Essays On Air Pollution, Climate Change, And Extreme Weather
Creators: name: Jiang, Xueting
Publisher: name: The Australian National University
Year: 2024

description: This thesis comprises four papers addressing air pollution, carbon dioxide emissions, and extreme weather. In the first study, I examine the factors driving the decrease in air pollution, particularly in light of the dominant role of coal combustion in the Chinese power plants. Using unit-level air pollution data from Chinese power plants during 2014-7, I employ the Index Decomposition Analysis model to quantify the contributions of eight factors to reducing three major air pollutants . The primary findings focus on aggregates of the ten wealthiest and twenty less affluent provinces. The dominant driver behind the decline in air pollution is the emission intensity of fossil fuel use, leading to a reduction of 69%-86% in the wealthier region and 65%-72% in the poorer region. Additionally, electricity consumption per unit of gross domestic product, the second-largest contributor to air pollution reduction, has lowered emissions of these key air pollutants from power plants by 7%-9% and 11%-12% in the wealthier and poorer regions, respectively. In the second study of this thesis, I quantify the gap in co-benefits of air pollution reduction between a swifter and a more gradual decarbonization scenario within the Chinese electric power sector. I integrate the Greenhouse Gas-Air Pollution Interactions and Synergies-ASIA and the Index Decomposition Analysis models to disentangle the effects of reduced air pollution resulting from both end-of-pipe measures and climate strategies. Results show that in a more rapid decarbonization scenario in the Chinese electric power sector, climate strategies yield between 10 to 87 times more co-benefits of air pollution reduction compared to a slower decarbonization scenario. The emission intensity factor is the most significant contributor to the co-benefits of reduced air pollution due to decarbonization. The third study explores how the oil market shocks associated with some, but not all, US recessions regarding the asymmetric response of carbon emissions. Previous research shows that, in the US, the elasticity of carbon emissions with respect to GDP is greater when GDP declines than when GDP increases. This study employs time-series data of monthly frequency to analyze each recession from 1973 to 2020 using econometric models. Results suggest asymmetric changes in carbon emissions in the 1973-5, 1980, 1990-1, and 2020 recessions but not in the 1981-2, 2001, or 2008-9 recessions. The former four recessions are associated with negative oil market shocks. The initial three recessions experienced supply shocks, while the one in 2020 resulted from a demand shock. These differences in recession causes can be attributed to shifts in oil consumption that cannot be accounted for by changes in GDP. Additionally, these variations are primarily driven by emissions from the transport and industrial sectors, which represent the primary oil consumers in the US. In the fourth study, I investigate the net impact of four categories of extreme weather shocks on economic growth. Utilizing panel data encompassing 179 countries from 1960 to 2019 and employing a fixed-effects weighted least squares (WLS) regression model, I analyze their influence on GDP per capita. I compare the effects of four types of extreme weather shocks: heat waves, cold spells, floods, and droughts. Notably, the aggregated findings for all countries in the sample indicate that floods are associated with a 0.5% increase in GDP per capita, primarily from lower-income nations. The primary driver of the positive effects of floods in these poorer nations is government expenditure, while investment and services contribute to these positive effects. Additionally, the positive impact of floods on GDP per capita becomes more pronounced over extended time periods. The other three categories of hazards do not have statistically significant effects on GDP per capita in either income group.