Bioeconomic models are analytical tools that integrate biophysical and economic models. These models allow for analysis of the biological and economic changes caused by human activities. The biophysical and economic components of these models are developed based on historical observations or theoretical relations. Technically these models may have various levels of complexity in terms of equation systems considered in the model, modeling activities, and programming languages. Often, biophysical components of the models include crop or hydrological models. The core economic components of these models are optimization or simulation models established according to neoclassical economic theories. The models are often developed at farm, country, and global scales, and are used in various fields, including agriculture, fisheries, forestry, and environmental sectors. Bioeconomic models are commonly used in research on environmental externalities associated with policy reforms and technological modernization, including climate change impact analysis, and also explore the negative consequences of global warming. A large number of studies and reports on bioeconomic models exist, yet there is a lack of studies describing the multiple uses of these models across different disciplines.
Ruda Zhang, Patrick Wingo, Rodrigo Duran, Kelly Rose, Jennifer Bauer, and Roger Ghanem
Economic assessment in environmental science means measuring and evaluating environmental impacts, adaptation, and vulnerability. Integrated assessment modeling (IAM) is a unifying framework of environmental economics, which attempts to combine key elements of physical, ecological, and socioeconomic systems. The first part of this article reviews the literature on the IAM framework: its components, relations between the components, and examples. For such models to inform environmental decision-making, they must quantify the uncertainties associated with their estimates. Uncertainty characterization in integrated assessment varies by component models: uncertainties associated with mechanistic physical models are often assessed with an ensemble of simulations or Monte Carlo sampling, while uncertainties associated with impact models are evaluated by conjecture or econometric analysis. The second part of this article reviews the literature on uncertainty in integrated assessment, by type and by component. Probabilistic learning on manifolds (PLoM) is a machine learning technique that constructs a joint probability model of all relevant variables, which may be concentrated on a low-dimensional geometric structure. Compared to traditional density estimation methods, PLoM is more efficient especially when the data are generated by a few latent variables. With the manifold-constrained joint probability model learned by PLoM from a small, initial sample, manifold sampling creates new samples for evaluating converged statistics, which helps answer policy-making questions from prediction, to response, and prevention. As a concrete example, this article reviews IAMs of offshore oil spills—which integrate environmental models, transport models, spill scenarios, and exposure metrics—and demonstrates the use of manifold sampling in assessing the risk of drilling in the Gulf of Mexico.
David I. Stern
The environmental Kuznets curve (EKC) is a hypothesized relationship between environmental degradation and GDP per capita. In the early stages of economic growth, pollution emissions and other human impacts on the environment increase, but beyond some level of GDP per capita (which varies for different indicators), the trend reverses, so that at high income levels, economic growth leads to environmental improvement. This implies that environmental impacts or emissions per capita are an inverted U-shaped function of GDP per capita. The EKC has been the dominant approach among economists to modeling ambient pollution concentrations and aggregate emissions since Grossman and Krueger introduced it in 1991 and is even found in introductory economics textbooks. Despite this, the EKC was criticized almost from the start on statistical and policy grounds, and debate continues. While concentrations and also emissions of some local pollutants, such as sulfur dioxide, have clearly declined in developed countries in recent decades, evidence for other pollutants, such as carbon dioxide, is much weaker. Initially, many understood the EKC to imply that environmental problems might be due to a lack of sufficient economic development, rather than the reverse, as was conventionally thought. This alarmed others because a simplistic policy prescription based on this idea, while perhaps addressing some issues like deforestation or local air pollution, could exacerbate environmental problems like climate change. Additionally, many of the econometric studies that supported the EKC were found to be statistically fragile. Some more recent research integrates the EKC with alternative approaches and finds that the relation between environmental impacts and development is subtler than the simple picture painted by the EKC. This research shows that usually, growth in the scale of the economy increases environmental impacts, all else held constant. However, the impact of growth might decline as countries get richer, and richer countries are likely to make more rapid progress in reducing environmental impacts. Finally, there is often convergence among countries, so that countries that have relatively high levels of impacts reduce them more quickly or increase them more slowly, all else held constant.
