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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Jing-Jin-Ji region (i.e., Beijing, Tianjin, and Hebei) is China’s key development region, but it is also the leading and most serious air pollution region in China. High fossil fuel consumption is the major source of both carbon dioxide (CO2) emissions and air pollutants. Therefore, it is important to reveal the source of CO2 emissions to control the air pollution in the Jing-Jin-Ji region. In this study, an interregional input-output model was applied to quantitatively estimate the embodied CO2 transfer between Jing-Jin-Ji region and other region in China using China’s interregional input-output data in 2010. The results indicated that there was a significant difference in the production-based CO2 emissions in China, and furthermore, the Jing-Jin-Ji region and its surrounding regions were the main regions of the production-based CO2 emissions in China. Hebei Province exported a large amount of embodied CO2 to meet the investment, consumption, and export demands of Beijing and Tianjin. The Jing-Jin-Ji regions exported a great deal of embodied CO2 to the coastal provinces of southeast China and imported it from neighboring provinces. [ABSTRACT FROM AUTHOR]

In recent years there has been a great deal of research into environmental pollution using a variety of techniques in response to growing environmental concerns. Convergence analysis, one of these techniques, helps determine whether the developing countries will catch up with the rich countries in pollution using unit root tests. However, the vast majority of the research in the field has generally used conventional unit root tests. Since many economic series contain structural breaks, using unit root tests that account for structural breaks is essential for accurate prediction. More specifically, if the series has a fractional process, conventional unit root tests may erroneously conclude that the departure from linearity is permanent. Moreover, the existing literature mainly uses gas emissions, such as carbon dioxide, which represent pollution weakly. Therefore, we use per capita ecological footprint (EF hereafter) as a more comprehensive pollution indicator of environmental degradation. In this direction, the study aims to determine whether BRICS-T countries' EF converges to the average of the BRICS-T for the 1992–2017 period. Besides the ADF unit root test, we employed the Fourier ADF unit root test, which considers the structural breaks, and the Fractional Frequency Fourier ADF unit root test, which accounts for structural breaks by considering fractional values. Our results showed that EF converges in Russia and Turkey according to the conventional ADF test, in China and Russia according to the Fourier ADF test, and in Brazil and China according to the Fractional Fourier Frequency test. [ABSTRACT FROM AUTHOR]

Understanding the temporal and spatial characteristics of carbon dioxide (CO2) emissions from municipal solid waste (MSW) and a quantitative evaluation of the contribution rate of the factors influencing the changes in CO2 emissions are important for pollution and emission reduction and the realization of the "double carbon" goal. This study analyzed the spatial and temporal evolution of waste generation and treatment based on panel data from 31 Chinese provinces over the past 15 years and then applied the logarithmic mean Divisia index (LMDI) model to study the driving factors of CO2 emissions from MSW. China's MSW production and CO2 emissions displayed a rising trend, and the overall CO2 emissions showed a geographical pattern of being high in the east and low in the west. Carbon emission intensity, economic output, urbanization level, and population size were positive factors that increased CO2 emissions. The most important factors driving CO2 emissions were carbon emission intensity and economic output, with cumulative contribution rates of 55.29% and 47.91%, respectively. Solid waste emission intensity was a negative factor in reducing CO2 emissions, with a cumulative contribution rate of -24.52%. These results have important implications for the design of policies to reduce CO2 emissions from MSW. [ABSTRACT FROM AUTHOR]

WIth the introduction of "carbon peak and neutrality" targets, China's power industry is under enormous pressure to reduce carbon dioxide (CO2) emissions, as it produces more than 40% of emissions. In response, China's power industry is actively reducing the investment in thermal energy and gradually shifting toward non-fossil energy sources. However, the CO2 reduction effect of these measures is still unknown. This study aims to analyze CO2 emissions from China's power industry from 2009 to 2018 from an entire lifecycle perspective, considering that CO2 emissions also exist in non-fossil power generation. The logarithmic mean Divisia index (LMDI) method is employed to identify the factors influencing CO2 emissions. Then, the modified STochastic Impacts by Regression on Population, Affluence and Technology model is used for comparative validation. The results show that (1) CO2 emissions from China's power industry increased significantly, from 276.5 million tons of CO2 equivalent (Mtce) in 2009 to 436.44 Mtce in 2018; (2) the investment intensity, investment structure, and emission intensity dampen CO2 emissions, with cumulative contribution rates of − 28.88%, − 11.89%, and − 3.16%, respectively. The investment efficiency, economic development level, and population size contribute to CO2 emissions, with cumulative contribution rates of 29.76, 24.68, and 1.07%, respectively; and (3) Investment into the hydropower contributes the least to CO2 emissions, followed by wind, nuclear, photovoltaic, and thermal power. These research findings suggest that the power industry should improve its investment decision-making capabilities and pay particular attention to the hydropower-led non-fossil energy sector. [ABSTRACT FROM AUTHOR]

