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气候变化是21世纪人类面临的重大挑战之一,并对自然系统和社会经济系统造成了各种负面影响。对气候变化的影响进行经济评估是气候变化研究中的重要问题。而可计算一般均衡框架下的综合评估模型（CGE_IAMs）是评估气候变化经济影响的有效手段之一,文中对气候变化影响经济评估的主要CGE_IAMs进行了文献调研,并对这些模型进行了比较分析。研究表明不同模型在温室气体排放、气候参数的处理方式以及气候影响的引入机制等方面有着较大区别,因而各模型对气候变化影响的经济评估结果也有一定的差异。此外,当前CGE_IAMs在评估气候变化经济影响时存在支撑数据未及时更新、方法不细致以及评估不全面等问题。未来该领域的相关研究应该更加关注于模型与支撑数据的精细化和开源化,此外还应加强CGE_IAMs中经济模块与复杂气候模式的耦合。 相似文献
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鉴于气候变化影响粮食安全问题的特殊性和复杂性,本文试图从自然科学和社会科学的交叉研究入手,提出一种新的研究的思路和方法,即:运用计量经济学模型对气候变化数据进行统计分析,使用计量经济学方法来评估气候这一外部驱动因素引发的社会经济系统变化与观测到的气候变化引发的社会经济系统变化之间的关系;在厘清“气候变化影响量”对粮食产量的影响的基础上,预估我国未来30年特别是经济社会发展两个关键节点2035年和2050年的粮食生产的气候变化风险,文章给出了一种新的研究视角,构建了研究内容和研究方法,力争实现定性研究与定量研究相结合,以科学预测为政策指导提供有力支撑。 相似文献
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该研究从综合评估模型(IAM)的模型耦合视角出发,介绍了当前损失函数的研究进展,主要从损失函数的构建方法、损失函数与IAM气候模块和经济模块的耦合以及IAM与气候模式的耦合角度分析了损失函数的耦合功能及其存在的科学问题,探讨了损失函数的改进方向。通过文献梳理发现,损失函数的构建方法上,主要采用专家判断法、元分析法和统计学方法,但各有优缺点;与气候模式的耦合功能上,损失函数多以温升为气候变化因素,降水等气候变化信息无法表达,且由全球尺度的年平均值进行标定,不能体现区域的差异和季节的变化,无法直接描述极端气候事件造成的巨大损失;与经济模块的耦合功能上,基于生产部门的损失函数缺乏间接损失评估功能,缺乏对经济增长的动态影响机制。针对上述IAM中气候变化对经济影响的反馈机制的不足,需重点从细化区域气候变化因素影响和细分经济产业部门两个方向重构损失函数,紧密连接气候模式与经济模块,全面评估气候变化经济损失,并需要从技术上解决损失函数在耦合经济模块与气候模式时出现的时空尺度不匹配问题,最终为IAM与气候模式甚至地球系统模式的耦合提供重要的解决方案。 相似文献
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全球气候变化对全球自然系统和社会经济产生了显著的影响,近百年来的气候变暖已经成为既定的科学事实。目前,世界各国对于发展低碳经济、建设低碳城市及承担碳减排任务以应对气候变化的共识不断得到加强,在此背景下,低碳社会、低碳生活等理念为人们所广泛关注和推崇。本文综述了气候变化的科学事实、碳排放与城市化之间的内在联系;阐述了低碳经济、低碳城市、"脱钩"理论、环境库兹涅茨理论、生态足迹和碳足迹理论的理论内涵;评述了目前低碳城市发展水平的测度指标体系、计算方法和评价标准,重点分析了人均碳排放、碳生产率、碳排放强度和碳能源排放系数等城市低碳发展水平指标,以及DPSIR模型、生态足迹模型、主成分分析和层次分析综合模型等低碳发展水平评价模型。最后,提出了目前该研究领域存在的科学问题,展望了未来的研究重点和发展方向。 相似文献
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由于美国地域广阔,地形、生态、气候和经济的多样性以及散居的人口和不同的生活方式,气候变化对美国的影响会有很大差异。这样一个拥有13．6多万亿美元GDP和3．02亿人口的大国如何应对气候变化取决于许多因素。这些因素包括对影响的严重性的认识、对影响的全部含意的理解能力以及在投资和政策决策中反映这种认知的程度。 相似文献
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陕北黄土丘陵沟壑区农户气候变化的适应行为及影响因素分析 总被引：2，自引：0，他引：2
从农户个体微观视角,研究农业的活动主体--农户的气候变化适应行为。选择陕北黄土丘陵沟壑区,采用问卷调查和半结构式访谈相结合的方法研究农户气候变化感知与适应行为,运用二元逻辑回归模型分析影响该地区农户适应行为的因素。结果表明：农户对气候变化趋势感知比较一致,认为近5 年夏季和冬季气温升高,降水减少,但与实际观测存在一定偏差。农户应对气候变化采取适应行为的比重并不高,只有57.8% 的农户表示采取了相应的措施来应对气候变化。农户适应行为受气候变化感知的影响,此外,家庭社会经济属性对农户采取适应行为的概率影响显著,而性别、年龄、文化程度等人口属性因素与农户采取适应行为的概率关系不大。 相似文献
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气象条件影响我国农业经济产出的计量经济分析 总被引：1，自引：0，他引：1
将计量经济学与气象学相结合,在经典C-D生产函数中引入气象因子构建气象计量经济模型,引入弹性和极差率的概念,定量分析了气象条件变化对我国农业经济产出的影响。结果表明:模型具有较高的拟合准确性,加入气象因子提高了对农业经济产出的整体拟合水平,初步证明了计量经济模型的合理性。受气象条件变化的综合影响,各行政区农业经济产出变化幅度为5%～85%,各区域农业经济产出变化幅度为3.4%～19.5%。计量经济学在气象领域的应用可以定量评估天气气候变化对社会经济的影响,为天气和气候变化研究提供了新方法,具有很好的应用前景。 相似文献
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The impact of climate change on agriculture has received wide attention by the scientific community. This paper studies how to assess the grain yield impact of climate change, according to the climate change over a long time period in the future as predicted by a climate system model. The application of the concept of a traditional ＂yield impact of meteorological factor （YIMF）＂ or ＂yield impact of weather factor＂ to the grain yield assessment of a decadal or even a longer timescale would be suffocated at the outset because the YIMF is for studying the phenomenon on an interannual timescale, and it is difficult to distinguish between the trend caused by climate change and the one resulting from changes in non-climatic factors. Therefore, the concept of the yield impact of climatic change （YICC）, which is defined as the difference in the per unit area yields （PUAY） of a grain crop under a changing and an envisaged invariant climate conditions, is presented in this paper to assess the impact of global climate change on grain yields. The climatic factor has been introduced into the renowned economic Cobb-Douglas model, yielding a quantitative assessment method of YICC using real data. The method has been tested using the historical data of Northeast China, and the results show that it has an encouraging application outlook. 相似文献
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Grain maize yield in the main arable areas of the European Community (E.C.) was calculated with a simulation model, WOFOST, using historical weather data and average soil characteristics. The sensitivity of the model to individual weather variables was determined. Subsequent analyses were made using climate change scenarios with and without the direct effects of increased atmospheric CO2. The impact of crop management (sowing date, irrigation and cultivar type) in a changed climate was also assessed. Scenario climate change generally results in larger grain yields for the northern E.C., similar or slightly smaller yields for the central E.C. and considerably smaller yields for the southern E.C. The various climate change scenarios used appear to give considerably different changes in grain yield, both for each location and for the E.C. as a whole. Management analyses show that for both current and scenario climates the largest grain yield will be attained by varieties with an early start of grain filling, that average irrigation requirements to attain potential grain yield in the E.C. will increase with climate change but will decrease with both increased CO2 and climate change, and that sowing at both current and scenarios climate should occur as early as possible.The U.S. Government right to retain a nonexclusive, royalty-free licence in and to any copyright is acknowledged. 相似文献
