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It has been a crucial challenge for countries worldwide to reduce carbon dioxide (CO2) emissions. China is developing policies among various industries to peak CO2 emissions as soon as possible and expects the reduction of CO2 emissions through financial development. This paper explores the influencing mechanism of financial development on CO2 emissions based on panel data from provinces in China, considering constraints of technological innovation and fossil energy dependence among provinces and periods. Empirical results consistently indicate that the effect of China's financial development on CO2 emissions per capita is significantly negative, and it also presents an inverted U-shaped pattern. In those provinces or periods witnessing lower technological innovation and higher fossil energy dependence, the financial development has more significant effect on carbon emission reduction. This result suggests that regions with deficiencies in the development of technology industries, lower proportions of the tertiary industry, and a higher percentage of the secondary industry need to focus more on the value of financial development. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to strengthen research concerning the impact of climate change on China's water resources, in order to avoid extreme weather events such as regional rainstorms and floods affecting the stable supply of energy and electricity. The goal is to reorganize energy industry allocation to make China's energy the most efficient, which would thus help achieve sustainable development goals. The biggest contribution and of this paper is not only to enlarge the scale and take into account the sustainable aspects, but also to consider climate change factors that have been ignored in the past as exogenous variables. For the first time, we take stock of the optimal allocation of new and tradition energy sources and carbon dioxide. This study uses the Dynamic ZSG-DDF model with exogenous variables and undesirable outputs to find a way to redistribute carbon dioxide, new energy, and tradition energy from different provinces on the premise of maximizing efficiency, thus achieving the goal of reducing carbon and saving the earth. The research results show that the energy efficiency of tradition energy is higher than that of new energy in various provinces. At the same time, the China government must comprehensively review the quota targets of new energy, tradition energy, and carbon dioxide (CO2) in each province as quickly as possible. Currently, the quotas of various provinces are seriously misplaced, and 26 provinces in total have great room for improvement. [ABSTRACT FROM AUTHOR]

Commercial department assumes the vital part in energy conservation and carbon dioxide emission mitigation of China. This paper applies the time-series data covering 2001–2015 and introduces the STIRPAT method to research the factors of commercial department's carbon dioxide emissions in China. The combination of STIRPAT method and ridge regression is first adopted to research carbon dioxide emissions of commercial department in China. Potential influencing factors of carbon dioxide emission, including economic growth, level of urbanization, aggregate population, energy intensity, energy structure and foreign direct investment, are selected to establish the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) model, where ridge regression is adopted to eliminate multicollinearity. The estimation consequences show that all forces were positively related to carbon dioxide emissions in China's commercial department except for energy structure. Energy structure is the only negative factor and aggregate population is the maximal influencing factor of carbon dioxide emissions. The economic growth, urbanization level, energy intensity and foreign direct investment all positively contribute to carbon dioxide emissions of commercial department. The findings have significant implications for policy-makers to enact emission reduction policies in commercial sector. Therefore, the paper ought to take into full consideration these different impacts of above influencing factors to abate carbon dioxide emissions of commercial sector. [ABSTRACT FROM AUTHOR]

The development and renewal of gas sensor technology have enabled more and more low-cost gas sensors to form a carbon monitoring network to meet the requirements of the city. In the context of China's commitment to achieving the "double carbon" target by 2060, this paper reviews the principles of four standard gas sensors and the application of several low-cost sensors in urban carbon monitoring networks, with the aim of providing a practical reference for the future deployment of carbon monitoring networks in Chinese cities. Moreover, the types, prices, and deployment of the sensors used in each project are summarized. Based on this review, non-dispersive infrared sensors have the best performance among the sensors and are commonly used in many cities. Lots of urban climate networks in cities were summarized by many reviews in the literature, but only a few sensors were studied, and they did not consider carbon dioxide (CO2) sensors. This review focuses on the dense CO2 urban monitoring network, and some case studies are also discussed, such as Seoul and San Francisco. To address the issue of how to better ensure the balance between cost and accuracy in the deployment of sensor networks, this paper proposes a method of simultaneously deploying medium-precision and high-precision fixed sensors and mobile sensors to form an urban carbon monitoring network. Finally, the prospects and recommendations, such as different ways to mitigate CO2 and develop an entire carbon monitoring system for future urban carbon monitoring in China, are also presented. [ABSTRACT FROM AUTHOR]

Spatiotemporal variations of industrial carbon emissions (IE) must be scientifically understood, which will be helpful to formulate reasonable emission reduction strategies. Given that spatial distribution of IE is irrelevant to space agents commonly used (such as population and nighttime light), estimation and spatialization methods for total carbon dioxide (CO2) emissions are not entirely suitable for IE. Therefore, this paper used greenhouse gases observing satellite level 4A product to estimate IE at the city level and used industrial land density to obtain the distribution of IE within the administrative districts. Sectoral emission inventories of 182 cities and a mosaic Asian anthropogenic emission inventory named MIX were used to verify the results. Then, spatiotemporal variation characteristics of China's IE were analyzed from multiple levels. Results showed that (1) the mean relative error of estimation results was 56.11%, among which 62 cities had relative error of less than 30%. Gridded IE in this paper had high consistency with MIX. (2) Cities with high IE experienced rapid growth from 2009 to 2012, followed by slower growth from 2012 to 2017. (3) Centroid of significant cold and hot spots moved to the southeast and northwest, respectively. Most cities with high annual IE growth had relatively low emission efficiency, mainly located in Inner Mongolia and Xinjiang. Aggregation of medium and high IE grids may represent high emission efficiency. Significant differences still exist between cities in IE, and sustainable development strategies should be formulated according to local conditions. Regions with high annual growth or low emission efficiency are the key to achieving IE reduction targets in future. [ABSTRACT FROM AUTHOR]

China is the largest carbon dioxide emitter in the world. Over the past decades, China has made great efforts to retard the carbon emission growth. This paper aims to quantify the driving forces of stagewise provincial carbon emission changes and expound the bottom-up provincial efforts aligned with China's carbon abatement targets. During 2007–2012, economic growth as the major contributing factor posed a great increase in provincial carbon emissions, especially in underdeveloped provinces such as Inner Mongolia, Anhui, Guangxi, Shaanxi, and Ningxia. During 2012–2017, the energy intensity effect replaced economic growth as the largest contributor to provincial carbon emission changes. In both two periods, industrial structure upgrading and renewable energy expansion are conducive to mitigate provincial carbon growth, and the power sector will continue to contribute to national decarbonization. Lastly, this paper provides insight into national and provincial carbon emission abatement. [ABSTRACT FROM AUTHOR]

With the increase of carbon dioxide concentration in the atmosphere, the living environment of human beings is seriously affected. As a high carbon emission industry in China, thermal power enterprises are the key areas of carbon emission reduction in China. This paper first uses Super-SBM model to measure the performance of China's 18 major thermal power enterprises in 2009–2018 from a static point of view. After considering the carbon emission constraints, it analyzes the degree of change in enterprise performance, and finds that the impact of carbon emission constraints on enterprise performance is not absolute. After that, with the help of Malmquist index model, this paper discusses the dynamic changes of thermal power enterprises' performance under carbon emission constraint in recent 10 yrs. The results show that the overall performance of carbon emission constraint is in a weak regression stage and summarizes the disadvantages of different enterprises. On the basis of the research conclusion, this paper puts forward countermeasures and suggestions to further improve the performance of Chinese thermal power enterprises under the carbon emission constraints in the future, which is conducive to different enterprises to optimize their own disadvantages. [ABSTRACT FROM AUTHOR]