Assessing the mitigation effect on greenhouse gas (GHG) reduction of the National VI Emissions Standard bears great significance in enhanced actions on climate change in China. This research established a reference scenario and National VI scenario to evaluate whether the National VI Emissions Standard could make a contribution to synergistic emission reduction. Here are the main conclusions: carbon dioxide (CO2) was the majority GHG emissions type, accounting for around 99% of GHG emissions between 2019 and 2025 for these two scenarios; implementing the National VI Emissions Standard will not mitigate total GHG emissions from 2019 to 2025, but the National VI Emissions Standard could help mitigate methane (CH4) and nitrous oxide (N2O), not CO2 if considering the mitigation effect from the perspective of GHG type. Based on the results and discussion, this research suggests to consider the economic and technical feasibility of incorporating carbon emissions limits into the National VII Emissions Standard while continuing the evaluation of the National VI Emissions Standard. [ABSTRACT FROM AUTHOR]

To realize "the future we want" proposed by the UN Development Agenda, it is highly necessary for China, which is the largest emitter of carbon dioxide (CO2) emissions, to find ways to boost the green innovation of domestic firms. Digital finance, as an emerging product of the contemporary digital economy, provides a new research perspective for green innovation. Based on 2011–2019 panel data on A-shared listed companies in China, this article establishes a regression model and provides empirical evidence that digital finance can promote green innovation by exerting resource and information effects. Our results reveal that (1) digital finance can stimulate enterprises' green innovation by increasing the coverage of digital finance and the depth of use. (2) Digital finance can significantly improve the quantity and quality of green innovation by alleviating enterprises' financial constraints and giving full play to the internal and external information effect. (3) The discussion shows that the effect of digital finance is heterogeneous and can more significantly and effectively stimulate the green innovations of enterprises with lower analyst optimism bias and higher synchronicity. [ABSTRACT FROM AUTHOR]

The extensive literature has debated the varying effects of factors on carbon dioxide (CO2) emissions. However, it has paid little attention to land freight structure (FS), including road and rail freight share, which may have different effects on CO2 emissions. Based on the data from 6 eastern provinces in China during 2005–2019, the panel threshold model is used to explore the dynamic influence mechanism of road and rail freight share on transport carbon emission intensity (CE), respectively. The results show different nonlinear relationships between the share of road and rail freight and transport carbon emission intensity. First, the effect of road freight share on carbon emission intensity is all positive across different stages of trade openness, while such effect goes through a process of increasing and then decreasing with the level of trade openness improving. Second, the driving effect of rail freight share on carbon emission intensity exhibits a "negative–positive-negative" feature as the level of trade openness increases. Third, trade openness generates a double-threshold effect on carbon emission intensity. The differentiated nonlinear effects provide significant evidence of the modal shift from road to rail freight, which would be effective to alleviate transport CO2 emissions. [ABSTRACT FROM AUTHOR]

CO2 emission performance evaluation is crucial to make abatement policies. Knowledge about the potential and costs of CO2 reduction could provide information guides for policymakers and help them implement targeted measures. However, relevant studies are rarely subdivided into detailed industrial sectors, and results are lack of inter-industry comparisons. To fill this gap, this study estimates provincial technical inefficiency, abatement potential, and shadow price of CO2 from fuel combustion in China's 25 industries in 2001–2017. Results show that China's industry could ideally reduce CO2 emissions by a further 22.01-33.27%, averaging 1645.96 MtCO2. Technical efficiency, abatement potential, and cost vary across provinces and industries and should therefore be fully considered when designing emission reduction targets and control policies. Provinces and industries with low technical efficiency, large-scale emissions, great abatement potential and low shadow price are the key to emission reduction. We thus identify key provinces and industries that need to take on more abatement responsibility. Those findings are of great significance to the formulation of carbon reduction targets and the implementation of abatement policies, and prove the feasibility of China's trans-regional carbon trading. It is suggested to prioritize key industries into the trading system and further promote inter-provincial cooperation through carbon trading. [ABSTRACT FROM AUTHOR]

