Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change

Gross primary productivity(GPP)of vegetation is an important constituent of the terrestrial carbon sinks and is significantly influenced by drought.Understanding the impact of droughts on different types of vegetation GPP provides insight into the spatiotemporal variation of terrestrial carbon sinks,aiding efforts to mitigate the detrimental effects of climate change.In this study,we utilized the precipitation and temperature data from the Climatic Research Unit,the standardized precipitation evapotranspiration index(SPEI),the standardized precipitation index(SPI),and the simulated vegetation GPP using the eddy covariance-light use efficiency(EC-LUE)model to analyze the spatiotemporal change of GPP and its response to different drought indices in the Mongolian Plateau during 1982-2018.The main findings indicated that vegetation GPP decreased in 50.53% of the plateau,mainly in its northern and northeastern parts,while it increased in the remaining 49.47%area.Specifically,meadow steppe(78.92%)and deciduous forest(79.46%)witnessed a significant decrease in vegetation GPP,while alpine steppe(75.08%),cropland(76.27%),and sandy vegetation(87.88%)recovered well.Warming aridification areas accounted for 71.39% of the affected areas,while 28.53% of the areas underwent severe aridification,mainly located in the south and central regions.Notably,the warming aridification areas of desert steppe(92.68%)and sandy vegetation(90.24%)were significant.Climate warming was found to amplify the sensitivity of coniferous forest,deciduous forest,meadow steppe,and alpine steppe GPP to drought.Additionally,the drought sensitivity of vegetation GPP in the Mongolian Plateau gradually decreased as altitude increased.The cumulative effect of drought on vegetation GPP persisted for 3.00-8.00 months.The findings of this study will improve the understanding of how drought influences vegetation in arid and semi-arid areas.

Interrupted Time Series Analysis of Military and Civilian Regimes in Nigeria: A Statistical Evidence from Gross Domestic Product (GDP)

Governments influence the economy by changing the level and types of taxes, the extent and composition of spending, and the degree and form of borrowing. Governments directly and indirectly influence the way resources are used in the economy. Higher taxes, fees, and greater regulations can stymie businesses or entire industries and the resulting impact is reflected on the country’s economy status (strong or weak). The growth rate of GDP is often used as an indicator of the general health of the economy. In broad terms, an increase in real GDP is interpreted as a sign that the economy is doing well. So it is important to study and pay more attention to country’s GDP growth rate. In this paper, an intervention analysis approach was applied to Nigeria GDP data in order to evaluate the performances of military and civilian rules in the country. Data on Nigeria GDP were collected and subjected to interrupted (intervention) time series model. Based on the Alkaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sigma2 values, the interrupted time series model ARIMA (1, 1, 0) with exogenous variables (per capita per capita GDP, intervention, year and yearAfter) was identified as the best model amongst other competing models. It was observed that the intervention (civilian rule) was significant at the 10% level of significance in increasing the Nigeria GDP by 10B US$ on the average since 2005 till 2021 while controlling for the effects of other determinants. Also, the ARIMA (1, 1, 0) forecasts indicate that the Nigeria GDP will continue increasing during the civilian rule. As a result, changing from military rule to civilian rule in Nigeria significantly increased the GDP of the country.

Interrupted Time Series Analysis of Military and Civilian Regimes in Nigeria: A Statistical Evidence from Gross Domestic Product (GDP)

