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The serious consequences of climate warming have increasingly become a globe agenda in recent decades. China has been actively participating in various initiatives to address global climate change and has made commitments to reduce carbon emissions. Although the logistics industry is regarded as the "new driving force of national economic development", its carbon intensity is relatively high. Therefore, whether the logistics industry can develop in a green and low-carbon way is very crucial. This paper takes the green logistics efficiency of China's provincial logistics industry as the research object. The Super-SBM model is used to measure the China's green logistics efficiency, then the general dynamic characteristics is depicted by kernel density analysis. With the GML (Global Malmquist-Luenberger) index model, the reasons for the changes in green logistics efficiency are explained. Finally, Moran's I index is used to analyze the spatial correlation of green logistics efficiency in each province. The results show that the green logistics efficiency in China is at a low level, but with an upward trend. China's green logistics efficiency has a significant positive spatial correlation, showing a zonal pattern of high in the east and low in the west, and a polarization phenomenon. In addition, the bottleneck of the overall development of green logistics efficiency in China depends on the level of green technology. Furthermore, the results also imply that green technology advancement is an inherent key factor for green logistics efficiency to achieve growth. [ABSTRACT FROM AUTHOR]

Global warming has endangered the natural ecosystem's balance, as well as human existence and development, and it is mostly caused by carbon dioxide. Identifying carbon emission characteristics and predicting carbon emission reasonably is helpful to provide indication for the effective design of emission reduction path. The most literature use a single prediction model; this paper predicts carbon emission using a number of strategies based on previous research. Considering the prediction accuracy, advantages, and disadvantages of each method, a new method combining autoregressive integrated moving average (ARIMA) model and NAR neural network (NAR-NN) is proposed; in addition, this paper attempts to explain the carbon emission characteristics and emission reduction paths of each region from the new perspective of clustering. First, the results show that China's carbon emission features can be divided into four categories: low-carbon demonstrative type, low-carbon potential type, high-carbon developed type, and high-carbon traditional type. Moreover, low-carbon demonstrative type includes merely Beijing and Shanghai, low-carbon potential type is distributed in the southeast coastal areas of China, the high-carbon developed type is mainly distributed in Northeast China, and the western region basically belongs to high-carbon traditional type. Second, ARIMA model and NAR-NN are the two best methods in terms of prediction effect, and the combined model has better prediction effect than the single model. Third, carbon emissions in most regions of China will increase in the next few years; the time of carbon peak in the east is earlier than that in the west regions of China. Beijing will probably be the first region in China to complete the carbon peak. Besides, there is a certain correlation between the carbon peak time and the type of carbon emission in each region. [ABSTRACT FROM AUTHOR]

As global warming becomes more prominent, the need to reduce carbon emissions to achieve China's carbon peak target is increasing. It is imperative to seek effective methods to predict carbon emissions and propose targeted emission reduction measures. In this paper, a comprehensive model integrating grey relational analysis (GRA), generalized regression neural network (GRNN) and fruit fly optimization algorithm (FOA) is constructed with carbon emission prediction as the research objective. Firstly, GRA is used for feature selection to find out the factors that have a strong influence on carbon emissions. Secondly, the parameter of GRNN is optimized using FOA algorithm to improve the prediction accuracy. The results show that (1) fossil energy consumption, population, urbanization rate and GDP are important factors affecting carbon emissions; (2) FOA-GRNN outperforms GRNN and back propagation neural network (BPNN), verifying the effectiveness of FOA-GRNN model for CO2 emission prediction. Finally, by analyzing the key influencing factors and combining scenario analysis with forecasting algorithms, the carbon emission trends in China for 2020-2035 are forecasted. The results can provide guidance for policy makers to set reasonable carbon emission reduction targets and adopt corresponding energy saving and emission reduction measures. [ABSTRACT FROM AUTHOR]