Understanding the effect of freight structure (FS) on carbon dioxide (CO2) emissions is significant for policymakers in the transport sector. However, few studies have investigated fully how the freight structure influences transport CO2 emissions. In the paper, a comprehensive freight structure index is proposed to assess the freight structure in 16 provinces of China during 2005–2019. Then, the heterogeneous effect of the freight structure and other factors on CO2 emissions are quantified by the quantile regression model developed. The mediating effect between freight structure and CO2 emissions is further examined. And the panel causality test is used to explore the directions of causality among variables. The main results indicate that (1) the level of freight structure shows a distribution of increase from western and central regions to eastern regions in 16 provinces. (2) Freight structure has a positive effect on transport CO2 emissions, and its effect is significantly heterogeneous in all quantiles. (3) Freight structure has an indirect impact on CO2 emissions through the scale effect. (4) The bidirectional causality is confirmed between economic growth, industrial structure, and information and communication technology with carbon emissions. A unidirectional causality from CO2 emissions to freight structure is also found. Finally, some policy recommendations are proposed for sustainable transport development in China. The results facilitate the formulation of the regionally tailored carbon emission reduction strategy in the transport sector. [ABSTRACT FROM AUTHOR]

To reduce carbon emissions effectively in Shandong province in New Normal economy, this paper presents a new way to reflect the situation of carbon emissions accurately from both time-perspective and spatial perspective. The characteristics of carbon emissions in Shandong province are presented during 2005-2014, and this paper explores the spatial autocorrelation of carbon emissions branches in 17 cities. The results show that the total amount of carbon emissions in Shandong province presented a steady growth, whereas the growth rate was low, and the proportion of coal consumption in energy structure was significantly higher than the national average. In addition, the spatial pattern of the carbon emissions in 17 cities presented obvious regionally distinction. Furthermore, the affecting factors governing carbon emissions are decomposed based on the Kaya identity. The results reveal that economic scale was the key factor to increase the carbon emissions, whereas energy efficiency was the main factor of carbon emissions reduction. Therefore, this paper draws government should promote carbon emissions reduction given priority to some regions virtually, while encouraging emission reduction in others. [ABSTRACT FROM AUTHOR]

This study examines the relationship between FDI and CO2 emissions in a nonlinear framework using data from countries along the "One Belt, One Road" from 1979 to 2017 as the sample. First, the linear analysis method was used to examine the stability of FDI and CO2 emissions in countries along the "One Belt, One Road." In addition, the BDS (Brock-Dechert-Scheinkman test) method and nonlinear Granger causality test are used to investigate the nonlinear relationship between variables. Finally, a threshold vector autoregressive model (TVAR) is used to analyze the dynamic change mechanism between China's FDI and CO2 emissions, and a threshold vector error correction model (TVECM) is used to test how the variables respond to deviations from equilibrium. Then, the Markov switching model is used to test the robustness of the results. The research results show that China, India, South Africa, and other countries all have a nonlinear causal relationship between FDI and CO2 emissions. At the same time, the comovement of FDI and CO2 emissions in China has obvious structural break features, which are relevant for the underlying regime. Furthermore, the results also show that the adjustment process of the FDI toward equilibrium is highly persistent in the first regime, and CO2 emissions will adjust to an equilibrium state at a faster speed in the second regime. Therefore, this paper puts forward different policy suggestions for different countries. For China, we should pay attention to the long-term benefits of FDI and introduce high-tech green FDI. [ABSTRACT FROM AUTHOR]

This paper investigates the impacts of new digital technology on carbon emissions in China. To better capture the feature of new digital technology, we distinctively measure it using the invention patent stock of new generation information technology industry. The empirical results show that the development of new digital technology significantly reduces carbon dioxide (CO2) intensity. The results are still robust after addressing the endogeneity issues using instrumental variable regressions. The carbon reduction mechanisms of new digital technology include reducing energy intensity, optimizing energy structure, and advancing treatment technology. We further confirm the spatial spillover and nonlinear effects of new digital technology on CO2 intensity and the moderating effect of urban digitalization in carbon reduction. The study suggests that the potential of new digital technology in reducing carbon emissions should be highly attentioned by governments. [ABSTRACT FROM AUTHOR]

Because of the harmful influence of CO2 emissions on the environment and humans, issues related to CO2 emissions have received considerable attention in recent years. Based on the pollution haven hypothesis and pollution halo effect, the uncertain effect of bilateral foreign direct investment (FDI) on CO2 emissions has recently been in focus. Moreover, because of the opposing capital flow of bilateral FDI, the interaction between inward FDI (IFDI) and outward FDI (OFDI) might have a trade-off effect on CO2 emissions. The accurate forecasting of CO2 emissions in China in light of effect of the bilateral FDI is important since the government can use it to regulate emissions' reduction. The grey multivariable Verhulst model (GMVM) was formulated in this paper with the goal of forecasting CO2 emissions in China by considering the nonlinear, independent, and interaction-related effects of bilateral FDI on them. To enhance the accuracy of prediction, this paper used the Fourier series and the grey prediction model for residual modifications. The empirical results showed that the IFDI and the item of the interaction of bilateral FDI promoted CO2 emissions, whereas OFDI reduced them in China. These results also verified the higher precision of the improved GMVM relative to other models. This paper also used improved GMVM to further forecast CO2 emissions and provided suggestions for the Chinese government to plan for foreign investment, including selectively implementing bilateral FDI, and focusing on the trade-off in its interaction-related effects. [ABSTRACT FROM AUTHOR]

With economic development and the acceleration of urbanization, China's energy demand has gradually increased and brought a lot of energy-related CO2 emissions. Energy-related CO2 emissions are affected by a variety of factors. Quantifying the correlation between energy-related CO2 and driving factors and constructing the driving factor system are conducive to predict the future energy-related CO2 emissions and analyze the impact of driving factors. In this paper, the improved grey relational analysis (IGRA) was proposed to screen the influencing factors of energy-related CO2 emissions considering the sample difference, and the factor analysis (FA) was used to reduce dimensionality of the influencing factors. Then, a carbon dioxide emission forecasting model based on the bacterial foraging optimization algorithm (BFO) and the least square support vector machine (LSSVM) was proposed. Empirical analysis results of Hebei show that the LSSVM optimized BFO significantly improves the accuracy of energy-related CO2 emissions forecasting, and IGRA-FA-BFOLSSVM model is significantly better than BP, PSOBP, SVM, and LSSVM models. The mean absolute percentage error (MAPE) of the proposed model is 0.374%. The forecasting results of the supplementary case show that the model has better generalization ability. In addition, education and technological progress have proven to be important drivers of energy-related CO2 emissions. Simultaneously, the research results can also offer more breakthrough points for policy makers to control carbon emissions. [ABSTRACT FROM AUTHOR]

