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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]

As global warming intensifies, reducing carbon emissions has become a global common mission. Tourism transportation is one of the important sources of carbon emissions, and reducing its carbon emissions is a key part of achieving China's carbon reduction goals. Based on the panel data of various provinces and cities in North China from 2000 to 2022, this paper calculates the carbon emissions of tourism transportation by using the carbon emission coefficients of different transportation modes in different segments. Moreover, the temporal and spatial evolution of the tourism economy is systematically analyzed. The Tapio decoupling model and LMDI addition decomposition model are used to analyze the relationship between carbon emissions and tourism economic growth and the effects of 11 influencing factors on carbon emissions. The results show that: (1) The carbon emission of tourism transportation in North China has experienced four stages: a steady growth period, a transitional adaptation period, a stable equilibrium period, and a drastic decline period. The overall carbon emission level of tourism transportation is as follows: Hebei Province > Shanxi Province > Inner Mongolia Autonomous Region > Beijing City > Tianjin City. (2) The decoupling coefficient between tourism traffic carbon emissions and economic development fluctuates but mainly shows a weak decoupling state. (3) In terms of influencing factors, passenger size and passenger density have the greatest impact on the carbon emissions of tourism transportation. [ABSTRACT FROM AUTHOR]

With the challenge posed by global warming, accurately estimating and managing carbon emissions becomes a key step for businesses, especially power generation companies, to reduce their environmental impact. Optuna–LightGBM–XGBoost, a novel power and carbon emission relationship model that aims to improve the efficiency of carbon emission monitoring and estimation for power generation companies, is proposed in this paper. Deeply exploring the intrinsic link between power production data and carbon emissions, this model paves a new path for "measuring carbon through electricity", in contrast to the emission factor method commonly used in China. Unit data from power generation companies are processed into structured tabular data, and a parallel processing framework is constructed with LightGBM and XGBoost, and optimized with the Optuna algorithm. The multilayer perceptron (MLP) is used to fuse features to enhance prediction accuracy by capturing characters that the individual models cannot detect. Simulation results show that Optuna–LightGBM–XGBoost can achieve better performance compared to existing methods. The mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) of the model are 0.652, 0.939, 0.136, and 0.994, respectively. This not only helps governments and enterprises to develop more scientific and reasonable emission reduction strategies and policies, but also lays a solid foundation for achieving global carbon neutrality goals. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy, China has become the world's largest carbon emitter. China not only has an obvious growth rate of industrial carbon emissions but also the intensity of agricultural carbon emissions is hovering at a high level. The development of China's agricultural economy has largely come at the expense of high emissions. Currently, under the background of global warming and difficulty in controlling greenhouse gas emissions, the development of low-carbon agriculture is an important way to realize the harmonious development of the ecological environment and economic growth and to promote the sustainable development of agriculture. The agricultural production efficiency is the main factor affecting the intensity of agricultural carbon emissions. Based on provincial panel data of China from 2010 to 2019, this paper establishes an indicator system and uses the super-efficiency SBM model to measure agricultural production efficiency. The regional agricultural carbon emissions were estimated using carbon-emission-related agricultural production activities. In order to study the nonlinear relationship between agricultural production efficiency and agricultural carbon emission intensity in the narrow sense, this paper uses a threshold regression model with agricultural carbon emissions as the threshold variable. Based on the analysis of China's agricultural production efficiency and agricultural carbon emissions from 2010 to 2019, an empirical test is conducted through a threshold regression model. The results show an "inverted U-shaped" relationship between agricultural production efficiency and agricultural carbon emission intensity. In areas with high agricultural production efficiency, the improvement of production efficiency can suppress the intensity of agricultural carbon emissions; in areas with low agricultural production efficiency, the improvement of production efficiency increases the intensity of agricultural carbon emissions. Finally, based on the research conclusions, this paper provides feasible suggestions and countermeasures for China's agricultural carbon emission reduction and improvement of agricultural production efficiency. [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]

In recent years, global warming has become increasingly severe, and the ecological and environmental problems facing mankind have become increasingly serious. As the main areas of transportation activities, cities are also the main places of carbon emissions. As a necessary condition for human's daily-life travel, it is particularly important to calculate the carbon emissions from urban transportation. Due to the different characteristics of economy and population in different regions of a city, the carbon emissions of urban buses show different characteristics in terms of temporal and spatial distribution. The developments of science and technology promote the application of big data analysis to specific practical life, enabling people to research and solve problems from a new perspective. This paper uses the GPS data of urban buses in Sanya City, China, to identify operation conditions from urban buses, and calculates the distance and time under different conditions. Based on the measured data of carbon emissions, this paper visualizes the distribution characteristics of carbon emissions by density analysis; explains the time distribution characteristics by the visual analysis of carbon emissions in different time periods, working days and rest days, and different energy types; and illustrates the spatial distribution characteristics by the spatial distributions of carbon emissions from Sanya's buses on working days and rest days, as well as in different routes, providing reference for a low-carbon development of urban green transport. [ABSTRACT FROM AUTHOR]

