Analysis of factors influencing carbon emissions in the Yangtze River Delta region and projections of carbon peak scenarios

Based on the supply-side perspective,the improved STIRPAT model is applied to reveal the mechanisms of supply-side factors such as human,capital,technology,industrial synergy,institutions and economic growth on carbon emissions in the Yangtze River Delta(YRD)through path analysis,and to forecast carbon emissions in the YRD from the baseline scenario,factor regulation scenario and integrated scenario to reach the peak.The results show that:(1)Jiangsu's high carbon emission pattern is the main reason for the YRD hindering the synergistic regulation of carbon emissions.(2)Human factors,institutional factors and economic growth factors can all contribute to carbon emissions in the YRD region,while technological and industrial factors can generally suppress carbon emissions in the YRD region.(3)Under the capital regulation scenario,the YRD region has the highest level of carbon emission synergy,with Jiangsu reaching its peak five years earlier.Under the balanced regulation scenario,the YRD region as a whole,Jiangsu,Zhejiang and Anhui reach the peak as scheduled.

Freeze-thaw cycles and associated geomorphology in a post-glacial environment:current glacial,paraglacial,periglacial and proglacial scenarios at Pico de Orizaba volcano,Mexico

The glacial history of Pico de Orizaba indicates that during the Last Glacial Maximum,its icecap covered up to~3000 m asl;due to the air temperature increasing,its main glacier has retreated to 5050 m asl.The retraction of the glacier has left behind an intense climatic instability that causes a high frequency of freeze-thaw cycles of great intensity;the resulting geomorphological processes are represented by the fragmentation of the bedrock that occupies the upper parts of the mountain.There is a notable lack of studies regarding the fragmentation and erosion occurring in tropical high mountains,and the associated geomorphological risks;for this reason,as a first stage of future continuous research,this study analyzes the freezing and thawing cycles that occur above 4000 m asl,through continuous monitoring of surface ground temperature.The results allow us to identify and characterize four zones:glacial,paraglacial,periglacial and proglacial.It was found that the paraglacial zone presents an intense drop of temperature,of up to~9℃ in only sixty minutes.The rock fatigue and intense freeze-thaw cycles that occur in this area are responsible for the high rate of rock disintegration and represent the main factor of the constant slope dynamics that occur at the site.This activity decreases,both in frequency and intensity,according to the distance to the glacier,which is where the temperature presents a certain degree of stability,until reaching the proglacial zone,where cycles are almost non-existent,and therefore there is no gelifraction activity.The geomorphological processes have resulted in significant alterations to the mountain slopes,which can have severe consequences in terms of risk and water.

Comparing trends of crop and pasture in future land-use scenarios for climate change mitigation

Revegetation of former agricultural land is a key option for climate change mitigation and nature conservation.Expansion and abandonment of agricultural land is typically influenced by trends in diets and agricultural inten-sification,which are two key parameters in the Shared Socioeconomic Pathways(SSPs).Datasets mapping future land dynamics under different SSPs and climate change mitigation targets stem from different scenario assump-tions,land data and modelling frameworks.This study aims to determine the role that these three factors play in the estimates of the evolution of cropland and pastureland in future SSPs under different climate scenarios from four main datasets largely used in the climate and land surface studies.The datasets largely agree with the rep-resentation of cropland at present-day conditions,but the identification of pastureland is ambiguous and shows large discrepancies due to the lack of a unique land-use category.Differences occur with future projections,even for the same SSP and climate target.Accounting for CO_(2)sequestration from revegetation of abandoned agri-cultural land and CO_(2)emissions from forest clearance due to agricultural expansion shows a net reduction in vegetation carbon stock for most SSPs considered,except SSP1.However,different datasets give differences in estimates,even when representative of the same scenario.With SSP1,the cumulative increase in carbon stock until 2050 is 3.3 GtC for one dataset,and more than double for another.Our study calls for a common classifica-tion system with improved detection of pastureland to harmonize projections and reduce variability of outcomes in environmental studies.

