Emissions of carbon from forestry and land‐use change in tropical Asia

Energy-related activities are a major contributor of greenhouse gas (GHG) emissions. A growing body of knowledge clearly depicts the links between human activities and climate change. Over the last century the burning of fossil fuels such as coal and oil and other human activities has released carbon dioxide (CO2) emissions and other heat-trapping GHG emissions into the atmosphere and thus increased the concentration of atmospheric CO2 emissions. The main human activities that emit CO2 emissions are (1) the combustion of fossil fuels to generate electricity, accounting for about 37% of total U.S. CO2 emissions and 31% of total U.S. GHG emissions in 2013, (2) the combustion of fossil fuels such as gasoline and diesel to transport people and goods, accounting for about 31% of total U.S. CO2 emissions and 26% of total U.S. GHG emissions in 2013, and (3) industrial processes such as the production and consumption of minerals and chemicals, accounting for about 15% of total U.S. CO2 emissions and 12% of total ...

This study employs land use and energy consumption data from Gansu Province spanning the years 2005 to 2020 and utilizes models to estimate carbon emissions and the corresponding carbon compensation values. The research calculated the carbon emissions and compensation for various administrative regions over different time periods, revealing the following insights: (1) There is a notable increasing trend in net carbon emissions due to land-use changes in Gansu Province, characterized by an initial swift rise, followed by a more gradual growth pattern. Construction land is identified as the primary contributor to carbon emissions, increasing from 26 million tons in 2005 to 55.3 million tons in 2020, which is an increase of 1.80 times; meanwhile, forested areas, as significant carbon sinks, show a slight increase in carbon absorption from 2.33 million tons in 2005 to 2.35 million tons in 2020. (2) The municipalities with high net carbon emissions are predominantly Lanzhou, Qingyang City, and Jiuquan City, which are marked by high levels of carbon emissions and low compensation rates. In contrast, regions with lower net carbon emissions are mainly found in the Gannan Tibetan Autonomous Prefecture, where emissions are minimal and compensation rates are relatively high. A similarity in the spatial distribution patterns of both net carbon emission intensity and total net carbon emissions is observed among these cities. Alterations in land use have a significant impact on regional carbon emissions. Investigating the spatiotemporal variations of land-use change and carbon compensation in Gansu Province is essential for comprehending the dynamics of regional carbon emissions, developing effective emission reduction strategies, and fostering low-carbon development.

Land use changes lead to changes in the functions of different types of carbon sources and sinks, which are key sources of carbon emissions. The study of carbon emissions and its influencing factors in the Aksu River Basin from the perspective of land use change is of great importance for the promotion of integrated protection and restoration of mountains, water, forests, fields, lakes, grasslands, sand, and ice in the basin and to help achieve the goal of carbon peaking and carbon neutrality. Based on four periods of land use data and socio-economic data from 1990 to 2020, the total carbon emissions from land use were measured, and the spatial and temporal trajectories of carbon emissions and their influencing factors were explored. The results showed that:① from 1990 to 2020, arable land, forest land, construction land, and unused land showed a general increasing trend, whereas grasslands and water areas showed a decreasing trend. The spatial change in land use types was mainly characterized by the conversion of grasslands and unused land into arable land, and 83.58 % of the arable land conversion areas were concentrated in the southwest of Wensu, Aksu, and the northern part of Awat. ② The total net carbon emissions in the basin showed a continuous growth trend from 1990 to 2020, with a cumulative increase of 14.78×104 t. The increase in arable land was a key factor causing an increase in net carbon emissions in the basin. ③ The spatial distribution pattern of land use carbon emissions in the basin was high in the middle and low in the fourth, with significant changes in net carbon emissions mainly in the southern part of Wensu, Aksu, Awat, and Alaer. ④ Human activities had the strongest driving effect on land use carbon emissions, with their effects gradually increasing from east to west. The contribution of average annual temperature to land use carbon emissions was mainly concentrated in the eastern part of Aksu and the northern part of Awat, whereas average annual rainfall had a strong inhibitory effect on the northern part of Wensu and the western part of Aheqi.