Several countries have weakened the carbon emission objectives to immediately revive the economy in the post-COVID-19 era. Therefore, it is a challenge worth addressing to readjust the economic development and carbon emissions after the COVID-19 pandemic. From the perspective of China's carbon emissions, this study shapes a multi-objective dynamic optimization model based on the material capital input and R&D support aspects. The proposed model imitates China's economic development, energy consumption, and carbon dioxide (CO2) emissions. The model provides theoretical suggestion for the government to revive economic development and reduce carbon emissions. In addition, this research paper compares the evolutionary path of carbon peak under the two scenarios. The first scenario requires maintaining the pre-epidemic development state and pace of carbon emission reduction, referred to as the baseline scenario (BS). The second scenario is termed the optimal scenario (OS) based on the model calculation. The study findings exhibit that China is not able to accomplish the 2030 CO2 emission peak objective, under the BS. However, China under the OS shall expectedly accomplish the 2030 carbon peak objective ahead of schedule, while the peak CO2 emissions shall be around 11.28 billion tons. Reportedly, at least 788 million tons of CO2 reduction contrasted with the BS. Furthermore, there is an 80.35% decline in energy intensity as compared to 2005. Consequently, the study results contribute theoretical guidance for the "green recovery" of China's economy and the adjustment of carbon emission reduction's path after the COVID-19 epidemic. Consistent with this, the research method also contributes to the theoretical research on carbon emissions at the national level while extending a new research perspective for the economic and environmental fields. [ABSTRACT FROM AUTHOR]

In China, urbanization has been rapidly developing since the country began its economic reform in 1978. With the expansion of the urban population size and the corresponding urbanization and industrialization, the rapid increase in CO2 emissions has become a major restraint on China's economic growth. However, current studies have not paid sufficient attention to the impact of the urban population size on CO2 emissions in China due to poor data availability. In this paper, we apply index decomposition analysis (IDA) to decompose CO2 emissions into five elements, and we investigate both the direct and indirect impacts of the urban population size on total CO2 emissions and per capita CO2 emissions in China. Additionally, we empirically study the impact on 175 Chinese cities at the prefecture level and above for the first time. The results show that the urban population size significantly promotes total CO2 emissions but curbs per capita CO2 emissions in cities in China. A 1% increase in the urban population size will lead to a nearly 1% increase in total CO2 emissions and a 0.3% decrease in per capita CO2 emissions. Regarding heterogeneity, the expansion of the urban population size in the large city group drives a greater increase in CO2 emissions than the expansion of the urban population size in other city groups. The main transmission pathways are through population density, economic agglomeration and energy intensity. Regarding the mechanism variables, high economic agglomeration leads to more CO2 emissions, while an increase in population density and energy efficiency results in carbon mitigation. Moreover, public green areas, foreign direct investment (FDI) and technology innovation are conducive to reducing CO2 emissions. [ABSTRACT FROM AUTHOR]

With the economic development, China has become the largest CO2 emissions country. China’s power industry CO2 emissions accounted for about 50% of total CO2 emissions. Therefore, exploring major drivers of CO2 emissions is critical to mitigating its CO2 emissions in power industry. Many studies considered the time series model to analyze the national influences factors of CO2 emissions. But this paper focuses on regional differences in CO2 emissions and adopts panel data models to explore the major impact factors of CO2 emissions in the power industry at the regional and provincial perspectives. The results indicate economic growth level plays a dominant role in reducing CO2 emissions. The power-consuming efficiency on the demand side has large potential to mitigate CO2 emissions, but its influences are different in three regions. The impacts of the electric power structure on CO2 emissions decline from the eastern region to the central and western regions. The influence of urbanization and industrialization also has significant regional differences. Therefore, the governments should consider the influencing factors and regional differences and formulate appropriate policies to decrease CO2 emissions in the power industry. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between the logarithms of carbon dioxide (CO2) emissions and real Gross Domestic Product (GDP) in China by applying fractional integration and cointegration methods. These are more general than the standard methods based on the dichotomy between stationary and non-stationary series, allow for a much wider variety of dynamic processes, and provide information about the persistence and long-memory properties of the series and thus on whether or not the effects of shocks are long-lived. The univariate results indicate that the two series are highly persistent, their orders of integration being around 2, whilst the cointegration tests (using both standard and fractional techniques) imply that there exists a long-run equilibrium relationship between the two variables in first differences, i.e. their growth rates are linked together in the long run. This suggests the need for environmental policies aimed at reducing emissions during periods of economic growth. [ABSTRACT FROM AUTHOR]

China has announced to launch a national emission trading system (ETS). The heterogeneity of marginal abatement cost (MAC) is prerequisite for trading, and the knowledge about the evolutionary characteristics of MAC is particularly necessary. However, the β convergence theory has been proved to be suitable yet rarely applied to the study of MAC of CO2. To fill this gap, this paper connects them creatively, and the convergence of MAC during 2001–2015 and the influential factors are explored by spatial panel data models. Results show that China's MAC converges during the study period whether the spatial effect is considered or not. When evaluating the convergence of MAC, the spatial effect should not be ignored, because it will improve the explanatory power of models and promote the convergence. The size of labor force, emission level, coal consumption, foreign direct investment, and industrial structure significantly affect the growth rate of MAC. Low-carbon policies could be formulated fully considering the factors and their spillover effects. Those findings are certainly significant in imposing carbon reduction targets and adopting policy instruments. In addition, a national ETS is more applicable to China's reality at this stage and suggested to introduce carbon tax in due course in the future. [ABSTRACT FROM AUTHOR]

Foresight of CO2 emissions from fuel combustion is essential for policy-makers to identify ready targets for effective reduction plans and to further improve energy policies and plans. A new method for forecasting the future development of China's CO2 emissions from fuel combustion is proposed in this paper by using grey forecasting theory. Although the existing fractional nonlinear grey Bernoulli model (denoted as FNGBM(1,1)) has been theoretically proven to enhance the adaptability to diverse sequences, its fixed integer-order differential derivative still impairs the performance to some extent. To this end, a varying-order differential derivative is introduced into the existing differential equation to enable a more flexible structure, thus improving the prediction ability of FNGBM(1,1). Specifically, because of the advantages of conformable fractional accumulation, the traditional differential derivative is first replaced by the conformable fractional differential derivative. As a consequence, the continuous conformable fractional nonlinear grey Bernoulli model (hereinafter referred to as CCFNGBM(1,1)) is proposed. To further increase the validity of the model, a metaheuristic algorithm, namely Grey Wolf Optimizer (GWO), is then applied to search for the optimal emerging coefficients for the proposed model. Two real examples and China's CO2 emissions from fuel combustion are considered to verify the effectiveness of the newly proposed model, the experimental results show that the newly proposed model outperforms other benchmark models in terms of forecasting accuracy. The proposed model is finally employed to forecast the future China's CO2 emissions from fuel combustion by 2023, accounting for 10,039.80 million tons. Based on the forecasts, several policy suggestions are provided to curb CO2 emissions. [ABSTRACT FROM AUTHOR]

Carbon dioxide emission is an important environmental issue, and it has also become an important reference factor for governments to formulate social and economic policies. The objective and accurate prediction of carbon dioxide emissions can provide reference and early warning for the implementation of the government's environmental strategy. The change of the original data of carbon dioxide emissions is S-type, but not saturated S-type. The grey Verhulst model is mainly used to describe the process with saturation state, which is suitable for modeling S-type data series. However, it is found that there are inherent errors and limitations in this model. In this paper, the grey action of the grey Verhulst model is improved, a new action Verhulst model is obtained, and its properties are studied. Finally, the new model is used to predict the carbon dioxide emissions of China and Russia, and ARIMA model is added for comparison. The results show that compared with the original Verhulst model, the simulation and prediction accuracy of the optimized Verhulst model are improved by more than 10%, and the ARIMA model underestimates the carbon dioxide emissions. From the result analysis, China and Russia need to formulate strong energy conservation and emission reduction policies, vigorously develop clean energy industry, and promote green production and lifestyle. [ABSTRACT FROM AUTHOR]