Governments influence the economy by changing the level and types of taxes, the extent and composition of spending, and the degree and form of borrowing. Governments directly and indirectly influence the way resources are used in the economy. Higher taxes, fees, and greater regulations can stymie businesses or entire industries and the resulting impact is reflected on the country’s economy status (strong or weak). The growth rate of GDP is often used as an indicator of the general health of the economy. In broad terms, an increase in real GDP is interpreted as a sign that the economy is doing well. So it is important to study and pay more attention to country’s GDP growth rate. In this paper, an intervention analysis approach was applied to Nigeria GDP data in order to evaluate the performances of military and civilian rules in the country. Data on Nigeria GDP were collected and subjected to interrupted (intervention) time series model. Based on the Alkaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sigma2 values, the interrupted time series model ARIMA (1, 1, 0) with exogenous variables (per capita per capita GDP, intervention, year and yearAfter) was identified as the best model amongst other competing models. It was observed that the intervention (civilian rule) was significant at the 10% level of significance in increasing the Nigeria GDP by 10B US$ on the average since 2005 till 2021 while controlling for the effects of other determinants. Also, the ARIMA (1, 1, 0) forecasts indicate that the Nigeria GDP will continue increasing during the civilian rule. As a result, changing from military rule to civilian rule in Nigeria significantly increased the GDP of the country.

Responses of gross primary productivity to different sizes of precipitation events in a temperate grassland ecosystem in Inner Mongolia, China

Changes in the sizes of precipitation events in the context of global climate change may have profound impacts on ecosystem productivity in arid and semiarid grasslands. However, we still have little knowledge about to what extent grassland productivity will respond to an individual precipitation event. In this study, we quantified the duration, the maximum, and the time-integrated amount of the response of daily gross primary productivity (GPP) to an individual precipitation event and their variations with different sizes of precipitation events in a typical temperate steppe in Inner Mongolia, China. Results showed that the duration of GPP-response (τR) and the maximum absolute GPP-response (GPPmax) increased linearly with the sizes of precipitation events (Pes), driving a corresponding increase in time-integrated amount of the GPP-response (GPPtotal) because variations of GPPtotal were largely explained by τR and GPPmax. The relative contributions of these two parameters to GPPtotal were strongly Pes-dependent. The GPPmax contributed more to the variations of GPPtotal when Pes was relatively small (<20 mm), whereas τR was the main driver to the variations of GPPtotal when Pes was relatively large. In addition, a threshold size of at least 5 mm of precipitation was required to induce a GPP-response for the temperate steppe in this study. Our work has important implications for the modeling community to obtain an advanced understanding of productivity-response of grassland ecosystems to altered precipitation regimes.

Responses in gross primary production of Stipa krylovii and Allium polyrhizum to a temporal rainfall in a temperate grassland of Inner Mongolia, China

In the arid and semi-arid areas of China, rainfall and drought affect the growth and photosynthetic activities of plants.Gross primary productivity(GPP) is one of the most important indices that measure the photosynthetic ability of plants.This paper focused on the GPP of two representative grassland species(Stipa krylovii Roshev.and Allium polyrhizum Turcz.ex Regel) to demonstrate the effect of a temporal rainfall on the two species.Our research was conducted in a temperate grassland in New Barag Right Banner, Hulun Buir City, Inner Mongolia Autonomous Region of China, in a dry year 2015.We measured net ecosystem productivity(NEP) and ecosystem respiration flux(ER) using a transparent chamber system and monitored the photosynthetically active radiation(PAR), air and soil temperature and humidity simultaneously.Based on the measured values of NEP and ER, we calculated the GPP of the two species before and after the rainfall.The saturated GPP per aboveground biomass(GPPAGB) of A.polyrhizum remarkably increased from 0.033(±0.018) to 0.185(±0.055) μmol CO2/(gdw·s) by 5.6-fold and that of S.krylovii decreased from 0.068(±0.021) to 0.034(±0.011) μmol CO2/(gdw·s) by 0.5-fold on the 1st and 2nd d after a 9.1 mm rainfall event compared to the values before the rainfall at low temperatures below 35℃.However, on the 1st and 2nd d after the rainfall, both of the saturated GPPAGB values of S.krylovii and A.polyrhizum were significantly lower at high temperatures above 35℃(0.018(±0.007) and 0.110(±0.061) μmol CO2/(gdw·s), respectively) than at low temperatures below 35℃(0.034(±0.011) and 0.185(±0.055) μmol CO2/(gdw·s), respectively).The results showed that the GPP responses to the temporal rainfall differed between S.krylovii and A.polyrhizum and strongly negative influenced by temperature.The temporal rainfall seems to be more effective on the GPP of A.polyrhizum than S.krylovii.These differences might be related to the different physiological and structural features, the coexistence of the species and their species-specific survival strategies.

