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Given the context of significant global warming and the intensification of extreme climate events in the last century, large-scale reforestation and afforestation have been recognized as effective strategies to mitigate the climate crisis. Since the 1970s, China has launched several afforestation programs aimed at regional ecological protection, playing an important role in reaching carbon neutrality by 2060. This study provided a detailed analysis of the growth suitability of the main planted conifers (Pinus sylvestris var. mongolica and Pinus tabulaeformis) and broadleaves (Populus spp., Robinia pseudoacacia) in the semi-arid northern China. We compared the radial growth trends of plantations and their responses to extreme droughts from 1980 to 2018. Growth of most plantations has significantly increased over time, but broadleaves showed recent growth reductions in the past decade, which may be related to tree age and reduced soil moisture. Nevertheless, under warmer climate scenarios, the growth of plantations is forecasted to continue increasing. Broadleaves showed a better post-drought recovery, probably linked to their anisohydric behavior, than conifers, which presented a better resistance to drought. Growth of conifers depended more on warmer temperature and better precipitation conditions during the growing season, whereas broadleaves mainly reacted to warm temperature. Additionally, pre-drought growth levels weakened resilience components, while post-drought precipitation compensated for drought-induced growth deficit. Growth and resilience were negatively related to tree age, while higher stand density reduced growth. This assessment and projections of growth and drought resilience indicate the sustainability of most plantations in semi-arid regions, but future warmer and drier conditions may lead to an uncertain future regarding forest health and reduce their carbon sink potential., (© 2024 John Wiley & Sons Ltd.)

Carbon emissions embodied in anthropogenic activities represent the major cause of global warming. Countries, regions, and cities have implemented comprehensive, multi-level and multi-scale measures to reduce emissions and move towards carbon neutrality. The demand for carbon emission reduction (CER) is made more challenging by different geographical locations, country-owned natural resources, and economic development stages. The main objectives of this paper are to conduct a bibliometric analysis to map the frontiers and directions of CER and to explore the paths and development models of CER from the perspective of spatio-temporal, multi-scale, multi-sectoral, and multi-responsible subjects. This study reveals that carbon emission evaluation and prediction, correlation and causal relationship analysis, and CER-related policy simulation and optimization are the most critical hotspots. Additionally, we point out the shortcomings of and future developments for the three study dimensions above. The bibliometric analysis also highlights the fact that a cooperative global value chain as well as amendable policies and mechanisms for CER will help with climate change mitigation and adaptation through the use of advanced carbon capture and storage technologies. We review the technical measures for and policy responses to CER adopted by different countries and industries at the theoretical and practical levels and provide new recommendations. Our work provides important information for climate actions in different countries and sectors and for developing more effective CER strategies and policies., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Understanding environmental determination of crop yield plays a critical role in agricultural. management in resource-limited areas. The climatic transition zone was a naturally ideal place to study. the relations between environmental factors and crop yield, due to its large annual variability of climatic factors and high speed of temperature increase under global warming. Our objectives were to identify the most critical environmental factor in determining spring wheat yield and analyze the convergence and divergence of water-yield relations for spring wheat in a typical climatic transition zone (semi-arid area). The study was conducted at two locations, Dingxi and Pengyang in Northwest China, with a long-term experiment (1987-2018) and two short-term irrigation experiments. Meanwhile, data of water use and spring wheat yield was collected from a series of previously published literature in the study area. The highest spring wheat yield was obtained under year pattern with higher soil water content at sowing (SWCS) and lower atmospheric dryness condition (ADC, the difference between reference evapotranspiration and precipitation during spring wheat growing season). SWCS was more important than precipitation during the growing season (PGS) in determining spring wheat yield in the study area. The relations between available water supply, water use, and spring wheat yield were convergence. However, SWCS had an impact on the relationship between yield and PGS and SWCS-yield relation was affected by ADC. We concluded that precipitation in 7 months before sowing was the dominant factor determining spring wheat yield in the climatic transition zone under global warming whereas the impact of high atmospheric evaporative demand resulted from the increasing temperature on crop yields and SWCS-yield relation must be taken into account for the analysis of environmental determination of spring wheat yield., (© 2021. The Author(s) under exclusive licence to International Society of Biometeorology.)

The increasing global warming trend has resulted in the mass loss of most glaciers. The Urumqi Vally, located in the dry and cold zone of China, and its widely dispersed glaciers are significant to the regional ecological environment, oasis economic development, and industrial and agricultural production. This is representative of glaciers in Middle Asia and represents one of the world's longest observed time series of glaciers, beginning in 1959. The Urumqi Headwater Glacier No. 1 (UHG-1) has a dominant presence in the World Glacier Monitoring Service (WGMS). This paper supplies a comprehensive analysis of past studies and future modeling of glacier changes in the Urumqi Valley. It has received insufficient attention in the past, and the mass balance of UHG-1 was used to verify that the geodetic results and the OGGM model simulation results are convincing. The main conclusions are: The area of 48.68 ± 4.59 km2 delineated by 150 glaciers in 1958 decreased to 21.61 ± 0.27 km2 delineated by 108 glaciers in 2022, with a reduction of 0.47 ± 0.04 km2·a−1 (0.96% a−1 in 1958–2022). The glacier mass balance by geodesy is −0.69 ± 0.11 m w.e.a−1 in 2000–2022, which is just deviating from the measured result (−0.66 m w.e.a−1), but the geodetic result in this paper can be enough to reflect the glacier changes (−0.65 ± 0.11 m w.e.a−1) of the URB in 2000–2022. The future loss rate of area and volume will undergo a rapid and then decelerating process, with the fastest and slowest inflection points occurring around 2035 and 2070, respectively. High temperatures and large precipitation in summer accelerate glacier loss, and the corresponding lag period of glacier change to climate is about 2–3 years. [ABSTRACT FROM AUTHOR]