Carbon dioxide (CO2) emissions result from human activities like burning fossil fuels. CO2 is a greenhouse gas, contributing to global warming and climate change. Efforts to reduce CO2 emissions include transitioning to renewable energy. Monitoring and reducing CO2 emissions are crucial for mitigating climate change. Strategies include energy efficiency and renewable energy adoption. In the past few decades, several nations have experienced air pollution and environmental difficulties because of carbon dioxide (CO2) emissions. One of the most crucial methods for regulating and maximizing CO2 emission reductions is precise forecasting. Four machine learning algorithms with high forecasting precision and low data requirements were developed in this study to estimate CO2 emissions in the United States (US). Data from a dataset covering the years 1973/01 to 2022/07 that included information on different energy sources that had an impact on CO2 emissions were examined. Then, four algorithms performed the CO2 emissions forecast from the layer recurrent neural network with 10 nodes (L-RNN), a feed-forward neural network with 10 nodes (FFNN), a convolutional neural network with two layers with 10 and 5 filters (CNN1), and convolutional neural network with two layers and with 50 and 25 filters (CNN2) models. Each algorithm's forecast accuracy was assessed using eight indicators. The three preprocessing techniques used are (1) without any processing techniques, (2) processed using max-min normalization technique, and (3) processed using max-min normalization technique and decomposed by variation mode decomposition (VMD) technique with 7 intrinsic mode functions and 1000 iterations. The latter with L-RNN algorithm gave a high accuracy between the forecasting and actual values. The results of CO2 emissions from 2011/05 to 2022/07 have been forecasted, and the L-RNN algorithm had the highest forecast accuracy. The L-RNN model has the lowest value of 1.187028078, 135.5668592, and 11.64331822 for MAPE, MSE, and RMSE, respectively. The L-RNN model provides precise and timely forecasts that can help formulate plans to reduce carbon emissions and contribute to a more sustainable future. Moreover, the results of this investigation can improve our comprehension of the dynamics of carbon dioxide emissions, resulting in better-informed environmental policies and initiatives targeted at lowering carbon emissions.

Rethinking wedges

The buildup of so-called “greenhouse gases” in the atmosphere — CO2 in particular-appears to be having an adverse impact on the global climate. This paper briefly reviews current expectations with regard to physical and biological effects, their potential costs to society, and likely costs of abatement. For a “worst case” scenario it is impossible to assess, in economic terms, the full range of possible non-linear synergistic effects. In the “most favorable” (although not necessarily “likely”) case (of slow-paced climate change), however, it seems likely that the impacts are within the “affordable” range, at least in the industrialized countries of the world. In the “third world” the notion of affordability is of doubtful relevance, making the problem of quantitative evaluation almost impossible. We tentatively assess the lower limit of quantifiable climate-induced damages at $30 to $35 per ton of “CO2 equivalent”, worldwide, with the major damages being concentrated in regions most adversely affected by sea-level rise. The non-quantifiable environmental damages are also significant and should by no means be disregarded. The costs and benefits of (1) reducing CFC use and (2) reducing fossil fuel consumption, as a means of abatement, are considered in some detail. This strategy has remarkably high indirect benefits in terms of reduced air pollution damage and even direct cost savings to consumers. The indirect benefits of reduced air pollution and its associated health and environmental effects from fossil-fuel combustion in the industrialized countries range from $20 to $60 per ton of CO2 eliminated. In addition, there is good evidence that modest (e.g. 25%) reductions in CO2 emissions may be achievable by the U.S. (and, by implication, for other countries) by a combination of increased energy efficiency and restructuring that would permit simultaneous direct economic benefits (savings) to energy consumers of the order of $50 per ton of CO2 saved. A higher level of overall emissions reduction — possibly approaching 50% — could probably be achieved, at little or not net cost, by taking advantage of these savings. We suggest the use of taxes on fossil fuel extraction (or a carbon tax) as a reasonable way of inducing the structural changes that would be required to achieve significant reduction in energy use and CO2 emissions. To minimize the economic burden (and create a political constituency in support of the approach) we suggest the substitution of resource-based taxes in general for other types of taxes (on labor, income, real estate, or trade) that are now the main sources of government revenue. While it is conceded that it would be difficult to calculate the “optimal” tax on extractive resources, we do not think this is a necessary prerequisite to policy-making. In fact, we note that the existing tax system has never been optimized according to theoretical principles, and is far from optimal by any reasonable criteria.

