京津冀地区碳排放时空格局变化及其驱动因子

人类对陆地生态系统的改变是碳排放增加的主要原因。在“双碳”目标背景下探索土地利用变化与碳排放的动态关系,有助于区域土地低碳可持续利用。研究基于土地利用转移视角,采用重心-标准差椭圆方法揭示了京津冀地区土地利用碳排放时空格局演化特征,评估了碳排放与生态环境、社会经济发展的协调程度,并借助改进的Kaya模型和LMDI分解模型定量分析了土地利用变化对碳排放的影响程度。结果表明:(1)建设用地的转入是土地利用碳排放增加的主要来源,引起碳排放量增加15844.36万t;耕地、草地向林地、水域的转变促进了地区固碳能力的提升。(2)土地利用碳排放空间分布格局呈现出东北-西南方向向中心进一步聚集的趋势,并且东-西向聚集趋势大于南-北向。(3)京津冀地区整体碳排放与生态环境的协调性呈向好趋势发展,但大部分地区碳排放与社会经济发展出现失衡现象,地区间碳生产力差异逐渐增大。(4)经济水平是促进碳排放增加的最显著因素,单位GDP用地强度是抑制碳排放增加的最主要因素。分析结果表明,严格控制建设用地的无序扩张是促进低碳土地利用的基础,低碳经济发展是促进地区减碳的重要途径。

Assessing the effects of vegetation and precipitation on soil erosion in the Three-River Headwaters Region of the Qinghai-Tibet Plateau,China

Soil erosion in the Three-River Headwaters Region(TRHR)of the Qinghai-Tibet Plateau in China has a significant impact on local economic development and ecological environment.Vegetation and precipitation are considered to be the main factors for the variation in soil erosion.However,it is a big challenge to analyze the impacts of precipitation and vegetation respectively as well as their combined effects on soil erosion from the pixel scale.To assess the influences of vegetation and precipitation on the variation of soil erosion from 2005 to 2015,we employed the Revised Universal Soil Loss Equation(RUSLE)model to evaluate soil erosion in the TRHR,and then developed a method using the Logarithmic Mean Divisia Index model(LMDI)which can exponentially decompose the influencing factors,to calculate the contribution values of the vegetation cover factor(C factor)and the rainfall erosivity factor(R factor)to the variation of soil erosion from the pixel scale.In general,soil erosion in the TRHR was alleviated from 2005 to 2015,of which about 54.95%of the area where soil erosion decreased was caused by the combined effects of the C factor and the R factor,and 41.31%was caused by the change in the R factor.There were relatively few areas with increased soil erosion modulus,of which 64.10%of the area where soil erosion increased was caused by the change in the C factor,and 23.88%was caused by the combined effects of the C factor and the R factor.Therefore,the combined effects of the C factor and the R factor were regarded as the main driving force for the decrease of soil erosion,while the C factor was the dominant factor for the increase of soil erosion.The area with decreased soil erosion caused by the C factor(12.10×10^3 km^2)was larger than the area with increased soil erosion caused by the C factor(8.30×10^3 km^2),which indicated that vegetation had a positive effect on soil erosion.This study generally put forward a new method for quantitative assessment of the impacts of the influencing factors on soil erosion,and also provided a scientific basis for the regional control of soil erosion.

Speech Enhancement Algorithm Based on MMSE Short Time Spectral Amplitude in Whispered Speech

An improved method based on minimum mean square error-short time spectral amplitude （MMSE-STSA） is proposed to cancel background noise in whispered speech. Using the acoustic character of whispered speech, the algorithm can track the change of non-stationary background noise effectively. Compared with original MMSE-STSA algorithm and method in selectable mode Vo-coder （SMV）, the improved algorithm can further suppress the residual noise for low signal-to-noise radio （SNR） and avoid the excessive suppression. Simulations show that under the non-stationary noisy environment, the proposed algorithm can not only get a better performance in enhancement, but also reduce the speech distortion.

