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China is currently the world’s largest carbon emitter and has pledged to achieve carbon neutrality by 2060, which requires significant reductions in emissions and the removal of carbon dioxide removal. Precise and efficient forestation has become a key strategy to increase carbon sequestration and mitigate the effects of climate change. In order to obtain a high-accuracy distribution area of potential forestation land, a variety of influencing factors on potential forestation land were selected in this study, including vegetation factors and environmental factors. Combining the two models “law of the minimum factors (LMF)” and “equal weight classification (EWC)” predicts the spatial distribution pattern of potential forestation land in China. And, we calculated the available forestation area in each province and predicted future forest coverage. The results showed that the potential forestation area using the LMF method reached approximately 5.65 × 105 km2, and the potential forestation area using the EMC method reached approximately 4.95 × 105 km2. Combining the two models, the final potential forestation area in China was approximately 6.29 × 105 km2, of which approximately 1.02 × 105 km2 was evaluated as a high-confidence area. If all potential forestation land obtained from the two models was successfully planted, China’s forest coverage rate would reach nearly 26 %. Two newly introduced vegetation factors, namely vegetation resilience and top vegetation succession, were considered in this study to make the prediction results of potential forestation distribution more accurate and reasonable. In addition, the results obtained in this study can provide certain assistance for the specific implementation of national forestation policies, and can provide a reference for promoting forestation actions and achieving the goal of carbon neutrality.

Afforestation is an important way to effectively reduce carbon emissions from human activities and increase carbon sinks in forest ecosystems. It also plays an important role in climate change mitigation. Currently, few studies have examined the spatiotemporal dynamics of future afforestation areas, which are crucial for assessing future carbon sequestration in forest ecosystems. In order to obtain the dynamic distribution of potential afforestation land over time under future climate change scenarios in China, we utilized the random forest method in this study to calculate weights for the selected influencing factors on potential afforestation land, such as natural vegetation attributes and environmental factors. The “weight hierarchy approach” was used to calculate the afforestation quality index of different regions in different 5-year intervals from 2021 to 2060 and extract high-quality potential afforestation lands in each period. By dynamically analyzing the distribution and quality of potential afforestation land from 2021 to 2060, we can identify optimal afforestation sites for each period and formulate a progressive afforestation plan. This approach allows for a more accurate application of the FCS model to evaluate the dynamic changes in the carbon sequestration capacity of newly afforested land from 2021 to 2060. The results indicate that the average potential afforestation land area will reach 75 Mha from 2021 to 2060. In the northern region, afforestation areas are mainly distributed on both sides of the “Hu Line”, while in the southern region, they are primarily distributed in the Yunnan–Guizhou Plateau and some coastal provinces. By 2060, the potential calculated cumulative carbon storage of newly afforested lands was 11.68 Pg C, with a peak carbon sequestration rate during 2056–2060 of 0.166 Pg C per year. Incorporating information on the spatiotemporal dynamics of vegetation succession, climate production potential, and vegetation resilience while quantifying the weights of each influencing factor can enhance the accuracy of predictions for potential afforestation lands. The conclusions of this study can provide a reference for the formulation of future afforestation plans and the assessment of their carbon sequestration capacity.

Industrialization has increased global carbon emissions, necessitating effective climate change mitigation measures. China, the most populous developing nation, faces the challenge of strategizing emissions to meet national carbon neutrality objectives. However, research on specific regions’ carbon emissions drivers and causal factors is limited, particularly across prefectural-level cities. This study estimates the spatial and temporal patterns of carbon emissions across China’s prefectural cities and utilizes both OLS regression and stepwise regression models to analyze the impact of various factors influencing carbon emissions in these cities. Results reveal the following: (1) The country’s overall 20-year carbon emissions continue to grow from 3020.29 Mt in 2001 to 9169.74 Mt in 2020, with an average annual growth rate of 5.71%; the eastern region has seen a gradual deceleration in emissions, whereas the western region continues to experience an increase. Carbon emissions in cities within each subregion consistently rise. (2) Carbon emissions in Chinese prefectural-level cities exhibit strong spatial autocorrelation and clustering (Z > 1.96, p < 0.05), with hot spots primarily in the eastern coastal areas and cold spots in the northwest to southwest regions. (3) Economic and demographic factors significantly increase carbon emissions, while climate and urbanization effects are more complex and variable. Economic growth and population increase are the most significant influencing factors, but regional variances exist in carbon emissions determinants in subregional prefectural cities. These insights provide valuable insights into national emission dynamics at the prefectural level, providing a theoretical basis for enhancing carbon emission strategies across various jurisdictions.

