방법 비교

	Toxic Release Inventory Analysis	Land-Change Driver Analysis
분야	Environmental Sociology	Environmental Sociology
계열	Process / pipeline	Process / pipeline
기원 연도	2004	2002
창시자	Michael Ash & T. Robert Fetter (using EPA TRI / RSEI)	Eric F. Lambin & Helmut J. Geist
유형	Toxicity-weighted spatial pipeline for distributional burden	Spatial-and-causal pipeline for explaining land-cover change
원전	Ash, M., & Fetter, T. R. (2004). Who Lives on the Wrong Side of the Environmental Tracks? Evidence from the EPA's Risk-Screening Environmental Indicators Model. Social Science Quarterly, 85(2), 441-462.	Geist, H. J., & Lambin, E. F. (2002). Proximate Causes and Underlying Driving Forces of Tropical Deforestation. BioScience, 52(2), 143-150.
별칭	TRI Distributional Analysis, Toxic Burden Disparity Analysis, RSEI-Based Exposure Analysis, Industrial Pollution Equity Analysis	LUCC Analysis, Land-Change Science, Land Use/Land Cover Change Analysis, Proximate-and-Underlying Driver Analysis
관련	4	3
요약	Toxic Release Inventory (TRI) analysis uses mandatory facility-level reports of industrial chemical releases to measure how the burden of toxic pollution is distributed across social groups. Rather than counting raw pounds of emissions, which treat a ton of an innocuous solvent the same as a ton of a potent carcinogen, the modern approach weights releases by toxicity and models how they disperse to populations. Michael Ash and T. Robert Fetter's 2004 study showed how the EPA's Risk-Screening Environmental Indicators (RSEI) model, built on TRI, can be used to assign toxicity- and exposure-weighted pollution to neighborhoods and to test for disparities. They found consistent income and racial gradients: lower-income people and African Americans are exposed to more industrial air pollution, both across and within cities. The analysis combines the spatial-disparity logic of environmental justice with a chemical-specific account of harm. The result is a far more defensible burden measure than emission counts alone.	Land-use and land-cover change (LUCC) analysis is the land-change-science method for detecting how the Earth's surface is being transformed and explaining why, with particular attention to the social drivers behind the change. Its defining move, formalized by Eric Lambin and Helmut Geist, is to separate proximate causes, the direct human activities such as agricultural expansion, wood extraction, and infrastructure that physically alter land cover, from underlying driving forces, the demographic, economic, technological, institutional, and cultural factors that operate at a distance and push the proximate causes. Their meta-analysis of tropical deforestation showed that single-factor explanations are rare and that change is usually produced by synergistic combinations of drivers. The analysis chains remote sensing of cover change to a structured causal attribution, giving social scientists a rigorous way to link maps of deforestation or urbanization to the human forces that produce them.