The process of carbon neutrality does have economic costs; however, few studies have measured the cost and the economic neutral opportunities. This paper uses a dynamic computable general equilibrium (CGE) model to simulate China's carbon neutrality path from 2020 to 2060 and analyzes its economic impact. This paper innovatively adjusts the CGE modeling technology and simulates the boundary of the Porter hypothesis on the premise of economic neutrality. The results show that the carbon neutrality target may reduce the annual GDP growth rate by about 0.8% in 2020–2060. To make the carbon pricing method under the carbon neutrality framework meet the strong version of the Porter hypothesis (or economic neutrality), China must increase its annual total factor productivity by 0.56–0.57% in 2020–2060; this is hard to achieve. In addition, the study finds that China's 2030 carbon target has little impact on the economy, but the achievement of the 2060 carbon neutrality target will have a significant effect. Therefore, the paper believes that the key to carbon neutrality lies in the coexistence of technological innovation and carbon pricing to ensure that we can cope with global warming with the lowest cost and resistance. [ABSTRACT FROM AUTHOR]

With the increase in global temperature, the global change situation dominated by climate warming is becoming more and more serious. Climate change will cause differences in the suitable areas of species in different periods. Ginkgo biloba L., a rare and endangered wild plant protected at the national level in China, is the oldest relict plant in the world. Because of severe climate change, only China's wild Ginkgo biloba has been preserved, yet China's wild Ginkgo biloba population is facing extinction risk. Ginkgo biloba has rich ornamental value, application value, economic value, medicinal value and ecological value. Not only can it produce economic and ecological benefits, but it can also produce huge social benefits. Based on the data of Ginkgo biloba sample distribution, bioclimatic variables and soil variables, this paper uses the MaxEnt model to simulate Ginkgo biloba suitable area under current and future different climate scenarios, and analyzes the changes in the potential suitable area of Ginkgo biloba in the future through ArcGIS 10.6. The results are as follows: (1) the results simulated by the MaxEnt model are AUC > 0.9, showing that the simulation results have a high accuracy; (2) the min temperature of the coldest month, precipitation of the wettest month, elevation, and temperature seasonality are the main environmental variables affecting the change in the Ginkgo biloba suitable area; (3) under future climate scenarios, the suitable area of Ginkgo biloba is predicted to expand in the future, covering most of the south and some northeast regions, and moderate temperature and precipitation changes under climate change are conducive for the growth of Ginkgo biloba; and (4) in the future, the distribution center of the suitable area will move to the northeast. According to the conclusions in this paper, it is expected to provide theoretical reference for cultivation and management, sustainable utilization and solution of ecological environment problems of Ginkgo biloba. [ABSTRACT FROM AUTHOR]

There have been fruitful publications on thermal comfort of urban residential buildings in China. However, there is a lack of reviews on this topic to perform a comprehensive analysis and find opportunities to meet occupants' thermal comfort needs while improving building energy efficiencies. This paper addresses this issue by presenting a systematic review on the advancements in research on thermal comfort in urban residential buildings in China. Firstly, two common thermal comfort research approaches, i.e., field studies and laboratory studies, are discussed. Secondly, eleven main thermal comfort evaluation indicators are summarized. Finally, this paper analyzes the thermal comfort survey data from different researchers, discusses the impacts of adaptive behaviors on human thermal comfort, and provides recommendations for future research on urban residential thermal comfort. It was found that people have higher and higher requirements for their indoor thermal environment as time goes by, especially in the winter; the thermoneutral temperature is higher in warmer climate regions in the summer but lower in the winter than in colder climate regions; the thermoneutral temperature tends to increase with the indoor air temperature due to an adaptation to the indoor thermal environment. The outcomes of this paper provide valuable information on thermal comfort behaviors of urban residents in different climate zones in China, which can serve as a resource for the academic community conducting future research on thermal comfort and assist policymakers in enhancing building energy efficiencies without compromising the occupants' comfort. [ABSTRACT FROM AUTHOR]

