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The geological storage of CO2 is a critical technique for reducing emissions, which significantly contributes to the mitigation of the greenhouse effect. Currently, CO2 is often geologically stored in coal seams, hydrocarbon reservoirs, and saline aquifers in order to store CO2 and improve the oil and gas recovery simultaneously. Shale formations, as candidates for CO2 storage, are drawing more attention because of their rich volumes. CO2 storage through shale formations in the Sichuan Basin, China, has tremendous potential because of the readily available CO2 injection equipment, such as abandoned shale gas wells. Therefore, we review the potential of using these wells to store CO2 in this paper. Firstly, we review the status of the geological storage of CO2 and discuss the features and filed applications for the most studied storage techniques. Secondly, we investigate the formation properties, shale gas field development process, and characteristics of the abandoned wells in the Sichuan Basin. Additionally, after carefully studying the mechanism and theoretical storage capacity, we evaluate the potential of using these abandoned wells to store CO2. Lastly, recommendations are proposed based on the current technologies and government policies. We hope this paper may provide some insights into the development of geological CO2 storage using unconventional reservoirs. [ABSTRACT FROM AUTHOR]

Methane (CH4) is the second-largest greenhouse gas emitted by human activity and natural sources after carbon dioxide (CO2). Its relatively short lifetime in the atmosphere (about 12 years) means that we can mitigate the human impacts of climate change in a relatively short period of time by reducing CH4 emissions. The creation of CH4 emissions management policies can be based on the distribution maps of surface CH4 concentration that are in large-scale and at high-resolution. The estimate of CH4 emissions with broad coverage are provided by currently extensively used satellite data supplemented with data from model simulations. However, it is at low spatial resolution. In this paper, through the combination of atmospheric CH4 observations from the TROPOMI sensor and wind data from the ECMWF global reanalysis, a straightforward divergence method is proposed to estimate the surface CH4 emissions in China from March 2019 to September 2022 at a resolution of 7 km × 7 km. This method was compared with the average annual CH4 emissions of Emissions Database for Global Atmospheric Research (EDGARv7.0), and the Root Mean Square Error (RMSE) is 2.53 kg/km2/h and within error envelop (EE) is 72.93%, which represents the proportion of reliable values under certain uncertain conditions. We estimated that the average annual CH4 emissions in China from 2019 to 2022 is 81 Tg, with the lowest emissions in 2021 (75 Tg) due to the impact of COVID-19. In 2021, the largest anthropogenic emissions in China are from agriculture, energy activities and livestock, accounting for 28% (20.8 Tg), 25% (18.9 Tg) and 19% (13.9 Tg) of total emissions, respectively, while wetlands, as the largest natural source, produce 14% (10.5 Tg) of CH4 emissions. [ABSTRACT FROM AUTHOR]

With the proposal of China's national "double carbon" strategic goal, carbon capture, utilization and storage (CCUS) technology has attracted more and more attention. Due to the high cost, high energy consumption and high risk of CCUS technology, this technology is still in the initial stage of development in China. Among them, CO2 geological storage is one of the risks, and the environmental monitoring technology of CO2 storage leakage is particularly important in the large-scale popularization and application of CCUS technology in China. On the basis of extensive research on the related literature concerning CO2 storage and leakage, this paper begins with the types and mechanisms of CO2 storage, analyzes the ways and risks of CO2 storage and leakage and then summarizes the existing environmental monitoring technologies of CO2 geological storage and leakage. In the future, China can promote the progress of CO2 geological storage monitoring technology and help achieve the goal of "double carbon" by strengthening the research on CO2 storage mechanism and main control factors, perfecting the risk assessment method of CO2 storage, constructing the monitoring technology system of the CO2 storage life cycle, and standardizing the CO2 storage risk response system. [ABSTRACT FROM AUTHOR]

China's economy has made significant strides in the past three decades. As a direct result of China's "one belt, one road" (OBOR) initiative, the country's rate of industrialization and urbanization is currently the fastest in the entire world. This rapid development is largely dependent on the enormous amounts of energy currently being consumed and forms the foundation of the world's high levels of carbon emissions. It is generally agreed that the production of greenhouse gases, particularly carbon dioxide, is the primary contributor to the current state of climate change. In this paper, a CO2 emission prediction model based on Bi-LSTM is constructed. In order to conduct empirical tests on the model, this study uses data from South Asian countries and China from 2001 to 2020. China's CO2 emissions from 2022 to 2030 were predicted along with those of other countries in order to study the combined effects of the scientific and technological progress, industrial structures, and energy structure factors affecting CO2 emissions. When compared with the LSTM and GRU methods, the Bi-LSTM model's results produced lower MAE, MSE, and MAPE values, indicating that it performs better. According to the findings, carbon emissions represent a significant problem that will become much worse in the future due to China and India's high emissions, particularly in the next 10 years, if the government does not implement policies that help reduce those emissions. [ABSTRACT FROM AUTHOR]