Due to the lack of carbon dioxide (CO2) emission data on China's logistics industry, this study uses the industry stripping method to calculate the embodied energy consumption CO2 emissions based on input-output tables for China's logistics industry from 1997 to 2017. And then the Log-Mean Divisia Index method is used to decompose the influencing factors of carbon emission from five aspects. The empirical study mainly focused on the application of carbon emission tools and approaches in China, and this resulted in four key findings. First, the use of transportation, warehousing, and postal industries as a proxy for the logistics is simply not in line with reality and could result in greatly underestimating the CO2 emissions of logistics. Second, the annual direct CO2 accounts for roughly 40% in the embodied CO2 emissions in the logistics industry. Third, construction industry makes a greater contribution to the embodied CO2 of the logistics industry, followed by manufacturing. Fourth, economic output, population size, industrial structure, and energy structure are factors that contributed to the increase of logistics CO2 emissions, while the restraining factors included energy intensity. There is immense scope for adjustment in the energy and industrial structures. [ABSTRACT FROM AUTHOR]

Since the nonlinear ARDL approach is introduced in advanced econometric, some old relationships are getting new empirical attention. Therefore, we examine the asymmetric macroeconomic determinants of carbon dioxide (CO2) emission in China, covering the time from 1971 to 2019 and using a nonlinear ARDL model. The study results show that GDP and industrialization have an asymmetric effect on CO2 emission in long-run in direction and magnitude, while agriculturalization has also an asymmetric effect on environmental pollution in magnitude, but not in the direction. In short, GDP and industrialization have also asymmetric response on CO2 emissions in the short term only in magnitude, but agriculturalization has an asymmetric response in magnitude and direction in short-run. Therefore, this empirical research is more applicable for policymakers in China. [ABSTRACT FROM AUTHOR]

Global warming is currently the biggest problem. China is the world's highest CO2 emitter. The Chinese authorities agreed to overcome global pollution per the current Paris treaty and have shown deep concern regarding global warming. Hence, policymakers are paying attention to economic policy uncertainty. Motivated by this issue, the study examines the effect of energy use, economic policy uncertainty, and economic growth on China's CO2 emissions from 1970 to 2018 by employing a novel dynamic ARDL simulation model. The findings show that energy use and economic growth have statistically substantial long-run and short-run positive effects on CO2 emissions. However, economic policy uncertainty has a statistically insignificant effect on CO2 emissions, due to firms' sustainability policies. Energy use can have valuable policy consequences, particularly for environmental sustainability. Therefore, based on the empirical findings, the crucial partnership and feedback on China's carbon emission policy should be carried forward. [ABSTRACT FROM AUTHOR]

Globally, urban has been the major contributor to greenhouse gas (GHG) emissions and thus plays an increasingly important role in its efforts to reduce CO2 emissions. However, quantifying city-level CO2 emissions is generally a difficult task due to lacking or lower quality of energy-related statistics data, especially for some underdeveloped areas. To address this issue, this study used a set of open access data and machine learning methods to estimate and predict city-level CO2 emissions across China. Two feature selection technologies including Recursive Feature Elimination and Boruta were used to extract the important critical variables and input parameters for modeling CO2 emissions. Finally, 18 out of 31 predictor variables were selected to establish prediction models of CO2 emissions. We found that the statistical indicators of urban environment pollution (such as industrial SO2 and dust emissions per capita) are the most important variables for predicting the city-level CO2 emissions in China. The XGBoost models obtained the highest estimation accuracy with R2 > 0.98 and lower relative error (about 0.8%) than other methods. The CO2 emissions predictive accuracy can be improved modestly by combing geospatial and meteorological interpolation predictor variables (e.g., DEM, annual average precipitation, and air temperature). We also observed an S-shape relationship between urban CO2 emissions per capita and urban economic growth when the rest variables were held constant, rather than a U-shaped one. The findings presented herein provide a first proof of concept that easily available socioeconomic statistical records and geospatial data at urban areas have the potential to accurately predict city-level CO2 emissions with the aid of machine learning algorithms. Our approach can be used to generate carbon footprint maps frequently for the undeveloped regions with scarce detailed energy-related statistical data, to assist policy-makers in designing specific measures of reducing and allocating carbon emissions reduction goal. [ABSTRACT FROM AUTHOR]