Response of Growing Season Gross Primary Production to El Nino in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on Bayesian Model Averaging

Gross primary production(GPP) plays a crucial part in the carbon cycle of terrestrial ecosystems.A set of validated monthly GPP data from 1957 to 2010 in 0.5°× 0.5° grids of China was weighted from the Multi-scale Terrestrial Model Intercomparison Project using Bayesian model averaging(BMA).The spatial anomalies of detrended BMA GPP during the growing seasons of typical El Nino years indicated that GPP response to El Nino varies with Pacific Decadal Oscillation(PDO) phases: when the PDO was in the cool phase,it was likely that GPP was greater in northern China(32°–38°N,111°–122°E) and less in the Yangtze River valley(28°–32°N,111°–122°E);in contrast,when PDO was in the warm phase,the GPP anomalies were usually reversed in these two regions.The consistent spatiotemporal pattern and high partial correlation revealed that rainfall dominated this phenomenon.The previously published findings on how El Nino during different phases of PDO affecting rainfall in eastern China make the statistical relationship between GPP and El Nino in this study theoretically credible.This paper not only introduces an effective way to use BMA in grids that have mixed plant function types,but also makes it possible to evaluate the carbon cycle in eastern China based on the prediction of El Nino and PDO.

Earthquake loss estimation by using Gross Domestic Product and population data

In the traditional method of earthquake loss estimation, all the social wealthes are classified according to their structural type and occupational use. Inventory data is collected and the total loss is estimated from each facility class separately. For many regions of the world, however, the vast amount of data required by this method is difficult or impossible to obtain. The traditional method is also unable to estimate quickly the loss from an unexpected disaster earthquake. It is difficult to give the necessary risk information to help the government to rescue and relief the earthquake disaster. This paper proposes a simple estimation method of earthquake loss based on macroscopic economical index of Gross Domestic Product (GDP) and population distribution data. A preliminary nonlinear relation among hazard loss, seismic intensity and social wealth was developed by means of some earthquake disaster records during 1980～1995. This method was applied to analyze several assumed earthquakes. The preliminary analysis results show that the new method is effective and reasonable for quick assessment of earthquake loss.

Sensitivity of Near Real-time MODIS Gross Primary Productivity in Terrestrial Forests Based on Eddy Covariance Measurements

As an important product of Moderate Resolution Imaging Spectroradiometer(MODIS), MOD17A2 provides dramatic improvements in our ability to accurately and continuously monitor global terrestrial primary production, which is also significant in effort to advance scientific research and eco-environmental management. Over the past decades, forests have moderated climate change by sequestrating about one-quarter of the carbon emitted by human activities through fossil fuels burning and land use/land cover change. Thus, the carbon uptake by forests reduces the rate at which carbon accumulates in the atmosphere. However, the sensitivity of near real-time MODIS gross primary productivity(GPP) product is directly constrained by uncertainties in the modeling process, especially in complicated forest ecosystems. Although there have been plenty of studies to verify MODIS GPP with ground-based measurements using the eddy covariance(EC) technique, few have comprehensively validated the performance of MODIS estimates(Collection 5) across diverse forest types. Therefore, the present study examined the degree of correspondence between MODIS-derived GPP and EC-measured GPP at seasonal and interannual time scales for the main forest ecosystems, including evergreen broadleaf forest(EBF), evergreen needleleaf forest(ENF), deciduous broadleaf forest(DBF), and mixed forest(MF) relying on 16 flux towers with a total of 68 site-year datasets. Overall, site-specific evaluation of multi-year mean annual GPP estimates indicates that the current MOD17A2 product works highly effectively for MF and DBF, moderately effectively for ENF, and ineffectively for EBF. Except for tropical forest, MODIS estimates could capture the broad trends of GPP at 8-day time scale for all other sites surveyed. On the annual time scale, the best performance was observed in MF, followed by ENF, DBF, and EBF. Trend analyses also revealed the poor performance of MODIS GPP product in EBF and DBF. Thus, improvements in the sensitivity of MOD17A2 to forest productivity require continued efforts.