The paper aims to investigate the influencing factors that drive the temporal and spatial differences of CO2 emissions for the transportation sector in China. For this purpose, this study adopts a Logistic Mean Division Index (LMDI) model to explore the driving forces of the changes for the transport sector's CO2 emissions from a temporal perspective during 2000–2017 and identifies the key factors of differences in the transport sector's CO2 emissions of China's 15 cities in four key years (i.e., 2000, 2005, 2010, and 2017) using a multi-regional spatial decomposition model (M-R). Based on the empirical results, it was found that the main forces for affecting CO2 emissions of the transport sector are not the same as those from temporal and spatial perspectives. Temporal decomposition results show that the income effect is the dominant factor inducing the increase of CO2 emissions in the transport sector, while the transportation intensity effect is the main factor for curbing the CO2 emissions. Spatial decomposition results demonstrate that income effect, energy intensity effect, transportation intensity effect, and transportation structure effect are important factors which result in enlarging the differences in city-level CO2 emissions. In addition, the less-developed cities and lower energy efficiency cities have greater potential to reduce CO2 emissions of the transport sector. Understanding the feature of CO2 emissions and the influencing factors of cities is critical for formulating city-level mitigation strategies of the transport sector in China. Overall, it is expected that the level of economic development is the main factor leading to the differences in CO2 emissions from a spatial-temporal perspective. [ABSTRACT FROM AUTHOR]

CO2 geological storage (CGS) is a crucial strategy for meeting CO2 emission reduction targets. However, the effect of microbial action on CGS in deep saline aquifer is unclear. In this paper, numerical simulations based on previous experimental data were conducted to investigate CO2 solubility-trapping and mineral-trapping by microbial-mediated process in CGS demonstration base in Ordos, north of China. The simulation results reveal that the presence of a microbial community has a positive influence on CGS, mainly in terms of the amount of CO2 that can be injected and the manner by which it is stored. With the effect of microbial action, the amount of injected CO2 was increased by 23.33% and the CO2 mineral sequestration increased from 1.12 to 2.05%. In addition, microbial action plays different roles at different stages of CGS. In the initial stage of CO2 injection, the microbial action promotes dissolution of calcite, oligoclase and clay minerals such as chlorite and kaolinite, providing a favorable condition (more K+, Ca2+, Na+, Mg2+, Fe2+ and HCO3−) for CO2 mineral-trapping. In the following stage of CGS, microbial action accelerates the precipitation of carbon-fixing minerals such as calcite and siderite, enhancing the CO2 storage security in deep saline aquifer. As such, secondary minerals such as ferroan dolomite and dawsonite were observed in advance in microbially mediated CO2–saline–sandstone interactions, further indicating that microbes play a positive role in CO2 mineral-trapping. Consequently, the microbial action can promote mineral capture of CO2 and increase the CO2 storage security in deep saline aquifer. These conclusions can provide a reference and basis for the suitability assessment of CGS in such deep saline aquifer like Erdos, China. [ABSTRACT FROM AUTHOR]

The anthropogenic emission of carbon dioxide (CO2) into the atmosphere is recognized as the main contributor to global climate change. To date, scientists have developed various strategies, including CO2 utilization technologies, to reduce global carbon emissions. This paper presents the global scientific landscape of the CO2 utilization research from 1995 to 2019 based on a bibliometric analysis of 1875 publications extracted from Web of Science. The findings indicate a major increase in the number of publications and citations received from 2015 to 2019, denoting a fast-emerging research trend. The dynamics of global CO2 utilization research is partly driven by China's policies and research funding to promote low-carbon economic development. Applied Energy is recognized as a core journal in this research topic. The utilization of CO2 is a multidisciplinary topic that has progressed by multidimensional collaborations at the country and organizations levels, while the formation of co-authorship networks at the individual level is mostly influenced by the authors' affiliations. Keyword co-occurrence analysis reveals a rapid evolution in the CO2 utilization strategies from chemical fixation in carbonates and epoxides to pilot-scale testing of power-to-gas technologies in Europe and the USA. The development of efficient power-to-fuel technologies and biological utilization routes (using microalgae and bacteria) will probably be the next research priorities in CO2 utilization research. [ABSTRACT FROM AUTHOR]

Energy saving and emission reduction are the most concerned issues in the world. Objective and accurate prediction of carbon emissions can provide reference and early warning for the implementation of the government's environmental strategy. Based on the traditional grey Verhulst model, this paper analyzes the main causes of its errors, introduces the extrapolation method to optimize the background value, and uses particle swarm optimization algorithm to optimize its parameters. In order to evaluate the performance of the models, six commonly statistical evaluation indicators are used to compare the new model with other optimization models and grey universal models, and the effect of the new model is basically better than other models. Finally, it is applied to the prediction of carbon dioxide emission of three kinds of coal in China. The results show that the increase of carbon dioxide related to raw coal slows down in 2016–2020, while the increase of carbon dioxide related to clean coal and other washed coal is 12.7097% and 19.2024%, respectively. Therefore, in order to prevent a strong rebound in carbon emissions, China should increase efforts to save energy and reduce emissions, and reduce energy consumption, especially coal consumption. [ABSTRACT FROM AUTHOR]

With increases in the economy and standards of living, energy consumption has grown significantly in China, which has resulted in serious local air pollution and greenhouse gas emissions. Because both carbon dioxide (CO2) and air pollutant emissions mainly stem from fossil energy use, a co-control strategy is simulated and compared with single control in China, using an integrated assessment model (Global Change Assessment Model-Tsinghua University (GCAM-TU)) in this paper. We find that end-of-pipe (EOP) control measures play an important role in reducing air pollution in the near future, but in the long run, optimizing the energy system is an effective way to control both emissions. Reducing air pollutant might take a "free-ride" of decarbonizing the energy system. Compared with a single control of air pollutants, a co-control strategy is likely to reduce the requirement of EOP control measures. The result guides the Chinese government to consider a systemic and scientific plan for decarbonizing the energy system and co-controlling CO2 and air pollutant, in order to avoid duplicate investments in infrastructure and lockup effect. The solution could be extended to many other developing countries, such as India and Africa, which is helpful to realize the goals of United Nations (UN) Sustainable Development Agenda. [ABSTRACT FROM AUTHOR]

As the largest carbon dioxide (CO2) emitter, China exists obvious regional inequality in per capita CO2 emissions. However, such inequality and its dynamic change for recent years have not been systematically studied. In this paper, we evaluate China's regional inequality in per capita CO2 emissions during 1997–2016 using the Theil index and decompose it into within-region and between-region components based on eight regions. Furthermore, we apply the decomposition analysis to explore the contribution of different factors to such inequality, including the carbonization index, energy intensity, energy structure, labor productivity, and employment rate. The results show that China's overall inequality in per capita CO2 emissions reduced first and increased thereafter during 1997–2016. Within-region inequality was the main source of overall inequality in 1997–2004, while between-region inequality contributed more during 2005–2016. Labor productivity and energy intensity were the two main drivers of overall inequality, but their contributions to the inequality between regions and within regions were quite different. Moreover, the impact of energy structure and carbonization index on regional inequality in per capita CO2 emissions significantly increased during 2012–2016, which was related to the efforts made by local governments to improve the energy mix. Policy implications were given according to the above conclusions to improve regional inequality in per capita CO2 emissions. [ABSTRACT FROM AUTHOR]