The development path from attaching importance to environmental protection to the theory of ecological conservation, then to piloting ecological civilization demonstration areas, marks that China’s ecological conservation has gradually moved from theoretical construction to practical exploration, based on the new idea that “lucid waters and lush mountains are invaluable assets.” It is still an ordeal for China’s ecological conservation in the context of global warming how to reduce carbon emission intensity while maintaining sustained economic growth. Under the dual constraints of peaking carbon dioxide emissions before 2030 and achieving carbon neutrality before 2060 (“dual carbon” goals), this paper employs the five national ecological civilization pilot demonstration areas (ECDAs) established in 2014 as quasi-natural experiments based on the panel data of 30 Chinese provincial regions from 2003 to 2019. Based on the analysis of the policy implementation background and the theoretical mechanism of its impact on carbon emission intensity, the synthetic control method and difference in differences method are adopted to test the impact of the construction of ECDAs on carbon emission intensity and examine the spatial spillover effect of the pilot policies. The study shows that the construction of ECDAs has significantly reduced carbon emission intensity as a whole, especially in Fujian, Guizhou, and Yunnan Provinces. Moreover, the conclusions successfully pass the robustness test. The mechanism analysis results demonstrate that the construction of ECDAs can lower carbon emission intensity through the positive incentives from boosting technological progress and developing green finance, and the reversal pressure mechanism of optimizing the energy structure and improving the market segmentation. The analysis results of the spatial spillover effect indicate that the construction of ECDAs plays a significant role in reducing carbon emission intensity in the region and its adjacent areas. Therefore, China should introduce ECDAs and the experience gained to more regions. Meanwhile, China should spare no effort to seek multi-dimensional paths to reduce carbon emissions in view of regional differences in green development, and strengthen cross-regional communications and cooperation to realize the goals of carbon emission reduction. [ABSTRACT FROM AUTHOR]

The severe global warming issue currently threatens humans' existence and development. Countries and international organizations have effectively implemented policies to reduce carbon emissions and investigate low-carbon growth strategies. Reducing carbon emissions is a hot topic that academics and government policy-making departments are concerned about.Through necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis(fsQCA), this paper investigates local governments' configuration linkage effect and path choice to improve carbon emission performance from six dimensions: energy consumption, industrial structure, technological innovation, government support, economic development, and demographic factors. The research findings include the following: (1) Individual condition does not represent necessary conditions for the government's carbon performance. Among the two sets of second-order equivalence configurations(S and Q) (five high-level carbon performance configurations), those dominated by economic development or low energy consumption can produce high-level carbon performance. Therefore, the six antecedent conditions dimensions work together to explain how the government can create high levels of carbon performance. (2)According to the regional comparison, China's eastern, central, and western regions exhibit similarities and differences in the driving forces behind high carbon emission performance. All three regions can demonstrate carbon emission performance when all the factors are combined. However, when constrained by the conditions of each region's resource endowment, the eastern region emphasizes the advantage of economic and technological innovation, the central region favors government support and demographic factors, and the western region prefers upgrading industrial structure based on a specific level of economic development. [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]

This paper proposes a novel aviation CO 2 emissions reduction method by introducing a market-based incentive mechanism. An aviation carbon emissions prediction model is proposed to characterize both the microscopic flight dynamics and the macroscopic air traffic demand trend. Based on emissions prediction, a linear climate response model is applied to evaluate the environmental impact of the aviation carbon emissions. Finally, a market-based framework for aviation CO 2 emissions reduction is established based on a two-phase carbon trading model, which provides a piecewise charge method through emission taxation and cap-and-trade. A case study is carried out to demonstrate the effectiveness of the proposed framework using a real-world dataset from the Hefei Air Traffic Control Zone in China. Three scenarios are defined to describe the possible development speed of sustainable aviation fuel application in the future: not applied, medium-speed development, and high-speed development. The results show that the carbon peak will be reached in 2025 in the high-speed development scenario, when the emission amount and associated environmental impact will be reduced by 23.7% and 22.3%, respectively. Thus, the market-based framework proposed in this paper can effectively reduce aviation CO 2 emissions and mitigate the effects of global warming. • An aviation CO 2 emissions prediction model is proposed considering both microscopic flight dynamics and macroscopic traffic demand. • A more efficient market-based CO 2 emission reduction framework is developed based on a two-phase carbon trading model by means of taxation and cap-and-trade. • Case study shows that the proposed method has a more optimal incentive for CO 2 emission reduction. [ABSTRACT FROM AUTHOR]

In recent years, concurrent climate extreme conditions (i.e., hot‐dry, cold‐dry, hot‐wet, and cold‐wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra‐extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Specifically, to reduce the uncertainties from models, we use a downscaled and bias‐corrected CMIP6 ensemble under two continuously‐warming scenarios to evaluate the impact of global warming on ultra‐extreme events over China. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. The frequency of extreme cold events is projected to be small. More frequent extreme hot‐wet events with concurrence in the same month and year would be expected for China under the continuously‐warming scenarios. This is particularly obvious for the west where more than 6 hot‐wet months are likely to take place under future warming scenarios. This may imply that more extreme heat waves and flooding events would coincide in the same month or year for China in the future. For univariate ultra‐extreme events, both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. When the global mean temperature is limited to 1.5°C rather than 2°C, the avoided impacts of hot‐wet and cold‐wet extremes concurring in the same month will be larger than those of dry‐related compound extremes. Overall, the results suggest that slowing down global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction. Plain Language Summary: In recent years, concurrent climate extreme conditions (e.g., hot‐dry, cold‐dry, hot‐wet, and cold‐wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra‐extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Here, we use a downscaled and bias‐corrected CMIP6 ensemble under two continuously‐warming scenarios to address this question. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. Both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. Overall, the results suggest that slowing down the global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction. Key Points: A downscaled and bias‐corrected CMIP6 ensemble is used to evaluate the impact of global warming on ultra extreme events over ChinaMore frequent extreme hot‐wet events with concurrence in the same month and year would be expected for ChinaBoth extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level [ABSTRACT FROM AUTHOR]

This article explores the impact of high-standard farmland construction policy on green and low-carbon agricultural development, such policy has been ignored in literature from the perspective of policy evaluation and it is a promising cure for global warming. The effectiveness and impact mechanism of policy implementation are analyzed, it concludes that the policy significantly reduces agricultural carbon emissions by 12.3%, which benefits from the improvement of agricultural production efficiency. This paper opens a new window for policy-making for modern agriculture and the momentous challenge of the global warming. [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]

