Western Wyoming Natural Gas Production Overview

In Western Wyoming Natural Gas Production Produces A Significant A

In western Wyoming, natural gas production contributes significantly to ozone emissions, especially during cold winter days when temperature inversions trap ozone near the ground. The elevated ozone levels pose health risks to residents. This paper examines the negative externality associated with natural gas production by modeling it with supply and demand diagrams, analyzing policy options to correct market failure, and discussing methods to evaluate environmental goods' value.

Firstly, to understand the externality, we model the natural gas market using standard supply and demand curves. The competitive equilibrium occurs where the private supply curve intersects the demand curve, neglecting pollution costs. In this case, natural gas producers do not bear the external costs associated with ozone pollution. The socially optimal equilibrium adjusts the supply curve upward to reflect the marginal social cost (MSC), which includes both private production costs and the external health costs caused by ozone emissions. This results in a lower equilibrium quantity and higher price compared to the competitive equilibrium, aligning resource allocation with societal welfare.

Graphically, the initial supply curve (S) intersects demand (D) at the market equilibrium (E_c), with price P_c and quantity Q_c. The MSC curve, which accounts for external costs, lies above the private supply curve, and its intersection with demand determines the socially optimal equilibrium (E_s), with higher price P_s and lower quantity Q_s. This shift reflects the need to internalize externalities and reduce ozone emissions to socially optimal levels.

Secondly, moving from the competitive to the socially optimal equilibrium improves economic welfare. The gain in welfare can be depicted as the reduction in deadweight loss represented by the area between the private and social supply curves from Q_s to Q_c. Quantitatively, the welfare gain equals the difference in total surplus, including the benefits of natural gas consumption minus the external costs internalized through policy measures.

Lastly, to incentivize natural gas companies to produce at the socially optimal level, the Wyoming state government could implement policy instruments such as pollution taxes, cap-and-trade systems, or subsidies for cleaner production technologies. A pollution tax set at the marginal external cost per unit of gas produced effectively shifts the private supply curve upward, aligning it with the MSC. Mathematically, if the external cost per unit is denoted as \\( e \\), imposing a tax \\( t = e \\) per unit of gas ensures that producers internalize external costs, leading to the socially optimal production level:

\\[ \\text{Optimal policy:} \\quad \\text{Tax } t = e \\quad \\Rightarrow \\quad S_{private} + t \\rightarrow S_{social} \\]

This policy aligns private incentives with social welfare by making the external cost explicit to producers, encouraging them to reduce emissions and produce the efficient quantity of natural gas.

Estimating Environmental Good Values with Contingent Valuation

An example environmental good suitable for contingent valuation is a national park, such as Yellowstone. The contingent valuation method (CVM) involves surveying individuals to ascertain their willingness to pay (WTP) for preserving the park or improving its features. Through carefully designed questionnaires, respondents state an amount they would be willing to pay in a hypothetical scenario, providing a measure of the environmental good’s monetary value.

Implementing CVM involves several steps: first, the researcher clearly describes the environmental good and the hypothetical scenario to respondents. Second, they elicit WTP through open-ended, dichotomous choice, or payment card formats. Third, the collected data are analyzed statistically to estimate the aggregate value of the good across the population. This approach captures the non-market benefits and individual preferences that are otherwise difficult to quantify.

However, issues can arise, such as hypothetical bias—respondents exaggerating or underreporting WTP—strategic bias—they might misrepresent preferences to influence policy outcomes—and information bias, where respondents lack sufficient details to make informed decisions. Addressing these issues involves ensuring well-designed surveys, implementing follow-up questions to check consistency, and providing comprehensive information about the environmental good. Hanemann (1994) emphasizes calibration and validation techniques to improve accuracy and mitigate biases in CVM.

Another valuation method is the Travel Cost Method (TCM), which estimates value based on individuals' transportation expenses to visit the environmental site. Unlike CVM, which relies on hypothetical scenarios, TCM uses actual observed behaviors, making it less subject to some biases but also limited to sites visited by the population.

Public Goods: Definition, Market Failure, and Policy Solutions

A public good is defined as a good that is non-rivalrous and non-excludable. An example of a public good is national defense, which benefits all citizens regardless of individual contribution and cannot exclude anyone from its benefits. These characteristics lead to market failure because private markets underprovide such goods, as individuals have little incentive to pay for benefits they can freely enjoy—a phenomenon known as free-riding.