This Special Issue aims to highlight research articles focusing on the geographical scale of glacier and lake ice variations, as well as the engineering scale of ice properties and their practical applications via laboratory tests and numerical modeling. Additionally, it includes research on ecosystems under lake ice. The main goal is successfully achieved through the joint efforts of authors, anonymous reviewers, and editorial managers. In total, 1 review article and 15 research articles are included in this Special Issue. These articles cover a wide range of topics, including water resources from Chinese mountain glacier variation; lake ice phenology at different latitudes and altitudes around the world; ice properties from laboratory experiments and numerical modeling; ice engineering with different purposes in China and the Arctic; and ecosystem under lake ice at different temporal and spatial scales. This Special Issue received contributions from researchers from different parts of China and from Chinese international cooperation partners because of its focus on "higher temperature ice" under global warming. All papers presented are innovative and of high quality. This Special Issue can promote research on ice properties and their applications in practices ranging from mountains to sea, especially in popular water ecosystem environments under ice during seasonal ice period. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions from wetlands have exacerbated global warming, attracting worldwide attention. However, the research process and development trends in this field remain unknown. Herein, 1865 papers related to wetlands GHG emissions published from January 2000 to December 2023 were selected, and CiteSpace and VOSviewer were used for bibliometric analysis to visually analyze the publications distribution, research authors, organizations and countries, core journal and keywords, and discussed the research progress, trends and hotspots in the fields. Over the past 24 years, the research has gone through three phases: the "embryonic" stage (2000-2006), the accumulation stage (2007-2014), and the acceleration stage (2015-2023). China has played a pivotal role in this domain, publishing the most papers and working closely with the United States, United Kingdom, Canada, Germany, and Australia. In addition, this study synthesized 311 field observations from 123 publications to analyze the variability in GHG emissions and their driving factors in four different types of natural wetlands. The results suggested that the average carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) fluxes in different wetlands were significantly different. River wetlands exhibited the highest GHG fluxes, while marsh wetlands demonstrated greater global warming potential (GWP). The average CO 2 , CH 4 and N 2 O fluxes were 60.41 mg m -2 ·h -1 , 2.52 mg m -2 ·h -1 and 0.05 mg m -2 ·h -1 , respectively. The GWP of Chinese natural wetlands was estimated as 648.72 Tg·CO 2 -eq·yr -1 , and CH 4 contributed the largest warming effect, accounting for 57.43%. Correlation analysis showed that geographical location, climate factors, and soil conditions collectively regulated GHG emissions from wetlands. The findings provide a new perspective on sustainable wetland management and reducing GHG emissions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Plant phenology plays an important role in nutrient cycling and carbon balance in forest ecosystems, but its response to the interaction of global warming and precipitation reduction remains unclear. In this study, an experiment with factorial soil warming (ambient, ambient +5 °C) and precipitation exclusion (ambient, ambient -50 %) was conducted in a subtropical Chinese fir (Cunninghamia lanceolata) plantation. We investigated the effects of soil warming, precipitation exclusion, and their interactions on Chinese fir phenology involving tree height and fine root growth. In the meantime, the impact of tree height growth and related climatic factors on fine root production was also assessed. The results showed that: (1) more variable phenology responses were observed in fine root growth than in tree height growth to the climatic treatments; the duration of fine root growth and tree height growth was significantly reduced by the precipitation exclusion and warming treatment, respectively; phenology differences of fine root and tree height growth caused by the solo warming and precipitation exclusion treatment were further enhanced by the combined treatment; and despite the greater inter-annual phenology stability of tree height growth than that of fine root growth, both of them showed insignificant response to all the climate treatments; (2) asynchrony of phenology between tree height and fine root growth was significantly enlarged by solo warming and precipitation exclusion treatments, and further enlarged by the combined treatment; (3) fine root production was significantly and positively correlated with air, and soil temperature, and tree height growth as well, which was altered by warming and precipitation exclusion treatments. Our results demonstrated that climatic changes significantly and differently alter phenology of, and extend the phenology asynchrony between, above and below ground plant components, and also highlight the climate-sensitive and variable nature of root phenology. Overall, these phenology responses to climatic change may weaken the close link between fine root production and tree height growth, which may result in temporal mismatch between nutrient demand and supply in Chinese fir plantation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Natural wetlands are among the most important sources of methane; thus, these areas are important for better understanding long-term temporal variations in atmospheric methane concentration. During the last 60 years, wetlands have experienced extensive conversion and global impacts from climate warming, which makes the estimation of methane emission from wetlands highly uncertain. In this paper, we present a modeling framework, integrating CH4MODWETLAND, TOPMODEL and TEM models, to analyze the temporal and spatial variations in CH4 emissions from natural wetlands (including inland wetlands, coastal wetlands, lakes and rivers) in China. Our analysis revealed an increase of 25.5 %, averaging 0.52g m-2 per decade, in national CH4 fluxes from 1950 to 2010, which was mainly induced by climate warming. Higher rates of increasing CH4 fluxes occurred in northeastern, northern and northwestern China, associated with large temperature increases. However, decreases in precipitation due to climate warming o_set the increase in CH4 fluxes in these regions. The CH4 fluxes from the wetland on the Qinghai Tibetan Plateau exhibited a lower rate of increase, which was approximately 25% of that simulated in northeastern China. Although climate warming has accelerated CH4 fluxes, the total amount of national CH4 emissions decreased by approximately 2.35 Tg (1.91-2.81 Tg), i.e., from 4.50 Tg in the early 1950s to 2.15 Tg in the late 2000s, due to a large wetland loss of 17.0 million ha. Of this reduction, 0.26 Tg 0.24-0.28 Tg) was derived from lakes and rivers, 0.16 Tg (0.13-0.20 Tg) from coastal wetlands, and 1.92 Tg (1.54-2.33 Tg) from inland wetlands. Northeastern China had the largest contribution to this reduction, with a loss of 1.68 Tg. The CH4 emissions were reduced by more than half in most regions in China except for the Qinghai Tibetan Plateau, where only a 23.3% decrease in CH4 was observed. [ABSTRACT FROM AUTHOR]

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]

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Flash drought is characterized by rapid onset and short-duration drought conditions caused by a combination of factors, including high evaporation, high temperature, and prolonged periods of little to no precipitation, leading to a sudden and severe decrease in soil moisture levels. In comparison to conventional drought, it is more susceptible to the effects of global warming and has the potential to become a common drought phenomenon in the coming years, necessitating further research. In this paper, we focused on flash drought events, specifically in forest parts of northeastern China that are included within the Greater Khingan Mountains (GKM), Lesser Khingan Mountains (LKM), and Changbai Mountains (CM), using daily soil moisture data as well as SPOT- VEGETATION NDVI satellite data from 2000 to 2020 and determined their impact on the forest NDVI. Our major findings are as follows. (1) The forest within GKM had the maximum area being affected by flash drought events. (2) The frequency ranged from 1 to 2 times, whereas the total duration varied between 20 and 55 days over the study area in a 21-year period. (3) Flash drought was most common in the plant-growing seasons. (4) The flash drought events had a negative influence on the forest NDVI. Our study contributes to a deeper understanding of the flash drought dynamics in forest areas of northeast China for flash drought monitoring, prediction, and management strategies in this region. [ABSTRACT FROM AUTHOR]

