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The partisan divide over climate change issues in the U.S. has been increasing over time, particularly after the signing of the Kyoto protocol in 1997. This divide has posed a substantial hurdle to implementing a unified climate policy in the U.S. In this paper, I employ the Shapley value decomposition approach to measure the partisan gap in state-level per capita energy-related CO 2 emissions. Despite a decrease in overall carbon emissions and stable per capita carbon inequality between states (measured by the Gini index), the inequality between the so called 'red' and 'blue' states widened over the period between 1997 and 2021. This is true regardless of whether each state is treated as a unit of analysis or assigned a weight equal to its population. Throughout the period, the divide by political party color was more pronounced than that by many other dimensions, such as income, climatic condition, and geographic location. These results also hold true when Mean Log Deviation is used as the measure of inequality. Driven primarily by disparity in transportation sector and coal use for electricity generation, the partisan gap was worse in 2021 than in 2020. While the partisan gap intensified, the carbon distribution changed from being top-concentrated to bottom-concentrated between 1997 and 2019, only to be reversed in the aftermath of the coronavirus pandemic in 2020 and subsequent economic recovery in 2021. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic lockdowns led to a sharp drop in socio-economic activities in China in 2020, including reductions in fossil fuel use, industry productions, and traffic volumes. China's economy suffered a serious negative effect from COVID-19. However, there is a "positive effect" on CO 2 emissions reduction. Here, for the first time, this paper constructs a new model named "Weighted Multi-regional Hypothetical Extraction Method (WMHEM)" based on a multiregional input-output model. It not only solves the problems of traditional HEM methods such as improper use of assumptions, excessive reliance on industry intermediate input, but also accurately reflects the impact of external shocks on the inter-industry linkages. By using the monthly economic data of each provinces in China during COVID-19 (except Hong Kong，Macao and Taiwan) an the latest Multi-regional input-output tables, the "economic negative effect" and "CO 2 emission positive effect" under COVID-19 in China are measured. Results show that COVID-19 lockdown was estimated to have reduced China's CO 2 emissions substantially between January and March in 2020, with the largest reductions in February. With the spread of coronavirus controlled, China's CO 2 emissions rebounded in April. In addition, key emission reduction sectors and key development encouraged sectors are selected by combining "economic negative effect" and "CO 2 emission positive effect" during COVID-19. Therefore, policies recommendations are put forward based on forward and backward linkages respectively which are from two ends of the supply chain to turn pandemic-related CO 2 emissions declines into firm climate action. • COVID-19 has negative effects on economy, but positive effects on CO 2 emissions. • A new model is used to measure economic and CO 2 emission effects during COVID-19. • Hubei had the biggest economic and CO 2 emission effects on China due to COVID-19. • Economic and CO 2 emission effects of COVID-19 are combined to select key sectors. [ABSTRACT FROM AUTHOR]

Achieving the peak of carbon dioxide (CO 2) emissions requires a large amount of green and low-carbon investment. Whether the green finance system can efficiently support the capital need for achieving the CO 2 emissions target in the context of the COVID-19 epidemic is a matter of concern. This paper constructs a system dynamics model (SD model) to illustrate the quantitative relationship between the green finance system and CO 2 emissions and introduce the COVID-19 epidemic as a variable to analyze ten simulation scenarios regarding the carbon emissions commitment realization under different green finance and economic growth status. It is shown that: (1) Regardless of the impact of COVID-19, China can meet its commitment by reaching its CO 2 emissions peak in 2030 and realizing a 20% non-fossil energy proportion in 2025; (2) Under the impact of the epidemic, the goal can not be obtained in all energy consumption scenarios when the government expenditure on the environment is low. The target year of reaching CO 2 emissions peak becomes 2033, 2037, and 2040. The results indicate that reducing government expenditure on environment protection makes the CO 2 emissions peak target less likely to be achieved within a given time frame. We also concluded with important policy implications according to the result of the simulations. Overall, this study makes a reference for other economies and researchers to quantitatively predict the interaction relationship between the green finance system and CO 2 emissions in the context of COVID-19, which provides policymakers with insights into a joint power of energy consumption upgrading and green capital guidance. • Providing empirical studies regarding the mechanism of the inhibitory effect of green finance on CO 2 emissions. • Using systems dynamics model to address the missing data problem. • Making solid suggestions under COVID-19 Epidemic. • Building the relationship between the green finance system and CO 2 emissions and making forecasts for "3060" mission result. [ABSTRACT FROM AUTHOR]

Increased clean energy production from household solar panels is a potentially vital component of a sustainable energy transition and decarbonisation that can reduce carbon dioxide emissions from many countries. This paper investigates multiple channels of wealth effects on household solar-panel uptake. There is evidence of impacts of both financial and non-financial assets on solar-panel uptake, but the evidence for financial assets is much more robust. Compared to the highest-asset quartile, proportional solar-panel uptake is three percentage points lower for the second-highest quartile of households based on financial assets, all else equal. This gap grows to six percentage points for households in the lowest quartile for financial assets. The results are robust across many models using probit, logit and linear probability formats. Knowledge of the relative magnitudes of impacts of wealth channels is important for policymakers who are considering supporting solar-panel uptake, particularly following the COVID-19 pandemic, when efficiency of public spending will be crucial. Our results reveal that means testing for solar policy support should be based on financial-asset thresholds, rather than non-financial assets or income. These are globally important policy lessons in creating a viable climate change adaptation strategy through solar electrification. [Display omitted] • Financial assets are more important than non-financial assets for residential solar uptake. • Two components of financial assets, private pensions and other assets, have similar impacts. • Liabilities also have a positive association with household solar-panel uptake. • In contrast, income is not a significant contributor to residential solar uptake. • Means testing for policy support should be based on financial-asset thresholds. [ABSTRACT FROM AUTHOR]