Increasing attention has been placed on the evaluation of CO abatement potential at national or sectoral level in China due to its largest energy consumption and carbon emission. However, few studies specialize in estimating provincial carbon reduction potential and its provincial differences. This paper estimates the carbon intensity abatement potential in China at provincial scale by exploring an environmental learning curve (ELC) model for carbon intensity. Per capita GDP, the proportion of the tertiary industry in GDP and energy intensity are selected as three independent variables. Based on the ELC model, the carbon intensity reduction potentials of 30 provinces in 2020 are estimated for business-as-usual and planned scenarios. The results indicate that China's total intensity abatement potential is 34.22 and 37.64 % in the two scenarios, respectively. For all provinces, energy intensity has the strongest positive learning ability among the three variables. Beijing, Tianjin, Liaoning, Jilin and Shanghai play major roles to cut down carbon emission intensity due to their large reduction potentials. However, the intensity reduction potentials in Qinghai, Ningxia, Xinjiang and Hainan are not obvious. [ABSTRACT FROM AUTHOR]

Information and communication technology (ICT) has exerted a great impact on the socio-economic development; however, this development has come with some potential influence on climate change. The academics are divided over this issue; some argue that ICT has contributed to carbon reduction, while others consider that ICT has increased carbon dioxide (CO2) emissions. Undoubtedly, the existing literature abounds in the relationship between ICT and CO2 emissions, but little attention has been paid to the spillover effect of ICT and CO2 emissions, especially in developing countries. Hence, with the panel data of 285 China's prefecture-level cities from 2004 to 2018, this study innovatively discusses the spatial and mechanism effects of ICT on CO2 emissions, further exploring the heterogeneous impact of ICT on CO2 emissions from multiple perspectives. The empirical results confirm the positive relationship between ICT and CO2 emissions and identify the spatial spillover effect in the relationship. Furthermore, notably, the intermediary effect of energy consumption on the impact of ICT on CO2 emissions is identified. Finally, due to the differences in the geographical position, population size, and urban agglomeration of prefecture-level cities in China, the impact of ICT on CO2 emissions varies in different cities. The findings not only contribute to advancing the existing literature but also have a significant and targeted policy guiding significance for the cities to maximize the favorable influences of ICT and promote the low-carbon transformation of the whole society. [ABSTRACT FROM AUTHOR]

This paper presents detrital modes, clay and carbonate cements and major and trace element contents of the sandstones from the lower Xiaganchaigou formation deposited during the Eocene/Oligocene transition in the Lenghu tectonic belt on the northern margin of the Qaidam Basin, NW Qinghai–Tibetan plateau. Our results show that the sandstones are mainly lithic feldspathic, with clay content varying from 4.1 to 42.5 vol. % (average 13.7 vol. %). These sandstones have low contents of Na2O, MgO, Al2O3, K2O, CaO and TiO2, with the average contents of 2.14 wt%, 0.78 wt%, 7.96 wt%, 2.11 wt%, 6.14 wt% and 0.15 wt%, respectively. The total content of major elements is < 30 wt%, indicating that the pore water was in an alkaline environment with high pressure, so that the decarboxylation reaction of the organic matter took place to generate a large amount of carbon dioxide with a loss of 13C. Consequently, the δ13C of the carbonate cement is negatively biased to − 6.4‰; the deep high temperature environment and the external fresh water leaching make the δ18O value of the carbonate cement gradually lose, reaching − 13.19‰, according to its precipitation temperature (78–108 °C) and pore water salinity (104–110), it is judged that carbonate cement is mainly formed in the middle diagenetic stage and early diagenetic stage. The rare earth elements are mainly authigenic by the results of ∑REE and Y/Ho, and the deposition characteristics of trace elements such as Zn, Th, U, Ni, Co, Cr, Sr/Cu, Ceanom, Ce/La, ∑REE, Eu/Eu*, La/Yb, V/(V + Ni) and the transformation of later diagenesis, combining the characteristic of the global climate and the influence of uplifting of the Qinghai–Tibetan Plateau, indicate that the lower Xiaganchaigou formation was in a dry, cold and anoxic reducing environment. [ABSTRACT FROM AUTHOR]

The mitigation of CO2 emissions requires a global effort with common but differentiated responsibilities. In this paper, we identify clusters of CO2 emissions across 72 countries. First, using the stochastic version of the IPAT and employing the dynamic common correlated effects technique, we identify three key determinants affecting CO2 emissions (non-renewables, population, and real GDP). In the second step, both hierarchical and non-hierarchical clustering methods are considered to identify the optimal number of clusters. We identify two to four clusters with different member countries, and in particular establish that in most cases, a 2-cluster solution appears to be optimal. The contents of clusters vary slightly according to the clustering methods for each period. The clustering results from using only the overall CO2 emissions indicate that the countries we consider form three clusters, with China and the USA each within a single member cluster. The remaining 70 countries form the third cluster. Our findings reflect the prominent roles of China and the USA in overall CO2 emissions. Analyses with sub-period and largest emitters reflect a different clustering structure. Some policy recommendations in setting emission reductions are made, considering different clusters across countries. [ABSTRACT FROM AUTHOR]

As the pillar of national economy, manufacturing industry is the largest primary energy consumer and emitter of carbon dioxide (CO2) in China. Therefore, capturing the determinants of CO2 emissions in manufacturing industry is extremely important for national efforts to mitigate carbon emissions. This paper explores the major driving forces behind CO2 emission changes in China's manufacturing industry during 2000–2015 from perspectives of the whole sector and 28 subsectors, by applying the temporal logarithmic mean Divisia index (LMDI) method. Moreover, an intersectoral LMDI model is built to uncover the intersectoral discrepancies of CO2 emissions among 28 subsectors. The temporal analysis indicates that industrial activity and energy intensity are crucial factors respectively contributing to the increase and mitigation of CO2 emissions. The intersectoral analysis reveals that energy intensity is the dominant factor responsible for the intersectoral discrepancies of CO2 emissions among 28 subsectors. The great mitigation towards CO2 emissions can be achieved if energy efficiency is largely improved in carbon-intensive subsectors. Priority should be given by governments to the industrial technology advancement, such as subsidies for energy-saving technological transformation and promotion of international advanced techniques and equipment, which can greatly improve production efficiency and mitigate emissions in manufacturing industry. [ABSTRACT FROM AUTHOR]

Effective management of surface emissions from abandoned gob methane (AGM) is crucial for mitigating greenhouse gas emissions and ensuring public safety. An important geochemical characteristic of near-surface AGM migration is the potential presence of abnormal component concentrations in near-surface soil gas over abandoned coal gobs. To investigate this phenomenon, a surface geochemical survey was conducted based on four survey lines in the Dongyi and Dongsan abandoned gob groups in the Xinghua Coal Mine, Jixi Basin, in China. The gas chromatography technique was used to analyze the concentrations of methane, carbon dioxide, ethane, propane, and butane in the collected 43 soil gas samples. The results revealed a significant anomaly of soil gas concentrations, particularly methane and carbon dioxide anomalies, in the near-surface soil over abandoned gobs. The background concentration for methane was determined to be 7.49 ppm, with an anomalous threshold set at 10 ppm based on a statistical analysis and an iterative method. This threshold could be confirmed by examining the coupling and decoupling relationship between methane, carbon dioxide, and C2–3 as well. A spatial correlation between regions exhibiting anomalous methane and carbon dioxide concentrations and the positions of gob areas, abandoned surface wells, and faults was observed. Abandoned and sealed coalbed methane surface wells and faults near gas-rich gob areas have the potential to act as conduits for AGM leakage to the surface. Furthermore, concentrations of methane, carbon dioxide, and C2–3 in soil gas over abandoned coal gobs were significantly higher compared to areas unaffected by mining activities. This suggests that elevated concentrations of methane, carbon dioxide, and C2–3 in soil gas may originate from underground AGM. [ABSTRACT FROM AUTHOR]

