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Wildfires are becoming more frequent due to the global climate change. Large amounts of greenhouse gases emitted by wildfires can lead to increases in extreme climate events. Accurately estimating the greenhouse gas carbon dioxide (CO2) emissions from wildfires is important for mitigation of climate change. In this paper, we develop a novel method to estimate wildfire CO2 emissions from the relationship between local CO2 emissions and XCO2 anomalies. Our method uses the WRF-Chem assimilation system from OCO-2 XCO2 retrievals which coupled with Data Assimilation Research Testbed (DART). To validate our results, we conducted three experiments evaluating the wildfire CO2 emissions over the conterminous United States. The four-month average wildfire emissions from July to October in 2015∼2018 were estimated at 4.408 Tg C, 1.784 Tg C, 1.514 Tg C and 2.873 Tg C, respectively. Compared to the average of established inventories CT2019B, FINNv1.5 and GFASv1.2 fire emissions, our estimates fall within one standard deviation, except for 2017 due to lacking of OCO-2 XCO2 retrievals. These results suggest that the regional carbon assimilation system, such as WRF-Chem/DART, using OCO-2 XCO2 retrievals has a great potential for accurately tracking regional wildfire emissions. [ABSTRACT FROM AUTHOR]

This study examines the effects of renewable energy consumption and environmental taxes on CO2 emissions in OECD countries from 1990 to 2022, employing the cross-sectional autoregressive distributed lag (CS-ARDL) approach. The findings reveal that both renewable energy consumption and environmental taxes significantly reduce CO2 emissions in both the short and the long term, emphasizing their crucial roles in climate change mitigation and sustainability promotion. Furthermore, this study identifies that industrialization and urbanization contribute to increased emissions, whereas foreign direct investment aids in emission reduction through the facilitation of green technology transfer. Economic growth is initially associated with higher emissions, but this trend reverses as economies mature and adopt sustainable practices. These results highlight the importance of continuous investment in renewable energy infrastructure and the implementation of robust environmental tax policies to achieve long-term sustainability goals. The integration of environmental considerations into economic and urban planning, along with leveraging foreign direct investment for technological advancements, is imperative for balancing economic growth with the necessity to reduce carbon emissions and effectively address climate change. This research provides a better understanding of the diverse factors influencing CO2 emissions and offers critical insights for policymakers. [ABSTRACT FROM AUTHOR]

Continued urban expansion (UE) has long been regarded as a huge challenge for climate change mitigation. However, much less is known about how UE affects carbon emissions (CEs), especially in the urban agglomerations of the Yellow River Basin (UAYRB), China. In this regard, this study introduced kernel density analysis, the Gini coefficient, and Markov chains to reveal the UE patterns and carbon emissions intensity (CEI) in the UAYRB at the county level, and explored the spatial heterogeneity of the impact of UE on CEI with the geographically and temporally weighted regression model. The results show that both CEI and UE in the UAYRB showed a steady growing trend during the study period. The kernel density of CEI and UE revealed that CEI in the UAYRB was weakening, while the UE rate continuously slowed down. The Gini coefficients of both CEI and UE in the UAYRB region were at high levels, indicating obvious spatial imbalance. The Markov transfer probability matrix for CEI with a time span of five years showed that CEI growth will still occur over the next five years, while that of UE was more obvious. Meanwhile, counties with a regression coefficient of UE on CEI higher than 0 covered the majority, and the distribution pattern remained quite stable. The regression coefficients of different urban landscape metrics on CEI in the UAYRB varied greatly; except for the landscape shape index, the regression coefficients of the aggregation index, interspersion and juxtaposition index, and patch density overall remained positive. These findings can advance the policy enlightenment of the high-quality development of the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Rail transportation plays a crucial role in reducing carbon emissions from the transportation system, making a significant contribution to environmental impact mitigation due to the efficiency of passenger and freight rail transportation. Accurate assessment of carbon emissions resulting from rail transit is essential to quantify the positive impact of this mode of transportation on overall urban transport emission reduction. Given that measuring carbon emissions throughout the lifecycle of rail transportation involves a wide array of factors, adopting a systematic framework for analyzing these aspects is crucial. This study conducts a comprehensive review of existing research related to carbon emissions in rail transportation and its mitigation. Initially, the distinct characteristics of carbon emissions associated with rail transportation are identified, along with the complexity involved in accurately measuring these emissions. Subsequently, a comparison and analysis are conducted regarding various models for measuring carbon emissions in rail transportation. Finally, the study examines some greenhouse gas emission measurement research within the railway system. Redirecting research efforts toward measuring carbon emissions in the rail transportation system is essential to help the development of robust and effective public policies. This measure will play a crucial role in emission reduction, climate change mitigation, and the promotion of more sustainable transportation. Furthermore, the identified results propose which LCA methodology offers a valuable framework improving the quality of railway transportation emissions for future generations. [ABSTRACT FROM AUTHOR]