This work focuses on the convergence of the numerical invariant measure for a stochastic age-dependent population–toxicant model with Markov switching. Considering that Euler–Maruyama (EM) has the advantage of fast computation and low cost, explicit EM was used to discretize the time variable. With the help of the p-th moment boundedness of the analytical and numerical solutions of the model, the existence and uniqueness of the corresponding invariant measures were obtained. Under suitable assumptions, the conclusion that the numerical invariant measure converges to the invariant measure of the analytic solution was proven by defining the Wasserstein distance. A numerical simulation was performed to illustrate the theoretical results.

Over the last several decades, China has taken multiple measures for afforestation and natural forest protection, including setting the goal of carbon neutrality by the middle of 21th century. In order to support the practice of relevant policies from the scientific perspective, it is essential to precisely estimate the carbon storage of arbor forest, as it plays an important role in the carbon cycle of ecosystems. In this study, we first used the latest four phases of national forest inventory data to investigate the variation of carbon storage for both natural and planted arbor forest in China during the covered period (1999-2018). Then we used machine leaning methods to simulate the carbon density based on various kinds of environmental factors and analyzed the contribution of each influencing factor. Our results demonstrate that the total carbon storage for arbor forest in China kept increasing over the last two decades, but this increment was mainly brought about by the continuous expansion of forest land. The gap of carbon sequestration between natural forest and planted forest showed a significant trend of reduction. Additionally, tree age was identified as the dominant factor for influencing the spatiotemporal variation of carbon density among all the independent variables while the impact of climatic factor was limited. Therefore, the future improvement of carbon sequestration of arbor forest in should mainly rely on additional projects of afforestation, reforestation, green space conservation and reduction of emissions in China. Conclusions of this study have important implications for policy makers and other stakeholders to evaluate the previous achievement of environmental projects and can also help to set future plans and finally realize the goals of carbon neutrality.

Carbon density change and land use change are important factors in the spatiotemporal evolution of ecosystem carbon stock. Accurately assessing regional carbon stock and analyzing its relationship with land use patterns and carbon density change are of great value to regional ecosystem protection and sustainable social and economic development. In order to effectively evaluate the carbon stock in North China, this study divided the target area into 5 sub-regions, and a variety of methods were used to calculate the carbon density in each sub-region over different time periods. The classic InVEST model was selected to evaluate carbon stock evolution under changes in land use and carbon density from 2000 to 2015. The results show that the carbon stock in North China in 2000, 2005, 2010 and 2015 were 1.301 × 1010 t, 1.325 × 1010 t, 1.332 × 1010 t and 1.366 × 1010 t, respectively, with a cumulative increase of 6.506 × 108 t. As two main factors, the land use type change and carbon density change showed different influences on the carbon stock of different regions and different ecosystems, but the former had a greater impact in North China during 2000–2015. Converting farmland to forest and grassland and converting bare land to grassland increased carbon stock, while converting farmland to building land reduced carbon stock. In addition, the carbon density of most land use types in each sub-region increased from 2000 to 2015, which further caused the increase in carbon stock. The carbon stock in North China had a significant spatial pattern of high in the east and low in the west, and this distribution pattern is closely related to land use. This research can provide scientific reference for land use management decision-making and sustainable carbon stock function in North China.

A grating coupler with a high coupling efficiency and low back reflections is designed and demonstrated on the thin film lithium niobate platform, which facilitates an efficient interface between a lithium niobate ridge waveguide and a standard single mode fiber. The excellent performances of the present grating coupler are enabled by a cavity-assisted grating structure and a top metal mirror, even though a uniform grating is adopted as the diffractive element. Experimentally, a coupling loss of −0.89 dB at 1552 nm is demonstrated with a 1-dB bandwidth of 45 nm. Low back reflections of

As more machine learning and deep learning models are applied in studying the quantitative relationship between the climate and terrestrial vegetation growth, the uncertainty of these advanced models requires clarification. Partial dependence plots (PDPs) are one of the most widely used methods to estimate the marginal effect of independent variables on the predicted outcome of a machine learning model, and it is regarded as the main basis for conclusions in relevant research. As more controversies regarding the reliability of the results of the PDPs emerge, the uncertainty of the PDPs remains unclear. In this paper, we experiment with real, remote sensing data to systematically analyze the uncertainty of partial dependence relationships between four climate variables (temperature, rainfall, radiation, and windspeed) and vegetation growth, with one conventional linear model and six machine learning models. We tested the uncertainty of the PDP curves across different machine learning models from three aspects: variation, whole linear trends, and the trait of change points. Results show that the PDP of the dominant climate factor (mean air temperature) and vegetation growth parameter (indicated by the normalized difference vegetation index, NDVI) has the smallest relative variation and the whole linear trend of the PDP was comparatively stable across the different models. The mean relative variation of change points across the partial dependence curves of the non-dominant climate factors (i.e., radiation, windspeed, and rainfall) and vegetation growth ranged from 8.96% to 23.8%, respectively, which was much higher than those of the dominant climate factor and vegetation growth. Lastly, the model used for creating the PDP, rather than the relative importance of these climate factors, determines the fluctuation of the PDP output of these climate variables and vegetation growth. These findings have significant implications for using remote sensing data and machine learning models to investigate the quantitative relationships between the climate and terrestrial vegetation.