Water resources represent an essential input to most human activities, but harnessing them requires significant infrastructure. Such water control allows populations to cope with stochastic water availability, preserving uses during droughts while protecting against the ravages of floods. Economic analysis is particularly valuable for helping to guide infrastructure investment choices, and for comparing the relative value of so called hard and soft (noninfrastructure) approaches to water management. The historical evolution of the tools for conducting such economic analysis is considered. Given the multimillennial history of human reliance on water infrastructure, it may be surprising that economic assessments of its value are a relatively recent development. Owing to the need to justify the rapid deployment of major public-sector financing outlays for water infrastructure in the early 20th century, government agencies in the United States—the Army Corps of Engineers and the Bureau of Reclamation—were early pioneers in developing these applications. Their work faced numerous technical challenges, first addressed in the drafting of the cost-benefit norms of the “Green Book.” Subsequent methodological innovation then worked to address a suite of challenges related to nonmarket uses of water, stochastic hydrology, water systems interdependencies, the social opportunity cost of capital, and impacts on secondary markets, as well as endogenous sociocultural feedbacks. The improved methods that have emerged have now been applied extensively around the world, with applications increasingly focused on the Global South where the best infrastructure development opportunities remain today. The dominant tools for carrying out such economic analyses are simulation or optimization hydroeconomic models (HEM), but there are also other options: economy wide water-economy models (WEMs), sociohydrological models (SHMs), spreadsheet-based partial equilibrium cost-benefit models, and others. Each of these has different strengths and weaknesses. Notable innovations are also discussed. For HEMs, these include stochastic, fuzz, and robust optimization, respectively, as well as co-integration with models of other sectors (e.g., energy systems models). Recent cutting-edge work with WEMs and spreadsheet-based CBA models, meanwhile, has focused on linking these tools with spatially resolved HEMs. SHMs have only seen limited application to infrastructure valuation problems but have been useful for illuminating the paradox of flood management infrastructure increasing the incidence and severity of flood damages, and for explaining the co-evolution of water-based development and environmental concerns, which ironically then devalues the original infrastructure. Other notable innovations are apparent in multicriteria decision analysis, and in game-theoretic modeling of noncooperative water institutions. These advances notwithstanding, several issues continue to challenge accurate and helpful economic appraisal of water infrastructure and should be the subject of future investigations in this domain. These include better assessment of environmental and distributional impacts, incorporation of empirically based representations of costs and benefits, and greater attention to the opportunity costs of infrastructure. Existing tools are well evolved from those of a few decades ago, supported by enhancements in scientific understanding and computational power. Yet, they do appear to systematically produce inflated estimations of the net benefits of water infrastructure. Tackling existing shortcomings will require continued interdisciplinary collaboration between economists and scholars from other disciplines, to allow leveraging of new theoretical insights, empirical data analyses, and modeling innovations.
Dennis Guignet and Jonathan Lee
Hedonic pricing methods have become a staple in the environmental economist’s toolkit for conducting nonmarket valuation. The hedonic pricing method (HPM) is a revealed preference approach used to indirectly infer the value buyers and sellers place on characteristics of a differentiated product. Environmental applications of the HPM are typically focused on housing and labor markets, where the characteristics of interest are local environmental commodities and health risks. Despite the fact that there have been thousands of hedonic pricing studies published, applications of the methodology still often grapple with issues of omitted variable bias, measurement error, sample selection, choice of functional form, effect heterogeneity, and the recovery of policy-relevant welfare estimates. Advances in empirical methodologies, increased quality and quantity of data, and efforts to link empirical results to economic theory will surely further the use of the HPM as an important nonmarket valuation tool.
Natural environments represent background settings for most outdoor recreation activities, which are important non-consumptive benefits that people obtain from nature. Recreation has been traditionally considered a non-market service because it is practiced free of charge in public spaces and therefore of secondary relevance for the economy. Although outdoor recreation in natural parks became relevant during the 19th century, the increased popularity of recreation after the Second World War required tools for the assessment of recreational benefits, which were not considered in the evaluation of investments in recreational facilities, and increasing spending for recreational equipment captured the attention of outdoor recreation as an economic sector. In the 1990s, it was observed that many recreational activities were commercialized and started being considered equally important to tourism as a means to boost the economy of local communities. The expansion of outdoor recreation is reflected in a growing interest in the economic aspects, including cost–benefit calculations of the investments in recreational facilities and research on appropriate methods to evaluate the non-market benefits of recreation. The first economic technique used for valuing recreation was the travel cost method that consisted in the assessment of a demand curve, where the demanded quantity is the number of trips to a specific site and the cost is the unit cost of travel to the destination. After this first intuition, the number of contributions on recreation valuation exponentially grew, and new methods were proposed, including methods based on stated preferences for recreation that can be used when travel cost data that reveal consumers’ behavior are not available. A regular assessment of recreational benefits has several advantages for public policy, including the evaluation of investments and information on visitor profile and preferences, income, and price elasticity, which are essential to understand the market of outdoor recreation and propose effective strategies and recreation-oriented management. The increasing environmental pressure associated with participation in outdoor recreation required effective conservation activities, which in turn posed limitations to economic activities of local communities who live in contact with natural resources. Therefore, a balance between environmental, social, and economic interests is essential for recreational destination to avail of benefits without conflicts among stakeholders.