방법 비교

	Environmental Kuznets Curve Estimation	STIRPAT Model
분야	Environmental Sociology	Environmental Sociology
계열	Regression model	Regression model
기원 연도	1995	1997
창시자	Gene M. Grossman & Alan B. Krueger	Thomas Dietz & Eugene A. Rosa; Richard York
유형	Reduced-form polynomial panel regression of pollution on income	Log-linear stochastic regression model of environmental impact drivers
원전	Grossman, G. M., & Krueger, A. B. (1995). Economic Growth and the Environment. The Quarterly Journal of Economics, 110(2), 353-377.	Dietz, T., & Rosa, E. A. (1997). Effects of population and affluence on CO2 emissions. Proceedings of the National Academy of Sciences, 94(1), 175-179.
별칭	EKC Estimation, Environmental Kuznets Curve, Income-Pollution Inverted-U Model, Grossman-Krueger Curve	Stochastic IPAT, STIRPAT Regression, Stochastic Impacts by Regression on Population Affluence and Technology, Dietz-Rosa Impact Model
관련	4	4
요약	Environmental Kuznets curve (EKC) estimation tests the hypothesis that environmental degradation first rises and then falls as a country grows richer, tracing an inverted-U relationship between per-capita income and pollution. The empirical pattern was popularized by Gene Grossman and Alan Krueger's 1995 study of how air and water quality vary with income across countries, which found that several pollutants worsen at low income but improve beyond a turning point. Methodologically, the EKC is estimated as a reduced-form regression of an environmental indicator on a polynomial, usually quadratic, in income, with the signs of the linear and squared terms determining whether the inverted-U holds and the coefficients pinning down the income level at which degradation peaks. The framework is named by analogy to Simon Kuznets's hypothesized inverted-U between income and inequality. David Stern's 2004 critical review documented how fragile many early EKC results were once proper panel econometrics, unit roots, and specification issues were taken seriously. EKC estimation remains a central, much-contested tool in environmental economics and sociology for studying the growth-environment relationship.	The STIRPAT model, short for Stochastic Impacts by Regression on Population, Affluence, and Technology, is a statistical reformulation of the IPAT identity that allows the drivers of environmental impact to be estimated and tested rather than merely asserted. Thomas Dietz and Eugene Rosa introduced it in 1997 to study national carbon dioxide emissions, recasting the deterministic accounting identity impact equals population times affluence times technology as a multiplicative stochastic model with an error term. Taking logarithms turns this into a linear regression whose coefficients are elasticities, the percentage change in impact associated with a one-percent change in each driver. This lets researchers ask whether impact rises strictly in proportion to population, as the original identity assumes, or whether there are increasing or decreasing returns to scale. Richard York, Rosa, and Dietz formalized and extended the approach in 2003, showing how additional drivers, quadratic terms, and panel structure can be incorporated within the same framework. STIRPAT has become the dominant quantitative tool in environmental sociology for analyzing the anthropogenic forces behind emissions, energy use, and ecological footprints.