The 2015 Paris Agreement has set out the climate change target of limiting global warming to 1.5 °C, which poses a serious challenge to countries to reduce emissions. As the world's largest carbon emitter, promoting the realization of the "dual-carbon" goal is the key to realizing China's green transformation and high-quality development. Chinese asset managers play active roles in the capital market as an important channel of asset allocation. Currently, the vast majority of Chinese asset managers hold high percentages of high-carbon industries in their portfolios, and lack quantitative data of their carbon footprints embodied in equity investments, which faces huge carbon-related risks. Therefore, it's an urgent need to comprehensively and scientifically measure financed emissions of Chinese asset managers, which is of great significance for asset managers' carbon risk management and sustainable investment. This paper develops a detailed inventory of carbon emissions for equity portfolios managed by Chinese asset managers from 2010 to 2020, which stands as a pivotal reference for in-depth analysis of emission characteristics. [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]

Greenhouse gas emissions from the use of fossil energy are the main drivers of global warming. China's dominant consumption of fossil energy necessitates adjustments in its energy consumption structure to break free from the carbon lock-in (CLI) phenomenon. Market-based environmental regulations, represented by the carbon trading market (CTM), play an important role in achieving the dual carbon goals of China. Using panel data of 270 prefecture-level cities in China from 2005 to 2020, this study applies a difference-in-difference model to identify the effect of CTM on urban CLI, analyze its transmission mechanism, and further examine the impact of urban characteristic heterogeneity on policy effects from multiple perspectives. Results show that the construction of CTM significantly reduces the degree of CLI of pilot cities; (2) CTM mainly affects urban CLI by promoting urban green technology innovation, industrial structure upgrading, and public green behavior; and (3) the inhibitory effect of CTM on CLI is more significant in cities with high carbon price, industrialization, and digital finance levels. The primary paths toward realizing carbon unlocking include optimizing the institutional design for CTM, enhancing the effective promotion and application of low-carbon technologies, cultivating the green awareness of the public, and increasing government investments in energy-saving and emission reduction techniques., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Organic materials play an important role in improving crop yield. However, due to variations in natural and field management practices, the impact of straw incorporation (NS) and biochar addition (NB) on soil organic carbon (SOC) sequestration and global warming potential (GWP) remains uncertain. This meta-analysis synthesizes the findings from 112 published studies, encompassing 897 samples, to assess the effects of NS and NB on crop yield, SOC, and GWP. The results reveal that Northeast China has the highest SOC stocks (40.80 Mg ha -1 ) and annual SOC sequestration (4.27 Mg ha -1  yr -1 ) compared to other regions. Notably, the NS and NB differ in their effect sizes on improving crop yield (7.68 % and 8.23 %, respectively) and SOC (6.92 % and 30.72 %, respectively), with opposing effects on GWP (increasing by 37.69 % in NS and decreasing by 23.94 % in NB). Following organic material application, climatic conditions, crop and field type, and soil properties affected SOC content and GWP. The main factors influencing variations in crop yield, SOC, and GWP were mean annual temperature and precipitation, initial SOC content, and soil pH, accounting for 57.46 %-60.29 %, 54.75 %-58.52 %, and 61.81 %-65.11 %, respectively. Considering the need to balance food demand, soil fertility and environmental benefits, biochar emerges as a recommended strategy for advancing future agriculture goals. In summary, this study quantitatively assessed the impact of organic material on crop yield, SOC, and greenhouse gas emissions, offering a scientific foundation for optimizing these factors under diverse regional conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Simple Summary: Since the 1990s, the golden cuttlefish (Sepia esculenta) has become the dominant cuttlefish fisheries target in the seas around China. In this paper, we aim to determine the current resource status and spatio-seasonal variation in the population in the East China Sea region. We found more juveniles at latitudes of 27.50–28.00° N and 29.00° N and more subadult individuals at 28.50° N in autumn, exhibiting different growth rates and resource densities. In addition, we found the majority of the catches were composed of parent groups in spring, while in autumn, the majority of the catches were composed of juvenile groups. We concluded that the subadult groups might have dispersed widely for feeding and growth along the latitude of 30.00° N and to the south in summer, and the southern area of the Yangtze River extending north was the spawning ground in spring. The groups of S. esculenta preferred to stay in areas with a stable water temperature of ~20.00 °C, and many S. esculenta juveniles might have adapted to endure the negative influence of the low oxygen content in summer. The depth range of the S. esculenta population was 10.00–133.00 m from spring to autumn, but this shrank to 66.00–107.00 m in winter. The golden cuttlefish (Sepia esculenta) is an important cephalopod species with a lifespan of approximately one year. The species plays a crucial role in marine ecological support services and is commercially valuable in fisheries. In the seas around China, this species has emerged as the main target for cuttlefish fisheries, replacing Sepiella maindroni since the 1990s. Variations in oceanographic conditions associated with global warming could significantly impact the temporal-spatial distribution of the species. In this study, we performed bottom trawling surveys with four cruises during 2018–2019 in the East China Sea region to determine the current resource status and seasonal-spatial variations in S. esculenta. We found that the average individual weight (AIW) values were 4.87 and 519.00 g/ind at stations located at 30.50° N, 124.00° E and 30.50° N, 124.50° E, respectively, with the aggregation of larvae and parent groups in spring. The species was not distributed north of 32.00° N in summer. The catch per unit effort by weight (CPUEw) value decreased in the order of 2772.50→2575.20→503.29→124.36 g/h, corresponding to latitudes of 34.50° N→34.00° N→33.50° N→32.50° N 121.50° E in autumn. The most suitable fishing areas were the south of the East China Sea region in spring; the south of the East China Sea region extending to the center and outer parts of the East China Sea region in summer; the south of the Yellow Sea close to the Haizhou Bay fishing ground and the forbidden fishing line region of the Lusi and Dasha fishing grounds in autumn; and the south and center of the East China Sea region in winter. The most suitable sea bottom temperature (SBT) values from spring to winter were 14.76–20.53 °C, 19.54–22.98 °C, 11.79–17.64 °C, and 16.94–20.36 °C, respectively. The most suitable sea bottom salinity (SBS) values were 31.53–34.80‰ in spring, 32.95–34.68‰ in summer, 31.51–34.77‰ in autumn, and 33.82–34.51‰ in winter. We concluded the following: (1) the southern and northern areas of the East China Sea region are spawning and nursery grounds, respectively, in spring; (2) the central distribution is located at a latitude of 28.00° N in autumn and winter; and (3) the southern area of the Yangtze River to the north is a spawning ground in spring, and the areas located at 29.00–34.50° N, 124.00–124.50° E, and 28.00–30.50° N, 125.50–126.50° E are nursery grounds. The results of this study provide useful guidance for appropriate fisheries management, thereby avoiding a collapse in the S. esculenta population, which has been experienced in other species in this area. [ABSTRACT FROM AUTHOR]

