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The reduction of the carbon emissions of construction industry is urgent. Therefore, it is essential to accurately predict the carbon emissions of the provincial construction industry, which can support differentiation emission reduction policies in China. This paper proposes a carbon emission prediction model that optimizes the backpropagation (BP) neural network by genetic algorithm (GA) to predict carbon emission of construction industry, or “GA-BP”. To begin with, the carbon emissions of construction industry in Sichuan Province from 2000 to 2020 are calculated by the emission factor method. Further, the electricity correction factor is introduced to eliminate the regional difference in electricity carbon emission coefficient. Finally, four factors are selected by the grey correlation analysis method to predict the carbon emission of construction industry in Sichuan Province from 2021 to 2025. The results show that the carbon emissions of construction industry in Sichuan Province have been trending up in the past two decades, with an average increase rate of 10.51%. The GA-BP model is a high-precision prediction model to predict carbon emissions of construction industry. The mean absolute percentage error (MAPE) of the model is only 6.303%, and its coefficient of determination is 0.853. Moreover, the carbon emissions of construction industry in Sichuan Province will reach 8891.97 million tons of CO 2 in 2025. The GA-BP model can effectively predict the future carbon emissions of construction industry in Sichuan Province, which provides a new idea for the green and sustainable development of construction industry in Sichuan Province.

The growth rate of atmospheric carbon dioxide (CO₂), the largest human contributor to human-induced climate change, is increasing rapidly. Three processes contribute to this rapid increase. Two of these processes concern emissions. Recent growth of the world economy combined with an increase in its carbon intensity have led to rapid growth in fossil fuel CO₂ emissions since 2000: comparing the 1990s with 2000-2006, the emissions growth rate increased from 1.3% to 3.3% y⁻¹. The third process is indicated by increasing evidence (P = 0.89) for a long-term (50-year) increase in the airborne fraction (AF) of CO₂ emissions, implying a decline in the efficiency of CO₂ sinks on land and oceans in absorbing anthropogenic emissions. Since 2000, the contributions of these three factors to the increase in the atmospheric CO₂ growth rate have been [almost equal to]65 ± 16% from increasing global economic activity, 17 ± 6% from the increasing carbon intensity of the global economy, and 18 ± 15% from the increase in AF. An increasing AF is consistent with results of climate-carbon cycle models, but the magnitude of the observed signal appears larger than that estimated by models. All of these changes characterize a carbon cycle that is generating stronger-than-expected and sooner-than-expected climate forcing.

Recent studies show that tropical hydroelectric reservoirs may be responsible for substantial greenhouse gas emissions to the atmosphere, yet emissions from the surface of released water downstream of the dam are poorly characterized if not neglected entirely from most assessments. We found that carbon dioxide (CO₂) emission downstream of Kariba Dam (southern Africa) varied widely over different timescales and that accounting for downstream emissions and their fluctuations is critically important to the reservoir carbon budget. Seasonal variation was driven by reservoir stratification and the accumulation of CO₂ in hypolimnetic waters, while subdaily variation was driven by hydropeaking events caused by dam operation in response to daily electricity demand. This “carbopeaking” resulted in hourly variations of CO₂ emission up to 200% during stratification. Failing to account for seasonal or subdaily variations in downstream carbon emissions could lead to errors of up to 90% when estimating the reservoir’s annual emissions. These results demonstrate the critical need to include both limnological seasonality and dam operation at subdaily time steps in the assessment of carbon budgeting of reservoirs and carbon cycling along the aquatic continuum.

The purpose of this paper is to explore whether the implementation of carbon emissions trading policy (CETP) promotes carbon finance, proxied by investment and financing facilitating carbon emissions reduction (IFCER), and reduces carbon emissions. Evidence shows that first, CETP is effective in stimulating IFCER and reducing carbon emissions. Second, the effects of CETP persist over time. Third, the effects of CETP taking effect in pilot regions can spill over to non-pilot regions nearby. Fourth, the effect is more pronounced in eastern and wealthy regions. Finally, R&D and industrial upgrading have a mediating effect linking CETP to IFCER and carbon emissions.

Carbon emissions and strategies for reducing them have become hot topics in recent years. This study firstly measured the total amount and the intensity of agricultural carbon emissions (i.e., agricultural carbon emission per capital) in China. The results show that China’s total carbon emission in 2016 was 272.022 million tons, which is 26.67% more than that in 2000, with an average annual increase of 1.67%. It then compared the regional differences of agricultural carbon emissions in China using the method of coefficient of variation and the Theil index. Following this, this paper finally provides scientific and technological support for the reduction of agricultural carbon emissions in China based on a matrix of carbon emission reduction strategies.