Due to the emission of carbon dioxide and other greenhouse gases, the global climate is warming. As the world's biggest emitter of carbon emissions, China faces a more severe challenge in reducing carbon emissions than developed countries. A reasonable prediction of the carbon peak in China will help the government to formulate effective emission reduction paths. This paper analyzes the changes in carbon emissions in China from 2004 to 2020, uses the STIRPAT model and scenario analysis method to predict carbon emissions from 2021 to 2030, and then calculates the carbon efficiency during carbon peaking to select the most effective carbon peak path for China. The results show that China's carbon emissions increased year by year from 2004 to 2020. Under the baseline scenario, China is unlikely to reach its carbon peak before 2030. Under the regulatory scenarios, China can reach its carbon peak before 2030. The peak values from high to low are seen with the rapid development-weak carbon control scenario, rapid development-intensified carbon control scenario, slow development-weak carbon control scenario and slow development-intensified carbon control scenario, respectively. Correspondingly, China will peak its carbon emissions in 2029, 2028, 2028 and 2028, respectively, according to these scenarios. The carbon efficiency under the rapid development-weak carbon control scenario is the highest, which means that accelerating the growth rate of population, GDP and urbanization while moderately carrying out the transformation of industrial structure and energy structure is an effective way to achieve the goal of "carbon peak by 2030". [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]

Achieving carbon neutrality is an important initiative to cope with global warming. Most studies on the role of tourism towards carbon neutrality were based on the carbon emission reduction (CER) perspective while the carbon sink (CS) perspective was lacking. Based on the quasi-natural experiment of high-standard scenic spots, this paper aimed to examine the effect of tourism resources development on regional CS with high-precision multi-source datasets of 2601 Chinese county units. According to the results of the time-varying difference-in-difference (DID) model regression, CNSA significantly deteriorated CS, but the findings of the rolling regression test confirmed that the adverse effect diminished over time. [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]

Against the backdrop of severe global warming, the low-carbon city pilot policy, with carbon emission reduction as its main objective, is an important initiative for China to fulfil its international commitment to carbon emission reduction and promote a green and low-carbon development strategy. However, none of the literature has yet evaluated whether the pilot low-carbon city policy promotes urban land use efficiency as a policy effect. In view of this, this paper measures urban land use efficiency from a low-carbon perspective using a global reference super-efficiency SBM model based on data from 186 prefecture-level cities in China from 2005–2017, and subsequently constructs a difference-in-differences method to test the true impact of low-carbon city pilot policies on urban land use efficiency and carbon emissions, and uses a propensity score matching method to test its robustness. It is found that: (1) the average level of urban land use efficiency in China is low and on a downward trend; (2) overall, cities are predominantly low-efficiency cities, with only the high-efficiency cities in Guangdong Province showing spatial agglomeration; and (3) the low-carbon city pilot policy reduces carbon emissions while also negatively affecting urban land use efficiency. Accordingly, this paper puts forward corresponding policy recommendations. [ABSTRACT FROM AUTHOR]

An analysis of carbon emissions of crop production provides paths for global warming mitigation. Existing studies have focused on the magnitude of the carbon emissions from crop production, which is unreasonable for inter-location comparison due to neglecting regional variations in cultivation technologies and planting scale. Different from the conventional idea, this paper estimated the carbon-emission density of crop production (CEDCP) based on carbon emissions per hectare of crop production. With the 30 Chinese provinces between 2000 and 2020 as the study area, temporal dynamics and spatial patterns of the CEDCP were explored, regional disparities of the CEDCP were discussed based on the Theil index, and the possibility of regional coordinated optimization for the CEDCP was explored by relying on the convergence tests. The results show that the average annual CEDCP in China was 1.462 t/hm2, reaching a peak of 1.576 t/hm2 in 2015. The national carbon-emission densities of agricultural materials, rice fields, soil management, and straw burning were 0.492 t/hm2, 0.390 t/hm2, 0.189 t/hm2, and 0.391 t/hm2, respectively. In many provinces, the CEDCP increased first and then decreased, presenting a spatial pattern of high in the eastern region and low in the western region. Regional disparities of CEDCP shrank early but expanded later, and the disparities within the western region had always contributed considerably to the overall disparities. The CEDCP had shown σ- and β- convergence in both national and regional scales, and the convergence process had positive spillover effects. These findings suggest that inter-provincial cooperation may facilitate the CEDCP to converge. [ABSTRACT FROM AUTHOR]

Research at the biophysical level constitutes the main approach to study the path through which climate influences infectious diseases, but the influence of socioeconomic factors on climate change and the spread of infectious diseases also cannot be ignored. In the current context, with its emphasis on carbon emission reduction, countries have begun to adopt "total control and trading" methods to reduce greenhouse gas emissions. This paper is based on the monthly relevant data of various provinces and cities from 2015 to 2019, adds the moderating variable of "carbon emission trading volume" based on current research results, establishes a static model of unbalanced panel fixed effects and a dynamic panel model, and deeply analyzes the impact of climate change on infectious disease popularity and the moderating effect of the carbon trading market. The study found that climate warming, frequent precipitation, and shortened sunshine duration will lead to an increase in the number of infectious disease cases and that the "carbon emission trading volume" variable plays a negative role in the positive correlation between climate change (temperature/precipitation) and the number of infectious disease cases. There are seasonal differences, and the moderating effect in summer/autumn is more obvious than that in winter/spring. [ABSTRACT FROM AUTHOR]

Due to global warming, the overheating risk in the severe cold region of China has attracted attention, but so far, no studies have examined summer overheating in this region. This paper aims to reveal the overheating risk in recent and future climates in the severe cold region of China. An 18-storey residential building in the severe cold region of China was monitored from May to September 2021 to validate the simulation data of the indoor temperature. Weather files of the typical meteorological year (TMY) from 2007 to 2020, observations in 2021, and forecasts for the climate in different carbon emission scenarios (2030, 2060) were used to simulate the indoor temperature and assess the overheating risk. The results revealed the severity of the overheating risk; the overheating hours in the south-facing bedroom were recorded as 884 h (24.07%) with the TMY weather data and 1043 h (28.40%) in 2030 and 1719 h (46.81%) in 2060 under the RCP8.5 carbon emission scenario. Thus, the low carbon emissions policy may significantly alleviate overheating. Moreover, to cope with climate change, it is suggested that the Chinese local design standards should consider the summer overheating risk and make the necessary adjustments. [ABSTRACT FROM AUTHOR]