Modeling Gross Primary Production by Integrating Satellite Data and Coordinated Flux Measurements in Arid and Semi-Arid China

Assessing large-scale patterns of gross primary production (GPP) in arid and semi-arid (ASA) areas is important for both scientific and practical purposes.Remote sensing-based models,which integrate satellite data with input from ground-based meteorological measurements and vegetation characteristics,improve spatially extended estimates of vegetation productivity with high accuracy.In this study,the authors simulated GPP in ASA areas by integrating moderate resolution imaging spectral radiometer (MODIS) data with eddy covariance and meteorological measurements at the flux tower sites using the Vegetation Photosynthesis Model (VPM),which is a remote sensing-based model for analyzing the spatial pattern of GPP in different land cover types.The field data were collected by coordinating observations at nine stations in 2008.The results indicate that in the region during the growing season GPP was highest in cropland sites,second highest in woodland sites,and lowest in grassland sites.VPM captured the temporal and spatial characteristics of GPP for different land covers in ASA areas.Further,Enhanced Vegetation Index (EVI) had a strong liner relationship with GPP in densely vegetated areas,while the Normalized Difference Vegetation Index (NDVI) had a strong liner relationship with GPP over less dense vegetation.This study demonstrates the potential of satellite-driven models for scaling-up GPP,which is a key component for studying the carbon cycle at regional and global scales.

Ocean-atmosphere Teleconnections Play a Key Role in the Interannual Variability of Seasonal Gross Primary Production in China

Since the 1950s,the terrestrial carbon uptake has been characterized by interannual variations,which are mainly determined by interannual variations in gross primary production(GPP).Using an ensemble of seven-member TRENDY(Trends in Net Land-Atmosphere Carbon Exchanges)simulations during 1951-2010,the relationships of the interannual variability of seasonal GPP in China with the sea surface temperature(SST)and atmospheric circulations were investigated.The GPP signals that mostly relate to the climate forcing in terms of Residual Principal Component analysis(hereafter,R-PC)were identified by separating out the significant impact from the linear trend and the GPP memory.Results showed that the seasonal GPP over China associated with the first R-PC1(the second R-PC2)during spring to autumn show a monopole(dipole or tripole)spatial structure,with a clear seasonal evolution for their maximum centers from springtime to summertime.The dominant two GPP R-PC are significantly related to Sea Surface Temperature(SST)variability in the eastern tropical Pacific Ocean and the North Pacific Ocean during spring to autumn,implying influences from the El Niño-Southern Oscillation(ENSO)and the Pacific Decadal Oscillation(PDO).The identified SST and circulation factors explain 13%,23%and 19%of the total variance for seasonal GPP in spring,summer and autumn,respectively.A clearer understanding of the relationships of China’s GPP with ocean-atmosphere teleconnections over the Pacific and Atlantic Ocean should provide scientific support for achieving carbon neutrality targets.

Empirical Analysis of Gross Domestic Product and Coal Import Based on VAR Model

The speed of China’s economic development is gradually accelerating, and the demand for energy is also constantly increasing, especially the demand for coal. In order to reveal whether the coal imports have an impact on China’s economic development, this paper constructs the VAR(6) model by selecting the quarterly data of coal imports (CIV) and gross domestic product (GDP) from 2002 to 2017, performing ADF (Augmented Dickey-Fuller) stationarity test and Johansen cointegration test. It shows that there is a long-term stable equilibrium relationship between coal imports and GDP. Then the impulse response function is used to obtain the relationship between coal imports and GDP. It is found that the impact of coal imports on GDP is greater than the impact of GDP on coal imports.