The contemporary global carbon (C) cycling involves the exchanges of C within and between the atmosphere, the oceans, and biosphere. The C may be transferred from one reservoir to another in seconds (e.g., the fixation of atmospheric carbon dioxide (CO2) by photosynthesis) or over millennia [e.g., the accumulation of fossil carbon (coal, oil, gas) through deposition and diagenesis of organic matter (OM)]. The focus of this chapter is on the exchange of CO2 occurring over the scale of months to a few centuries that are important for the cycling of C over years to decades with the focus on human influence starting from Industrial Era (1750). The cycling of C is important because it approximates the flows of energy around the Earth. The increased use of fossil fuels has led to increase in atmospheric concentration of CO2 and methane (CH4), which are the two most important greenhouse gases (GHGs). Addition of GHGs to the atmosphere enhances the greenhouse effect and is the main cause of the global warming. The rate and extent of the warming depend, in part, on changes in global C cycle. The processes responsible for adding C to, and withdrawing it from, the atmosphere are the part of the global C cycling. Some of the processes that add C to the atmosphere such as the combustion of fossil fuels and changes in land use and land management are under direct human control. Similarly human beings can control removal of CO2 through afforestation and/or reforestation as well as restoration of degraded lands . Others, such as the accumulation of carbon in the oceans or on land as a result of changes in global climate are not under direct human control except through controlling rates of greenhouse gas (GHG) emissions and therefore, climatic change. Because CO2 is more important GHG, and is expected to continue to be in the future, understanding the global C cycle is a vital part of managing the global climate. This chapter will address, first, the natural flows of C on the Earth, then the anthropogenic sources of C to the atmosphere and the sinks of carbon on land and in the oceans that have kept the atmospheric accumulation of CO2 lower than it would otherwise have been. Since 1750, the atmospheric concentration of CO2 has increased by ~44% from 278 ± 5 ppm in 1750 to 400.0 ± 0.1 ppm in 2015, corresponding to atmospheric burden of 260 ± 5 Pg C, largely as a result of fossil fuel combustion, but also from changes in land use and management. At the beginning of Industrial Revolution, the emissions of CO2 were from land use and land use change; now the emissions are largely (~90%) from fossil fuels. The decadal annual rates of fossil fuel CO2 emissions increased from 3.1 ± 0.2 Pg C yr−1 in 1960s to 9.3 ± 0.5 Pg C yr−1 for 2006–2015, while land use CO2 emission decreased from 1.5 ± 0.5 Pg C yr−1 to 1.0 ± 0.5 Pg yr−1 over the same period. The total global anthropogenic CO2 emission from 1750 to 2015 is estimated at 600 ± 70 Pg C, of which, fossil fuels and cement production is estimated at 410 ± 20 Pg C and land use change emission at 190 ± 65 Pg C. About 43% of the total anthropogenic CO2 emission or 260 ± 5 Pg C remained in the atmosphere, while ocean and terrestrial ecosystems sinks were 28 and 27%, respectively. The decadal atmospheric CO2 growth increased from 1.7 ± 0.1 Pg C yr−1 in the 1960s to 4.5 ± 0.1 Pg C yr−1 during 2006–2015, with ocean and terrestrial sinks increasing roughly in line with atmospheric increase over the last 50 years. Although there is no clear signal globally of a saturation of land sink strength, there are some indications suggesting that the ocean total CO2 uptake rate may have declined in recent decades.

Annual CO2 emissions (in 1000 Mt) %

The power industry, primarily its branch based on fossil fuel resources, is the major source of anthropogenic carbon dioxide emissions into the atmosphere of the planet. Based on direct measurements and some paleogeophysical data, it is shown that the carbon dioxide content in the atmosphere is currently increasing at a rate that exceeds by an order of magnitude its increase over the last several thousand years. Some methods for solving the global problem of reducing CO2 emissions caused by combustion of fossil fuels and basic technologies used for this purpose are considered. The main CO2 emission sources are identified. Based on the analysis of international statistical data, it is shown that large-scale fossil-coal-fired power-generating plants account for the greater part of the emissions. The existing CO2 capture technologies and those under development and intended for use in industrial power generation are compared and the prospects of their practical implementation are evaluated. The increase in the cost of the electric power generated within the full cycle of capturing CO2 from flue gases of large-scale power stations and storing it using the best-established technologies ready for introduction has been assessed. The basic factors that increase the cost of the power generated using the technologies for carbon dioxide emission sequestration have been determined and potential change in the prices in the future have been considered. The presented results suggest that the problem of drastic reduction in CO2 emissions requires a comprehensive approach and cannot be solved by efforts of only a limited number of industrially developed countries. The necessity of coordinated introduction of measures aimed at carbon dioxide emission sequestration not only in the “large-scale” power generation but also in other industries is shown.