Spatiotemporal changes and influencing factors of the intensity of agricultural water footprint in Xinjiang, China

Xinjiang Uygur Autonomous Region,the largest agricultural high-efficiency water-saving arid area in China,was adopted to explore the coupling relationship between agricultural water consumption and economic benefits,which is of great significance to guiding the efficient utilization and sustainable development of agricultural water resources.This study utilizes an indicator,termed the Agricultural Water Footprint Intensity(short as AWFI,which means the amount of water resource consumed per unit of agricultural GDP),to study the economic benefits of agricultural water in Xinjiang from 1991-2018.In addition,the Theil index,a measure of the imbalance between individuals or regions,was used to study the evolution in the spatial differences in water efficiency,and the Logarithmic Mean Divisia Index(LMDI)method was applied to quantify the factors driving the AWFI.The results showed that AWFI in Xinjiang has experienced three stages:obvious decline,stable and slow decline,which decreased from 16114 m^(3)/10^(4) CNY to 2100 m^(3)/10^(4) CNY,decreasing by 86.97%.The Theil index indicated that the spatial evolution of 14 prefectures(cities)resembled an inverted N-shaped Kuznets curve over time.Among the influencing factors,the contributions of water-saving technology and planting structure to the change in the AWFI in Xinjiang,China from 1991 to 2018 were 154.03%and−37.98%,respectively.The total contribution to AWFI of the total population,urbanization rate,and production scale was−16.06%.This study concluded that further improvements in the economic benefits of agricultural water consumption can be obtained by continuing to promote more efficient or“water-conservation”irrigation technologies(engineering aspects),adjusting the planting structure(policy guidance aspects),and intensive management of cultivated land(management aspects).

A distribution-free test of independence based on a modified mean variance index

Cui and Zhong(2019),(Computational Statistics&Data Analysis,139,117–133)proposed a test based on the mean variance(MV)index to test independence between a categorical random variable Y with R categories and a continuous random variable X.They ingeniously proved the asymptotic normality of the MV test statistic when R diverges to infinity,which brings many merits to the MV test,including making it more convenient for independence testing when R is large.This paper considers a new test called the integral Pearson chi-square(IPC)test,whose test statistic can be viewed as a modified MV test statistic.A central limit theorem of the martin-gale difference is used to show that the asymptotic null distribution of the standardized IPC test statistic when R is diverging is also a normal distribution,rendering the IPC test sharing many merits with the MV test.As an application of such a theoretical finding,the IPC test is extended to test independence between continuous random variables.The finite sample performance of the proposed test is assessed by Monte Carlo simulations,and a real data example is presented for illustration.

Spatiotemporal evolution and driving factors for GHG emissions of aluminum industry in China

China’s aluminum(Al)production has released a huge amount of greenhouse gas(GHG)emissions.As one of the biggest country of primary Al production,China must mitigate its overall GHG emission from its Al industry so that the national carbon neutrality target can be achieved.Under such a background,the study described in this paper conducts a dynamic material flow analysis to reveal the spatiotemporal evolution features of Al flows in China from 2000 to 2020.Decomposition analysis is also performed to uncover the driving factors of GHG emission generated from the Al industry.The major findings include the fact that China’s primary Al production center has transferred to the western region;the primary Al smelting and carbon anode consumption are the most carbonintensive processes in the Al life cycle;the accumulative GHG emission from electricity accounts for 78.14% of the total GHG emission generated from the Al industry;China’s current Al recycling ratio is low although the corresponding GHG emission can be reduced by 93.73% if all the primary Al can be replaced by secondary Al;and the total GHG emission can be reduced by 88.58% if major primary Al manufacturing firms are transferred from Inner Mongolia to Yunnan.Based upon these findings and considering regional disparity,several policy implications are proposed,including promotion of secondary Al production,support of clean electricity penetration,and relocation of the Al industry.

Decomposition and decoupling analysis of carbon dioxide emissions in African countries during 1984–2014

The potential for mitigating climate change is growing worldwide,with an increasing emphasis on reducing CO_(2)emissions and minimising the impact on the environment.African continent is faced with the unique challenge of climate change whilst coping with extreme poverty,explosive population growth and economic difficulties.CO_(2)emission patterns in Africa are analysed in this study to understand primary CO_(2)sources and underlying driving forces further.Data are examined using gravity model,logarithmic mean divisia index and Tapio's decoupling indicator of CO_(2)emissions from economic development in 20 selected African countries during 1984-2014.Results reveal that CO_(2)emissions increased by 2.11%(453.73 million ton)over the research period.Gravity centre for African CO_(2)emissions had shifted towards the northeast direction.Population and economic growth were primary driving forces of CO_(2)emissions.Industrial structure and emission efficiency effects partially offset the growth of CO_(2)emissions.The economic growth effect was an offset factor in central African countries and Zimbabwe due to political instability and economic mismanagement.Industrial structure and emission efficiency were insufficient to decouple economic development from CO_(2)emissions and relieve the pressure of population explosion on CO_(2)emissions in Africa.Thus,future efforts in reducing CO_(2)emissions should focus on scaleup energy-efficient technologies,renewable energy update,emission pricing and long-term green development towards sustainable development goals by 2030.