Improving transport efficiency and optimizing freight structure are the core of achieving sustainable transport development. However, few studies have conducted an in-depth analysis of freight structure optimization and transport efficiency at the provincial level in China. Hence, a new economy and carbon dioxide (CO2) emissions evaluation model based on the slack-based measurement data envelopment analysis (SBM-DEA) with undesirable outputs was proposed to analyze the transport efficiency and optimize freight structure in five provinces of China from 2005 to 2019. And optimization potential of the freight and passenger transport sector was uncovered in the provinces. The results showed that: (1) the regional difference in transport efficiency was significant, which indicated room for improvement in these provinces. (2) Freight structure was estimated and optimized by the slack variables of the evaluation model. The slack variables of railway, road, and waterway freight transport were large among these provinces. (3) The optimization potential of the freight transport sector was greater than that of the passenger transport in the regions. It indicated optimizing freight structure should be prioritized over passenger transport. Finally, some implications were put forward for sustainable transport development in China. The results provide significant insight into freight structure optimization as well as novel perspectives into the formulation of carbon mitigation strategies. [ABSTRACT FROM AUTHOR]

This paper attempts to examine energy efficiency, carbon dioxide (CO2) emission efficiency, and related abatement costs of China's regions in 2012 by considering embodied carbon caused by trade. To this end, a combined approach with environmentally extended input–output analysis (EEIO) and DEA is proposed. We first apply the EEIO model to measure CO2 emissions of 30 China's regions from the consumption perspective, which can identify the described embodied carbon. According to the amounts of CO2 emissions with respect to the production-based and consumption-based principles, 30 regions are divided into two groups, i.e., emission-importing regions and emission-exporting regions. For emission-importing regions, CO2 emissions from the consumption perspective exceed that from the production-based perspective. The emission-exporting regions exhibit the opposite case. Our results show that energy efficiency and CO2 emission abatement costs will be underestimated in both groups when ignoring the embodied carbon emissions. The emission-importing regions evidence higher efficiency scores and abatement costs than those emission-exporting regions, and there is a significant difference between these two groups in terms of energy efficiency and CO2 emission efficiency, whereas there is no significant difference with respect to CO2 emission abatement costs. It is interesting that emission-exporting regions enjoy a slight increase in CO2 emission efficiency whereas emission-importing regions suffer from a decrease. Some useful policy implications are achieved. [ABSTRACT FROM AUTHOR]

A large amount of carbon dioxide emissions have drawn more and more attention recently. Existing regional research is mainly based on the classification of geographical location, without considering the differences in urbanization. Using panel data of 30 provinces in China during the period of 1997–2014, this paper investigates the impact of population, per capita GDP, energy intensity, urbanization, industry proportion and tertiary industry proportion on CO2 emissions. Taking into account regional differences, 30 provinces in China are divided into four regions according to the features of "urbanization–CO2 emissions." The results show that the impacts of population and per capita GDP on CO2 emissions in the LU–LC region are higher than the other three regions. The energy intensity has positive effect on CO2 emissions in the four regions. The impact of energy intensity on CO2 emissions in HU–HC and HU–LC regions is greater than the other two regions. Meanwhile, the impact of urbanization on CO2 emissions differs across regions. The urbanization has a significant negative effect on CO2 emissions in the HU–LC region, indicating the urbanization increases CO2 emissions. However, the urbanization has a positive effect on CO2 emissions in the LU–HC region, indicating the urbanization increases CO2 emissions in the region. The impact of industry proportion is not statistically significant in all the regions, while the impact of tertiary industry proportion on CO2 emissions is negatively significant in the HU–LC and LU–HC regions, which indicates that the adjustment and upgrading of industrial structure play important roles in the decrease in carbon emissions. [ABSTRACT FROM AUTHOR]

As an important cause of global warming, CO2 concentrations and their changes have aroused worldwide concern. Establishing explicit understanding of the spatial and temporal distributions of CO2 concentrations at regional scale is a crucial technical problem for climate change research. High accuracy surface modeling (HASM) is employed in this paper using the output of the CO2 concentrations from weather research and forecasting-chemistry (WRF-CHEM) as the driving fields, and the greenhouse gases observing satellite (GOSAT) retrieval XCO2 data as the accuracy control conditions to obtain high accuracy XCO2 fields. WRF-CHEM is an atmospheric chemical transport model designed for regional studies of CO2 concentrations. Verified by ground- and space-based observations, WRF-CHEM has a limited ability to simulate the conditions of CO2 concentrations. After conducting HASM, we obtain a higher accuracy distribution of the CO2 in North China than those calculated using the classical Kriging and inverse distance weighted (IDW) interpolation methods, which were often used in past studies. The cross-validation also shows that the averaging mean absolute error (MAE) of the results from HASM is 1.12 ppmv, and the averaging root mean square error (RMSE) is 1.41 ppmv, both of which are lower than those of the Kriging and IDW methods. This study also analyses the space-time distributions and variations of the XCO2 from the HASM results. This analysis shows that in February and March, there was the high value zone in the southern region of study area relating to heating in the winter and the dense population. The XCO2 concentration decreased by the end of the heating period and during the growing period of April and May, and only some relatively high value zones continued to exist. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the carbon dioxide (CO) emissions reduction performance of 30 mainland provinces in China over 2005-2009. First, the log-mean Divisia index (LMDI) technique is used to decompose the changes in CO emissions at the provincial level into 4 effects that are carbon intensity effect, energy mix effect, energy intensity effect and gross domestic product (GDP) effect. Next, two indicators, decoupling index and rescaled decoupling index, are implemented to evaluate the performance of 30 provinces in CO emission reduction from 2005 to 2009. The decomposition result shows that the GDP growth is mainly responsible for the CO emissions increase, while the energy intensity effect is the key factor for the decrease in CO emissions in each province. Moreover, according to the evaluation, the performance of each province in what concerns the CO emission reduction varies significantly. Most provinces in China made significant efforts in emissions reduction during 2005-2009, while some provinces only made weak efforts or even no efforts in decoupling progress. [ABSTRACT FROM AUTHOR]

Recovery energy has been one of important components of energy consumption structure in China. Recovery energy is a type of secondary energy, which is generated from various sources. Since the estimation of recovery energy from some sources such as coke oven gas and other coal gas contains some uncertainties, recovery energy can be represented as an interval variable. The extant studies in the literature ignore recovery energy in the evaluation of energy efficiency or carbon dioxide ( $$\text {CO}_{2}$$ ) emissions efficiency. As a part of energy supply in China, recovery energy may influence regional primary energy consumption and $$\text {CO}_{2}$$ emissions significantly. In this context, this paper proposes an interval slacks-based measure approach to evaluate energy efficiency and $$\text {CO}_{2}$$ emissions efficiency in the presence of recovery energy in China. In the described approach, the indicator of $$\text {CO}_{2}$$ emissions is incorporated based on the weak disposability assumption, and recovery energy is modeled as a dual factor. The optimal exact data on recovery energy of each region can be obtained. Based on the proposed approach, the measures of energy saving potential and $$\text {CO}_{2}$$ emissions reduction potential are derived. Findings resulting from the model application show that there are great disparities in regional efficiencies, and the inefficiency of Chinese regions is largely driven by those inefficient regions in the central and western areas. Notably, the development of recovery energy can help to improve regional energy efficiency and $$\text {CO}_{2}$$ emissions efficiency in China. [ABSTRACT FROM AUTHOR]