A review of CO2 utilization and emissions reduction: From the perspective of the chemical engineering

Evaluating and simulating the impacts of land use patterns on carbon emissions in coal resource-based regions: A case study of shanxi province, China

Land use directly affects the carbon emissions and carbon stock of the ecosystem, and indirectly affects the carbon emissions from anthropogenic activities, which occur more frequently in coastal regions. Taking Nantong City as an example, detailed carbon emission projects were classified and calculated for different land use types by combining land use images of five typical years. Based on the complex relationship between land use carbon emissions and socio-economic factors, the system dynamics model (SD) was used to simulate the land use carbon emissions from 2005 to 2060, and to construct carbon-neutral policy scenarios. Compared with inlands, carbon emissions from land use in Nantong are more pronounced than inland areas, and unique land use types, such as shallows, play an important role as carbon sinks. Total land use carbon emissions show an upward trend from 2005 to 2020 and carbon emissions from construction land dominate. Under the natural development condition, the total net carbon emissions of Nantong are about 4,298,250 tons in 2060, failing to achieve carbon neutrality. The scenario with all four policies adjusted (LO, IO, TP, and PC) has the best emission reductions, peaking at 10,949,010 tons of net carbon emissions in 2029 and reducing them to 1,370,202 tons in 2060, which is the scenario closest to the carbon-neutral target. Overall, this study provides a meaningful conclusion for the study of land use carbon emission characteristics and low-carbon pathways in coastal cities, which can guide the formation of government policies.

This paper attempts to develop a calculation model to estimate the carbon dioxide (CO2) emissions during the mixing process of asphalt mixtures and explore energy-saving and emission reduction technologies. Based on a comprehensive analysis of the mixer’s working mechanism, mixing quality requirement, and theoretical deductions, a CO2 emission model for the mixing process of asphalt mixtures is established. The model highlights the significant impact of mixing time on both mixing quality and carbon emissions. The model demonstrates that the mixing quality improves with an increase in mixing time, but the degree of improvement diminishes after an initial significant enhancement, eventually stabilizing. Importantly, excessive mixing time does not significantly improve the mixing quality; conversely, an extended mixing time has a notable impact on carbon emissions. Results show that when the deviation of the asphalt content is changed from 0.3% to 0.2% for a 5% asphalt content mixture, the mixing time and resulting CO2 emissions increase by 14%; similarly, when the deviation is 0.1%, the mixing time and resulting CO2 emissions increase by nearly 40%. Additionally, the agitator’s capacity also significantly influences the CO2 emissions. For a project of a given scale, increasing the agitator capacity leads to a reduction in total carbon emissions during the mixing process. Compared to a type 1500 agitator, employing agitators of types 3000, 4000, and 5000 can achieve reductions in total CO2 emissions by 26.3%, 32.9%, and 36.8%, respectively. Therefore, for large-scale engineering projects aiming to minimize CO2 emissions during the mixing process, it is essential to determine the optimal mixing time to avoid excessive mixing and select a larger capacity agitator, preferably type 4000 or higher. These findings could support the development of effective emission reduction measures in the field of road construction, thereby contributing to the achievement of emission reduction targets and promoting the advancement of sustainable road development.

Fighting carbon leakage through consumption-based carbon emissions policies: Empirical analysis based on the World Trade Model with Bilateral Trades

Accurately measuring carbon dioxide (CO2) emissions is critical for effectively implementing carbon reduction policies, and China’s increased investment in reducing CO2 emissions is expected to significantly impact the world. In this study, the potential of shallow learning for predicting CO2 emissions was explored. Data included CO2 emissions, renewable energy consumption, and the share of primary, secondary, and tertiary industries in China from 1965 to 2021. These time-series data were converted into labeled sample data using the sliding window method to facilitate a supervised learning model for CO2 emission prediction. Then, different shallow learning models with k-fold cross-validation were used to predict China’s short-term CO2 emissions. Finally, optimal models were presented, and the important features were identified. The key findings were as follows. (1) The combined model of RF and LASSO performed best at predicting China’s short-term CO2 emissions, followed by LASSO and SVR. The prediction performance of RF was very fragile to the window width. (2) The sliding window method is used to convert time series predictions into supervision learning problems, and historical data can be used to predict future carbon dioxide emissions. To ensure that the feature data are real, the model can predict CO2 emissions for up to six years ahead. (3) Cross-validation and grid search were critical for optimizing China’s CO2 emissions prediction with small datasets. (4) By 2027, carbon dioxide emissions will continue to grow and reach 10.3 billion tons. It can be seen that the task of China to achieve its carbon peak on schedule is very heavy. The results indicate that an increase in renewable energy consumption and adjustments in industrial structure will continue to play an important role in curbing China’s CO2 emissions.