Abnormal Action Detection Based on Parameter-Efficient Transfer Learning in Laboratory Scenarios

Laboratory safety is a critical area of broad societal concern,particularly in the detection of abnormal actions.To enhance the efficiency and accuracy of detecting such actions,this paper introduces a novel method called TubeRAPT(Tubelet Transformer based onAdapter and Prefix TrainingModule).Thismethod primarily comprises three key components:the TubeR network,an adaptive clustering attention mechanism,and a prefix training module.These components work in synergy to address the challenge of knowledge preservation in models pretrained on large datasets while maintaining training efficiency.The TubeR network serves as the backbone for spatio-temporal feature extraction,while the adaptive clustering attention mechanism refines the focus on relevant information.The prefix training module facilitates efficient fine-tuning and knowledge transfer.Experimental results demonstrate the effectiveness of TubeRAPT,achieving a 68.44%mean Average Precision(mAP)on the CLA(Crazy LabActivity)small-scale dataset,marking a significant improvement of 1.53%over the previous TubeR method.This research not only showcases the potential applications of TubeRAPT in the field of abnormal action detection but also offers innovative ideas and technical support for the future development of laboratory safety monitoring technologies.The proposed method has implications for improving safety management systems in various laboratory environments,potentially reducing accidents and enhancing overall workplace safety.

Research on Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse Reinforcement Learning Theory

The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits.In addition,owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios,it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters.Therefore,an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed.First,numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset.Subsequently,a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory.Furthermore,a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function,and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed.Finally,the proposed strategy is verified based on real driving scenarios.The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the“emergency degree”of obstacle avoidance and the state of the vehicle.Moreover,this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories,effectively improving the adaptability and acceptability of trajectories in driving scenarios.

Physical-Layer Secret Key Generation for Dual-Task Scenarios

Physical-layer secret key generation(PSKG)provides a lightweight way for group key(GK)sharing between wireless users in large-scale wireless networks.However,most of the existing works in this field consider only group communication.For a commonly dual-task scenario,where both GK and pairwise key(PK)are required,traditional methods are less suitable for direct extension.For the first time,we discover a security issue with traditional methods in dual-task scenarios,which has not previously been recognized.We propose an innovative segment-based key generation method to solve this security issue.We do not directly use PK exclusively to negotiate the GK as traditional methods.Instead,we generate GK and PK separately through segmentation which is the first solution to meet dual-task.We also perform security and rate analysis.It is demonstrated that our method is effective in solving this security issue from an information-theoretic perspective.The rate results of simulation are also consistent with the our rate derivation.

Improved Unit Commitment with Accurate Dynamic Scenarios Clustering Based on Multi-Parametric Programming and Benders Decomposition

Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.

Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.

Land Use Dynamics and Ecosystem Service Value Changes in the Yangtze River Delta Urban Agglomeration Under Different Scenarios

Urban agglomerations,serving as pivotal centers of human activity,undergo swift alterations in ecosystem services prompted by shifts in land utilization.Strengthening the monitoring of ecosystem services in present and future urban agglomerations contributes to the rational planning of these areas and enhances the well-being of their inhabitants.Here,we analyzed land use conversion in the Yangtze River Delta(YRD)urban agglomeration during 1990-2020 and discussed the spatiotemporal response and main drivers of changes in ecosystem service value(ESV).By considering the different development strategic directions described in land use planning policies,we predicted land use conversion and its impact on ESV using the Future Land Use Simulation(FLUS)model in three scenari-os in 2025 and 2030.Results show that:1)from 1990 to 2020,land use change is mainly manifested as the continuous expansion of con-struction land to cultivated land.Among the reduced cultivated land,82.2%were occupied by construction land.2)The land use types conversion caused a loss of 21.85 billion yuan(RMB)in ESV during 1990-2020.Moreover,the large reduction of cultivated land area led to the continuous decline of food production value,accounting for 13%of the total ESV loss.3)From 2020 to 2030,land use change will mainly focus on Yangzhou and Zhenjiang in central Jiangsu Province and Taizhou in southern Zhejiang Province.Under the BAU(natural development)and ED(cultivated land protection)scenarios,construction land expansion remains dominant.In contrast,under the EP(ecological protection)scenario,the areas of water bodies and forest land increase significantly.Among the different scenarios,ESV is highest in the EP scenario,making it the optimal solution for sustainable land use.It can be seen that the space use conflict among urban,agriculture and ecology is a key factor leading to ESV change in the urban agglomeration of Yangtze River Delta.There-fore,it is crucial to maintain spatial land use coordination.Our findings provide suggestions for scientific and rational land use planning for the urban agglomeration.

