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The Tibetan Plateau, a unique cold and dry region recognized as the Earth’s third pole, is primarily composed of alpine grasslands (>60 %). While a warming climate in the plateau is being recorded, phenology of alpine grasslands and its climatic dependencies are less investigated. This study tests the feasibility of the frequently observed Moderate Resolution Imaging Spectroradiometer (MODIS) time series (500 m, 8 days) in examining alpine phenology in the plateau. A set of phenological metrics are extracted from the MODIS Normalized Difference Vegetation Index (NDVI) series in each year, 2000–2010. A nonparametric Mann-Kendall trend analysis is performed to find the trends of these phenological metrics, which are then linked to monthly climatic records in the growing season. Opposite trends of phenological change are observed between the east and west of the plateau, with delayed start of season, peak date, and end of season in the west and advanced phenophases in the east. The correlation analysis indicates that precipitation, with a decreasing trend in the west and increasing trend in the east, may serve as the primary driver of the onset and peak dates of greenness. Temperature increases all over the plateau. While the delay of the end of season in the west could be related to higher late season temperature, its advance in the east needs further investigation in this unique cold region. This study demonstrates that frequent satellite observations are able to extract phenological features of alpine grasslands and to provide spatiotemporally detailed base information for long-term monitoring on the plateau under rapid climate change. The Tibetan Plateau, a unique cold and dry region recognized as the Earth’s third pole, is primarily composed of alpine grasslands (>60 %). While a warming climate in the plateau is being recorded, phenology of alpine grasslands and its climatic dependencies are less investigated. This study tests the feasibility of the frequently observed Moderate Resolution Imaging Spectroradiometer (MODIS) time series (500 m, 8 days) in examining alpine phenology in the plateau. A set of phenological metrics are extracted from the MODIS Normalized Difference Vegetation Index (NDVI) series in each year, 2000–2010. A nonparametric Mann-Kendall trend analysis is performed to find the trends of these phenological metrics, which are then linked to monthly climatic records in the growing season. Opposite trends of phenological change are observed between the east and west of the plateau, with delayed start of season, peak date, and end of season in the west and advanced phenophases in the east. The correlation analysis indicates that precipitation, with a decreasing trend in the west and increasing trend in the east, may serve as the primary driver of the onset and peak dates of greenness. Temperature increases all over the plateau. While the delay of the end of season in the west could be related to higher late season temperature, its advance in the east needs further investigation in this unique cold region. This study demonstrates that frequent satellite observations are able to extract phenological features of alpine grasslands and to provide spatiotemporally detailed base information for long-term monitoring on the plateau under rapid climate change.

Solar radiation controls biological, chemical and other processes in the atmosphere, hydrosphere, biosphere and lithosphere. Solar radiation is energy source for forest ecosystem, power to maintain and develop the ecosystem, and has important effects on plant photosynthesis, transpiration and carbon exchange. Gongga Mountain is located in southeast edge of Tibetan Plateau, and is a typical and representative alpine ecosystem. According to the protocols for standard radiation observation and measurement of Chinese Ecosystem Research Network (CERN), the Alpine Ecosystem Observation and Experiment Station of Gongga Mountain, Chinese Academic of Sciences has been carrying out long-term radiation monitoring. In this dataset, we report 22 radiation indicators (total 97 KB) collected from the automatic radiation system after data processing and quality control and assessment during 1998–2018. It provides basic data for carbon cycle, water cycle and energy cycle of alpine ecosystem under global change. Solar radiation controls biological, chemical and other processes in the atmosphere, hydrosphere, biosphere and lithosphere. Solar radiation is energy source for forest ecosystem, power to maintain and develop the ecosystem, and has important effects on plant photosynthesis, transpiration and carbon exchange. Gongga Mountain is located in southeast edge of Tibetan Plateau, and is a typical and representative alpine ecosystem. According to the protocols for standard radiation observation and measurement of Chinese Ecosystem Research Network (CERN), the Alpine Ecosystem Observation and Experiment Station of Gongga Mountain, Chinese Academic of Sciences has been carrying out long-term radiation monitoring. In this dataset, we report 22 radiation indicators (total 97 KB) collected from the automatic radiation system after data processing and quality control and assessment during 1998–2018. It provides basic data for carbon cycle, water cycle and energy cycle of alpine ecosystem under global change.