Nowadays environmental issues have been of great concern to the world, among which the problem of global warming caused by greenhouse gas emissions is particularly prominent. All countries in the Kyoto Protocol and the Paris Agreement have committed to control greenhouse gas emissions, and China, as the largest carbon emitter, has assumed a heavier burden. China has been striving to develop low-carbon technologies such as hydrogen, nuclear, wind, and solar energy, but the most attention should be paid to CCUS, which many scholars have high expectations that CCUS can help China reduce emissions to some extent. Therefore, this paper presents a prediction that CCUS can reduce 3.8% of carbon emissions for China in 2040 when CCUS emission reductions increase at a rate of 30%. The power and chemical industries could reduce carbon emissions by 2.3% and 17.3%, respectively. [ABSTRACT FROM AUTHOR]

The massive use of energy has caused a rapid increase in global carbon dioxide emissions, resulting in a series of environmental problems such as climate warming. Investment in the energy industry can guide funds into green and clean production, reduce carbon emissions in the energy industry, and promote the green development of the energy industry. This paper considers the energy, the environment, the economy, and other factors and focuses on energy consumption and investment structure. Taking 30 provinces in China as research samples, a dynamic spatial Durbin model is established. The results show that the first-order term of carbon emissions has a driving force of 0.5068% for current carbon emissions at a significance level of 1% and that the increase in current carbon emissions will lead to a continued increase in carbon emissions in the next period. The increase in the carbon emissions of neighbouring provinces will increase their carbon emissions through the spatial spillover effect. Whether in the short term or long term, the increase in energy investment and the optimization of the energy investment structure can reduce carbon emissions. The above conclusions can provide a reference for the formulation of government environmental policies. [ABSTRACT FROM AUTHOR]

Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted widespread attention. Aiming at the problem of carbon dioxide emissions forecasting, this paper proposes a new hybrid forecasting model of carbon dioxide emissions, which combines the marine predator algorithm (MPA) and multi-kernel support vector regression. For further strengthening the prediction accuracy, a novel variant of MPA is proposed, called EGMPA, which introduces the elite opposition-based learning strategy and the golden sine algorithm into MPA. Algorithm test results show that EGMPA can effectively improve the convergence speed and optimization accuracy. The carbon dioxide emission data of China from 1965 to 2020 are taken as the research objects. Root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed model. The proposed multi-kernel support vector regression model is used to forecast China's carbon dioxide emissions during the "14th Five-Year Plan" period. The results show that the proposed model has RMSE of 37.43 Mt, MAE of 30.63 Mt, and MAPE of 0.32%, which significantly improves the prediction accuracy and can accurately and effectively predict China's carbon dioxide emissions. During the "14th Five-Year Plan" period, China's carbon dioxide emissions will continue to show an increasing trend, but the growth rate will slow down significantly. [ABSTRACT FROM AUTHOR]

Carbon emissions are important factors causing global warming, which requires global efforts to deal with. In this paper, we investigate the mechanism of financial innovation on reducing carbon emissions in China by constructing a financial innovation development index with factors of green finance as well as fintech development. Empirical results show that financial innovation contributes to reduce carbon intensity by promoting energy structure transition as well as public fiscal expenditure on energy conservation and environmental protection. Moreover, heterogeneity exists in the effect of financial innovation on carbon emission reduction. Financial innovation has a significant role in reducing carbon intensity in eastern regions, but has a relatively small influence on central and western regions. Furthermore, financial innovation has a lag effect on reducing carbon intensity. [ABSTRACT FROM AUTHOR]

