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Economic valuation
Central to the discussion on air quality degradation has been the effect it has on individuals and to the environment and also the costs it may impose on both health care and air clean-up plans (Leger, 2001). Given that air quality improvement is an environmental public good, economic valuation methods have thus been considered as the most appropriate tools for attaching monetary values for changes in air quality (Saelensminde, 1999; Wardman and Bristow, 2004; Wang and Whittington, 2005; Wang and Zang, 2008). Notably, attaching monetary values to air quality improvements is generally considered a difficult undertaking as air quality is unpriced and therefore no established markets for its trading (Hanemann, 1994; Diena et al., 1998). Therefore, economic valuation is the name given to the process of assigning monetary values to non market goods and servives such as air quality, pollination services and biological diversity. Subsequently, environmental economists are able to avail vital information for use in policy formulation and in the management of such non market goods (Freeman, 1993; Hanemann, 1994; Carson, 2000).
There are two methods that environmental economists have used in the valuation of non market goods (Mitchell and Carson, 1989; Bateman et al., 2002; Mendelsohn and Olmstead, 2009), namely: the revealed methods and the stated preference methods. The revealed preference methods employ surrogate markets to find correlations between the real market behaviour and the non market good in question. They rely on market observations to capture the value of environmental goods that are not themselves traded in any market but are in a way connected with other marketed goods. Revealed preference methods use regression techniques to estimate the underlying values associated with the behavioural decisions. Most commonly used revealed preference techniques include the hedonic pricing, travel cost, damage function and the quality adjusted life year. Conversely, revealed preference methods have a serious methodological problem that they only capture the use values of the valuation good in question. Since these methods fail to isolate the non-use elements of value, stated preference approaches are often employed.
In stated preference approaches, surveys or experimental interactions with respondents are used to construct hypothetical markets for the good under valuation. The hypothetical markets are, in this case, used to induce individuals to reveal either their willingness to pay (WTP) for the provision of a non market good that increases their utility or their willingness to accept (WTA) compensation for the provision of a non market good that decreases their utility. The most common forms of stated preference techniques are the DCEs and the CV method. As use and non use elements of value are both captured in these techniques, they are more preferred in the economic valuation of the non market goods than the revealed preference methods.
CHAPTER ONE General introduction
1.0 Introduction
1.1 Economic valuation
1.2 Contingent valuation
1.3 Respondent uncertainty in contingent valuation .
1.4 Discrete choice experiments .
1.5 Motorized emissions
1.6 Statement of the problem .
1.7 General objective of the study .
1.8 Specific objectives of the study
1.9 Hypotheses of the study
1.10 Approaches and methods of the study .
1.11 Outline of the study .
CHAPTER TWO Stated preferences for improved air quality management in Nairobi, Kenya
Abstract .
2.1 Introduction
2.2 Theoretical framework
2.3 Survey design
2.4 Empirical results and discussion
2.5 Conclusions and recommendations
CHAPTER THREE Comparing welfare estimates across stated preference and uncertainty elicitation formats for air quality improvements in Nairobi, Kenya
3.1 Introduction
3.2 Methodological framework .
3.3 Study area and data collection
3.4 Results
3.5 Conclusions and recommendations
CHAPTER FOUR Scope effects of respondent uncertainty in contingent valuation: Evidence from motorized emission reductions in the city of Nairobi, Kenya
4.1 Introductio
4.2 Respondent uncertainty and scope sensitivity in contingent valuation
4.3 Motorized emissions in the City of Nairobi .
4.4 Survey design
4.5 Findings and discussion
CHAPTER FIVE tated preferences for motorized emission reduction attributes in Kenya: A discrete choice experiment:
CHAPTER SIX Summary, Conclusions and Recommendations .