An Empirical Assessment of the Impact of Nigerian all Share Index, Market Capitalization, and Number of Equities on Gross Domestic Product

A stock exchange is an exchange where stock brokers and traders can buy and sell shares of stock, bonds, and other securities. All listings are included in the Nigerian Stock Exchange All Shares index. In terms of market capitalization, the Nigerian Stock Exchange is the third largest stock exchange in Africa. Objectives: The paper assesses the impact of Nigerian Stock Market (all share index, market capitalization, and number of equities) on Gross domestic product (Economic Growth). Materials and Methods: Regression analysis and ordinary least square technique were employed. Result and Discussion: The series was stationary at 1%, 5%, and 10% α level;the residuals were normally distributed but not serially correlated at 5% α level. All Share Index, Market Capitalization and Total Number of listed Equities have a joint and individual significant effect on Economic Growth (Gross Domestic Product) with Total Number of listed Equities having a negative (opposite) linear relationship with the Gross Domestic Product. The Durbin-Watson statistics (R2 = 0.9910 = 1.3686) suggest that the model is not spurious and it is devoid of positive and negative autocorrelation (DW = 1.3686 > dl = 1.07 and DW = 1.5033 ?-?du = 2.17). Therefore, it can produce meaningful result when used for forecasting a positive relationship between gross domestic product, all share index and market capitalization with a 99.1% R-square value. Significant Positive connection between all share index, market capitalization, the number of equities and gross domestic product suggests that government policies and bills aimed towards rapid development of the capital market should be initiated.

COVID-19 mortality and gross domestic product loss:A wake-up call for government leaders

Government leaders have struggled to reduce the infection and deaths due to coronavirus disease 2019(COVID-19)as well as to keep the economy and businesses open.There is a large variation of mortality and damage to economy among countries.One possible cause leading to the large variation is the manner in which countries have delt with COVID-19.Some countries or regions such as China,New Zealand,and Taiwan,acted quickly and aggressively by implementing border closures,lockdown,school closures,mass testing,etc.On the other hand,many European countries,United States,and Brazil delayed their decisions to implement these restrictions and measures.No study has assessed the correlation between gross domestic product(GDP)and COVID-19 mortality.In the present study,there was a negative correlation between GDP and COVID-19 mortality suggesting that countries that failed to control the virus(larger COVID-19 mortality)would see a larger decline in GDP.Governmental leaders should act fast and aggressively when making decisions because data shows that countries who have run after two hares have caught neither.Furthermore,citizens of each country need to do their own part by following guidelines and practicing social distancing and mask wearing,which are considered the most effective,easiest,and cheapest measures that can be taken,so that repeated lockdowns can be avoided.

Gross Final Product and GFP-Driven Economic Growth

After analyzing the defects of gross domestic product(GDP) as a statistical indicator, this paper identifies and defines the concept of gross final product(GFP), emphasizing the attribute of GFP as the final product of a natural process and the actuator of the entire economic system. The paper also investigates the roles of foreign trade, production investment, public goods and private goods in economic growth under the GFP perspective, and explores the possibility for the GFP analytical framework to explain the economic growth process.

黄河流域植被总初级生产力对持续性干旱水分亏缺的响应

利用植被总初级生产力和高精度分层土壤含水量数据分析了2001—2020年黄河流域生长季总初级生产力时空分布和变化特征,量化了流域不同深度土壤湿度之间的相关关系,探究了不同植被类型在生长季的总初级生产力对土壤连续水分亏缺事件的响应关系。结果表明:黄河流域总初级生产力空间上呈东高西低、南高北低的分布特征,生长季耕地累积总初级生产力最大、林地次之、草地最小,研究期内流域总初级生产力整体呈上升趋势,耕地相对增速最大、草地次之、林地最小;流域土壤湿度在空间和时间上均有显著差异,表层土壤含水量波动对深层土壤含水量的影响随深度增加而减小,林地和耕地表层与深层土壤含水量的相关性远高于草地;流域草地和耕地总初级生产力对土壤水分的深度敏感区间为0~40 cm,林地的深度敏感区间为0~100 cm,在深度敏感区间内,随着土壤连续水分亏缺天数的增加,草地和耕地总初级生产力依次经历短暂平稳、加速下降、降速趋缓的变化过程,而林地总初级生产力则表现出短暂下挫、长时间平稳、加速下降的变化特征。