Freeze–thaw (FT) erosion intensity may exhibit a future increasing trend with climate warming, humidification, and permafrost degradation in the Qinghai–Tibet Plateau (QTP). The present study provides a reference for the prevention and control of FT erosion in the QTP, as well as for the protection and restoration of the regional ecological environment. FT erosion is the third major type of soil erosion after water and wind erosion. Although FT erosion is one of the major soil erosion types in cold regions, it has been studied relatively little in the past because of the complexity of several influencing factors and the involvement of shallow surface layers at certain depths. The QTP is an important ecological barrier area in China. However, this area is characterized by harsh climatic and fragile environmental conditions, as well as by frequent FT erosion events, making it necessary to conduct research on FT erosion. In this paper, a total of 11 meteorological, vegetation, topographic, geomorphological, and geological factors were selected and assigned analytic hierarchy process (AHP)-based weights to evaluate the FT erosion intensity in the QTP using a comprehensive evaluation index method. In addition, the single effects of the selected influencing factors on the FT erosion intensity were further evaluated in this study. According to the obtained results, the total FT erosion area covered 1.61 × 106 km2, accounting for 61.33% of the total area of the QTP. The moderate and strong FT erosion intensity classes covered 6.19 × 105 km2, accounting for 38.37% of the total FT erosion area in the QTP. The results revealed substantial variations in the spatial distribution of the FT erosion intensity in the QTP. Indeed, the moderate and strong erosion areas were mainly located in the high mountain areas and the hilly part of the Hoh Xil frozen soil region. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau stands as one of China's largest middle and low latitude permafrost regions. However, the effects of global warming and human activities have led to permafrost thawing, inducing surface instability and posing significant threats to infrastructure and indigenous communities. The deployment of Lu Tan-1 (LT-1), China's premier L-band synthetic aperture radar (SAR) satellite constellation, offers a novel opportunity to assess these changes. This paper evaluates the deformation of critical engineering corridors, such as the Qinghai-Tibet Railway (QTR) and the Qinghai-Tibet Highway (QTH), utilizing time-series InSAR techniques with LT-1 SAR constellation data. We introduce both Stacking InSAR and a multi-baseline persistent scatterer multitemporal (MT-InSAR) method to enhance permafrost and engineering corridor deformation detection capabilities. Results obtained through the MT-InSAR approach reveal line-of-sight (LOS) deformation velocities of permafrost in the Beiluhe region ranging from -90 mm/y to approximately 70 mm/y, with an average velocity amplitude of 0.06 m/y. Differential displacement between alpine meadows and alpine deserts across the Beiluhe region is successfully discerned using LT-1 SAR data. Deformation velocities of QTR, QTH were found to be lower than that of permafrost, with average velocities of 0.027 m/y. These findings underscore the LT-1 SAR constellation's potential to serve as a valuable SAR data source for monitoring engineering corridor deformation within the Tibetan Plateau permafrost region. [ABSTRACT FROM AUTHOR]

Our understanding of how net global warming potential (NGWP) and greenhouse gas intensity (GHGI) is affected by management practices aimed at food security with respect to rice agriculture remains limited. In the present study, a 5 year field experiment was conducted in China to evaluate the effects of integrated soil-crop system management (ISSM) on NGWP and GHGI after accounting for carbon dioxide (CO2) emissions from all sources (methane, CH4, and nitrous oxide, N2O, emissions, agrochemical inputs, Ei, and farm operations, Eo) and sinks (i.e., soil organic carbon, SOC, sequestration). For the improvement of rice yield and agronomic nitrogen use efficiency (NUE), four ISSM scenarios consisting of different nitrogen (N) fertilization rates relative to the local farmers' practice (FP) rate were carried out, namely, N1 (25% reduction), N2 (10% reduction), N3 (FP rate) and N4 (25% increase). The results showed that compared with the FP, the four ISSM scenarios, i.e., N1, N2, N3 and N4, significantly increased the rice yields by 10, 16, 28 and 41% and the agronomic NUE by 75, 67, 86 and 82%, respectively. In addition, compared with the FP, the N1 and N2 scenarios significantly reduced the GHGI by 14 and 18%, respectively, despite similar NGWPs. The N3 and N4 scenarios remarkably increased the NGWP and GHGI by an average of 67 and 36%, respectively. In conclusion, the ISSM strategies are promising for both food security and environmental protection, and the ISSM scenario of N2 is the optimal strategy to realize high yields and high NUE together with low environmental impacts for this agricultural rice field. [ABSTRACT FROM AUTHOR]