As a basic properties of forest vegetation, forest succession law is the basis of understanding forest community, managing forest and utilizing forest rationally. Typical evergreen broad-leaved forest is a zonal vegetation in the subtropical area of east China. The existing vegetation is mostly in different secondary succession stages due to human and natural disturbance. Plant species composition is an important indicator of the long-term terrestrial ecosystem observation of National Ecosystem Research Network of China (CNERN). It affects the biogeochemical cycle, productivity, carbon sequestration, biodiversity and ecosystem services of forest ecosystems. According to CNERN monitoring standards, Zhejiang Tiantong Forest Ecosystem National Observation and Research Station finished three investigations at three succession plots and established a dataset on species composition during 2008 and 2017. The dataset included species name, abundance, mean diameter and biomass of woody plants in the plot. The species composition database provides critical data for in-depth studies of forest species diversity, structure and function under succession or environment change, and can support forest management and ecosystem service evaluation in this region. As a basic properties of forest vegetation, forest succession law is the basis of understanding forest community, managing forest and utilizing forest rationally. Typical evergreen broad-leaved forest is a zonal vegetation in the subtropical area of east China. The existing vegetation is mostly in different secondary succession stages due to human and natural disturbance. Plant species composition is an important indicator of the long-term terrestrial ecosystem observation of National Ecosystem Research Network of China (CNERN). It affects the biogeochemical cycle, productivity, carbon sequestration, biodiversity and ecosystem services of forest ecosystems. According to CNERN monitoring standards, Zhejiang Tiantong Forest Ecosystem National Observation and Research Station finished three investigations at three succession plots and established a dataset on species composition during 2008 and 2017. The dataset included species name, abundance, mean diameter and biomass of woody plants in the plot. The species composition database provides critical data for in-depth studies of forest species diversity, structure and function under succession or environment change, and can support forest management and ecosystem service evaluation in this region.

As a kind of important renewable resources, grassland resources have significant influence on human’s daily life. China is a country with abundant grassland resources. The scientific use of grassland resources would contribute to the sustainable development of animal husbandry, national unity and the stability of the country. However, grassland resources are facing with more and more problems, with the development of agriculture, industry, animal husbandry, population growth, and the impact of global warming. Therefore, obtaining accurate real-time information of the growth condition of grassland is quite important. People can use this information carrying on the scientific management of grassland resources, thus protecting grassland resources and keeping the sustainable development of animal husbandry. Traditional observation method is mainly ground experiment, which would cost lots of time and money. Remote sensing data has the advantage of near-real time, dynamic observation and contains image with large scale. But a single type of remote sensing data cannot meet the needs of high temporal-spatial grassland biomass observation. This study intends to use data fusion method to generate high temporal-spatial remote sensing data. Then combining with ground survey data , we established the parametric and non-parametric model. Eventually we developed the optimal aboveground biomass model for Qinghai Lake Basin and generate the biomass time series with 30 meter resolution and 8 day interval during 2000—2015. We then analyzed the grassland trend in Qinghai Lake basin during the past 16 years. The main work and the conclusions of our findings are as follows: (1) According to the actual situation of Qinghai Lake Basin, we developed the optimal fusion model from three prospects: the selection of generating synthetic NDVI, the comparison between different image (different MODIS product and TM image in different years), and the development of data fusion algorithm. We finally generated the synthetic NDVI series of Qinghai Lake Basin. The selection of the fusion scheme would directly affect the precision of the vegetation index, and then impact the accuracies of the construction of the biomass model. Based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, we used MCD43A4 as the input MODIS file. We then chose data in the same year, in adjacent year, and data with 2-yr intervals. Based on the landcover type, we used decision tree to choose different windows for different vegetation types: 350m for croplands; 950m for forest; 750m for grassland and other vegetation types. We have synthetic NDVI time series with relatively high spatial and temporal resolution. It can tell more spatial details on the vegetation variation compared with MODIS data. (2) Based on the measured data and the fusion vegetation index data, the parametric models and a non-parametric model were established and compared. Finally, the experimental results show that the support vector machine (SVM) model has good accuracy. Based on this model, the data set of 30-m data series of grassland aboveground biomass in Qinghai Lake area in the past 16 years was established. We built the model in the following four steps: We generated the synthetic NDVI series with the optimal data fusion scheme; combined with the 291 field samples and vegetation index data, we generated the biomass estimation model of Qinghai Lake region; We chose the optimal model for biomass estimation according to the test data. We finally generated the biomass series with 320 scenes. Biomass estimation model with synthetic NDVI (r=0.85, RMSE=74.45g/m2) can not only maintain accuracies of the models based on MODIS