Vegetation changes and factors have a profound influence on the local ecology, the economy, and the long-term durability of human construction. This study focuses on the impacts of climate change and human activity on vegetation changes on the Qinghai-Tibet Plateau and aims to develop a dataset of human activity levels on the plateau. Sen and Mann-Kendall trend analysis was used to evaluate the spatial distribution of vegetation NDVI and its trends, as well as the lagged response of plant growth to climatic circumstances. Using a geodetector model, the effects of meteorological and anthropogenic intensity data were examined. The study’s findings show that, although anthropogenic influences and ecosystem vulnerability caused a decline in the region’s vegetation, a stable climate and a healthy ecosystem supported the growth of vegetation. From 2000 to 2017, the area where vegetation grass improved significantly accounted for the highest proportion, reaching 34.22%. Different anthropogenic intensities are distributed spatially, and this interplay of anthropogenic intensities and climatic factors affects the distribution of vegetation greenness more than each element acting alone. The study of how human activity and climate change affect vegetation greenness can offer practical recommendations for maintaining the Qinghai-Tibet Plateau’s natural ecology. It is crucial to responsibly safeguard the Qinghai-Tibet Plateau’s environment in response to the nation’s ecological civilization.

Thin-film lithium niobate (TFLN) based traveling-wave modulators maintain simultaneously excellent performances, including large modulation bandwidth, high extinction ratio, low optical loss, and high modulation efficiency. Nevertheless, there still exists a balance between the driving voltage and modulation bandwidth. Here, we demonstrate an ultra-large bandwidth electro-optic modulator without compromising the driving voltage based on the TFLN platform on a silicon substrate, using a periodic capacitively loaded traveling-wave electrode. In order to compensate the slow-wave effect, an undercut etching technique for the silicon substrate is introduced to decrease the microwave refractive index. Our demonstrated devices represent both low optical and low microwave losses, which leads to a negligible optical insertion loss of 0.2 dB and a large electro-optic bandwidth with a roll-off of 1.4 dB at 67 GHz for a 10 mm-long device. A low half-wave voltage of 2.2 V is also achieved. Data rates up to 112 Gb s−1 with PAM-4 modulation are demonstrated. The compatibility of the proposed modulator to silicon photonics facilitates its integration with matured silicon photonic components using, e.g., hybrid integration technologies.

Digital soil mapping approaches related to soil organic matter (SOM) are crucial to quantify the process of the carbon cycle in terrestrial ecosystems and thus, can better manage soil fertility. Recently, many studies have compared machine learning (ML) models with traditional statistical models in digital soil mapping. However, few studies focused on the application of hybrid models that combine ML with statistical models to map SOM content, especially in loess areas, which have a complicated geomorphologic landscape. In this study, the trend prediction used two ML models, i.e., gradient boosting modeling and random forest (RF), and a traditional stepwise multiple linear regression plus interpolated residuals generated from two classic geostatistical models, i.e., ordinary kriging and inverse distance weighting, and a high accuracy surface modeling (HASM) were implemented to map SOM content in the Dongzhi Loess Tableland area of China. A total of 145 topsoil samples and heterogeneous environmental variables were collected to develop the hybrid models. Results showed that 18 variables related to soil properties, climate variables, terrain attributes, vegetation indices, and location attributes played an important role in SOM mapping. The models that incorporate ML algorithms and interpolated residuals to predict SOM variation were found to have a better ability to handle complex environment relationships. The HASM model outperformed traditional geostatistical models in interpolating the residuals. In contrast, RF combined with HASM residuals (RF_HASM) gave the best performance, with the lowest mean absolute error (1.69 g/kg), root mean square error (2.30 g/kg), and the highest coefficient of determination (0.57) and concordance correlation coefficient (0.69) values. Moreover, the spatial distribution pattern obtained with RF_HASM yielded a spatial distribution of SOM that better fit the actual distribution pattern of the study area. In conclusion, these results suggest that RF_HASM is particularly capable of improving the mapping accuracy of SOM content at the regional scale.