从Scenarios到状态图的算法分析

采用Scenarios进行需求建模能够反映从用户角度观察到的系统的行为,状态图是系统行为的精确描述。从Scenario到状态图的自动转换是指软件开发过程中从UML需求模型自动生成行为模型的过程,其研究对于细化系统行为,保持开发过程中模型间的一致性有重要意义。对其研究现状及进展进行了介绍,并对现有算法进行了总结和分类,进一步指出这些算法的不足和今后需要改进的方向。

The Change of North China Climate in Transient Simulations Using the IPCC SRES A2 and B2 Scenarios with a Coupled Atmosphere-Ocean General Circulation Model

This paper applies the newest emission scenarios of the sulfur and greenhouse gases, namely IPCC SRES A2 and B2 scenarios, to investigate the change of the North China climate with an atmosphere-ocean coupled general circulation model. In the last three decades of the 21st century, the global warming enlarges the land-sea thermal contrast, and hence, causes the East Asian summer (winter) monsoon circulation to he strengthened (weakened). The rainfall seasonality strengthens and the summer precipitation increases significantly in North China. It is suggested that the East Asian rainy area would expand northward to North China in the last three decades of the 21st century. In addition, the North China precipitation would increase significantly in September. In July, August, and September, the interannual variability of the precipitation enlarges evidently over North China, implying a risk of flooding in the future.

Quantitative Simulation of Dynamic Changes in Cultivated Land in Areas of Reclamation and Returning Cultivated Land to Forest or Pastures under RCPs Climate Scenarios

Northeast China as one of important agricultural production bases is an area under reclamation and returning cultivated land to forests or pastures. Therefore, it is of great practical significance in guaranteeing the sustainable development and national food security to study the spatial and temporal variation of cultivated land in Northeast China under future climate scenarios. In this study, based on data of land use, natural environment and social-economy, dynamics of land system（DLS） model was used to to simulate the spatial distribution and changing trends of cultivated land in the typical areas of reclamation and returning cultivated land to forest or pastures in Northeast China during 2010-2030 under land use planning scenario and representative concentration pathways（RCPs） scenarios quantitatively.The results showed that the area of cultivated land had an overall decreasing trend under the land use planning scenario, but the area of upland field increased slightly from 2000 to 2010 and then declined greatly, while the area of paddy field continuously declined from 2000 to 2030. Under the Asia-Pacific Integrated model（AIM）scenario, the total area of cultivated land had a tendency to increase considerably,with the upland field expanding more obviously and the paddy field declining slightly.In addition, the cultivated land showed a greater decreasing trend under the model for energy supply strategy alternatives and their general environmental impact（MESSAGE） scenario compared to the land use planning scenario. Moreover, analysis on the conversion between different land use types indicated that the reclamation and returning cultivated land to forests or pastures was likely to continue under future scenarios, but the frequency of occurrence could decrease as the time goes by. The conclusions can provide significant decision-making information for the rational agricultural planning and cultivated land protection in Northeast China to adapt to the climate change.

Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios: A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.

Changes in demand and supply of ecosystem services under scenarios of future land use in Vorarlberg, Austria

Ecosystem services(ES)are highly impacted by human-induced land-use change.Progressive urbanization and agricultural land abandonment in Western Europe necessitate assessments of future land-change impacts on ES to ensure sustainable service management.The present study aims at evaluating future demand and supply of three key services(flood protection,nearby recreation and biodiversity)in the mountainous region of Vorarlberg,Austria.We mapped the ES for the referenced time step 2016 and two scenarios for 2050,assuming the continuation of current land-change trends and pressure on landscape development.Results for the referenced landscape in 2016 show the highest ES supply for intermediate levels,while ES supply was low in the lowlands and valley bottoms and in high-elevation areas.We found a high positive correlation of ES with the distribution of forested areas.In contrast,service demand was highest in lowelevation areas and decreased with increasing elevation.This indicates that densely settled and intensively used agricultural areas currently suffer from ES undersupply.The projected future development of land use showed an increase in both supply and demand of the selected ES.The overall service supply increased more than the respective demand due to some reforestation of open land.As forests were found to be important synergistic areas for overall service provision,we expect decreasing demand on related services.Locally,demand was found to exceed the supply of ES,especially in the densely populated Rhine valley-requiring further policy interventions.Such ES-related information may contribute to regional policy making and ensure the long-term provision of ESs for future generations.

Simulation of Land-use Scenarios for Beijing Using CLUE-S and Markov Composite Models

This study investigated and simulated land use patterns in Beijing for the year 2000 and the year 2005 from the actual land use data for the year 1995 and the year 2000,respectively,by combining spatial land allocation simulation using the CLUE-S model,and numerical land demand prediction using the Markov model.The simulations for 2000 and 2005 were confirmed to be generally accurate using Kappa indices.Then the land-use scenarios for Beijing in 2015 were simulated assuming two modes of development:1) urban development following existing trends;and 2) under a strict farmland control.The simulations suggested that under either mode,urbanized areas would expand at the expense of land for other uses.This expansion was predicted to dominate the land-use conversions between 2005 and 2015,and was expected to be accompanied by an extensive loss of farmland.The key susceptible to land-use changes were found to be located at the central urban Beijing and the surrounding regions including Yanqing County,Changping District and Fangshan District.Also,the simulations predicted a considerable expansion of urban/suburban areas in the mountainous regions of Beijing,suggesting a need for priority monitoring and protection.