我国生态产品总值统计核算问题研究

文章在分析GEP概念内涵、分类与核算方法相关研究的基础上,充分借鉴国际统计核算的经验,结合我国GEP统计核算实践,从通用性和可操作性出发,建立了一套与自然资源部门土地利用现状分类相匹配、符合我国实际的GEP核算分类标准,重新划分了森林、草地、湿地、农田、城市、荒漠、海洋等生态系统的分类范围。同时,基于GEP概念、特征、逻辑准则,提出了我国生态产品清单筛选的五个基本原则,讨论了现有GEP实物量和价值量核算方法存在的不足,指出了核算方法选取时需注意把握的问题及未来改进方向。

中国温带草地物候对气候变化的响应及其对总初级生产力的贡献

气候变暖引起的植物物候变化影响了陆地生态系统功能和碳循环。目前研究着重关注温带和热带森林物候变化趋势、驱动因素,关于干旱半干旱地区草地物候变化及其对生态系统总初级生产力(gross primary productivity, GPP)影响仍知之甚少。因此,开展草地植物物候与生产力之间的关系研究对预测草地生态系统响应未来气候变化和区域碳循环至关重要。基于1982—2015年气象资料和GIMMS NDVI3g数据,分析了中国温带草原植被返青期(start of the growing season, SGS)和枯黄期(end of the growing season, EGS)变化及其对气候的响应,并借助一阶差分法量化物候对GPP动态变化的贡献。结果表明:(1)季前1—2个月的夜间温度增温会显著提前SGS,而当月至季前2个月的白天温度对SGS有着微弱的促进作用;季前3个月的累积降水对SGS提前作用最为强烈,累积太阳辐射在各个时期对SGS影响相对较弱。(2)不同季前时间尺度昼夜温度对草地EGS均表现出相反的作用,短期累积降水对EGS起到显著延迟的区域范围最大,太阳辐射随着季前时间的增加对草地枯黄期的延迟作用逐渐转变为提前作用。(3)EGS对草地GPP年际变化趋势的相对贡献率强于返青期。研究结果有助于深化陆地生态系统与气候变化、碳循环之间相互作用的认识,为草地适应未来气候变化和生态建设提供科学依据。

散射辐射对鄱阳湖平原典型稻田总初级生产力的影响

探明散射辐射变化对稻田生态系统总初级生产力(Gross primary productivity,GPP)的影响可为稻田碳汇能力评估和产量估算提供参考。本研究以鄱阳湖平原双季稻田为研究对象,利用涡度相关(Eddy covariance,EC)系统对稻田CO_(2)通量进行了连续两年(2017—2018年)的定位观测,选取水稻生育中期数据,按照散射辐射比例(Diffuse fraction,DF)进行分段,分析了散射辐射对稻田GPP的影响,探明并量化了不同DF条件下散射辐射与其他气象因素对稻田GPP的影响机制。结果表明:不同类型辐射对GPP的影响存在差异,GPP随着直接光合有效辐射(Direct photosynthetically active radiation,PAR_(dir))的增加先迅速增长,随后达到饱和;在不同DF条件下,早晚稻GPP随着散射光合有效辐射(Diffuse photosynthetically active radiation,PAR_(dif))的变化趋势存在差异,当DF为0.1~0.4时,早稻GPP随PAR_(dif)无明显变化趋势,晚稻GPP随PARdif呈上升趋势(决定系数R^(2)为0.23),当DF为0.4~0.7时,早晚稻GPP随PAR_(dif)呈下降趋势(R^(2)为0.38、0.02),当DF为0.7~1.0时,早晚稻GPP随PAR_(dif)呈明显上升趋势(R2为0.32、0.89),可见PAR_(dif)是影响水稻GPP的重要因素。早晚稻GPP与DF呈二次曲线关系(R^(2)为0.45、0.67),早晚稻光能利用效率(Light use efficiency,LUE)则与DF呈显著的线性正相关关系(R^(2)为0.68、0.82),早晚稻最优DF为0.48和0.40。DF变化同时引起气温(Air temperature,T_(a))和饱和水汽压差(Water vapor pressure deficit,VPD)的变化,进而对水稻GPP产生协同影响。气象因素与水稻GPP的通径分析结果表明:不同DF条件下,气象因素对水稻GPP的影响存在明显差异。总体而言,T_(a)和VPD升高分别对水稻GPP起促进和抑制作用,当DF为0.1~0.4、0.4~0.7和0.7~1.0时,影响早稻GPP的主要气象因素为T_(a)、PAR_(dir)和PAR_(dif),影响晚稻GPP的主要气象因素则为PAR_(dif)、PAR_(dir)和PAR_(dif)。