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This study aims to evaluate the performance of two mainstream downscaling techniques: statistical and dynamical downscaling and to compare the differences in their projection of future climate change and the resultant impact on wheat crop yields for three locations across New South Wales, Australia. Bureau of Meteorology statistically- and CSIRO dynamically-downscaled climate, derived or driven by the CSIRO Mk 3.5 coupled general circulation model, were firstly evaluated against observed climate data for the period 1980–1999. Future climate projections derived from the two downscaling approaches for the period centred on 2055 were then compared. A stochastic weather generator, LARS-WG, was used in this study to derive monthly climate changes and to construct climate change scenarios. The Agricultural Production System sIMulator-Wheat model was then combined with the constructed climate change scenarios to quantify the impact of climate change on wheat grain yield. Statistical results show that (1) in terms of reproducing the past climate, statistical downscaling performed better over dynamical downscaling in most of the cases including climate variables, their mean, variance and distribution, and study locations, (2) there is significant difference between the two downscaling techniques in projected future climate change except the mean value of rainfall across the three locations for most of the months; and (3) there is significant difference in projected wheat grain yields between the two downscaling techniques at two of the three locations. 相似文献
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Estimates of impact of climate change on crop production could be biased depending upon the uncertainties in climate change scenarios, region of study, crop models used for impact assessment and the level of management. This study reports the results of a study where the impact of various climate change scenarios has been assessed on grain yields of irrigated rice with two popular crop simulation models- Ceres-Rice and ORYZA1N at different levels of N management. The results showed that the direct effect of climate change on rice crops in different agroclimatic regions in India would always be positive irrespective of the various uncertainties. Rice yields increased between 1.0 and 16.8% in pessimistic scenarios of climate change depending upon the level of management and model used. These increases were between 3.5 and 33.8% in optimistic scenarios. At current as well as improved level of management, southern and western parts of India which currently have relatively lower temperatures compared to northern and eastern regions, are likely to show greater sensitivity in rice yields under climate change. The response to climate change is small at low N management compared to optimal management. The magnitude of this impact can be biased upto 32% depending on the uncertainty in climate change scenario, level of management and crop model used. These conclusions are highly dependent on the specific thresholds of phenology and photosynthesis to change in temperature used in the models. Caution is needed in using the impact assessment results made with the average simulated grain yields and mean changes in climatic parameters. 相似文献
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An interdisciplinary investigation was conducted to assess the impact of
climate change on grain yields using an economy--climate model (C-D-C). The
model was formulated by incorporating climate factors into the classic
Cobb-Douglas (C-D) economic production function model. The economic meanings
of the model output elasticities are described and elucidated. The C-D-C
model was applied to the assessment of the impact of climate change on grain
yields in China during the past 20 years, from 1983 through 2002. In the
study, the land of China was divided into eight regions, and both the C-D-C
and C-D models were applied to each individual region. The results suggest
that the C-D-C model is superior to the classic C-D model, indicating the
importance of climate factors. Prospective applications of the C-D-C model
are discussed. 相似文献
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The Effect of Spatial Scale of Climatic Change Scenarios on Simulated Maize, Winter Wheat, and Rice Production in the Southeastern United States 总被引：2，自引：0，他引：2
