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.
Ruda Zhang, Patrick Wingo, Rodrigo Duran, Kelly Rose, Jennifer Bauer, and Roger Ghanem
Vincent Moreau and Guillaume Massard
The concept of metabolism takes root in biology and ecology as a systematic way to account for material flows in organisms and ecosystems. Early applications of the concept attempted to quantify the amount of water and food the human body processes to live and sustain itself. Similarly, ecologists have long studied the metabolism of critical substances and nutrients in ecological succession towards climax. With industrialization, the material and energy requirements of modern economic activities have grown exponentially, together with emissions to the air, water and soil. From an analogy with ecosystems, the concept of metabolism grew into an analytical methodology for economic systems. Research in the field of material flow analysis has developed approaches to modeling economic systems by assessing the stocks and flows of substances and materials for systems defined in space and time. Material flow analysis encompasses different methods: industrial and urban metabolism, input–output analysis, economy-wide material flow accounting, socioeconomic metabolism, and more recently material flow cost accounting. Each method has specific scales, reference substances such as metals, and indicators such as concentration. A material flow analysis study usually consists of a total of four consecutive steps: (a) system definition, (b) data acquisition, (c) calculation, and (d) interpretation. The law of conservation of mass underlies every application, which implies that all material flows, as well as stocks, must be accounted for. In the early 21st century, material depletion, accumulation, and recycling are well-established cases of material flow analysis. Diagnostics and forecasts, as well as historical or backcast analyses, are ideally performed in a material flow analysis, to identify shifts in material consumption for product life cycles or physical accounting and to evaluate the material and energy performance of specific systems. In practice, material flow analysis supports policy and decision making in urban planning, energy planning, economic and environmental performance, development of industrial symbiosis and eco industrial parks, closing material loops and circular economy, pollution remediation/control and material and energy supply security. Although material flow analysis assesses the amount and fate of materials and energy rather than their environmental or human health impacts, a tacit assumption states that reduced material throughputs limit such impacts.
Air pollution has been a major threat to human health, ecosystems, and agricultural crops ever since the onset of widespread use of fossil fuel combustion and emissions of harmful substances into ambient air. As a basis for the development, implementation, and compliance assessment of air pollution control policies, monitoring networks for priority air pollutants were established, primarily for regulatory purposes. With increasing understanding of emission sources and the release and environmental fate of chemicals and toxic substances into ambient air, as well as atmospheric transport and chemical conversion processes, increasingly complex air pollution models have entered the scene. Today, highly accurate equipment is available to measure trace gases and aerosols in the atmosphere. In addition, sophisticated atmospheric chemistry transport models—which are routinely compared to and validated and assessed against measurements—are used to model dispersion and chemical processes affecting the composition of the atmosphere, and the resulting ambient concentrations of harmful pollutants. The models also provide methods to quantify the deposition of pollutants, such as acidifying and eutrophying substances, in vegetation, soils, and freshwater ecosystems. This article provides a general overview of the underlying concepts and key features of monitoring and modeling systems for outdoor air pollution.