As the top emitter of carbon dioxide worldwide, China faces a considerable challenge in reducing carbon emissions to combat global warming. Carbon emissions from coal consumption is the primary source of carbon dioxide emissions in China. The decomposition of the driving factors and the quantification of regions and industries needs further research. Thus, this paper decomposed five driving factors affecting carbon emissions from coal consumption in China, namely, carbon emission intensity, energy structure, energy intensity, economic output, and population scale, by constructing a Kaya-Logarithmic Mean Divisia Index (Kaya-LMDI) decomposition model with data on coal consumption in China from 1997 to 2019. It was revealed that the economic output and energy intensity effects are major drivers and inhibitors of carbon emissions from coal consumption in China, respectively. The contribution and impact of these driving factors on carbon emissions from coal consumption were analyzed for different regions and industrial sectors. The results showed that carbon emissions from coal consumption increased by 3211.92 million tons from 1997 to 2019. From a regional perspective, Hebei Province has the most significant impact on carbon emissions from coal consumption due to the effect of economic output. Additionally, the industrial sector had the most pronounced influence on carbon emissions from coal consumption due to the economic output effect. Finally, a series of measures to reduce carbon emissions including controlling the total coal consumption, improving the utilization rate of clean energy, and optimizing the energy structure is proposed based on China's actual development. [ABSTRACT FROM AUTHOR]

The impact of climate warming on world agricultural production and food security has become increasingly obvious. As an important source of carbon emissions and the carbon sink system, the green development of agriculture is one of the important ways to alleviate the crisis and achieve China' s goals of reaching peak carbon emissions and carbon neutrality. Exploiting the potential of agricultural carbon sinks and accurately calculating agricultural net carbon emissions are of great significance to promoting the green development of agriculture meeting the ' dual carbon' goals. Based on the dual characteristics of the agricultural carbon cycle, this paper sorted out the logical relationship between agricultural green development and the agricultural carbon cycle, incorporated the role of agricultural carbon sinks into the calculation framework of agricultural carbon emissions, and measured China' s agricultural net carbon emissions from 2000 to 2019 by time and region. The decoupling model of agricultural net carbon emissions from agricultural economic growth was constructed. The results of the study found that the total amount of carbon emissions from agricultural sources increased from 277.899 million t in 2000 to 308.383 1 million t in 2019, with an average annual growth rate of 0.52%, but the agricultural carbon emission intensity showed a trend of continuous decline; the agricultural carbon sink increased from 99.539 9 million t in 2000 to 133.039 million t in 2019, with an average annual growth rate of 1.46%, and the agricultural carbon sink resources were considerable. The intensity of agricultural carbon source emissions was higher in the west than in other regions and there was a clear gap with the eastern region. The total amount of agricultural carbon source emissions, carbon sinks, and net carbon emissions all showed a medium-high pattern. The decoupling relationship between agricultural economic growth and agricultural net carbon emissions was mainly manifested in two decoupling states: strong decoupling and weak decoupling. Nearly 50% of the provinces achieved the ideal strong decoupling state in terms of agricultural green development, while the remaining provinces were in a weak decoupling state, and the decoupling state was generally ideal. Finally, this paper puts forward targeted countermeasures and suggestions to promote the green development of agriculture and help achieve the ' dual carbon' goals: we should accurately locate the carbon source; ensure the ' double bottom line' of food security and pig production capacity under low-carbon agriculture; resolve the resource endowment differences between regions; explore the potential of the agricultural carbon trading market; create a new path for farmers to increase their income, maintain the strong and improve the weak; and strive to achieve the ' package' of strong decoupling in agriculture to help achieve the ' dual carbon' goals. [ABSTRACT FROM AUTHOR]

The increase of carbon emissions accelerates global warming and causes environmental problems. Therefore, research on emission peak and carbon neutrality is of broad interest. In this paper, based on the relationships among carbon emissions, carbon absorption and urban population growth, a delayed differential equations model is established. Then, we analyze the existence and stability of a positive equilibrium point of the system and substitute the data for simulations. Eventually, according to theoretical analysis and numerical simulations, some suggestions for China to achieve the goal of emission peak and carbon neutrality are given. [ABSTRACT FROM AUTHOR]

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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]

Addressing global warming is one of the most pressing environmental challenges and a crucial agenda for humanity. In this literature study, we employed bibliometrics to reproduce nearly two decades of research on carbon emission reduction in China, the largest carbon emitter worldwide. The scientometrics analysis was conducted on 1570 academic works published between 2001 and 2021 concerning China's carbon emission reduction to characterize the knowledge landscape. Using CiteSpace and VOSviewer, the basic characteristics, research forces, knowledge base, research topic evolution, and research hotspots were identified and revealed. The analysis results show that the attention to and research on China's carbon emissions have increased in recent years, giving rise to leading institutions and relatively stable core journal groups in this field. The research disciplines are relatively concentrated, but the research collaboration needs strengthening. The research hotspots are mainly carbon emission causes, impacts, and countermeasures in China, and the research frontiers have been constantly advanced and expanded. In the future, research on countermeasures needs more effort, and research cooperation needs to strengthen. The changing landscape of hotspot clusters reveals China's transition towards a low-carbon economy. Through comprehensive analysis of the potential and obstacles to China's transition to low-carbon development, we identified three promising areas of action (low-carbon cities, low-carbon technologies and industries, and transforming China's energy system) and proposed research directions to address remaining gaps systematically. [ABSTRACT FROM AUTHOR]