Climate warming has gradually become a major problem threatening human survival, and countries have begun to pay attention to carbon emissions. Energy conservation and emission reduction has become a central task in China's economic development since the 14th Five-Year Plan. As the main force of carbon emissions in China, thermal power industry is bound to become the focus of attention in China's low-carbon development strategy and energy conservation and emission reduction. Moreover, with the marketization of the power industry, the state has joined the market competition at the power generation sectors and the power sale sectors, and implemented the "opening the middle of the two pipes." Therefore, the coverage of influence of carbon emissions and carbon investment behavior of power generation companies is not limited to itself, but will also be extended to the supply chain level. Based on the above background, this paper evaluates the scientific rationality of low-carbon investment projects of thermal power enterprises from the perspective of low-carbon supply chain, which not only can help enterprises achieve a win–win situation of economic and environmental benefits, but also contribute to the carbon emission reduction of the entire supply chain, thereby promoting China's entire social and economic energy conservation and emission reduction work. [ABSTRACT FROM AUTHOR]

With greater industrialization and urbanization, the amount of carbon dioxide (CO2) in the atmosphere has increased rapidly, and global warming has become a great challenge for humankind. Land is an important resource for human survival and development. The analysis of CO2 emissions from various land uses is crucial for controlling the level of land CO2 emissions and increasing the effectiveness of land use. This study develops a methodological framework to explore the spatiotemporal spillover effect and efficiency in regional land use carbon emissions (LUCE) and the change of land use structure efficiency (LUSE). The CO2 emissions of various land use types and the amount of indirect CO2 emissions of related industries from 1995 to 2015 were calculated. Based on CO2 emission measurement and spatial autocorrelation test, a spatial Durbin model is constructed to explore the spatial spillover effect (SSE) of LUCE. A Super-slack-based DEA model with undesirable outputs is proposed to measure the LUSE and the LUCE of various provinces from 1990 to 2015. Results show that the LUCE in China show an increasing trend overall, with emissions increasing nearly four times from 1990 to 2015. This increase is caused mainly by indirect CO2 emissions, that is, energy consumption generated by human activities. The local Moran's I showed a downward trend with the increase in years, indicating that regions with similar LUCE were gradually dispersed and that regional differences existed. The LUSE in China presents a mainly decreasing trend from 0.96 in 1990 to 0.69 in 2015. The spatial distribution of CO2 emissions is negatively correlated with land use efficiency. Economic growth, technological input and energy intensity are the core factors affecting regional LUCE and have significant spatial spillover characteristics. The 30 provinces are separated into three subareas by a comparison of the LUSE and the LUCE, and recommendations are made for how to improve the LUSE and CO2 emission reduction control of each subarea. [ABSTRACT FROM AUTHOR]

China has to deal with the twin challenges of economic structural reform and carbon emission reduction against global warming. While investing in and constructing new infrastructure is great for the economy, it has also added to the carbon emissions of major cities. The product design industry has recently become increasingly interested in creating and pricing cultural and creative goods in specific provinces. Thanks to the burgeoning global cultural and creative sector, a new platform has opened up for the evolution and modernization of China's ancient cultural practices. Cultural creativity has broken the rigid design and production pattern of traditional products from a business point of view, increasing their economic advantages and competition. Also, this study examines ICT's main and moderate effect on carbon emissions in the 27 provinces of China's economy from 2003 to 2019 using panel estimators. The estimated outcomes show the positive contribution to environmental damages by physical capital, tourism, cultural product prices, innovative, creative prices, and trade openness, while ICT significantly reduces emissions. Besides the moderate role of the digital economy on physical capital, tourism, CP, ICP, and tourism significantly reduce CO2 emissions. However, the granger causality outcomes also show a robust analysis. Furthermore, this study also proposes some interesting policies to obtain environmental sustainability. [ABSTRACT FROM AUTHOR]