China is in a phase of rapid urbanization and economic development; in addition, the country's carbon dioxide (CO) emissions are increasing. Using a multivariate vector error correction model, this paper investigates the relationship between urbanization, income growth and CO emissions in China. The empirical evidence shows that the three variables are cointegrated, indicating a long-term relationship among urbanization, income growth and CO emissions. Moreover, a Granger causality test reveals that urbanization is the reason for income growth in China. There is also evidence that both urbanization and income growth lead to CO emissions. Hence, authorities should pay more attention to mitigating the negative effects on the environment when developing and implementing policies that promote urbanization and income growth. However, CO emissions do not cause changes in income and urbanization in China. Therefore, China should enforce stricter policies for reducing CO emissions. [ABSTRACT FROM AUTHOR]

This paper analyzes the spatial and temporal distribution of CO2 emissions intensity and energy intensity in China by using spatial measuring method from 2000 to 2013 and estimates the potential for CO2 emissions reduction. The results obtained in this study include: (1) Both CO2 emissions intensity and energy intensity are declining; (2) the spatial distribution of carbon emission intensity and energy intensity in China shows the characteristics of lower from north to south; (3) China's total growth of energy consumption and carbon emissions is clearly slowing, which will peak before 2030; the carbon emission reduction potential in China is great with 167,316.91 million tons, and Shanxi, Inner Mongolia and Hebei have the greatest potential to reduce CO2 emissions with 29,885.8 Mt, 32,704.49 Mt and 34,222.1 Mt, respectively; (4) the differences of CO2 emissions intensity and energy intensity among provinces are distinctive. This study can provide a reference for the sustainable development of China's energy and environment. [ABSTRACT FROM AUTHOR]

Inland freshwater lakes have been widely considered as significant sources of CO2 to the atmosphere. However, long-term measurements of CO2 dynamics in lakes are still lacking, but are necessary due to their large temporal variations. Herein, we present the long-term dynamics of water parameters in Lake Donghu from 2002 to 2016, and further calculate the partial pressure of CO2 (pCO2) based on the measurements of pH, water temperature, and alkalinity from 2008 to 2016. The results revealed that a significantly high pCO2 occurred during the winter in Lake Donghu (p < 0.01), whereas no significant spatial difference was observed (p = 0.37). Statistical analysis indicated that the pCO2 in the lake was only positively correlated with the total phosphorus (TP) concentration (p < 0.05). A multilinear regression model provided the best predictors for the pCO2; however, it only explained 16% of the observed pCO2 variability. This indicates the complex factors that influenced the pCO2 in Lake Donghu between 2008 and 2016. Our estimated CO2 flux revealed that Lake Donghu acted as a small CO2 source to the atmosphere during this period, with a mean CO2 flux of 10.8 ± 37.4 mg m−2 day−1 corresponding to a mean CO2 emission of 0.13 ± 0.43 Gg year−1. The CO2 emission fluxes in Lake Donghu were much lower than the mean CO2 fluxes reported for other lakes in China and globally. Furthermore, the long-term evolution of the CO2 flux indicated that Lake Donghu has shifted between acting as a CO2 source and sink, which highlights the need for long-term monitoring to accurately evaluate CO2 emissions from lakes. [ABSTRACT FROM AUTHOR]

The (ultra-)mafic mine tailings pond revealed a weathering discrepancy in the tailings profile, which provided a valuable analog to assess the role of carbonate and silicate weathering of the basalt. In this study, drill-cores samples were selected from the Wanniangou V–Ti–Fe mine tailings pond (Sichuan province, China) to investigate the mineralogicand geochemical characteristics in the tailings profile. The results reveal (1) the tailings pond profile consist of upper and lower layers. The upper layer is composed of carbonate weathering (1.4 %), which was formed in the initial stages of tailings exposure and represented a minimal weathering degree. (2) The lower layer was primarily observed at the aquifer zone of the tailings pond, and was consistent with 0.45 % carbonate weathering and 48.4 % silicate weathering. (3) The weathering discrepancy in the tailings profile could be due to the sulfide oxidation and aerobic/flowing aquifer, which facilitate the water-tailings reactions. The tailings profile provides an analog to studying basalt weathering, as it spans both carbonate and silicate weathering. This research reinforces the idea that silicate weathering is predominant in basaltic areas and plays a crucial role in regulating atmospheric CO2 (carbon dioxide) levels on Earth. [ABSTRACT FROM AUTHOR]

This paper investigates the technical inefficiency, shadow price and substitution elasticity of $$\hbox {CO}_{2}$$ emissions of China based on a provincial panel for 2001-2010. Using linear programming to calculate a quadratic parameterized directional output distance function, we show that China's technical inefficiency increases over the period implying further scope for $$\hbox {CO}_{2}$$ emissions reduction in the medium and longer term at best by 4.5 and 4.9 % respectively. Our results (notwithstanding regional differences) highlight increases in the shadow price of $$\hbox {CO}_{2}$$ abatement (1,000 Yuan/ton in 2001 to 2,100 Yuan/ton in 2010). Additionally, increasingly steep substitution elasticity highlights the difficult reality of reducing China's $$\hbox {CO}_{2}$$ emissions. [ABSTRACT FROM AUTHOR]

Vigorously developing high-tech industry has been considered to be an effective way to coordinate economic growth with excessive carbon dioxide (CO2) emissions. However, previous studies have not explored the heterogeneous impacts of high-tech industry on CO2 emissions in regions with different levels of high-tech industry development, and not distinguished the direct and indirect impacts. Based on STIRPAT model, this study investigates the impacts of high-tech industry development on CO2 emissions in China between 2005 and 2016. Adopting the K-medians cluster method, effects in regions with high, middle, and low levels of high-tech industry development are considered. Indirect effects of high-tech industry development on CO2 emissions by affecting industry structure upgrades and economic growth are explored. Empirical results illustrate a positive U-shaped nonlinear link between the level of high-tech industry development and CO2 emissions at the national level and regional (high, middle, and low) level. In terms of indirect impacts, high-tech industry development attenuates the reduction of CO2 emissions due to industry structure upgrades, and promotes economic growth to increase CO2 emissions slightly. The indirect impact intensity gradually decreases as the level of high-tech industry development decreases across three regions. Reasonable implications of our findings are proposed. [ABSTRACT FROM AUTHOR]

Observations of atmospheric carbon dioxide (CO) from satellites offer new data sources to understand global carbon cycling. The correlation structure of satellite-observed CO can be analyzed and modeled by geostatistical methods, and CO values at unsampled locations can be predicted with a correlation model. Conventional geostatistical analysis only investigates the spatial correlation of CO, and does not consider temporal variation in the satellite-observed CO data. In this paper, a spatiotemporal geostatistical method that incorporates temporal variability is implemented and assessed for analyzing the spatiotemporal correlation structure and prediction of monthly CO in China. The spatiotemporal correlation is estimated and modeled by a product-sum variogram model with a global nugget component. The variogram result indicates a significant degree of temporal correlation within satellite-observed CO data sets in China. Prediction of monthly CO using the spatiotemporal variogram model and space-time kriging procedure is implemented. The prediction is compared with a spatial-only geostatistical prediction approach using a cross-validation technique. The spatiotemporal approach gives better results, with higher correlation coefficient ( r), and less mean absolute prediction error and root mean square error. Moreover, the monthly mapping result generated from the spatiotemporal approach has less prediction uncertainty and more detailed spatial variation of CO than those from the spatial-only approach. [ABSTRACT FROM AUTHOR]

