Understanding how carbon dioxide emissions (CO₂) can be decoupled from economic growth is an important part of planning for climate change mitigation. A variety of critical environmental theories contend that the oppression of marginalized groups is interconnected with the mistreatment and destruction of nature. As a result, social equity, or the removal of barriers of structural inequality, often coincide with environmental quality and reduced environmental degradation. To date, there is limited research on the dialectical relationship between inequality, economic growth, and the environment. The present study seeks to further understand the relationship between social inequality and the environment by assessing how gender equality decouples economic growth from CO₂ emissions. We construct a fixed-effects panel regression model with robust standard errors that accounts for clustering in 140 nations to assess how gender inequality interacts with GDP per capita to influence CO₂ emissions per capita. Our findings indicate that in nations with more gender equality, the association between GDP per capita and CO₂ emissions is much lower than in nations with higher levels of gender inequality.

The STochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model has become a widely employed methodological approach within social science research, largely used to understand the complex relationships between human social systems and the non-human environment. The general assumption of the model is that anthropogenic environmental impacts are a multiplicative function of population, affluence, and technology. While previous STIRPAT research has examined the impact of technology in terms of urbanization, estimating the specific effect of urban population, we argue that this measure is better understood as a proxy for modernization. As an alternative, we frame urbanization as a multidimensional driver of environmental change, and we operationalize the technology dimension through cross-national data on impervious surface area, or what we call ‘terrestrial technology’. To demonstrate the potential of this example for environmental sociology, we draw from political economy to show how operationalizing technology offers a stronger, more nuanced understanding of the socioeconomic drivers of environmental degradation. Analytically, we employ a spatial regression model that estimates the effect of terrestrial technology on total carbon emissions for 173 countries. Our results show that impervious surface area is positively related to total carbon output and thus should be considered an operational measure of technology in future STIRPAT analyses.

Many proponents of organic farming claim that it is a sustainable alternative to conventional agriculture due to its reliance on natural agro-inputs, such as manure based fertilizers and organic pesticides. However, in this analysis we argue that although particular organic farming practices clearly benefit ecosystems and human consumers, the social context in which some organic farms develop, limit the potential environmental benefits of organic agriculture. Specifically, we argue that certified organic farming’s increased reliance on agro-inputs, such as organic fertilizers and pesticides, reduces its ability to decrease global water pollution. We review recent research that demonstrates the environmental consequences of specific organic practices, as well as literature showing that global organic farming is increasing its reliance on agro-inputs, and contend that organic farming has its own metabolic rift with natural water systems similar to conventional agriculture. We use a fixed-effects panel regression model to explore how recent rises in certified organic farmland correlate to water pollution (measured as biochemical oxygen demand). Our findings indicate that increases in the proportion of organic farmland over time increases water pollution. We conclude that this may be a result of organic farms increasing their reliance on non-farm agro-inputs, such as fertilizers.