Extreme weather is an unexpected shock to the socioeconomic, which is likely to create climate risks in the process of global warming mitigation. The aim of this study is to investigate the impact of extreme weather on prices of China's regional emission allowances, by using the panel data of four representative pilots in China (Beijing, Guangdong, Hubei, and Shanghai) from April 2014 to December 2020. The overall findings reveal that extreme weather, especially extreme heat, has a short-term lagged positive impact on carbon prices. In particular, the specific performance of extreme weather under different conditions is as follows: (i) carbon prices in tertiary-dominated markets are more sensitive to extreme weather, (ii) extreme heat has a positive effect on carbon prices while extreme cold does not, and (iii) the positive impact of extreme weather on carbon market is significantly stronger during compliance periods. This study provides the decision-making basis for emission traders to avoid losses caused by market fluctuations. [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]

The rainfall distribution in southwest China is uneven, and the rainstorm threshold cannot use in a unified standard. This paper synthesizes a calculation method for the extremely heavy precipitation threshold and the provision of the rainstorm threshold in meteorological operation. It calculates the daily precipitation rainstorm threshold at 400 national ground stations in southwest China. The rainstorm events from 1961 to 2021 were statistically analyzed using the rainstorm threshold and analyzing the spatial-temporal variation characteristics. The results show that the number of single-station rainstorm events and the average precipitation of single-station rainstorm events in southwest China decreased from east to west. The number and frequency of single-station rainstorm events in Guizhou, Sichuan, Tibet, and Chongqing are increasing, while the number of single-station rainstorm events in Yunnan is decreasing. There is no apparent spatial distribution pattern for the continuous rainstorm events in the southwest region. From 1961 to 2021, the number and frequency of rainstorm events at a single station in southwest China followed an upward trend. The number of rainstorm events at a single station increased by 16.7 times · (10a)−1, and the frequency of rainstorms increased by 9.9% · (10a)−1. The continuous rainstorm events show an increasing trend, with an increase of 0.1 times · (10a)−1. Using the rainstorm threshold in southwest China, the early warning threshold for rainstorm disasters can be adjusted. The temporal and spatial characteristics of rainstorm events since 1961 can analyze the changes occurring in rainstorm events under global warming and provide data to support the response of southwest China to climate change. [ABSTRACT FROM AUTHOR]

In the face of global warming, air pollution, and other difficulties, electric vehicles have become an industry strongly supported by various countries due to their good environmental protection characteristics. In the context of big data, people are exposed to more and more information, and the convenience brought by big data is also increasing. Based on this background, the development of green and intelligent vehicles is getting faster and faster. This paper is aimed at studying the application of machine learning algorithms in the development and consumption trends of green and intelligent vehicles in the context of big data. This paper proposes machine learning algorithms based on big data, as well as support vector machine algorithms and so on. Machine learning algorithms specialize in how computers simulate or implement human learning behaviors to acquire new knowledge or skills and to reorganize existing knowledge structures to continuously improve their performance. The test results of this paper show that, starting from 2014, China has begun to vigorously develop green and intelligent vehicles. In 2014, the production volume of green and intelligent vehicles in China was 3,675, and the sales volume was 2,790. The development of green and intelligent vehicles is not very good and has not been fully accepted by the public. However, since 2017, the production and sales of green and intelligent vehicles have been slowly increasing. By 2020, the production of green and intelligent vehicles will be 24,360 and the sales will be 24,090. It can be seen that with the development of time, green and intelligent vehicles are gradually being recognized. [ABSTRACT FROM AUTHOR]

In the context of global warming, frequent heat wave disasters have seriously threatened the safety of human life and property. The urban agglomeration, as the main region with a high concentration of population and economy, is susceptible to heat weaves due to the existing urban heat island effect. In this study, we investigated the temporal and spatial characteristics of heat waves (heat index, HI) in China from 2000 to 2020 and assess the vulnerability of 19 urban agglomerations to heat waves from the perspective of exposure, sensitivity and adaptability. The results show that: (1) In the past 20 years, the frequency and intensity of HI (greater than 26.67 °C) both showed an upward trend. (2) Shandong Peninsula, Central Henan, Yangtze River Delta, Middle Reaches of Yangtze River, and Mid-southern Liaoning urban agglomerations always maintain a high vulnerability. (3) From 2000 to 2020, the vulnerability of Beijing-Tianjin-Hebei, Yangtze River Delta, Chengdu-Chongqing, Middle reaches of Yangtze River, Guangdong-Fujian-Zhejiang, Harbin-Changchun and Mid-southern Liaoning urban agglomerations were always dominated by exposure. The vulnerability of Shandong Peninsula, Beibu Gulf and Central Henan urban agglomeration has always been dominated by sensitivity. The vulnerability of North Tianshan Mountain, Lanzhou-Xining, Guanzhong and Hu-Bao-E-Yu urban agglomeration has always been dominated by inadequate adaptability. (4) Recently, the factors that contributed most to exposure, sensitivity and adaptability were population density, the proportion of outdoor workers and water supply, with contribution rates of 38%, 55% and 26%, respectively. This study can provide a scientific basis for the rational allocation of resources among urban agglomerations, effectively formulating policies and guiding population migration from high temperature disasters., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Food is the material basis for human survival. Therefore, food security is a top priority for the people's livelihood and the sustainable development and future destiny of human beings. In the context of global warming in recent decades, the Arctic region has experienced more significant temperature anomalies than the midlatitudes due to the "Arctic amplification," and the rate of sea ice reduction has accelerated, which has an important impact on climate change in the middle and high latitudes, especially the frequent occurrence of extreme climate disasters that seriously affect food security and China's agricultural production. However, little research has been conducted on the role of changes in this important system of Arctic sea ice in China's agricultural production. Therefore, this paper analyzes the interannual variability and multi-year trends of Arctic sea ice concentration, CO2, air temperature, precipitation and China's major crop yield data to explore the possible effects and mechanisms of the rapid decrease in Arctic sea ice on China's grain production. From the analysis, it was found that the yield of major grains (rice, maize, wheat and soybean) in China was closely related to the Arctic sea ice anomaly in the previous summer and autumn, and the influence process was primarily through the dynamic process of the Arctic sea ice anomaly affecting the meridional temperature gradient and the positive and negative Arctic Oscillation phases, which in turn affected the air temperature anomalies in Eurasia and China, and finally led to the anomalous changes in Chinese grain yield. Based on this, a prediction model of China's major grain yield was established by stepwise nonlinear multiple regression analysis, which is a good fit and is expected to increase China's major crop yield by 11.4% in 2022 compared with last year. This presents new ideas and methods for future grain yield assessment in China and has far-reaching guidance for the stability and development of national and regional economies worldwide. [ABSTRACT FROM AUTHOR]