High-precision (±0.1 ppm) and high-frequency (hourly averaged) in situ measurements of atmospheric carbon dioxide (CO) were made for the first time from August 2005 to July 2007 at Yanbian, China using a non-dispersive Infrared (NDIR) analyzer with National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) standards. The results of these measurements are presented in this paper and are used to investigate the regional representativeness of regional background data at Yanbian and determine the CO emission source regions in Northeast Asia. The phase of the monthly variations at Yanbian reflects the special regional characteristics, which were overall in excellent agreement with other observatories in the middle-to-high latitudes in the Northern Hemisphere. Applying a hybrid receptor model to the regional emission source events in cold period (November-April), we estimated the distribution of the major CO emissions in the northeast Asia. The results indicated that the strongest potential emission areas contributing to Yanbian are the Beijing & Tianjin metropolitan areas, southwestern part of Shandong Province including Jinan, and Vladivostok. The results of this study reveal the usefulness of in situ CO measurements at Yanbian in establishing the scientific foundation for monitoring the large CO emission areas in northern China and Russia. Continued monitoring of CO at Yanbian within a regional network should provide significant contributions to both understanding the global/regional carbon cycle and constraining 'top-down' emissions in Northeast Asia. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems may act as a source or a sink for the atmospheric greenhouse gases, carbon dioxide (CO), methane (CH), and nitrous oxide (NO), depending on land use and management. This paper reviews the literature on carbon, CH, and NO fluxes from terrestrial ecosystems in China, and analyzes its national greenhouse gas budget. Carbon storage in biomass and soils in Chinese terrestrial ecosystems decreased in the past 300 years, due to deforestation and expansion of cultivated land, and reached a minimum in the late 1970s. Since then, carbon storage has increased at an estimated rate of 0.19 to 0.26 Pg C yr, mainly owing to reforestation and afforestation. CH emission from natural wetlands decreased from 6.65 Tg CH yr in 1990 to 5.71 Tg CH yr in 2000 owing to the decrease in wetland area. CH emission from flooded rice fields was 7.41 Tg CH yr. At the same time, aerobic soils took up atmospheric CH at a rate of 2.56 Tg CH yr. Nitrous oxide emission from forestlands, grasslands, and farmlands was positively correlated with precipitation at a national scale, and the emission rate was positively correlated with the CH uptake rate of forestlands and grasslands ( P<0.01). Natural NO sources were estimated to be 419 Gg N yr and anthropogenic sources (from farmlands) to be 292 to 476.3 Gg N yr, with a mean of 372.6 Gg N yr. The integrated budget of greenhouse gasses indicates that Chinese terrestrial ecosystems act as a small net sink for global warming potential (GWP), ranging from 0.04 to 0.32 Pg CO-eq yr, in a striking contrast to terrestrial ecosystems globally, which are a source of 2.75 to 6.78 Pg CO-eq yr. The ratios of anthropogenic to natural sources of CH and NO are much larger in Chinese terrestrial ecosystems than they are in global averages, reflecting greater human disturbance of terrestrial ecosystems in China. [ABSTRACT FROM AUTHOR]

Carbon isotopic compositions of soil CO2 in rainy season (July) from two natural soil profiles (DHLS & DHS) in the monsoon evergreen broadleaf forest in the Dinghushan Biosphere Reservoir (DBR), South China, are presented. Turnover and origins of soil CO2 are preliminarily discussed in this paper. Results show that the content of soil CO2 varies between 6120 and 18718 ppmv, 13 and increases with increasing depth until 75 cm, and then it declines. In DHLS, soil CO2 δ 13C ranges from -24.71‰ to -24.03‰, 2 showing a significant inverse correlation (R =0.91) with the soil CO2 content in the same layer. According to a model related to soil CO2 δ 13C, the soil CO2 is mainly derived from the root respiration (>80%) in DHLS. While in DHS, where soil CO2 δ 13C ranges from -25.19‰ to -22.82‰, soil CO2 is primarily originated from the decomposition of organic matter (51%--94%), excluding the surface layer (20 cm, 90%). Radiocarbon data suggest that the carbon in soil CO2 is modern carbon in both DHLS xand DHS. Differences in 14C ages between the "oldest" and "youngest" soil CO2 in DHLS and DHS are 8 months and 14 months, respectively, indicating that soil CO2 in DHLS has a faster turnover rate than that in DHS. The Δ 14C values of soil CO2, which range between 100.0‰ and 107.2‰ and between 102.5‰ and 112.1‰ in DHLS and DHS, respectively, are obviously higher than those of current atmospheric CO2 and SOC in the same layer, suggesting that soil CO2 is likely an important reservoir for Bomb- 14C in the atmosphere. [ABSTRACT FROM AUTHOR]

Battery storage is critical for integrating variable renewable generation, yet how the location, scale, and timing of storage deployment affect system costs and carbon dioxide (CO2) emissions is uncertain. We improve a power system model, SWITCH-China, to examine three nationally uniform battery deployment strategies (Renewable-connected, Grid-connected, and Demand-side) and a heterogeneous battery deployment strategy where each province is allowed to utilize any of the three battery strategies. Here, we find that the heterogeneous strategy always provides the lowest system costs among all four strategies, where provinces with abundant renewable resources dominantly adopt Renewable-connected batteries while those with limited renewables dominantly adopt Demand-side batteries. However, which strategy achieves the lowest CO2 emissions depends on carbon prices. The Renewable-connected strategy achieves the lowest CO2 emissions when carbon prices are relatively low, and the heterogeneous strategy results in the lowest CO2 emissions only at extremely high carbon prices. Heterogeneous battery strategy, with each province flexibly choosing different battery strategies, achieves the lowest power system costs. However, this non-uniform strategy only achieves the lowest CO2 emissions at extremely high carbon prices. [ABSTRACT FROM AUTHOR]

Purpose: We sought to identify the effects of elevationally (1560−2160 m) driven climate (mean annual air temperatures of + 4.0 to + 10 oC and mean annual precipitation of 900–1200 mm), forest type and soil property variations on soil greenhouse gas (GHG) fluxes in the Qinling Mountains, China. Methods: Soil GHGs were monitored at five temperate forest sites using static chambers for two full years. The related edaphoclimatic parameters were also monitored. Results: The cumulative soil CO2 and N2O emissions and the CH4 uptake from the five sites ranged between 3.82 to 7.48 t CO2-C ha−1 yr−1, 1.19 to 2.25 kg N2O-N ha−1 yr−1, and 0.89 to 5.67 kg CH4-C ha−1 year−1, respectively. Average soil CO2 emissions decreased with increasing site elevation, whereas CH4 and N2O fluxes showed no clear relationship with site elevation. All sites were net CH4 sinks. Occasionally, soils switched to net sources of CH4 during summer or autumn rainfall events at all sites. All sites were net N2O sources although short periods of soil N2O uptake were observed during freeze–thaw cycles and during summer rainfall. The source strength of N2O and CH4 differed during summer and winter and snowfall which had direct effects on soil water content. Conclusions: Site elevation could be used as a predictive variable for constructing regional soil CO2 emissions budgets, whereas soil CH4 and N2O fluxes are driven by a more complex interplay of climatic and soil biochemical variables, which are more difficult to integrate on a regional scale. [ABSTRACT FROM AUTHOR]