E. A. Tsvetsinskaya L. O. Mearns T. Mavromatis W. Gao L. McDaniel M. W. Downton 《Climatic change》2003,60(1-2):37-72
We use the CERES family of crop models to assess the effect of different spatial scales of climate change scenarios on the simulated yield changes of maize (Zea mays L.), winter wheat (Triticum aestivum L.),and rice (Oryza sativa L.) in the Southeastern United States. The climate change scenarios were produced with the control and doubled CO2 runs of a high resolution regional climate model anda coarse resolution general circulation model, which provided the initial and lateral boundary conditions for the regional model. Three different cases were considered for each scenario: climate change alone, climate change plus elevated CO2, and the latter with adaptations. On the state level,for most cases, significant differences in the climate changed yields for corn were found, the coarse scale scenario usually producing larger modeled yield decreases or smaller increases. For wheat, however, which suffered large decreases in yields for all cases, very little contrast in yield based on scale of scenario was found. Scenario scale resulted in significantly different rice yields, but mainly because of low variability in yields. For maize the primary climate variable that explained the contrast in the yields calculated from the two scenarios is the precipitation during grain fill leading to different water stress levels. Temperature during vernalization explains some contrasts in winter wheat yields. With adaptation, the contrasts in the yields of all crops produced by the scenarios were reduced but not entirely removed. Our results indicate that spatial resolution of climate change scenarios can be an important uncertainty in climate change impact assessments, depending on the crop and management conditions. 相似文献
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MEAN AND VARIANCE CHANGE IN CLIMATE SCENARIOS: METHODS, AGRICULTURAL APPLICATIONS, AND MEASURES OF UNCERTAINTY 总被引：12，自引：2，他引：10
Our central goal is to determine the importance of including both mean and variability changes in climate change scenarios in an agricultural context. By adapting and applying a stochastic weather generator, we first tested the sensitivity of the CERES-Wheat model to combinations of mean and variability changes of temperature and precipitation for two locations in Kansas. With a 2°C increase in temperature with daily (and interannual) variance doubled, yields were further reduced compared to the mean only change. In contrast, the negative effects of the mean temperature increase were greatly ameliorated by variance decreased by one-half. Changes for precipitation are more complex, since change in variability naturally attends change in mean, and constraining the stochastic generator to mean change only is highly artificial. The crop model is sensitive to precipitation variance increases with increased mean and variance decreases with decreased mean. With increased mean precipitation and a further increase in variability Topeka (where wheat cropping is not very moisture limited) experiences decrease in yield after an initial increase from the 'mean change only case. At Goodland Kansas, a moisture-limited site where summer fallowing is practiced, yields are decreased with decreased precipitation, but are further decreased when variability is further reduced. The range of mean and variability changes to which the crop model is sensitive are within the range of changes found in regional climate modeling (RegCM) experiments for a CO2 doubling (compared to a control run experiment). We then formed two types of climate change scenarios based on the changes in climate found in the control and doubled CO2 experiments over the conterminous U. S. of RegCM: (1) one using only mean monthly changes in temperature, precipitation, and solar radiation; and (2) another that included these mean changes plus changes in daily (and interannual) variability. The scenarios were then applied to the CERES-Wheat model at four locations (Goodland, Topeka, Des Moines, Spokane) in the United States. Contrasting model responses to the two scenarios were found at three of the four sites. At Goodland, and Des Moines mean climate change increased mean yields and decreased yield variability, but the mean plus variance climate change reduced yields to levels closer to their base (unchanged) condition. At Spokane mean climate change increased yields, which were somewhat further increased with climate variability change. Three key aspects that contribute to crop response are identified: the marginality of the current climate for crop growth, the relative size of the mean and variance changes, and timing of these changes. Indices for quantifying uncertainty in the impact assessment were developed based on the nature of the climate scenario formed, and the magnitude of difference between model and observed values of relevant climate variables. 相似文献