Impacts of household living consumption on energy use and carbon emissions in China based on the input–output model

China must urgently accelerate its decrease of energy use,optimize its energy structure,reduce CO2 emissions,and promote the early realization of an ecological civilization.Simultaneously,meeting the growing consumer demand is one of the reasons for the increase in energy use.This study investigates the impacts of household consumption on energy use and CO2 emissions from the perspective of the lifestyle of Chinese residents.On the basis of the input–output model of 30 provinces,we analyze the current situation of energy use and CO2 emissions in different regions(spatial scale)with economic development and income improvement(time scale),investigate the pulling effect of household consumption in different provinces on industrial sectors,examine the influencing factors of indirect CO2 emissions from food,clothing,housing,and transportation in key regions,and explore the policy implications of the transition to a low-carbon lifestyle in different provinces.Results show that the fuel structure of Chinese residents should be optimized further.Total household energy consumption and total CO2 emissions considerably increased.In 2012,total household energy consumption accounted for nearly 30%of total energy consumption,while indirect CO2 emissions accounted for 66.3%of total household emissions.With regard to the structures of indirect household energy consumption,the housing sector accounted for the largest proportion,reaching 23.4%in indirect energy consumption in 2012.The pulling effect of the housing sector on industrial sectors was also evident.The decomposition analysis showed that the rapid increase in indirect household CO2 emissions was primarily due to the increase in per capita living expenditure.The consumption structures in different provinces produced various impacts,and the energy intensity effect was identified as an important factor for reducing indirect household CO2 emissions.

广东省煤炭消费的动态演变及其驱动机制

Guangdong Province,as one of China’s fast-developing regions,an important manufacturing base,and one of the national first round low-carbon pilots,still faces many challenges in controlling its total energy consumption.Coal dominates Guangdong’s energy consumption and remains the major source of CO_(2).Previous research on factors influencing energy consumption has lacked a systematic analysis both from supply side(factors related to scale,structure,and technologies)and demand side(investment,consumption,and trade).This paper develops the logarithmic mean Divisia index(LMDI)method that focuses on the supply side and the structural decomposition analysis(SDA)method that focuses on the demand side to systematically identify the key factors driving coal consumption in Guangdong.Results are as follows:(1)Supply side analysis indicates that economic growth has always been the most important factor driving coal consumption growth,while energy intensity is the most important constraining factor.Industrial structure and energy structure have different impacts on coal consumption control during different development phases.(2)Demand side analysis indicates that coal is consumed mainly for international exports,inter-provincial exports,fixed capital formation,and urban household.(3)Industries with the fastest coal consumption growth driven by final demand have experienced significant shifts.Increments in industrial sectors were mainly driven by inter-provincial exports and urban household consumption in recent years.(4)Research on energy consumption in subnational regions under China’s new development pattern of“dual circulation”should not only focus on exports in the context of economic globalization but also pay more attention to inter-provincial exports on the background of strengthened interregional connections.

Trends and driving forces of low-carbon energy technology innovation in China’s industrial sectors from 1998 to 2017: from a regional perspective

Low-carbon energy technology(LC)innovation contributes to both environmental protection and economic development.Using the panel data of 30 provinces/autonomous regions/municipalities in China from 1998 to 2017,this paper constructs a two-layer logarithmic mean Divisia index(LMDI)model to uncover the factors influencing the variation of the innovation of LC in China’s industrial sectors,including the alternative energy production technology(AEPT)and the energy conversation technology(ECT).The results show that China’s industrial LC patent applications rapidly increased after 2005 and AEPT patent applications outweighed ECT patent applications all the time with a gradually narrowing gap.Low-carbon degree played the dominant role in promoting the increase in China’s industrial LC patent applications,followed by the economic scale,R&D(research and development)efficiency,and R&D share.Economic structure contributed to the increases in LC patent applications in the central and the western regions,while led to the decreases in the eastern region,the north-eastern region,and Chinese mainland.Low-carbon degree and economic scale were two main contributors to the growths of both industrial AEPT patent applications and ECT patent applications in Chinese mainland and the four regions.Several policy recommendations are made to further promote industrial innovation in China.