In the context of global warming, low-carbon economy with low energy consumption, low pollution and low emissions as the main characteristics has become the focus of attention. The paper takes 30 provincial-level units in China as the basic research object, uses DEA model to measure China's carbon emission performance in 2005, 2010, 2015 and 2019, and uses spatial visualization and trend surface analysis method to analyze its spatial-temporal rule. On this basis, geographical detector is used to analyze the influencing factors and the results show that: (1) China's carbon emission performance is at a relatively low level, except for that of provinces such as Beijing, Shanghai, Hainan and Qinghai while other provinces still need more efforts to perform better. In addition, the carbon emission performance fluctuates during the research period. Specifically, the change trend of east-west direction is greater than that of north-south direction. (2) The spatial difference of the performance is significant. In general, areas of high performance are mainly distributed in the eastern and southern regions while areas at low performance level mainly in the western and northern parts. There is an evident spatial trend of mixed distribution, initially forming the development trend of „overall mixed distribution and partial aggregation“. (3) Government intervention has the greatest influence on carbon emission performance, followed by ownership structure, foreign investment, energy structure, technological progress, industrial structure, and degree of opening to the outside world. In addition, interaction factors have a greater impact on performance than single factors, and show nonlinear enhancement characteristics. [ABSTRACT FROM AUTHOR]

Abstract With the development of China's economy, the use of fossil energy has become more and more, resulting in increasing carbon emissions. CO 2 emissions have caused global warming, threatening humans and creatures on Earth. In order to effectively suppress the growth of carbon emissions, it is necessary to analyze the influencing factors of carbon emissions and apply them to predict carbon emissions. This paper presents sixteen potential influencing factors and uses grey relational analysis to identify the factors that have a strong correlation with carbon emissions. The principal component analysis (PCA) is used to extract the four principal components, which reduce the redundancy of the input data. The long short-term memory (LSTM) method is established to predict carbon emissions in China. We use back propagation neural network (BPNN) and Gaussian process regression (GPR) to compare LSTM method. The simulation results show that the prediction accuracy of carbon emissions based on LSTM is better than that of BPNN and GPR, indicating the effectiveness of PCA and LSTM in prediction of carbon emissions. Finally, this paper provides the theoretical basis for China to reduce carbon emissions by studying prediction of carbon emissions. Highlights • Grey correlation analysis is applied to identify factors associated with carbon emissions. • Principal components analysis is used to simplify input variables and obtain the main factors affecting carbon emissions. • The long short-term memory model is used for the first time to predict carbon emissions. • The direction of reducing carbon emissions in China is given by analyzing the prediction results. [ABSTRACT FROM AUTHOR]

With the aggravation of carbon emissions and global warming, the probability of various natural disasters and environmental risks will increase significantly. Therefore, carbon emission reduction and low-carbon development have become the focus of environmental economic research. This paper uses the information about the opening of high-speed railway (HSR) in China to comprehensively explore the effect of infrastructure construction on urban carbon total factor productivity (CTFP) in China, which is calculated by the novel non-parametric method named biennial non-radial Luenberger productivity indicator (BLPI). Our results reveal that in China, an extra HSR line significantly improves the CTFP of local cities by 0.63%, while the CTFP of related surrounding cities increases by 6.3%. We confirm the robustness of our empirical findings and perform the mechanism analysis from several aspects. The mechanism analysis shows that the HSR improves the CTFP of local cities mainly via industrial upgrading and technological progress, and improves the CTFP of surrounding cities via industrial agglomeration and factor spillover. Besides, the impact of the HSR is more pronounced for the core cities, large cities, and innovative cities. Our results suggest that the HSR makes cities more carbon efficient, which is a non-negligible advantage of infrastructure construction in reducing environmental risks. • Governments around the world have been committed to reducing external environmental risks by adopting urbanization. • This paper analyzes the spillover effect of the onening of HSR on CTFP in China. • This paper provides a novel framework, which uses the LPI combined with the BNDDF to measure CTFP. • An extra HSR line significantly improves the CTFP of local cities by 0.63% and surrounding cities by 6.3%. • The mechanism analysis reveals four channels, including industrial upgrading, technological progress, industrial agglomeration, and factor spillover. [ABSTRACT FROM AUTHOR]

The rapid development of public buildings has greatly increased the country's energy consumption and carbon emissions. Excessive carbon emissions contribute to global warming. This paper aims to measure the carbon emissions in the operation of public buildings, and to identify the multiple influencing factors of carbon emissions in operational public buildings. First, the spatial and temporal variation characteristics of carbon emissions from public buildings in 30 provinces of China from 2008–2019 are analyzed. Second, a green building index is constructed, and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is utilized to explore the relationship between each influencing factor and carbon emissions, using spatial and temporal geographically weighted regression analysis. The results show that the effects of population, urbanization rate, GDP per capita, green building index, and industrial structure on carbon emissions from public buildings all show spatial correlation and differences. There are east-west differences in the operational carbon emissions of public buildings in China's provinces. Cluster evolution shows a spatially increasing trend from west to east. To some extent, policymakers can develop appropriate policies for different provinces through the findings. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of transportation industry brings economic benefits, at the same time, the problem of environmental pollution is becoming more and more serious. The emission of CO 2 intensifies the trend of global warming. How to measure the development of environment and economy is a huge challenge for us. This paper proposes a data envelopment analysis (DEA) method based on slacks-based measure (SBM) to evaluate the environmental and economic performance of the transportation industry from 2015 to 2017. According to the economic region, 30 provinces in China are divided into northeast, east, central and western regions for efficiency analysis. The model can determine the low efficiency decision units and finally put forward improvement suggestions for the low efficiency areas. [ABSTRACT FROM AUTHOR]

