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Given the increasingly severe challenges of global climate change and sustainable development, accurate prediction of daily carbon dioxide (CO 2) emissions has become crucial. However, current research on real-time daily forecasts remains relatively scarce, often limited to annual predictions and point estimates. This paper proposes an innovative integrated model, based on quantile regression and Attention-Bidirectional Long Short-Term Memory (BILSTM), specifically designed for probabilistic prediction of daily carbon emissions. Testing the model on daily carbon emission data from major carbon-emitting countries including China, the United States, India, Russia, and Italy has demonstrated its superior performance across various metrics, including mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R2), and normalized average width of prediction intervals (PINAW), compared to other benchmark models. This achievement not only highlights the model's effectiveness in predicting daily carbon emissions but also provides policymakers with a powerful tool for real-time monitoring and assessment of carbon emissions. Furthermore , the application of probabilistic forecasting methods enables decision-makers to gain a more comprehensive understanding of the potential range of future carbon emissions, providing strong support for the formulation of targeted policies. [ABSTRACT FROM AUTHOR]

The issue of climate change has become an increasing concern for the international community. At the same time, climate change and, particularly, global warming affects the amount of energy required in China and the structure of this demand, thereby influencing the amount of CO 2 (carbon dioxide) emissions produced. To investigate the possible influence of climate change on China's CO 2 emissions, this paper utilizes the degree-day method to calculate the heating degree days (HDD) and cooling degree days (CDD) in 29 provinces in China from 1995 to 2011. A series of appropriate econometric models are subsequently estimated to measure the impact of climate change on CO 2 emissions per capita. To enhance the explanatory power of the estimation results, several indicators, such as income per capita, industry structure, urbanization rate and population density, are incorporated in the estimation equations as control variables. The estimation results indicate that the feedback effect of climate change on China's carbon emissions is statistically significant but not large in magnitude. During the sample period between 1995 and 2011, approximately 1.687% of China's increased total CO 2 emissions could be attributed to climate change. Moreover, the impacts of climate change on different regions of China are notably different. For example, the influence of HDD on CO 2 emissions per capita are most significant in the middle and southern regions, while the impacts of CDD are most significant in the eastern and northern regions. However, there is no obvious evidence that climate change has affected CO 2 emissions in the western region. [ABSTRACT FROM AUTHOR]

Several studies have shown the benefits of using timber in the construction sector in terms of climate change, which has received the support of decision-makers. However, few studies focus on other environmental indicators. The environmental impacts (i.e., such as human health, ecosystem quality, and resources) of material substitutions, consequence of the increasing use of wood, are still lacking in the literature. Studies that compare different structural materials focus either on a single building or on one structural system that would be representative on a larger scale. However, the environmental benefits of some projects are not easily extrapolated to the scale of a given region with a variety of architectures. Higher-order effects, beyond the building scale, can indeed imply changes in the resource availability according to the unconstrained suppliers and resources. The objective of this paper is to assess the life cycle environmental consequences of wood substitutions at a regional scale for the non-residential construction sector. The increasing use of wood structures for non-residential buildings in the province of Quebec (Canada) is used as a case study. The method includes the development of material substitution factors by comparing several structures. The substitution factors show that, on average, wood can replace, simultaneously, 0.59 and 4.54 times the weight of steel and concrete, respectively. However, steel substitution has more advantages than concrete substitution. Among the tested parameters, the variability of the material substitution factors implies more uncertainties in the results. The originality of our study lies in the limitations that may change the conclusions drawn from the substitution at a large scale. With three substitution scenarios for four impact categories, the results showed an advantage of using wood in seven of the twelve combinations. The ecosystem quality indicator is the most unfavorable and shows opposite trends to climate change, human health, and resource indicators. Considering average material substitution factors combined with the maximum potential use of wood in non-residential (NR) structures in Quebec in 2050, wood can contribute to avoid 2.6 Mt of carbon dioxide equivalent (CO 2 eq.). This amount is equivalent to 3.5% of Quebec's CO 2 eq. emission reduction target by 2050 compared to 1990. • The environmental consequences of wood substitution at the regional level for the non-residential sector are assessed. • The average scenario suggests that the use of wood can avoid up to 2.62 Mt of CO 2 eq. in 2050. • The variability of material substitution factors implies the greatest variability in the results. • With three substitution scenarios for four impact categories, the results showed an advantage for wood increased use in seven of the twelve combinations. [ABSTRACT FROM AUTHOR]