This study seeks to dissect the basic factors that can elucidate the efficiency and innovation in biomass utilization to control carbon dioxide (CO2) emission and economic growth nexus particularly at the time that the worldwide CO2 emission is at an all-time high and COVID-19 is ravaging the word. We use data principally from the World Bank Indicators covering the period 1990–2016 to study the nexus among biomass utilization, economic growth, and CO2 emission based on the moderating role of biotechnology in China. On the basis of the results of our preliminary tests, we apply the autoregressive distributed lag (ARDL) for this analysis and employ the nonlinear autoregressive distributed lag (NARDL) as a robust check and also deploy the vector error correction model (VECM) to determine the direction of causality. We find that long-run relationship exists among the factors in this study. We additionally find that biotechnology has a critical but negative relationship with CO2 emission in China. Through hierarchical multiple regression analysis and PROCESS macro for mediation, moderation, and conditional process, we establish that biotechnology significantly moderates the relationship between biomass utilization and CO2 emission in China. Again, we discover that biomass utilization significantly decreases CO2 emission in China. Through the ARDL, NARDL, and VECM, we find empirical support for the growth hypothesis in China. We conduct a series of diagnostic tests that prove the robustness of our estimates. Based on our empirical evidence, this study recommends that China seeks sustainable economic development and environmental sustainability simultaneously by prioritizing biomass utilization and biotechnological innovation in the country. [ABSTRACT FROM AUTHOR]

With a large agricultural sector, China is greatly affected by natural disasters caused by extreme weather events. Because the occurrence of natural disasters is closely related to the sharp increased consumption of energy and the massive emissions of carbon dioxide, this research examines relevant data from 2013 to 2017 in four major regions of China that cover 30 provincial administrative regions. Using the two-stage dynamic DEA model, we evaluate total efficiency value, two-stage efficiency value, and the efficiencies of energy consumption, CO2 emissions, and crop disaster areas, setting CO2 as the link between the production stage (first stage) and the crop damage stage (second stage). The research findings show that overall efficiency in China is generally low, whereby the total efficiencies of eastern and northeastern China are higher than those of central and western China. The efficiency value of the first stage (production stage) is greater than that of the second stage (crop damage stage), and the efficiency of most administrative regions' second stage is below 0.3, which is the main reason for the country's low overall efficiency. There is little difference between China's CO2 and energy consumption efficiency scores, but the efficiency values of crop disaster areas fluctuate greatly. The efficiency scores of various indicators in the eastern region are generally higher and more balanced, and the total efficiency scores exhibit a decreasing trend from east to west. Therefore, it is necessary to implement the environmental policy of controlling energy consumption and early warning of natural disasters in the central and western regions, and promote the R&D industry and technological innovation of carbon dioxide emission reduction and disaster control in the economically developed eastern regions. [ABSTRACT FROM AUTHOR]

CO2 emission reduction has become a binding target in China's economic and social development. The mining industry is China's primary energy consumer and therefore has an important influence on China's CO2 emissions. Herein, we conduct a comprehensive analysis of data from the Chinese mining industry between 2004 and 2015. And we employ meta-frontier data envelopment analysis to analyse the CO2 emission efficiency of the Chinese mining industry and identify the driving factors that influence the observed dynamic changes in CO2 emission efficiency. The main practical conclusions of our research are as follows: (1) the CO2 emission efficiency of China's mining industry had grown continuously during the study period; (2) during the study period, the CO2 emission efficiency of China's mining industry did not change consistently with changes in different geographical regions; (3) the overall efficiency of management in China's mining sector was low; (4) the small and medium enterprises (SMEs) in China's mining sector played a significant role in the improvement of the CO2 emission efficiency. The empirical results provide constructive policy implications that should be considered by the policy makers for the more sustainable development of China's mining sector. [ABSTRACT FROM AUTHOR]