Accurate and effective accounting of grassland aboveground biomass (AGB) is essential for grassland carbon storage accounting and pastoral agriculture sustainability. In this study, we combined AGB field survey data and remote sensing data to build a suitable model to estimate the grassland AGB in the Three-River Source Region (TRSR) of China. Three machine learning models were used to simulate the grassland AGB from 2001 to 2019, including support vector machine (SVM), random forest (RF), and high accuracy surface modeling (HASM). The results show that (1) the HASM achieved better results than the RF and SVM models (R2 = 0.8459 > 0.72 > 0.5858; RMSE = 29

To identify relationships among entities in natural language texts, extraction of entity relationships technically provides a fundamental support for knowledge graph, intelligent information retrieval, and semantic analysis, promotes the construction of knowledge bases, and improves efficiency of searching and semantic analysis. Traditional methods of relationship extraction, either those proposed at the earlier times or those based on traditional machine learning and deep learning, have focused on keeping relationships and entities in their own silos: extracting relationships and entities are conducted in steps before obtaining the mappings. To address this problem, a novel Chinese relationship extraction method is proposed in this paper. Firstly, the triple is treated as an entity relation chain and can identify the entity before the relationship and predict its corresponding relationship and the entity after the relationship. Secondly, the Joint Extraction of Entity Mentions and Relations model is based on the Bidirectional Long Short-Term Memory and Maximum Entropy Markov Model (Bi-MEMM). Experimental results indicate that the proposed model can achieve a precision of 79.2% which is much higher than that of traditional models.

Exploring land use change is crucial to planning land space scientifically in a region. Taking the ecological conservation area (ECA) in western Beijing as the study area, we employ ArcGIS 10.2, landscape pattern index and multiple mathematical statistics to explore the temporal and spatial variation of land use from 2000 to 2020. Patch-generating Land Use Simulation (PLUS), Future Land Use Simulation (FLUS) and Markov models were used to simulate and predict the current land use in 2020. The models were evaluated for accuracy, and the more accurate PLUS model was selected and used to simulate and predict the potential land use in the study area in 2030 under two management scenarios. The main findings of this research are: (1) From 2000 to 2020, the construction land increased constantly, and the area of cultivated land and grassland decreased significantly. (2) For predicting the spatial distribution of land use in the study area, the PLUS model was more accurate than the FLUS model. (3) The land-use prediction of the study area in 2030 shows that the area of grassland, forest and water is approximately equal to their corresponding value in 2020, but the construction land increased constantly by occupying the surrounding cultivated land. According to this research, the continuous decrease of cultivated land in favor of increasing construction land will cause losses to the ecological service function of the ECA, which is not beneficial to the sustainable development of the region. Relevant departments should take corresponding measures to reduce this practice and promote sustainable development, particularly in the southern and western areas of the ECA where there is less construction land.

Satellite observation is one of the main methods used to monitor the global distribution and variation of atmospheric carbon dioxide (CO2). Several CO2 monitoring satellites have been successfully launched, including Japan’s Greenhouse Gases Observing SATellite (GOSAT), the USA’s Orbiting Carbon Observatory-2 (OCO-2), and China’s Carbon Dioxide Observation Satellite Mission (TanSat). Satellite observation targeting the ground-based Fourier transform spectrometer (FTS) station is the most effective technique for validating satellite CO2 measurement precision. In this study, the coincident observations from TanSat and ground-based FTS were performed numerous times in Beijing under a clear sky. The column-averaged dry-air mole fraction of carbon dioxide (XCO2) obtained from TanSat was retrieved by the Department for Eco-Environmental Informatics (DEEI) of China’s State Key Laboratory of Resources and Environmental Information System based on a full physical model. The comparison and validation of the TanSat target mode observations revealed that the average of the XCO2 bias between TanSat retrievals and ground-based FTS measurements was 2.62 ppm, with a standard deviation (SD) of the mean difference of 1.41 ppm, which met the accuracy standard of 1% required by the mission tasks. With bias correction, the mean absolute error (MAE) improved to 1.11 ppm and the SD of the mean difference fell to 1.35 ppm. We compared simultaneous observations from GOSAT and OCO-2 Level 2 (L2) bias-corrected products within a ±1° latitude and longitude box centered at the ground-based FTS station in Beijing. The results indicated that measurements from GOSAT and OCO-2 were 1.8 ppm and 1.76 ppm higher than the FTS measurements on 20 June 2018, on which the daily observation bias of the TanSat XOC2 results was 1.87 ppm. These validation efforts have proven that TanSat can measure XCO2 effectively. In addition, the DEEI-retrieved XCO2 results agreed well with measurements from GOSAT, OCO-2, and the Beijing ground-based FTS.

Herein, we describe a copper‐catalyzed sequential C(sp2)−H/C(sp3)−H annulation of indoles with propargylic alcohols for the synthesis of diversely functionalized hydrobenzo[b]carbazole scaffolds. The reaction involves a Friedel‐Crafts addition, 1,5‐H shift, 6π electrocyclization, and dearomatization‐aromatization cascade. Importantly, this is a report of the use of 2‐(diarylmethyl)indoles as three‐carbon synthons in a (3+3) annulation reaction involving C(sp3)−H indole functionalization under oxidant‐free conditions. [ABSTRACT FROM AUTHOR]