The manufacture of products in the industrial sector is the principal source of carbon emissions. To slow the progression of global warming and advance low-carbon economic development, it is essential to develop methods for accurately predicting carbon emissions from industrial sources and imposing reasonable controls on those emissions. We select a support vector machine to predict industrial carbon emissions from 2021 to 2040 by comparing the predictive power of the BP (backpropagation) neural network and the support vector machine. To reduce noise in the input variables for BP neural network and support vector machine models, we use a random forest technique to filter the factors affecting industrial carbon emissions. The statistical results suggest that BP's neural network is insufficiently adaptable to small sample sizes, has a relatively high error rate, and produces inconsistent predictions of industrial carbon emissions. The support vector machine produces excellent fitting results for tiny sample data, with projected values of industrial carbon dioxide emissions that are astonishingly close to the actual values. In 2030, carbon emissions from the industrial sector will have reached their maximum level. [ABSTRACT FROM AUTHOR]

Global warming continues to be an intimidating factor for environmental protection, and reducing carbon emissions is an effective way to deal with the phenomenon. However, the energy sector is a significant contributor to greenhouse gas emissions. Therefore, investment in environmental regulations and research and development (R&D), is critical for fostering a low-carbon growth model. This study focuses on 30 provinces in China from 2004 to 2019. We used the spatial Durbin model to investigate how the spatial spillover effect of R&D and environmental regulation impacts carbon emissions. In addition, we applied the dynamic threshold panel model to mitigate potential problems of endogeneity. The results reveal that carbon emissions have a considerable spatial correlation in both temporal and spatial dimensions, exhibiting high and low-value accumulation characteristics. Furthermore, the combined effect of R&D intensity, environmental regulation, and energy consumption were found to contribute to the increase in carbon emissions across China's provinces, and they also suggest different influencing mechanisms. The spillover effects of increased carbon emissions in neighboring regions also contribute to the increase in local carbon emissions. The study also found that R&D and stringent environmental regulations measures strongly moderate the link between energy consumption and carbon emissions. In promoting carbon reduction, by breaking the dynamic equilibrium in China, the provincial investment outflow on R&D intensity could be optimized, and the regional levels should focus more on tightening environmental regulatory measures and promoting the development of energy-conserving technologies. [ABSTRACT FROM AUTHOR]

Intensifying climate change significantly impacts residential electricity consumption, especially in developing countries, such as China, that are experiencing rapid income growth. By combining meteorological and monthly household consumption survey data, this study explores the response function of residential electricity consumption to temperature in China from a micro perspective. Future residential electricity demands and related CO2 emissions are then forecast under different climate scenarios. Overall, the response function is U-shaped, and one additional day above 34 °C will increase monthly residential electricity consumption by 2.11%. Global warming will more likely increase the electricity burden on low-income groups. There will be notable seasonal changes in electricity demand in the future, and the largest increase will occur in August. The total demand for residential electricity caused by temperature change will show a fluctuating growth trend, from 0.8% and 1% in 2025 to 2% and 2.9% in 2060 under the RCP4.5 scenario and RCP8.5 scenario, respectively; meanwhile, this demand will be accompanied by a cumulative increase in carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

China is the second-largest economy in the world after the USA and the largest contributor of CO2 emissions in the globe followed by the USA. In this study, we have included two factors that could affect the CO2 emissions in China i.e. financial inclusion and education. Five different proxies of financial inclusion have been estimated through the ARDL technique and four of them have confirmed the favorable impact of financial inclusion on the environmental quality in China. Similarly, the role of education proved to be beneficial in reducing CO2 emissions in China. Among the control variables, GDP and population also exerted a negative impact on CO2 emissions, whereas, the research and development activities encourage carbon emissions. Our findings suggest that financial inclusion and education can play an important role in the fight against global warming. Funds should be transferred to those firms, businesses, and individuals who are involved in eco-innovations. Government should try to educate people on the environment and the harmful effects of global warming. Knowledge helps to build up technology that is more energy-efficient; hence, innovation through knowledge should be promoted to fortify the positive impacts of education on the eco-system. [ABSTRACT FROM AUTHOR]

