questions about satisfaction (with factors), importance (of factors) and perceptions (of the impacts of coal mining on those factors) were examined separately, and 2 indices, one combining satisfaction and importance (Index of Dis-Satisfaction – IDS), and the other combining satisfaction and perceptions of impacts (Index of Dis- Satisfaction and Negative Impacts – IDSNI) were constructed. Indicators related to health and relationship were deemed – by the entire sample, and by each sub-sample – to be the most important factors; these were also the factors with which people from all the study areas were most satisfied. People living in coal mining areas were most dissatisfied with factors relating to environmental quality (air quality and water safety) and the economy (real estate prices and inflation), while people in non-coal mining areas were most dissatisfied with factors only relating to the economy. People from all the study regions expressed most concern about the impacts of mining on the factors relating to the environment and health. Both IDS and IDSNI indicate that in coal mining areas both environmental issues and economic issues were of high priority, and environmental issues were paramount. IDS indicates that in non-coal mining areas, economic issues and social issues (the quality of government, education and property safety) were of most concern.

Available objective wellbeing indicators were regressed against subjective wellbeing indicators, controlling for sociodemographic factors (such as age, family size and gender). The relationships between some subjective and objective indicators were statistically significant (e.g. higher levels of family income were associated with higher satisfaction with family income, and higher levels of PM10 were associated with lower levels of satisfaction with air quality), but some were not (the objective indicators of housing conditions did not always predict satisfaction with housing). These relationships were always mediated by sociodemographic variables indicating that subjective and objective indicators are not 'perfect' substitutes for each other. These results indicate that it is both possible and necessary to use subjective indicators of wellbeing in addition to traditionally used objective indicators to inform public policy in coal mining regions. Moreover, this study demonstrates approaches that can be used to explore the relationship between objective and subjective indicators which could be used to inform future policy makers about when it is most/least appropriate to use only objective or only subjective indicators.

Using principal component analysis, the 29 wellbeing factors were collapsed into 6 life domains: human capital, economy, social capital, institutional capital, living conditions and natural environment. Factor scores relating to each domain were retained for use as dependent variables in regression models. The sample was divided in two (rural and urban), and variables denoting proximity to coal mining and sociodemographic factors were included as regressors so that the impacts of mining on satisfaction with life domains and on LS could be assessed while controlling for other potentially confounding factors. Factor scores from each domain and measures of global life satisfaction were each regressed against numerous factors known to influence subjective assessments of wellbeing.

Urban residents were found to be relatively insensitive to the impacts of coal mining. Although people living in places with or close to coal mines in rural areas ("Rural With" and "Rural Close") had statistically significant lower levels of satisfaction across multiple life domains (the natural environment, the economy and society) than those living further away from mines, they were, however, more satisfied with their living conditions. After controlling for confounding sociodemographic factors, the analysis revealed that rural residents living in areas adjacent to coal mines had experienced lower levels of satisfaction with life overall than those living more than 10km away from mines. It was possible to use coefficients from the LS model to infer that a similar 'loss' of life satisfaction would be 'engineered' by reducing family income by 20,000 Yuan per annum; although that estimate should be treated as illustrative only since the model did not control for all potential statistical problems. The 'loss' in global LS was greatest for those who lived in rural areas adjacent to mines whose family were dependent upon non-coal mining industries for income: their LS was significantly lower than the LS of people whose families were dependent upon coal mining (even after controlling for income). This 'loss' of life satisfaction could be equivalently engineered by a reduction in family income of 47,000 Yuan per annum. Here too, the estimate is illustrative only.

These results suggest that the net impacts of coal mining for those who live in rural areas adjacent to mines are negative and that to mitigate these negative impacts, addressing environmental issues is a priority. Relocating people who live in coal mining areas, delivering more mining jobs to local residents and/or providing monetary compensation could also directly improve their life satisfaction (wellbeing). Delivering more jobs to local people is likely to be a less costly pathway to redistribute the benefits and costs of coal mining. It will not only improve the local economy in terms of improving local residents' incomes, reducing income disparity and achieving fairness but it could also help prevent the degradation of social capital that can occur when numerous nonresidential workers with limited meaningful attachment to local communities, are brought in from other regions.

This investigation furthers the understanding of coal mining or mining impacts on the wellbeing of host communities. It provides some useful information to address the negative impacts of coal mining or mining and to improve local wellbeing, and offers a new tool for mining impact assessment and compensation. Importantly, this approach to assessing net impacts and the trade-offs associated with the coal mining industry could potentially be used to assess the net impacts and trade-offs of a wide range of other industries and/or policy and development choices (e.g. the tourism industry, construction of dams), worldwide.

Item ID:	47252
Item Type:	Thesis (PhD)
Keywords:	China, coal mining, community health, health, mining, negative impacts, satisfaction, Shanxi, wellbeing
Date Deposited:	15 Feb 2017 21:58
FoR Codes:	05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050205 Environmental Management @ 50% 14 ECONOMICS > 1402 Applied Economics > 140205 Environment and Resource Economics @ 50%
SEO Codes:	96 ENVIRONMENT > 9606 Environmental and Natural Resource Evaluation > 960699 Environmental and Natural Resource Evaluation not elsewhere classified @ 60% 96 ENVIRONMENT > 9607 Environmental Policy, Legislation and Standards > 960799 Environmental Policy, Legislation and Standards not elsewhere classified @ 40%
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