The asymmetry of economic growth and the carbon intensity of well-being

Greiner, Patrick Trent; McGee, Julius Alexander

doi:10.1080/23251042.2019.1675567

Cited by 9 publications

(16 citation statements)

References 34 publications

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“…Building on the Monte Carlo experiments, we now present an empirical example. To date, a majority of the sociological studies that examine asymmetry have done so by focusing on the anthropogenic drivers of environmental change (Clement and York 2017; Greiner and McGee 2020; Huang and Jorgenson 2018; McGee and York 2018; York 2012; York and Light 2017). We build on this tradition by revisiting York’s (2012) study of the asymmetric effects of economic growth/decline on CO 2 emissions.…”

Section: An Empirical Example: Economic Growth and Co2 Emissionsmentioning

confidence: 99%

What Goes Up Might Not Come Down: Modeling Directional Asymmetry with Large-N, Large-T Data

Thombs

Huang

Fitzgerald

2021

Sociological Methodology

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Modeling asymmetric relationships is an emerging subject of interest among sociologists. York and Light advanced a method to estimate asymmetric models with panel data, which was further developed by Allison. However, little attention has been given to the large- N, large- T case, wherein autoregression, slope heterogeneity, and cross-sectional dependence are important issues to consider. The authors fill this gap by conducting Monte Carlo experiments comparing the bias and power of the fixed-effects estimator to a set of heterogeneous panel estimators. The authors find that dynamic misspecification can produce substantial biases in the coefficients. Furthermore, even when the dynamics are correctly specified, the fixed-effects estimator will produce inconsistent and unstable estimates of the long-run effects in the presence of slope heterogeneity. The authors demonstrate these findings by testing for directional asymmetry in the economic development–CO2 emissions relationship, a key question in macro sociology, using data for 66 countries from 1971 to 2015. The authors conclude with a set of methodological recommendations on modeling directional asymmetry.

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Section: An Empirical Example: Economic Growth and Co2 Emissionsmentioning

confidence: 99%

What Goes Up Might Not Come Down: Modeling Directional Asymmetry with Large-N, Large-T Data

Thombs

Huang

Fitzgerald

2021

Sociological Methodology

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“…All variables are log-transformed. Based on previous research (e.g., Greiner and McGee 2019;Jorgenson and Clark 2012), we expect that all three variables will be positively associated with carbon emissions.…”

Section: Carbon Emissions: Dependent and Independent Variablesmentioning

confidence: 99%

“…Specifically, with a focus on the sociology of environmental impacts, we analyze the lagged associations of demographic and economic change for two separate environmental outcomes: land development (in a county-level analysis; n = 3,026) and carbon emissions (in a cross-national analysis; n = 146). 1 In our research note, we focus on the use of uni-directional estimation techniques in the environmental social sciences (e.g., Ehrhardt-Martinez et al 2002;Elliott and Clement 2014;Greiner and McGee 2019;Hyde and Vachon, 2018;Jorgenson et al 2007;Muñoz et al 2018;Prechel and Istvan 2016;York et al 2003). For the analysis, we use the term uni-directional to represent generally when a dependent variable is regressed on independent variables, thus differentiating from techniques that estimate mediating or reciprocal associations between variables (cf.…”

Section: Introductionmentioning

confidence: 99%

“…Environmental scholars have only occasionally utilized other quantitative techniques (e.g., cross-lagged panel or bi-directional models) to address more directly these temporal issues (Clement et al 2020;Sorge and Neumann 2019;Thombs et al 2021). Instead, many continue to employ panel models to estimate uni-directional associations between an independent variable and an outcome measure over time (e.g., Elliott and Clement 2014; Greiner and McGee 2019;Hyde and Vachon, 2018;Jorgenson et al 2007;Muñoz et al 2018;Prechel and Istvan 2016). 2 When using regression models to estimate uni-directional associations, one common strategy to address the issues of reverse causation and simultaneity bias is to incorporate a temporal lag, which ensures that the values for the independent variable are observed prior to the values of the dependent variable.…”

Section: Introductionmentioning

confidence: 99%

“…In our research note, we focus on the use of uni‐directional estimation techniques in the environmental social sciences (e.g., Ehrhardt‐Martinez et al. 2002; Elliott and Clement 2014; Greiner and McGee 2019; Hyde and Vachon, 2018; Jorgenson et al. 2007; Muñoz et al.…”

Section: Introductionmentioning

confidence: 99%

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The Lagged Environmental Consequences of Demographic and Economic Change

Clement¹,

Pino²,

York³

et al. 2021

Sociological Inquiry

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Quantitative sociologists conducting environmental research often use temporally lagged variables to estimate the social drivers of ecological change. To highlight the relevance of temporal lags for this scholarship, we specifically look at the longitudinal relationship between demographic and economic change and two different environmental outcomes: land development and carbon emissions. For land development, we run longitudinal spatial regression models to assess whether increasing the lag time changes the slope estimates for in‐migration and out‐migration at the county level across the contiguous United States (n = 3,026). For carbon emissions, we use cross‐national data in Prais–Winsten models to assess changes in the lagged estimates for GDP, urbanization, and age structure (n = 146). Results from these analyses indicate that the slope estimates continue to be statistically significant, but the magnitudes of these coefficients change with increased lag time. We propose that scholars use a more systematic approach when assessing the temporal duration of socio‐ecological change.

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Design for Emergency: Inclusive Housing Solution

Giglio

Sansotta

2023

The Urban Book Series

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The paper describes a study on the growing emergency of homelessness, of which alarming data are estimated at national and European levels and which the Cohesion policies of the European Community are addressing. Thanks to the recent launch of the Collaboration Platform on Homelessness to stimulate dialog, improve data collection and monitoring and strengthen cooperation between all actors involved in the fight against the phenomenon. The emerging concept of ‘Design for Emergency’ highlights the historical link between temporary and emergency living regarding the welfare and health implications of the weak. The aim is twofold: to define a theoretical and design model that can be repeated, contributing on the one hand to a process of social reintegration for fragile realities and on the other to the circularity of construction processes and the recovery of resources and components, through innovative housing solutions, with characteristics of modularity, disassembly and dry connections. The results, deriving from a deductive scalar methodological approach, concern: (i) data collection is inherent to the issues addressed, the emergency conditions; (ii) a critical analysis of the data acquired and systematized; (iii) methodological and design experimentation. The research hopes for repeatable results in diverse marginal contexts, respecting the disparate needs not only of the users but of the place where the temporary installation will be needed. This is an aspect in which the intervention of the municipal administrations and all possible stakeholders involved is fundamental and which at the moment may represent a limitation, albeit a surmountable one of the research.

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The asymmetry of economic growth and the carbon intensity of well-being

Cited by 9 publications

References 34 publications

What Goes Up Might Not Come Down: Modeling Directional Asymmetry with Large-N, Large-T Data

What Goes Up Might Not Come Down: Modeling Directional Asymmetry with Large-N, Large-T Data

The Lagged Environmental Consequences of Demographic and Economic Change

Design for Emergency: Inclusive Housing Solution

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