Global warming, mainly caused by rising carbon dioxide (CO 2) emissions in the atmosphere, has led to severe effects worldwide. Hence, reducing carbon emissions has become an urgent need to mitigate the adverse effects. To do so, accurately forecasting CO 2 emissions is crucial for policymakers in formulating climate and energy policies. Existing CO 2 emission forecast models rely on various longitudinal economy, energy or environment data collected at the same periods, e.g., yearly, as indicators. However, these data usually come from diverse sources with different sampling frequencies, e.g., monthly vs. daily. Therefore, in this paper, we introduce for the first time a Mixed-frequency data Sampling Grey system Model (MSGM) that can exploit the potential information in high-frequency sampling (HF) data of economic and social indicators to effectively forecast annual CO 2 emissions. In MSGM, (1) we directly model low-frequency CO 2 emissions and HF economic-energy influence indicators without undertaking same-frequency conversion, thereby avoiding potential information loss and enhancing prediction accuracy; (2) we introduce polynomial weight functions to address the frequency mismatch, and its parameters are obtained through search optimization algorithms; (3) unlike existing grey system models, the whitening equation of MSGM adopts a reduced-order form to address the exponential explosion issue caused by HF data accumulation; (4) the time and cumulative terms are also introduced separately to explore the effects of temporal development and historical data on future CO 2 emissions. Extensive Monte-Carlo simulations show that MSGM exhibits high accuracy on both training and testing datasets and outperforms many other state-of-the-art rival techniques like MIDAS, AR-MIDAS, AMTGM, MLRM, BPNN, SVR, ARIMA and TVGBM. We further demonstrate the potential of MSGM to forecast China's CO 2 emissions under three distinct scenarios. Out-of-sample forecasting results indicate that only under a tight-contraction scenario does the growth rate of CO 2 emissions decrease. To the best of our knowledge, the MSGM is the first technique that can exploit different frequency data to effectively forecast CO 2 emissions. [Display omitted] • A novel grey system forecast model based on mixed-sampling frequency is proposed. • Introducing more higher frequency data from economic and social indicators. • Selecting popular local search algorithms to find optimal unstructured parameters. • The CO 2 emission forecast for 2022–2026 is carried out under three distinct scenarios. [ABSTRACT FROM AUTHOR]

Effective short-term prediction of regional voltage load is of great significance to the implementation of energy saving and emission reduction policies in China. Accurate prediction of real-time demand voltage can reduce power waste and carbon emissions, make outstanding contributions to delaying global climate warming, and is conducive to global environmental protection and sustainable development. On the short-term load forecasting of power system, a variant model of RNN-LSTM is tested in this paper. It effectively solves the problem of gradient explosion and disappearance caused by large amount of data input in classical RNN. On the basis of this model, optimization experiments are carried out under different super parameters to achieve better prediction results. The experimental results show that the accuracy of test set reaches 99.8%, which proves that the method proposed in this paper has certain reference value. [ABSTRACT FROM AUTHOR]

This study explores the impact of political uncertainty on sustainable urban development by examining carbon emission trading systems (ETS) in four major markets in China (Beijing, Shanghai, Guangdong, and Hubei) from 2014 to 2022. As an alternative to carbon taxes, carbon ETS markets have become increasingly popular due to their success in reducing greenhouse gas emissions. However, their effectiveness is often hindered by political instability and uncertainty. Utilising logistic regression and AR(1)-GARCH estimations, we identify a negative relationship between political uncertainty and carbon trading volume. Our study also reveals significant variations in the responses of these markets to political uncertainty. The paper contributes to the understanding of how ETS markets operate in a complex and constantly changing political environment. We suggest that policymakers need to consider the impact of political uncertainty on carbon trading when designing and implementing urban policies that promote sustainable development. Additionally, our research contributes to the development of urban policies that can be effectively implemented in both developed and developing regions. • Local government official turnovers are used as the measurement of political uncertainty. • Daily transaction data from four carbon ETS markets (Beijing, Shanghai, Hubei and Guangdong) in China • Considered the probability and proportion of zero transaction day • Logistic regression and AR(1)-GARCH estimations • A negative relationship between political uncertainty and carbon transaction volume [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]

In order to cope with global climate warming, measurement of the low-carbon utilization efficiency (LCUE) of cultivated land, considering carbon sink and carbon emission effects, is proposed. To address this, based on the data of 30 provinces in China, this study conducts a LCUE evaluation system by the MinDS-U-M productivity index model in order to analyze the spatiotemporal patterns and driving factors of LCUE with the geographic detector model and GTWR model. The results show the following: (1) Over the past 20 years, the average LCUE value exhibits a slow increasing trend from 2001 to 2021, which ranges from 0.9864 to 1.0272. Provinces with mid-level LCUE ranging from 1.0000 to 1.0990 account for the highest proportion in each period. (2) The annual growth rate of LCUE in the central region is the highest, where the promotion of green technology and farmland protection policies have played important roles. (3) According to the Geodetector analysis, urbanization rate (UR), irrigation index (IR), grain output value (GOV), precipitation (PR), arable land area (ALA), and environmental pollution control (EPC) are important drivers of the spatial difference of LCUE. (4) The GTWR model shows that the positive effects of ALA and SRT have always been concentrated in the main grain-producing areas over time. UR and PR have strong explanatory power for the space/time differentiation of LCUE, especially in eastern coastal regions. IR has an increasing effect on LCUE in the Western region, and the positive effect of EPC on the LCUE is concentrated in the central region. In order to coordinate regional LCUE contradictions, it is suggested to be wary of land resource damage caused by economic development, warn about the impacts of climate change, and strengthen the supervision of land remediation projects in order to achieve sustainable land management. [ABSTRACT FROM AUTHOR]