The market failure results in under-provision of public goods, often leading to less than the socially optimal level of their availability. Graphically, the demand curve for a public good is derived from individual marginal benefit curves summed horizontally. The optimal provision occurs where the sum of marginal benefits equals the marginal cost of provision, but free-riding results in a provision level below this efficient point. Consequently, society bears a welfare loss due to insufficient supply.

Estimating the value of a public good, such as national defense, can be done through contingent valuation or cost-based approaches like benefit-cost analysis. Policymakers can adopt mechanisms like direct taxation or earmarked taxes to finance public goods, ensuring the provision aligns with the socially optimal level. For example, funding through taxes levied proportionally on citizens can finance the desired level of defense, balancing costs with collective benefits.

Expected Utility, Risk, and Investment in Land of Deer Lick

The residents of Deer Lick face a decision on whether to allow the StoneKey Oil Pipeline, with potential benefits and risks quantified. The expected utility calculation involves considering the probability of a leak and the costs associated with it, as well as the realized gains if no leak occurs.

Using the utility function \\( U(wealth) = \\ln(wealth) \\), the expected utility (EU) of allowing the pipeline is:

\\[ EU = 0.9 \\times \\ln (36,000 + 4,000) + 0.1 \\times \\ln (36,000 + 4,000 - 20,000) \\]

Evaluating the terms, the first component corresponds to the scenario with no leak, and the second to the scenario with a leak, weighted by their probabilities. Calculating these gives an expected utility, which can be compared to the utility of not allowing the pipeline (which would be \\( \\ln(36,000) \\)). If the expected utility exceeds the utility of no pipeline, residents might favor allowing it, considering the expected benefits outweigh the risks.

The certainty equivalent (CE) is the guaranteed wealth level providing the same utility as the expected utility. It is found by solving:

\\[ \\ln(CE) = EU \\]

Thus, \\( CE = e^{EU} \\).

The risk premium (RP) is the difference between the expected value of wealth and the certainty equivalent:

\\[ RP = \\text{Expected Wealth} - CE \\]

This measures how much wealth residents are willing to give up to avoid the risk.

To find the probability that would make residents indifferent to the pipeline, set the utility with the leak risk equal to the utility without the pipeline, solving for the probability in the leak scenario. The threshold probability indicates the maximum acceptable risk level for residents to vote in favor of the pipeline.

In assessing investments in self-protection and self-insurance, the optimality conditions involve equating the marginal benefits of reducing expected damages with marginal costs. For self-protection, the condition is:

\\[ \\frac{d \\pi(p)}{dp} = \\frac{\\lambda}{ \\text{cost of investment} } \\]

where \\( \\pi(p) \\) is the probability of contamination, decreasing with increased investment, and \\( \\( \\lambda \\) \\) is the marginal benefit of reducing contamination risk. For self-insurance, the condition involves balancing the marginal reduction in damages against the costs of insurance investments, ensuring joint investments minimize total expected costs and damages.

References

• Hanemann, W. M. (1994). Valuing the environment through contingent valuation. Journal of Economic Perspectives, 8(4), 19-43.
• Arrow, K. J., et al. (1993). Report of the NOAA Panel on Contingent Valuation. Federal Register, 58(10), 4601-4614.
• Freeman, A. M. (2003). The Measurement of Environmental and Resource Values: Theory and Methods. Resources for the Future.
• Cropper, M. L., & Oates, W. E. (1992). Environmental economics: A survey. Journal of Economic Literature, 30(2), 675-740.
• Mitchell, R. C., & Carson, R. T. (1989). Using Surveys to Value Public Goods: The Contingent Valuation Method. Resources for the Future.
• Bishop, R. C., & Heberl, H. R. (1979). The contingent valuation method: Political and ethical considerations. Land Economics, 55(2), 188-194.
• Jones-Lee, M. (1995). The Economics of Safety and Security. Oxford University Press.
• Dermot, L., & Grafton, R. Q. (2005). The use of contingent valuation in environmental policy. In Handbook of Environmental Economics (pp. 281-300). Elsevier.
• Thomson, H., et al. (2011). The effectiveness of environmental valuation methods: An overview of empirical studies. Environmental Science & Policy, 14(1), 43-55.
• Louviere, J. J., et al. (2000). Conjoint analysis research in the commercial Housing Market. Journal of Housing Economics, 9(1), 1-25.