Purpose: After almost 10 years, people wonder if green finance has been able to attain its objectives in terms of controlling climate change. Persistent global warming and climate deregulation manifested by melting glaciers, droughts and floods, are all of these determinants that have called into question the efficiency of green finance. Design/methodology/approach: Green finance is a way to support climate action through investments. It has proven that this is a viable financial instrument and that it can be used by governments and private companies to plan for the future of our planet. Findings: Based on an analysis of articles published in top international journals from 2016 to 2022, about the relationship between green technology and financial services in China, this paper aims to present an overview of green finance, its importance for the planet, its objectives and its instruments. Research limitations/implications: This study's contribution is to shed light on the aspects that may have limited its effectiveness, such as the absence of incentives, the absence of climate costs and above all the absence of finance green standards. Originality/value: The results have shown that there is still a significant gap in green finance before inclusive green growth can be achieved. Inclusive green growth. All stakeholders need to increase the level of investment in green finance. The green investment financing gap is the result of inconsistencies in sustainability and policies. Therefore, governments must intervene to impose appropriate policies and regulations to compel the financial sector to engage in sustainable development. All of these factors make the concept of green finance just an illusion. [ABSTRACT FROM AUTHOR]

The uneven changes in potential evapotranspiration (PET) in response to temperature rise are called the 'evapotranspiration paradox' phenomenon, which is expected to intensify further under a warming climate. In this paper, we explored the spatial–temporal changes in the future 'evapotranspiration paradox' phenomenon over China and its 10 major river sub‐regions under different climate change scenarios. Thus, this paper uses four global climate model outputs under seven shared socioeconomic pathway‐based scenarios (SSP1‐1.9, SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, SSP4‐3.4, SSP4‐6.0 and SSP5‐8.5) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Considering the latest IPCC's 6th Assessment Report (AR6), this research emphasizes the 2021–2040 (near‐term), 2041–2060 (mid‐term) and 2081–2100 (long‐term) periods to anticipate the 'evapotranspiration paradox' phenomenon. In this study, PET is estimated based on the modified Penman–Monteith (P‐M) method (considering CO2). Furthermore, the paradox phenomenon in this study is defined considering two pivotal conditions: the surface temperature increases but the evaporation decreases (Type I), and the temperature decreases but the evaporation still tends to increase (Type II). The results show that there were only Type I 'evapotranspiration paradoxes' that existed in the historical period, which were dominant especially before the 1990s. Nearly 50% of the areas experienced the Type I 'evapotranspiration paradox' phenomenon that occurred during 1975–1994 and 1995–2014. Spatially, it covered 100% of the area of the Southeast River (SER) and the Liaohe River (LR) during 1975–1994 and the area of the SER, the HAR, the HHR and the LR during 1995–2014. In the future, the interdecadal growth rate of PET in China is projected to be the highest under the SSP5‐8.5 and the lowest under the SSP3‐7.0 with spatial variation. Importantly, the largest areas of approximately 36% and 45% with the Type I phenomenon are inclined to occur under the SSP1‐1.9 and SSP4‐6.0, respectively, over the long‐term period (2081–2100). The area with the Type I phenomenon will be less than 20% in the near‐term, and it is less than 12% in the mid‐term period. For the Type II evapotranspiration paradox, the uppermost 45% of the area is expected to experience the Type II phenomenon under SSP1‐1.9 during the mid‐term period, while it is 30% under SSP1‐2.6 during the long‐term period. However, this study's findings provide the scientific basis for formulating adaptation and mitigation strategies to combat 'evapotranspiration paradox'‐related extremes at regional scales. [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]

As the second-largest contributor to historical global warming, methane emissions must be controlled to slow down temperature increase and achieve climate benefits. Due to a lack of knowledge about the specific sources and processes, a quantitative approach will lead to inaccurate estimation. In this paper, a typical local distribution company with more than 100 years of operation history was chosen. Detailed procedures of pipeline constructions and accidents were investigated, and critical steps leading to methane emissions were clarified. Then emission quantification methodologies for all processes were proposed, including a new pipeline connection, new regulator connection, emergency repair and third-party damages. As a basis for emission estimation, the distribution of parameters, such as diameter, length and pressure, was counted. Then the emission rates of all projects were calculated, and emission factors were established. The average emission rates of the new pipeline connection, new regulator connection, emergency repair, third-party damage (medium pressure) and third-party damage (low pressure) were 234 kg, 147 kg, 217 kg, 17,282 kg and 62 kg, respectively. In addition, the total methane emission in China from these sources was estimated to be about 5 × 104 t, which is large enough to attract attention. The work in this paper aims to establish a reasonable framework to evaluate venting methane emissions from the distribution process of natural gas. [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]