Green infrastructure (GI) plays a pivotal role in contemporary urban infrastructure. Green infrastructure investment (GII) provides a fresh perspective for controlling urban carbon emissions in the context of global climate change. Based on theoretical analysis, we employed panel data from Chinese cities to examine the effects and operating mechanisms of GII on urban carbon emissions. The research reveals that the incremental GII can notably decrease urban carbon emissions, and various robustness tests and endogeneity checks corroborate this finding. However, when considering the cumulative effect, the GII stocks do not appear to influence urban carbon emissions; GII mitigates urban carbon emissions by drawing in pollution control talents, improving the efficiency of household waste treatment, increasing urban green spaces, and heightening public attention to the environment. Relative to cities in the central-western region, northern cities, smaller cities, resource-based cities, smart pilot cities, and cities with a lesser environmental emphasis, GII is more effective in curbing carbon emissions in eastern cities, southern cities, larger cities, non-resource-intensive cities, cities not in the smart pilot initiative, and cities with a stronger environmental focus. This research enhances the understanding of GI's environmental outcomes and the determinants of urban carbon emissions from an investment viewpoint. It also dissects the four operative mechanisms through which GII lowers urban carbon emissions, offering a novel interpretation of GII for the variance in carbon emission levels across cities with diverse traits. [ABSTRACT FROM AUTHOR]

China has set an ambitious national determined contributions target to peak CO 2 emissions by 2030 and aims to be carbon neutral by 2060. One of China's top energy consumers and CO 2 contributors, the steel industry accounts for 13% of the country's total consumption and 15% of CO 2 emissions. In response to the present global warming trend, there is an urgent need to cut energy consumption and CO 2 emissions to promote low-carbon growth. This paper attempts to provide a detailed analysis of the steel industry's CO 2 emission reduction path to carbon neutrality. Further efforts in terms of industrial structure, material efficiency, energy efficiency, and low- or zero-carbon technologies are required to achieve a low-carbon transition. Given that decarbonization is a significant priority in the steel industry in many countries, this study focuses on the goals and technological paths of low-carbon emissions projects to optimize existing technology priorities and propose development directions. Deep decarbonization is also projected to be achieved by combining CCUS and hydrogen metallurgy technologies. The steel industry requires collaborative efforts to standardize CO 2 emission accounting methodologies and establish a carbon trading system to strengthen carbon emission management. • The steel industry is one of the key contributors to carbon neutrality which needs to accelerate decarbonization. • Reducing energy consumption and CO 2 emissions to keep up with the current global warming trend are reviewed. • This paper selects low-carbon emission projects to optimize current technology priorities and provide development directions. • Policy recommendations and next steps for low-carbon transition are given. [ABSTRACT FROM AUTHOR]

The increasing contribution of refrigeration systems to global warming has led to widespread demand for low global warming potential (GWP) refrigerants as alternatives. This paper proposed a comprehensive environmental assessment method for evaluating the carbon footprint of units charged with low-GWP refrigeration. Experimental and numerical studies were used to investigate the carbon footprint impact of using alternative refrigerants at different ambient temperatures in China. Initially, the model was validated through experimental case studies on the use stage of cold storage units. Subsequently, a comparison was made between the cooling capacity (CC) and energy efficiency ratio (EER) of cold storage units charged with different refrigerants. Additionally, the impact of alternative refrigerants in the use stage was analyzed by numerical calculations, and the adaptability of the cold storage unit to the ambient temperature and the national energy structure was also studied. The results indicate that among the four stages of the cold storage unit, the use stage is the hotspot of carbon footprint due to the large amount of energy consumption and the existence of refrigerant leakage and other behaviors. Utilizing low-GWP alternative refrigerants could significantly reduce the carbon footprint of the cold storage units' use stage by 10.56%–16.39%, thereby lowering the whole stage carbon footprint. Furthermore, the ambient temperature and national energy structure could also effectively reduce the carbon footprint of cold storage units' whole stage by 1%–27.17%. Furthermore, after adopting low GWP refrigerants, the life-cycle cost (LCC) of the units have also decreased by 6.1%–12%. Taking into account both environmental impact and economic considerations, it is recommended to directly replace R404A with R448A in the units in regions where the average ambient temperature is above 15 °C. • A carbon footprint evaluation model for the units was established. • Influencing factors of carbon footprint are studied numerically and experimentally. • By GHG assessments, the optimal refrigerant and external conditions were determined. • Alternative refrigerants reduce carbon emissions for cold storage units by 8.8%–24.92%. [ABSTRACT FROM AUTHOR]

Global warming caused by the excessive combustion of fossil fuels has become the focus of the international community. China's carbon emission reduction policy plays a vital role in international climate change, carbon emission neutrality and domestic environmental pollution control. This paper summarises the progress of China's Emission Trading Scheme (ETS) pilots from 2011 to 2021, including trading volume, carbon emissions price, and the effect of carbon emission abatement. By calculating the carbon emissions intensity decrease rate of China's ETS pilot and non-pilot regions, the ETS and carbon emissions tax are compared in terms of emission reduction effectiveness, emission reduction cost and implementation resistance to provide theoretical support for the Chinese government to select and develop carbon emission reduction tools. The development of the Clean Development Mechanism in China is also compared with China Certified Emission Reduction (CCER), and the offsetting rules of CCER are summarised. After the whole analysis and assessment, the results show that from 2013 to 2018, the average carbon emissions intensity decline rate in the ETS pilots was 7.3% and that in non-pilots was 4.29%. Some problems of China's carbon emissions trading market are also concluded. The trading volume is small (only 43.4 × 106 t in 2020) and is discrepant greatly in the different ETS pilots. The carbon emissions price is low (only 33.45 Yuan/t in 2020), with high volatility and large price differences among the ETS pilots. Based on the existing problems, this paper proposes some suggestions for improving China's emission trading market and provides theoretical and decision-making support for promoting carbon emission reduction and neutralisation for China. [Display omitted] [ABSTRACT FROM AUTHOR]