China-US trade holds great significance for the world’s political and economic landscape. Since 2018, the US government has imposed additional tariffs on Chinese exports on the grounds of the US trade deficit with China. However, the transfer of pollutants embodied in trade and the differences in environmental costs between China and the US have not been widely recognized. In this study, we quantify the embodied carbon emissions (the “virtual” emissions associated with trade and consumption) in China-US trade by constructing a carbon dioxide emissions inventory and a multiregional input-output model. The study shows that the US benefits from a trade surplus of environmental costs by importing energy-intensive and pollution-intensive products from China, which increases China’s environmental pollution and abatement costs. In 2017, 288 Mt CO 2 emissions were associated with products produced in China but finally consumed in the US, and only 46 Mt CO 2 were associated with the US products that were consumed in China. From this perspective, China-US trade results in a net transfer of 242 Mt CO 2 per year from the US to China, accounting for approximately 5% of the total CO 2 emissions in the US. More importantly, for Chinese products exported to the US, the carbon emissions embodied in one unit of economic value amount to 0.92 kg/$ (RMB: USD=6.8:1), but for US products exported to China, the carbon emissions embodied in one unit of economic value amount to 0.53 kg/$, which means China will incur environmental costs that are 74% higher than those of the US while enjoying the same economic benefits. This environmental trade deficit has burdened China with higher environmental costs thaneconomic benefits. To address this environmental trade deficit, China should actively promote further industrial upgrading and energy structure adjustment and increase investment in innovation and R&D, thereby increasing the value added per unit of export products and reducing the environmental cost of producing export products.

Reducing agricultural carbon emissions (ACE) is a key point to achieve green and sustainable development in agriculture. Based on the ACE statistics of Jilin Province in China from 1998 to 2018, this article considers the sources of ACE in depth, and fourteen different carbon sources are selected to calculate ACE. Besides, the paper explores the variation characteristics of ACE in Jilin Province, their structure, and the relationship between the intensity and density of the dynamic changes in ACE in the province in terms of time. Finally, this paper uses the Kaya identity and logarithmic mean Divisia index (LMDI) to analyze the influential factors in ACE. The results show the following: (1) During 1998–2018, the amount of ACE in Jilin Province increased, with an average annual growth rate of 1.13%. However, the chain growth rate has been negative in recent years, which reflects that carbon emission reduction has been achieved to a certain extent. (2) The characteristics of ACE in Jilin Province during the years is that of the low-intensity, high density category. Furthermore, agricultural resource input is the main source of the planting industry’s carbon emissions. From the perspective of animal husbandry, the proportion of CH4 decreased, while the proportion of N2O is relatively stable. (3) Based on the LMDI decomposition model, production efficiency, industrial structure, and labor are the three main factors that reduce ACE in Jilin Province. The economic level is the main factor of ACE, and it will be the most important factor leading to an increase in ACE in the short term. On the basis of comprehensive analysis, this article puts forward reasonable suggestions in terms of policy improvement, production mode and industrial structure adjustment, technological innovation, and talent introduction.

With 80% of the world's carbon emissions coming from urban areas and most part of the world still experiencing ever accelerated process of urbanization, China faces huge pressure to achieve the carbon emission peaking in 2030 and realizes the goal of carbon neutrality before 2050. Therefore, this study explored the spatial variability of CO 2 emissions from urban construction land among 30 provinces in China, analyzed its driving factors and estimated their potentials for emission reductions from 2000 to 2018. The results demonstrate that: (1) according to the IPCC model, both the carbon emission amounts and carbon emission intensity from urban construction land showed an upward trend from 2000 to 2018. (2) Decomposition analysis of logarithmic mean Divisia index revealed that economic level has positive impact on carbon emissions. Energy efficiency and energy structure are the negative contribution factors to the carbon emissions, and the energy efficiency effect played a more important inhabiting factor. (3) The carbon emission reduction potential indexes was provided to estimate the carbon emission reduction potential of 30 provinces in China; it indicated that 17 provinces have their carbon emission reduction potential indexes less than 1, and they confront with mandatory push to reduce carbon emission under the current national policy. Finally, promoting clean energy and applying internet of things into energy transport corridor system and more low-carbon land planning policies are suggested to facilitate more effective implementation of carbon emissions reduction actions in China.

China is one of the world’s largest energy consumers and carbon emitters, and the situation of carbon emission reduction is serious. This paper forecasts the future trend of China’s carbon emissions by constructing a system dynamics model of China’s carbon emissions. The results show that China cannot fulfill its commitment to peak its carbon emissions in 2030 as scheduled. Secondly, the Logarithmic Mean Divisia Index model (LMDI) was used to analyze the influencing factors of China’s carbon emissions. The contribution rates of the five factors to China’s carbon emissions are as follows: economic development (226.30%), technological innovation (−105.92%), industrial structure (−26.55%), population scale (11.44%) and energy structure (−5.28%). Finally, this paper formulates five carbon emission reduction paths according to the size and direction of various factors that affect China’s carbon emissions. The paths of carbon emission reduction were simulated by using the system dynamics model of China’s carbon emissions. It is found that technological innovation is the key pathway for China to realize its commitment to carbon emission reduction. Slowing economic growth will delay the arrival time of peak carbon emissions and increase the intensity of carbon emissions. Optimizing the industrial structure, reducing the population scale and adjusting the energy structure can reduce the peak and carbon emissions in China, but the effect is small.

Based on input–output table to measure inter-industry R&D technology spillover, this paper introduces inter-industry R&D technology spillover into panel econometric model for carbon dioxide (CO2) emissions factor analysis. Using the panel data of 34 industrial sectors in China from 2005 to 2014, the results reveal that there is an inverted “U-shaped” nonlinear relationship between R&D technology spillover (intensity) and carbon emission; it is estimated that R&D technology spillover can reduce carbon emission currently; the indirect impact of R&D technology spillovers or spillovers intensity through R&D intensity on carbon emissions is also beneficial to carbon emission reduction; at last, this study suggests that industrial sectors should improve R&D intensity and strengthen technical exchanges and cooperation with other related sectors for the purpose of R&D technology spillover increase and CO2 emissions reduction.