近十年青海湖水生态系统生产总值变化及其影响因素

受气候变化和人类活动影响,湖泊水生态系统及其服务功能发生改变,研究湖泊水生态系统生产总值动态变化及其影响因素,对于维护湖泊健康生命、实现湖泊功能永续利用具有重要意义。选择位于气候敏感区和生态脆弱区的青海湖作为研究对象,根据青海湖水生态系统特征及当前保护状况,筛选调节服务及文化服务2类8个评估指标,构建湖泊水生态系统生产总值指标体系,核算2010—2020年青海湖水生态系统生产总值,分析其变化趋势及主要影响因素。结果表明,2010—2020年青海湖水生态系统生产总值总体呈波动上升趋势,变化范围为6903.47亿~7848.55亿元;调节服务是青海湖水生态系统主要的服务类型,占比高达91%。近十年,气候调节和水质净化价值有所减少,其他服务功能价值均呈增加趋势。水源涵养价值增加最多,增长760.70亿元;气候调节价值下降最多,减少658.59亿元。偏最小二乘回归分析表明,水温、水位是影响青海湖水生态系统生产总值的主要因素。气候变化影响下,水温升高引起初级生产力增加及鱼类数量增长,同时近年来水体矿化度下降有利于水生生物生长,提高了固碳释氧和物种保育价值。水位与水面面积增加引起水源涵养、洪水调蓄价值增长;蒸发量减少导致气候调节价值下降。人类合理开发利用与保护作用下,物种保育、休闲旅游和科研教育价值均得到了显著提升。本研究量化了青海湖水生态系统对人类的贡献,为变化环境下退化水生态系统修复、生态保护措施效果定量评价提供了科学依据。建议定期核算青海湖水生态系统生产总值,跟踪评估气候变化与人类活动共同影响下青海湖水生态系统变化,对维持青藏高原东北部生态安全具有重要作用。

晋中市生态系统生产总值核算与空间利用研究

基于功能价格法对生态系统生产总值(GEP)核算框架进行构建,分供给服务、调节服务、文化服务三大类别共15个核算科目对2019年山西省晋中市GEP进行核算,并根据不同价值量的分布进行空间分析与利用评价。结果表明,2019年晋中市GEP为1 719.32亿元,供给服务、调节服务和文化服务价值分别占10.3%、71.6%、18.1%;不同生态系统类型所提供的GEP由大到小依次为森林、湿地、农田、草地;各县(市、区)GEP由大到小依次为和顺县、左权县、寿阳县、昔阳县、榆社县、平遥县、榆次区、灵石县、太谷区、祁县、介休市,整体呈东高西低的分布态势;最后依据GEP不同服务类别的空间分布及其重要程度对研究区开展了生态产品主导服务分区和空间利用评价。