Decomposition of influencing factors and its spatial-temporal characteristics of vegetable production:A case study of China

China is the largest producer and consumer of vegetables,its vegetable industry is playing an important role in the domestic agricultural sector and global vegetable export market.It is important to promote the long-term sustainable development of Chinese vegetable industry for food security and quality of people’s lives.To find out the intrinsic way to promote the development of Chinese vegetable industry,this paper analyzed the influencing factors of Chinese vegetable production by utilizing the LMDI method and demonstrated the spatial-temporal characteristics of vegetable production through application of the Arc-GIS spatial autocorrelation analysis method.The results showed that the influencing factors of vegetable production were the cultivated land area,multiple cropping index,vegetable planting proportion and vegetable yield per hectare in China.The major driving factor had changed from vegetable planting proportion to vegetable yield per hectare.The influencing degrees of factors on vegetable production are different in different regions,regionalization is therefore a major feature of Chinese vegetable production.The government should take production technology,regionalization-driven effect,and marketing integration into consideration to promote the development of Chinese vegetable industry.

Mean Index for Non-periodic Orbits in Hamiltonian Systems

In this paper,we define mean index for non-periodic orbits in Hamiltonian systems and study its properties.In general,the mean index is an interval in R which is uniformly continuous on the systems.We show that the index interval is a point for a quasi-periodic orbit.The mean index can be considered as a generalization of rotation number defined by Johnson and Moser in the study of almost periodic Schr¨odinger operators.Motivated by their works,we study the relation of Fredholm property of the linear operator and the mean index at the end of the paper.

Drivers of the development of global climate-change-mitigation technology:a patent-based decomposition analysis

The development of the climate-change-mitigation technology has received widespread attention from both academic and policy studies.Nevertheless,very few studies have explained how and why economies contribute differently to global development.This paper decomposed the development of the global climate-change-mitigation technology,proxied by patent-based indicators,from 1996 to 2015 into several predefined factors.The results show that the worldwide surge of climate-change-mitigation-technology patents from 1996 to 2011 is driven by increased concentration on green invention,improved research intensity,and enlarged economic scale,while the falling of patent counts from 2011 to 2015 is predominantly due to less concentration on green invention.Among different climate-change-mitigation technologies,the type-specific development is attributed to different dominant factors,and the resulting priority change can reflect the shift of both global research and development(R&D)resource and market demand.Regarding regional contributions,the resulting economy-specific contributions to each driving factor can be used to design the policies to promote the development of the global climate-change-mitigation technology.

Ensemble Prediction of Monsoon Index with a Genetic Neural Network Model

After the consideration of the nonlinear nature changes of monsoon index,and the subjective determination of network structure in traditional artificial neural network prediction modeling,monthly and seasonal monsoon intensity index prediction is studied in this paper by using nonlinear genetic neural network ensemble prediction（GNNEP）modeling.It differs from traditional prediction modeling in the following aspects： （1）Input factors of the GNNEP model of monsoon index were selected from a large quantity of preceding period high correlation factors,such as monthly sea temperature fields,monthly 500-hPa air temperature fields,monthly 200-hPa geopotential height fields,etc.,and they were also highly information-condensed and system dimensionality-reduced by using the empirical orthogonal function（EOF）method,which effectively condensed the useful information of predictors and therefore controlled the size of network structure of the GNNEP model.（2）In the input design of the GNNEP model,a mean generating function（MGF）series of predictand（monsoon index）was added as an input factor;the contrast analysis of results of predic- tion experiments by a physical variable predictor-predictand MGF GNNEP model and a physical variable predictor GNNEP model shows that the incorporation of the periodical variation of predictand（monsoon index）is very effective in improving the prediction of monsoon index.（3）Different from the traditional neural network modeling,the GNNEP modeling is able to objectively determine the network structure of the GNNNEP model,and the model constructed has a better generalization capability.In the case of identical predictors,prediction modeling samples,and independent prediction samples,the prediction accuracy of our GNNEP model combined with the system dimensionality reduction technique of predictors is clearly higher than that of the traditional stepwise regression model using the traditional treatment technique of predictors,suggesting that the GNNEP model opens up a vast range of possibilities for operational weather prediction.