Simple Summary: In this study, we describe the well-preserved fossil seed cones of Nothotsuga found in the Mula Basin, Litang County, Sichuan Province, southwestern China. By detailed morphological comparisons, a new fossil species, Nothotsuga mulaensis Z. Li et J. L. Dong sp. nov. is named. This discovery suggests that Nothotsuga had a more northern distribution during the Miocene. In addition, it suggests that the Mula region had a humid and warm climate during this period. The ancient landform presents an intermountain lake basin, which would have been surrounded by higher mountains. After the Miocene, due to the uplift of the Tibetan Plateau, Nothotsuga was either unable to adapt to the extinction of the local environmental or was able to migrate to several scattered places in southern China. In this paper, we describe a new fossil species, Nothotsuga mulaensis Z. Li & J.L. Dong sp. nov. The discovery of the fossil species was based on well-preserved fossil seed cones that were found in the Mula Basin in Xiamula village, Litang County, Sichuan Province, southwestern China. The shapes of these fossils were characterized by ovate seed cones, rhombic or suborbicular scales with auriculate bases, and the bracts were ligulate-spathulate in shape. This finding suggests that Nothotsuga once had a wide distribution range in China and that it also inhabited the eastern Tibetan Plateau (TP). Nothotsuga mulaensis was distributed in an intermountain lake basin, at altitudes from 2000 to 2300 m, in a warm and humid environment. This finding also suggests that the eastern TP may have provided good habitat for Nothotsuga during the Miocene. In addition, we propose that the uplift, accompanied by the severe cooling and aridification that occurred after the Miocene, caused the disappearance of this species of Nothotsuga in the eastern TP. [ABSTRACT FROM AUTHOR]

Global warming is expected to increase the atmospheric evaporative demand and make more surface water for evapotranspiration, aggerating water sources' social and ecological shortage. Pan evaporation, as a routine observation worldwide, is an excellent metric to indicate the response of terrestrial evaporation to global warming. However, several non-climatic effects, such as instrument upgrades, have destroyed the homogenization of pan evaporation and limited its applications. In China, 2400s meteorological stations have observed daily pan evaporation since 1951. The observed records became discontinuous and inconsistent due to the instrument upgrade from micro-pan D20 to large-pan E601. Here, combining the Penpan model (PM) and random forest model (RFM), we developed a hybrid model to assimilate different types of pan evaporation into a consistent dataset. Based on the cross-validation test, on a daily scale, the hybrid model has a lower bias (RMSE=0.41 mm day -1 ) and better stability (NSE=0.94) than the two sub-models and the conversion coefficient method. Finally, we produced a homogenized daily dataset of E601 across China from 1961 to 2018. Based on this dataset, we analyzed the long-term trend of pan evaporation. Pan evaporation showed a -1.23±0.57 mm a -2 downward trend from 1961-1993, primarily caused by decreased pan evaporation in warm seasons over North China. After 1993, the pan evaporation in South China increased significantly, resulting in a 1.83±0.87 mm a -2 upward trend across China. With better homogeneity and higher temporal resolution, the new dataset is expected to promote drought monitoring, hydrological modeling, and water resources management. Free access to the dataset can be found at https://figshare.com/s/0cdbd6b1dbf1e22d757e., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

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 this study, CMIP6 and single model initial‐condition large ensemble (SMILE) simulations were applied to reveal possible changes in the spatial coverage (RSC) of summer extreme precipitation (≥99th percentile) in China. Four different approaches were applied to provide a comprehensive assessment of RSC changes. Results indicate that the trend of RSC in summer for the period 1961–2020 in China is underestimated by raw simulations of CMIP6 multi‐model ensembles. Further analyses suggest that the confidence in model simulations reflecting the observed change can be improved based on the two observation constraint approaches and SMILE‐based approach. In addition, the reliability of the spatial distribution can also be improved. Projection results indicate that the RSC of summer extreme precipitation increases consistently with the increment of global warming across the different approaches. Among them, the results based on observational constraints and the SMILE‐based approach show enhanced reliability and present larger RSC changes than by directly using CMIP6 ensembles. In conclusion, extreme precipitation in summer is expected to become more widespread in China as the level of global warming increases. Plain Language Summary: Extreme precipitation causes severe impacts on agriculture, economy, and human health. However, it is projected by models to change in China but with low consistency. Thus, this paper provides an assessment of the projected characteristics of the ratio of spatial coverage (RSC) for extreme precipitation in China combined with multiple evidence and approaches. CMIP6 and single model initial‐condition large ensemble (SMILE) simulations are applied. Four approaches based on the statistics principle and physical interpretability are conducted to comprehensively assess change in RSC. Results reveal that extreme precipitation will become more widespread in China. Among them, results based on observation constraint approaches and SMILE‐based approach, which show enhanced accuracy and reliability, present larger RSC than directly using CMIP6 ensembles. It indicates that extreme precipitation will be more widespread in China combined with multiple evidence and methods. This study provides some scientific foundation for climate mitigation and adaptation. Key Points: Observed trend in RSC in China increased significantly in summer for the period 1961–2020 but is underestimated by CMIP6 multi‐model ensemblesConfidence is improved based on observation constraint and SMILE‐based approaches to raw model simulationsExtreme precipitation in summer will become more widespread, as supported by multiple evidence lines and approaches [ABSTRACT FROM AUTHOR]

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Global warming poses a prominent challenge to every country. As one of the largest developing countries, China has experienced rapid economic transformation in its pursuit of sustainable development. This paper delves into a quasi-natural environmental experiment conducted in China, specifically focusing on the low-carbon city program initiated in 2010. Our study uncovers the phenomenon of regional competition among Chinese local governments with regard to carbon emissions, which in turn generates spillover effects stemming from the policy. To identify the effects of the low-carbon city policy on both economic and environmental development, we employ the spatial difference-in-differences approach. Our analysis suggests that reductions in carbon emissions in the pilot cities will lead to a decrease in carbon emissions and economic performance in the surrounding areas. Finally, by exploring firm-level data, we find that the policy significantly impacts large private firms operating in non-secondary industries. [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]