Scenarios在人机交互中的使用

Scenarios由于可通过抽象来说明行为和结果并且可以通过具体的例子来阐述一个较高水平的设计 ,因而被用于系统的开发中 .文章通过分析几个典型的在人机交互 (HCI)中使用的scenarios。

Estimation of future water resources of Xiangjiang River Basin with VIC model under multiple climate scenarios

Variation trends of water resources in the Xiangjiang River Basin over the coming decades have been investigated using the variable infiltration capacity(VIC) model and 14 general circulation models'(GCMs') projections under the representative concentration pathway(RCP4.5) scenario. Results show that the Xiangjiang River Basin will probably experience temperature rises during the period from 2021 to2050, with precipitation decrease in the 2020 s and increase in the 2030 s. The VIC model performs well for monthly discharge simulations with better performance for hydrometric stations on the main stream of the Xiangjiang River than for tributary catchments. The simulated annual discharges are significantly correlated to the recorded annual discharges for all the eight selected target stations. The Xiangjiang River Basin may experience water shortages induced by climate change. Annual water resources of the Xiangjiang River Basin over the period from 2021 to 2050 are projected to decrease by 2.76% on average within the range from-7.81% to 7.40%. It is essential to consider the potential impact of climate change on water resources in future planning for sustainable utilization of water resources.

Measurements and Characterization for the Vehicle-to-Infrastructure Channel in Urban and Highway Scenarios at 5.92GHz

In the future smart transportation system, reliable vehicle-to-infrastructure(V2 I) communication is very important to ensure vehicle driving safety and to improve vehicle driving efficiency. In this paper, V2 I channel measurements at 5.92 GHz are conducted in typical urban and highway scenarios.The frequency and bandwidth of transmission, as well as the deployment of the RSU(roadside unit) and the OBU(on board unit), are selected by considering the recommendation proposed by 3 GPP TR 36.885. Then,based on the measured data, the key channel characteristic parameters of the V2 I channel are extracted,including path loss, root-mean-square delay spread,stationarity distance, and Doppler spread, etc. Also,the statistical characteristics of the parameters, including time-varying and Doppler characteristics, are investigated and characterized. The work in this paper helps researchers design technology and communication systems in similar scenarios.

Modelling scenarios of land use change in northern China in the next 50 years

Modelling scenarios of land use change and their impacts in typical regions are helpful to investigate the mechanism between land use and ecological systems and process the land use allocation under the ecological security. A system dynamics (SD) model with the aim to modelling scenarios of land use change and assessing ecological impact in northern China in the next 50 years is developed here. The accuracy assessment with the historic data from 1990 to 2001 indicated the SD model is robust. After the different 'what-if' scenarios controlled by GDP, population, market, and technology advancement were built, the different scenarios of land use change in northern China from 2000 to 2050 were simulated with their ecological impact assessed. The result suggested that such factors as GDP, population, market and technology have a strong relationship with land use structural change in northern China. It also indicated that such measures as strict controlling of population increase, importing some food to keep the supply-demand balance in the region, and improving agricultural technology will be the guarantee of regional sustainable development with fast economic growth and the obvious land use structural improvement at the same time.

Uncertainty of Climate Response to Natural and Anthropogenic Forcings Due to Different Land Use Scenarios

The A.M.Obukhov Institute of Atmospheric Physics,Russian Academy of Sciences （IAP RAS） climate model （CM） of intermediate complexity is extended by a spatially explicit terrestrial carbon cycle module.Numerical experiments with the IAP RAS CM are performed forced by the reconstructions of anthropogenic and natural forcings for the 16th to the 20th centuries and by combined SRES （Special Report on Emission Scenarios） A2-LUH （Land Use Harmonization） anthropogenic scenarios for the 21st century.Hereby,the impact of uncertainty in land-use scenarios on results of simulations with a coupled climate-carbon cycle model is tested.The simulations of the model realistically reproduced historical changes in carbon cycle characteristics.In the IAP RAS CM,climate warming reproduced in the 20th and 21st centuries enhanced terrestrial net primary production but terrestrial carbon uptake was suppressed due to an overcompensating increase in soil respiration.Around year 2100,the simulations the model forced by different land use scenarios diverged markedly,by about 70 Pg （C） in terms of biomass and soil carbon stock but they differed only by about 10 ppmv in terms of atmospheric carbon dioxide content.