The synergetic reduction of CO2 and PM2.5 emissions has received much attention in China in recent years. A comprehensive evaluation of the synergy between CO2 emission reduction (CER) and PM2.5 emission reduction (PER) would provide valuable information for developing synergetic control policies. Thus, we constructed a comprehensive CO2-PM2.5-emission-reduction index system and evaluated the synergy between CER and PER, using the coupling coordination degree (CCD) and relative development degree (RDD) model in China's 329 cities from 2003 to 2017. The spatiotemporal characteristics of the CCD were analyzed on the national, regional, and urban scales. Furthermore, we used the spatial autocorrelation analysis, kernel density estimation, and Dagum Gini coefficient to investigate the spatial autocorrelation, evolutionary characteristics, and regional differences of the CCD. The results indicate that (1) the synergy between CO2 and PM2.5 emissions' reductions showed an upward trend, and the lowest CCD values occurred in NW and Shanghai on the regional and urban scales, respectively; (2) the CCD showed obvious spatial clustering characteristics, with 75% of the cities located in the "High–High" or "Low–Low" clustering zones in the Moran scatter plots in 2017; (3) the polarization of CCD in SC, MYR, and SW showed intensified trends; (4) and the hypervariable density was the largest contributor to the overall difference in the CCD. Our findings suggest that more attention should be paid to the top-level design of the policies, technological innovation, and cross-regional or intercity cooperation. [ABSTRACT FROM AUTHOR]

Modeling the heat and carbon dioxide (CO2) exchanges in agroecosystems is critical for better understanding water and carbon cycling, improving crop production, and even mitigating climate change, in agricultural regions. While previous studies mainly focused on simulations of the energy and CO2 fluxes in agroecosystems on the North China Plain, their corrections, simulations and driving forces in East China are less understood. In this study, the dynamic variations of heat and CO2 fluxes were simulated by a standalone version of the Simple Biosphere 2 (SiB2) model and subsequently corrected using a Random Forest (RF) machine learning model, based on measurements from 1 January to 31 May 2015–2017 in eastern China. Through validation with direct measurements, it was found that the SiB2 model overestimated the sensible heat flux (H) and latent heat flux (LE), but underestimated soil heat flux (G0) and CO2 flux (Fc). Thus, the RF model was used to correct the results modeled by SiB2. The RF model showed that disturbances in temperature, net radiation, the G0 output of SiB2, and the Fc output of SiB2 were the key driving factors modulating the H, LE, G0, and Fc. The RF model performed well and significantly reduced the biases for H, LE, G0, and Fc simulated by SiB2, with higher R2 values of 0.99, 0.87, 0.75, and 0.71, respectively. The SiB2 and RF models combine physical mechanisms and mathematical correction to enable simulations with both physical meaning and accuracy. [ABSTRACT FROM AUTHOR]

The transportation sector is a major source of greenhouse gases and air pollutants, and it has a crucial effect on the synergistic reduction of NOx and carbon. In order to find the energy-efficient vehicle technologies with the highest net reduction potential and lowest net reduction cost over the life cycle, this study traced the CO2 and NOx emission streams of 33 energy-efficient technologies, hidden in the supply chain during the production phase, through structural path analysis, and measured the emission reductions during the use phase using the emission factor method. Moreover, we applied structural decomposition analysis to quantify the three main drivers, including emission intensity, industrial structure, and final demand, of changes in CO2 and NOx emissions from 11 transport subsectors during 2012–2018. Results indicate that CO2 emissions of the transport sector more than doubled from 2012 to 2018; however, the influence of NOx was less significant. The final demand of the road subsector was the most significant driver contributing to CO2 emission changes, with an increase of 109.27 Mt. The emission intensity of road transportation caused the greatest mitigation effect on NOx emission changes, with a decrease of 1902 Kt. The findings of the scenario analysis demonstrate that the most efficient action of the pure electric technology for passenger cars reduces 20.92 Mt NOx emissions, and the parallel hybrid technology for heavy trucks offers the greatest cost effectiveness with a net abatement of 2577 Mt CO2 over its life cycle. Consequently, the aggressive development of new energy technology has become a prerequisite strategy to synergistically reduce CO2 and NOx emissions. [ABSTRACT FROM AUTHOR]

Urban residential carbon dioxide (CO2) emissions have increased sharply along with the rapid urbanization process. Few studies have considered the different effects of influencing factors between Northern and Southern China, and the analysis of CO2 per unit area from the spatial perspective is also rarely involved. Using the spatial Durbin model (SDM), this study aimed at revealing the influencing factors (including income, inequality, population density, urban morphology, etc.) on CO2 per capita and CO2 per unit area during 2001–2018 between Northern and Southern China. The results showed that the Northern cities had higher carbon emissions and a faster growth rate, and the high-high clusters were also mainly located in the Northern cities. The Gini coefficient was correlated adversely with CO2, while income imposed a positive effect on carbon emissions. The negative coefficients of the quadratic term of the GDP per capita demonstrated that the residential carbon emissions have the potential to decrease when the income increases to a certain level. The indirect effects of income and the Gini showed that spatial spillover effects exist. Urban population density and the ratio of residential area to built-up area had an opposite effect on CO2 per capita and CO2 per unit area, and they have a bigger impact on the CO2 per unit area. This study revealed the different roles of various factors in reducing CO2 per unit area from the spatial perspective and CO2 per capita from the non-spatial perspective between the Northern and Southern regions, which could help policymakers to design targeted mitigation measures in the residential sector in China, providing references for developing countries to jointly reduce carbon emissions to promote the mitigation of global climate change. [ABSTRACT FROM AUTHOR]