It is globally acceptable that carbon dioxide (CO2) emissions are one of the greenhouse gases are considered the main factor influencing global warming and environmental degradation. The present study focuses on China, the world's largest carbon emitter. The study aims to capture the time-frequency dependency of economic growth and CO2 emissions in China for the time period 1950–2016 using a wavelet coherence approach, which allows us to investigate both the long-run and short-run causal links of the estimated variables. In order to capture the long-run and causal linkage between economic growth and CO2 emissions, the study employs Maki cointegration, wavelet coherence, Toda-Yamamoto causality, Fourier Toda-Yamamoto causality, and nonparametric Granger causality tests. The findings of this study reveal that (i) there is a significant vulnerability between economic growth and CO2 emissions throughout the 2000s both the short-term and medium-term; (ii) there is long-run cointegration linkage between economic growth and CO2 emissions in China; (iii) economic growth in China has an important power for predicting CO2 emissions over the selected study period, especially in the short-term and medium-term; and (iv) it was observed that there is positive correlation between economic growth during the 1980s and 1990s in the short-term only. The outcome of the Toda-Yamamoto causality, Fourier Toda-Yamamoto causality, and nonparametric Granger causality tests underlines that economic growth is a robust policy variable for predicting CO2 emissions in China. [ABSTRACT FROM AUTHOR]

In the context of the rapid development of the Belt and Road (B&R) Initiative, the continuous transfer of Sino-US trade to the B&R countries is an important means to mitigate the threat of Sino-US trade, and the environmental impact of this transfer should be considered, so as to provide a scientific basis for China's policy formulation about achieving this possible trade transfer with minimized environmental impacts. This study proposes a multiregional input-output model and analyzes the impact on carbon dioxide (CO2) emissions of transferring the Sino-US trade to the B&R countries for two types of scenarios. The results show the following: (1) A transfer of either the import trade or the export trade increases global and Chinese CO2 emissions by 81.76 Mt and 24.84 Mt, respectively. When both the import trade and export trade are transferred, the increases in CO2 emissions are only 0.22% and 0.26%, respectively. (2) Globally, the changes in international trade-embodied CO2 emissions are responsible for most of the global emission changes, especially the CO2 emissions exported from Russia, India, and many Southeast Asian countries to China. (3) Different from the impact on global emissions, the increases in Chinese domestic production-based CO2 emissions influence China's total CO2 emissions. Due to the imported CO2 emissions, the consumption-based CO2 emissions are affected to a greater degree and increase by 70.30 Mt, accounting for only 0.86% of the CO2 emissions in 2015. Finally, some policy implications are proposed. [ABSTRACT FROM AUTHOR]

This study aims to explore the dynamic association among crop production, livestock production, power consumption in agriculture, forest area, and carbon dioxide (CO2) emissions. Based on the annual data of China, spanning the period 1990 to 2016, the study applied the auto-regressive distributed lag (ARDL) bounds testing approach. In addition, the fully modified ordinary least squares (FMOLS) canonical cointegration regression (CCR) and the Granger causality tests are employed to check the robustness of the ARDL estimations. The ARDL-bounds testing approach indicated that all variables share a long-run connection. The long- and short-run ARDL estimations confirmed that crop production, as well as livestock production, has a significant positive effect on CO2 emissions in both cases. However, power consumption in agriculture and forest area has a negative effect on it, indicating that both variables reduce CO2 emissions in the long and short run. These results stood robust under various regression estimators and confirmed the findings of the ARDL method. Additionally, the results of the causality approach specified that a unidirectional causality is running from crop production, power consumption in agriculture, and forest area to CO2 emissions. The causality between livestock production and CO2 emissions is bidirectional. Therefore, the directions of this connection also validate the outcomes under various techniques used for robustness. These findings suggest that the government must reconsider its policies related to agricultural and livestock production and adopt environment-friendly practices in the agriculture sector that may reduce the carbon footprints in the long run. [ABSTRACT FROM AUTHOR]

