Reducing Carbon Footprint Inequality of Household Consumption in Rural Areas: Analysis from Five Representative Provinces in China

Abstract

Household consumption carbon footprint and inequality reductions are vital for a sustainable society, especially for rural areas. This study, focusing on rural China, one of the fastest growing economies with a massive population, explored the carbon footprint and inequality of household consumption using the latest micro household survey data of 2018 linked to environmental extended input–-output analysis. The results show that in 2018 in rural China, the average household carbon footprint is 2.46 tons CO2-eq per capita, which is around one-third of China’s average footprint, indicating the large potential for further growth. Housing (45.32%), transportation (20.45%), and food (19.62%) are the dominant contributors to the carbon footprint. Meanwhile, great inequality, with a Gini coefficient of 0.488, among rural households is observed, which is largely due to differences in type of house built or purchased (explaining 24.44% of the variation), heating (18.10%), car purchase (12.44%), and petrol consumption (12.44%). Provinces, average education, and nonfarm income are among the important factors influencing the inequality. In the process of urbanization and rural revitalization, there is a high possibility that the household carbon footprint continues to increase, maintaining high levels of inequality. The current energy transition toward less carbon-intensive fuels in rural China is likely to dampen the growth rates of carbon footprints and potentially decrease inequality. Carbon intensity decrease could significantly reduce carbon footprints, but increase inequality. More comprehensive measures to reduce carbon footprint and inequality are needed, including transitioning to clean energy, poverty alleviation, reduction of income inequality, and better health care coverage.

Significant but Inequitable Cost-Effective Benefits of a Clean Heating Campaign in Northern China

“…To reveal the effect of the campaign on inequality, the number of premature deaths attributable to residential emissions is plotted against the population share under the two scenarios without and with campaign implementation in Figure S6. The results were summarized based on corresponding Gini coefficient values as an index of the degree of inequality. , Notched Lorenz curves and Gini coefficients indicated inequality in both cases. In fact, the campaign led to increased curvilinearity, and the Gini coefficient value rose from 0.13 to 0.34, suggesting that inequality was aggravated by the campaign.…”

Economic growth and household energy footprint inequality in China

“…Residence and health care consumption are the main factors contributing to the differences in energy footprints across groups. Thus, the size and inequality of the energy footprint can be reduced by improving housing conditions, developing energy-saving habits, and balancing health care provision [ 51 ]. As China’s real estate market and health care sector continue to develop and improve, the equality of China’s household energy footprint will further improve [ 52 , 53 ].…”

The Polarizing Trend of Regional CO2 Emissions in China and Its Implications

“…Economists often use the Gini coefficient to quantitatively compare income inequalities . Recently, some researchers have altered the methodology for calculating Gini coefficients to investigate the CO 2 emission inequalities across different income groups. , Here, in this research, we further changed the variables in the Gini coefficient calculations to directly show the difference in CO 2 emissions among Chinese provinces instead of population groups. Originally, Gini coefficient is derived from the Lorenz Curve.…”