The carbon trading market is an effective way to mitigate the threat of climate warming. In the context of carbon neutrality, what is the carbon emission trading scheme's carbon reduction effect? This paper answers the question by examining the carbon efficiency of China's iron and steel industry. This study first constructs CO 2 emission efficiency by employing the data envelopment approach. It then uses the synthetic control method to identify the impact of the carbon emission trading scheme on the CO 2 efficiency of the iron and steel industry. The results show that CO 2 emission efficiency in pilot provinces increased at a 5% significance level compared with the counterfactual scenario. The carbon trading market plays a vital role in industrial CO 2 emission efficiency improvement in Guangdong, Shanghai, Tianjin, Hubei, and Chongqing. From a dynamic perspective, the policy effect of enhancing CO 2 efficiency becomes more prominent over time. Moreover, the effectiveness degree of the carbon emission trading scheme varies across pilot provinces, caused of regional policy differences. Based on the findings, we put forward targeted suggestions for improving the newly-established national carbon trading market. • The DEA model and the synthetic control method are employed in the study. • Impact of carbon market on CO 2 efficiency of iron and steel industry is verified. • The heterogeneity of carbon market effects is explored from regional perspective. • Carbon market significantly promotes industrial CO 2 efficiency in pilot provinces. • China should develop the national carbon market to achieve carbon neutrality. [ABSTRACT FROM AUTHOR]

Global warming can be solved with carbon trading. When carbon emissions become a commodity, it can be a powerful motivator for companies to optimize production capacity and innovate technologically. The goal of this paper is to examine the way in which China's carbon emissions trading policy (CET) influences firm technological innovation in seven pilot regions, using a sample of firms in eight high-carbon industries. We employ difference-in-difference (DID) as well as DID-based propensity score matching (PSM-DID) models to investigate the influence of China's carbon emission policy on firms' technological innovation by using 4745 observations of 749 A-share listed firms in eight high-carbon industries in mainland China from 2007 to 2021. According to the findings, China's CET policy significantly boosts firms' technological innovation, and this beneficial effect persists even after a series of robustness tests that change the policy's implementation year and the technological innovation measure. Further analysis shows that this positive effect is heterogeneous across high-carbon industries and firms with different ownership. In addition, based on the external governance perspective, we find that CET policy performs better in firms in high-carbon industries with a sizable portion of ownership held by institutional investors. This study suggests the effectiveness of CET policy in promoting firms' technological innovation, confirms empirical evidence for Porter's hypothesis, and provides a basis for Chinese firms to achieve their emission reduction targets by improving technological innovation. [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]

Low-carbon consumption is inevitable for international societies to respond to reduce global warming because it restricts the generation of carbon emissions from the consumer's side. The consumption behaviours of Chinese consumers are identified as having high level of waste, excessive energy demand, lack of awareness and unreasonable consumption. These high-carbon consumption behaviours should be subject to effective regulatory measures imposed by the Chinese government. This article explores the current policies and practices of China in promoting low-carbon consumption and suggests the Chinese government make clearer rules and standards for the labelling and public procurement of low-carbon products and service expand low-carbon development pilot programs in China and strengthen the education of Chinese consumers. [ABSTRACT FROM AUTHOR]

Global warming has become one of the major threats to the security of human survival, security, and sustainable development. Agricultural production has been widely suspected as one of the main sources of anthropogenic carbon emissions. Analyzing the changing characteristics and influencing factors of agricultural carbon emissions is of great significance for the mitigation of global climate change and the sustainable development in agriculture. Taking China, a large agricultural country, as an example, this study used the empirical model to quantify carbon emissions from agricultural inputs from 1991 to 2019, and analyzed the driving factors using ridge regression. We found that agricultural carbon emissions in China have been on the rise in the past 30 years, but at a markedly slower pace. From 2008 to 2019, the average annual growth rate of agricultural carbon emissions was 1.47%, down significantly from 2.92% between 1991 and 2007. The carbon emissions per unit of planting area showed an overall increasing trend, which grew from 179.35 t ce/km2 to 246.26 t ce/km2, with an average annual growth rate of 1.13%. The carbon emissions per unit of agricultural output mainly showed a decreasing trend, which decreased from 0.52 kg ce/CNY to 0.06 kg ce/CNY, with an average annual rate of change of −7.42%. China's agricultural carbon emissions were closely related to macro-policies. Fertilizer inputs, agricultural industry structure, and energy use intensity were significantly positively correlated with carbon emission intensity. The degree of urban feedback to rural areas, public investment in agriculture, and large-scale planting were significantly negatively correlated with carbon emission intensity, but the impacts of these factors had a "lag effect". In order to reduce carbon emissions from agriculture and promote development in green agriculture, we suggest that the government should further increase the degree of urban feedback to rural and public investment in the agricultural sector. In addition, large-scale agricultural production should be encouraged to increase resource efficiency and reduce carbon emissions. [ABSTRACT FROM AUTHOR]

The study of household carbon emissions (HCEs) has become one of the most important topics in sustainability research due to the growing awareness of its significances in responding to global warming. This paper aims to identify the trends and characteristics of HCEs research for the period 1993–2019 through the bibliometric analysis approach. The impacts of most productive journals, countries, institutions and authors are evaluated by citation analysis, while the co-authorship network between various countries, institutions as well as the co-occurrence among different keywords are presented. The results of this paper provide a detailed picture on HCEs-related articles from 1993 to 2019. Both the number of publications on HCEs and the cooperation among countries and institutions have increased during the period. Keyword analysis suggests that the revelation of current status of HCEs and corresponding influencing factors are the main lines of HCEs research, and the prediction of HCEs should become the focus of future research. The valuable results obtained from this study can help the academic professionals keep informed of the latest developments in the HCEs-related field. • We employed bibliometric analysis to examine the evolution in HCEs Research. • HCEs research is at its rapid growth stage of development. • USA and China are the most active countries in HCEs research. • The quantification of HCEs and corresponding influencing factors are the main line. • The evolution trend of HCEs should be the focus of future research. [ABSTRACT FROM AUTHOR]