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As carbon dioxide and other greenhouse gasses accumulate in the atmosphere and contribute to rising global temperatures, it is important to examine how a changing climate may affect natural and managed ecosystems. In this series of papers, we study the impacts of climate change on agriculture, water resources and natural ecosystems in the General Circulation Model (GCM)-derived climate change projections, described in Part 1, to drive the crop production and water resource models EPIC (Erosion Productivity Impact Calculator) and HUMUS (Hydrologic Unit Model of the United States). These models are described and validated in this paper using historical crop yields and streamflow data in the conterminous United States in order to establish their ability to accurately simulate historical crop and water conditions and their capability to simulate crop and water response to the extreme climate conditions predicted by GCMs. EPIC simulated grain and forage crop yields are compared with historical crop yields from the US Department of Agriculture (USDA) and with yields from agricultural experiments. EPIC crop yields correspond more closely with USDA historical county yields than with the higher yields from intensively managed agricultural experiments. The HUMUS model was validated by comparing the simulated water yield from each hydrologic basin with estimates of natural streamflow made by the US Geological Survey. This comparison shows that the model is able to reproduce significant observed relationships and capture major trends in water resources timing and distribution across the country. 相似文献
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We investigated the effect of two different spatial scales of climate change scenarios on crop yields simulated by the EPIC crop model for corn, soybean, and wheat, in the central Great Plains of the United States. The effect of climate change alone was investigated in Part I. In Part II (Easterling et al., 2001) we considered the effects ofCO2 fertilization effects and adaptation in addition to climate change. The scenarios were formed from five years of control and 2 ×CO2 runs of a high resolution regional climate model (RegCM) and the same from an Australian coarse resolution general circulation model (GCM), which provided the initial and lateral boundary conditions for the regional model runs. We also investigated the effect of two different spatial resolutions of soil input parameters to the crop models. We found that for corn and soybean in the eastern part of the study area, significantly different mean yield changes were calculated depending on the scenario used. Changes in simulated dryland wheat yields in the western areas were very similar, regardless of the scale of the scenario. The spatial scale of soils had a strong effect on the spatial variance and pattern of yields across the study area, but less effect on the mean aggregated yields. We investigated what aspects of the differences in the scenarios were most important for explaining the different simulated yield responses. For instance, precipitation changes in June were most important for corn and soybean in the eastern CSIRO grid boxes. We establish the spatial scale of climate changescenarios as an important uncertainty for climate change impacts analysis. 相似文献
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Integrating global and regional analyses of the effects of climate change: A case study of land use in England and Wales 总被引：3，自引：0，他引：3
M. L. Parry J. E. Hossell P. J. Jones T. Rehman R. B. Tranter J. S. Marsh C. Rosenzweig G. Fischer I. G. Carson R. G. H. Bunce 《Climatic change》1996,32(2):185-198
The purpose of this paper is to exemplify a means by which an integrated assessment can be made of global and regional effects on land use of climate change. This is achieved by use of data on the effects of climate change on world food prices as inputs to a regional land use allocation model.Data on world prices are drawn from a recent global study of climate change and crop yields. In a case study of England and Wales a land allocation model is used to infer changes of land use that are the product of the integrated effect of climate-induced global price changes and climate-related changes of yield in England and Wales. This combination of changed prices and yield potential is used to calculate the land use providing the highest returns for each of 155,235 1 km2 cells of land in England and Wales for a future assumed for the year 2060 (without climate change) and then for that same environment with climate change. The difference between these two is then treated as an estimated effect resulting from climate change. 相似文献