China is the largest source of carbon emissions in the world, and the Chinese iron and steel sector is the main contributor to the growth in greenhouse gas emissions. In addition to excessive greenhouse gas emissions, climate change and global warming have seriously threatened the survival of animals, plants and even humans. To cope with global climate problems, it is our responsibility to establish environment-friendly iron and steel enterprises. In this study, data envelopment analysis (DEA) is used to evaluate the environmental protection performance of China's iron and steel enterprises based on the panel data during 2015–2017. We first establish an environment-related index system for the industry and build a range-adjusted measure model. Then, the global Malmquist productivity index based on DEA is introduced to analyse environmental efficiency and environmental productivity. The results show that most Chinese iron and steel enterprises achieved high environmental efficiency. However, many enterprises exhibited low environmental productivity and especially Shougang Group, HBIS Tangsteel and Anyang Iron and Steel continued to decline from 2015 to 2017. In general, Valin Hengyang and Shaanxi Iron and Steel achieved better performance in environmental protection. Based on the above results, the environmental problems in our country are still very serious. Thus, the government needs to formulate suitable environmental regulations to reduce the gap between the global frontier and contemporaneous frontier over two periods. Furthermore, entrepreneurs should introduce some advanced environmental technologies to improve their technical level and environmental protection level. [ABSTRACT FROM AUTHOR]

On one divide, energy types have been linked with the varying degree of environmental damage. Another perspective argued on the severity of the damaged base on per capita and/or population consumption pattern. As such, this study investigates the nexus of per capita natural gas consumption-carbon dioxide emissions and per capita income-carbon dioxide emissions in the case of the People of the Republic of China. This study objectively expanded to illustrate whether the N-shaped environmental Kuznets curve hypothesis holds in the case of China or not. The employed autoregressive distributed lag bound testing approach incorporated additional explanatory variables (urbanization) within the N-shaped EKC hypothesis over the period 1971–2018. Importantly, the results show an evidence of inverted N-shaped EKC relationship. In addition, the study posits a positive relationship between natural gas consumption and carbon dioxide emissions and between urbanization and carbon dioxide emissions. Thus, the study proposes renewable energy development and decongestion of the urban centers as a means of controlling global warming. [ABSTRACT FROM AUTHOR]

The Permian–Triassic mass extinction was marked by a massive release of carbon into the ocean-atmosphere system, evidenced by a sharp negative carbon isotope excursion. Large carbon emissions would have increased atmospheric pCO2 and caused global warming. However, the magnitude of pCO2 changes during the PTME has not yet been estimated. Here, we present a continuous pCO2 record across the PTME reconstructed from high-resolution δ13C of C3 plants from southwestern China. We show that pCO2 increased from 426 +133/−96 ppmv in the latest Permian to 2507 +4764/−1193 ppmv at the PTME within about 75 kyr, and that the reconstructed pCO2 significantly correlates with sea surface temperatures. Mass balance modelling suggests that volcanic CO2 is probably not the only trigger of the carbon cycle perturbation, and that large quantities of 13C-depleted carbon emission from organic matter and methane were likely required during complex interactions with the Siberian Traps volcanism. The Permian–Triassic mass extinction was accompanied by a massive release of carbon into the ocean-atmosphere system, but the magnitude of change is not well known. Here, the authors present a new record of C3 plants from southwestern China which shows that atmospheric pCO2 increased by a factor of six during this event. [ABSTRACT FROM AUTHOR]

In recent years, global warming has become increasingly devastating, leading to severe consequences, such as extreme weather events and sea-level rise. To reduce carbon dioxide emissions, it is essential to recognize different emission sources and key driving factors. Three main carbon emission sources from the period between 1990 and 2017 were identified in China: the energy industry, fuel combustion in other industries, and industrial process. For each source, a driving force model was developed via multiple linear regression. Based on these models, forecasts of the carbon intensity and total CO2 emissions were obtained from 2018 to 2030. The results demonstrate that the CO2 emission intensity and total emissions will continue to decrease but more effort will be required to achieve the goal of Paris Agreement. [ABSTRACT FROM AUTHOR]