As the second most abundant greenhouse gas after carbon dioxide (CO2), methane not only plays an important role in global and regional photochemical reactions, but also has an important impact on energy balance and climate change. To explore the long-term trends and spatiotemporal variation of methane concentration over China, we verified the accuracy of the column-averaged, dry air-mixing ratio of CH4 (abbreviated as XCH4 hereafter) merged by SCIAMACHY and GOSAT products, utilizing the data of six surface observation stations in China and the surrounding areas. The root mean square error (RMSE) was mostly less than 2.5%, and the correlation coefficients (r) were 0.77, 0.84, 0.66, 0.42, 0.62 and 0.75. Furthermore, we analyzed the temporal and spatial variation patterns of the XCH4 concentration over China from 2003 to 2020. The results showed that the XCH4 concentration had an increasing trend over China from 2003 to 2020; the average growth rate was 6.64 ppb·a−1, and the value range of the increase rate was from 4.66 ppb·a−1 to 8.46 ppb·a−1. The lowest XCH4 concentration was located over Tibet (1764.03 ppb), and the high values were located in the Sichuan Basin, Central China (Hunan, Hubei, and Henan) and East China (Anhui and Jiangxi) (1825–1845 ppb). The XCH4 concentration was higher in autumn and summer, low in winter and spring, and had obvious seasonal variations. Human factors such as population density, GDP and energy consumption have a significant impact on the XCH4 concentration over China. [ABSTRACT FROM AUTHOR]

Methane is the second most important greenhouse gas after carbon dioxide. The intensity and distribution of methane source/sink in China are unknown. We collected the column-averaged dry air mixing ratio of CH4 (abbreviated as XCH4 hereafter) from TROPOMI for the period from 2018 to 2021, to study spatial distribution and temporal change of atmospheric CH4 concentration, providing clues and foundations for understanding the source/sink in China. It was found that the distribution of XCH4 is roughly high in the East, low in the West, high in the South and low in the North. Additionally, an evidently positive linear relationship between XCH4 and population density was witnessed, suggesting anthropogenic emissions may account for a large portion of total methane emissions. XCH4 exhibits evident seasonal characteristics, with the peak in summer and trough in winter, regardless of the different regions. Moreover, we used XCH4 anomalies to identify the emission sources and found its great potential in the detection of methane emission from mining plants, landfill, rice fields and even geological fracture zones. [ABSTRACT FROM AUTHOR]

The concentration of atmospheric carbon dioxide (CO2) has increased rapidly worldwide, aggravating the global greenhouse effect, and coal-fired power plants are one of the biggest contributors of greenhouse gas emissions in China. However, efficient methods that can quantify CO2 emissions from individual coal-fired power plants with high accuracy are needed. In this study, we estimated the CO2 emissions of large-scale coal-fired power plants using Orbiting Carbon Observatory-2 (OCO-2) satellite data based on remote sensing inversions and bottom-up methods. First, we mapped the distribution of coal-fired power plants, displaying the total installed capacity, and identified two appropriate targets, the Waigaoqiao and Qinbei power plants in Shanghai and Henan, respectively. Then, an improved Gaussian plume model method was applied for CO2 emission estimations, with input parameters including the geographic coordinates of point sources, wind vectors from the atmospheric reanalysis of the global climate, and OCO-2 observations. The application of the Gaussian model was improved by using wind data with higher temporal and spatial resolutions, employing the physically based unit conversion method, and interpolating OCO-2 observations into different resolutions. Consequently, CO2 emissions were estimated to be 23.06 ± 2.82 (95% CI) Mt/yr using the Gaussian model and 16.28 Mt/yr using the bottom-up method for the Waigaoqiao Power Plant, and 14.58 ± 3.37 (95% CI) and 14.08 Mt/yr for the Qinbei Power Plant, respectively. These estimates were compared with three standard databases for validation: the Carbon Monitoring for Action database, the China coal-fired Power Plant Emissions Database, and the Carbon Brief database. The comparison found that previous emission inventories spanning different time frames might have overestimated the CO2 emissions of one of two Chinese power plants on the two days that the measurements were made. Our study contributes to quantifying CO2 emissions from point sources and helps in advancing satellite-based monitoring techniques of emission sources in the future; this helps in reducing errors due to human intervention in bottom-up statistical methods. [ABSTRACT FROM AUTHOR]