Carbon dioxide emission and greenhouse gas emissions are considered core issue in the world that influence agricultural production and also cause climate change. The present study seeks to investigate the linkage of methane emissions, nitrous oxide emissions, carbon dioxide emission, and greenhouse gas emissions with agricultural gross domestic product in China. The long-term association was checked by using an autoregressive distributed lag (ARDL) bounds testing approach, fully modified least squares method, and canonical cointegrating regression analysis. The results from long-run analysis exposed that carbon dioxide emission and greenhouse gas emissions have positive coefficients that demonstrate the long-run linkage with the agricultural gross domestic product having p values of 0.5709 and 0.3751, respectively. Similarly, results also revealed that agricultural methane emissions and agricultural nitrous oxide emissions have a negative association with the agricultural gross domestic product having p values of 0.1737 and 0.0559. China is a huge emitter of CO2 emission and greenhouse gas emissions. Possible conservative policies are required to form the Chinese government to tackle this challenge to decrease CO2 emission in order to increase agricultural production. [ABSTRACT FROM AUTHOR]

To investigate the effects of government environmental regulation and industrial structure changes on carbon dioxide emissions, this research analyzes annual data over the period 2003–2015 covering 30 provinces in China (except for Tibet) with the panel smooth transition regression (PSTR) model. The empirical results show obvious non-linear effects of environmental regulation and industrial structure on carbon dioxide emission. Taking the effects of both linearity and non-linearity into consideration, environmental regulation will reduce carbon emissions with the change of industrial structure rationalization (SR). When the level of industrial structure optimization (SH) is low, environmental regulation promotes carbon dioxide emissions. When the level of industrial structure optimization (SH) is high, environmental regulation plays a significant inhibitive role on carbon dioxide emissions. The Kuznets relationship between GDP per capita (PGDP) and carbon dioxide emissions is influenced by local industrial structure rationalization and industrial structure optimization. Therefore, environmental regulation policies suitable for local conditions should be made based on the transition of the local industrial structure. [ABSTRACT FROM AUTHOR]

Thermal power plants are the main source of carbon dioxide emissions in China. Beijing-Tianjin-Hebei and their neighborhood provinces are the most polluted regions in China. Environmental efficiencies of 528 thermal power plants were evaluated through metafrontier epsilon-based measure, which aimed to overcome the invalid inferences of radial or non-radial model. We also analyzed the heterogeneity of environmental efficiency across different regions by considering environmental technology differences. Bootstrap regression was used in order to testify three different hypotheses to address the disadvantages of conventional regression. We found that environmental efficiency in Beijing and Tianjin is higher than the other regions and is becoming divergent. In addition, coal consumption intensity negatively affects environmental efficiency. Large-scale power stations are more environmental efficient than smaller ones. Longer equipment utilization hour can enhance energy performance of power stations, which can decrease carbon emissions and increase environmental efficiency. It is better to promote technology transfer from regions with higher environmental efficiency to regions with lower environmental efficiency. Low-carbon technologies should be promoted to decrease carbon emissions. [ABSTRACT FROM AUTHOR]

China, the largest developing country, is the world largest cement producer and the largest cement-consuming nation. Although China's cement output reached its peak in 2014, regions, i.e., Fujian and Yunnan provinces, were no peaking until 2016. At the same time, rare studies referred to China's cement consumption and CO2 emissions from the perspective of cement consumption at the provincial level. We developed the S-Logistic, polynomial model, and ARIMA model to study the peaking time of cement consumption at the provincial level, and we also projected China's cement consumption and CO2 emissions toward 2030. Meanwhile, the discrepancies of peaking time and cumulative cement consumption per capita (CCCPC) among provinces were also studied based on GDP per capita and urbanization rate (UR). The results are that the CCCPC respectively in the range of 22–34 ton, 18–25 ton, and 17–27 ton in the eastern, intermediate, and western zone when cement consumption reached its peak. We draw the following conclusions that the CCCPC in 2030 could reach ~ 43 ton and the projected cement consumption is ~ 1252.72 Mt, which accounts for 50% of that in 2017, and cement CO2 emissions are at the range of 488.19–510.90 MtCO2 in 2030. Furthermore, capacity replacement, controlling new capacity and eliminating backward capacity are significant of greenhouse gas emission reduction not only for China, but also for the global cement industry. [ABSTRACT FROM AUTHOR]