NDVI (r=0.85, RMSE=73.20g/m2); it can also increase the spatial resolution of the biomass from 500m to 30m, and increase the time resolution up to 8 days. (3) The degradation condition of grassland in in Qinghai Lake area was analyzed. We found that during the past 16 years, grassland resources in this area have changed greatly. Grassland in the south lakeshore and the mountainous area in the northern part of the basin showed large degradation, while in the middle of the Qinghai Lake Basin, grassland showed growing tendencies. Grassland with apparent degradation accounted for 8.5% of the basin,while grassland with apparent growth account for 24.5% of the basin. The degradation of grassland were partly contributed by global warming; while the unscientific use of grassland resources is another critical issue caused the land degradation. In addition, as a tourist hot spot, In recent years, tourists number in Qinghai Lake Basin increased dramatically, which would also contribute to the grassland degradation in the local area. As a kind of important renewable resources, grassland resources have significant influence on human’s daily life. China is a country with abundant grassland resources. The scientific use of grassland resources would contribute to the sustainable development of animal husbandry, national unity and the stability of the country. However, grassland resources are facing with more and more problems, with the development of agriculture, industry, animal husbandry, population growth, and the impact of global warming. Therefore, obtaining accurate real-time information of the growth condition of grassland is quite important. People can use this information carrying on the scientific management of grassland resources, thus protecting grassland resources and keeping the sustainable development of animal husbandry. Traditional observation method is mainly ground experiment, which would cost lots of time and money. Remote sensing data has the advantage of near-real time, dynamic observation and contains image with large scale. But a single type of remote sensing data cannot meet the needs of high temporal-spatial grassland biomass observation. This study intends to use data fusion method to generate high temporal-spatial remote sensing data. Then combining with ground survey data , we established the parametric and non-parametric model. Eventually we developed the optimal aboveground biomass model for Qinghai Lake Basin and generate the biomass time series with 30 meter resolution and 8 day interval during 2000—2015. We then analyzed the grassland trend in Qinghai Lake basin during the past 16 years. The main work and the conclusions of our findings are as follows: (1) According to the actual situation of Qinghai Lake Basin, we developed the optimal fusion model from three prospects: the selection of generating synthetic NDVI, the comparison between different image (different MODIS product and TM image in different years), and the development of data fusion algorithm. We finally generated the synthetic NDVI series of Qinghai Lake Basin. The selection of the fusion scheme would directly affect the precision of the vegetation index, and then impact the accuracies of the construction of the biomass model. Based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, we used MCD43A4 as the input MODIS file. We then chose data in the same year, in adjacent year, and data with 2-yr intervals. Based on the landcover type, we used decision tree to choose different windows for different vegetation types: 350m for croplands; 950m for forest; 750m for grassland and other vegetation types. We have synthetic NDVI time series with relatively high spatial and temporal resolution. It can tell more spatial details on the vegetation variation compared with MODIS data. (2) Based on the measured data and the fusion vegetation index data, the parametric models and a non-parametric model were established and compared. Finally, the experimental results show that the support vector machine (SVM) model has good accuracy. Based on this model, the data set of 30-m data series of grassland aboveground biomass in Qinghai Lake area in the past 16 years was established. We built the model in the following four steps: We generated the synthetic NDVI series with the optimal data fusion scheme; combined with the 291 field samples and vegetation index data, we generated the biomass estimation model of Qinghai Lake region; We chose the optimal model for biomass estimation according to the test data. We finally generated the biomass series with 320 scenes. Biomass estimation model with synthetic NDVI (r=0.85, RMSE=74.45g/m2) can not only maintain accuracies of the models based on MODIS NDVI (r=0.85, RMSE=73.20g/m2); it can also increase the spatial resolution of the biomass from 500m to 30m, and increase the time resolution up to 8 days. (3) The degradation condition of grassland in in Qinghai Lake area was analyzed. We found that during the past 16 years, grassland resources in this area have changed greatly. Grassland in the south lakeshore and the mountainous area in the northern part of the basin showed large degradation, while in the middle of the Qinghai Lake Basin, grassland showed growing tendencies. Grassland with apparent degradation accounted for 8.5% of the basin,while grassland with apparent growth account for 24.5% of the basin. The degradation of grassland were partly contributed by global warming; while the unscientific use of grassland resources is another critical issue caused the land degradation. In addition, as a tourist hot spot, In recent years, tourists number in Qinghai Lake Basin increased dramatically, which would also contribute to the grassland degradation in the local area.

The area and volume of Chinese fir plantation is the largest in China. Based on a long-time biomass production observation on the Chinese fir plantation comprehensive observation field by Huitong National Forest Ecosystem Research Station, Hunan (Huitong Station). The dataset integrated annual biomass production of the Chinese fir plantation in Huitong Station in the period of 2007–2020, which both comprised the dry weight of trunk, branches, leaves, fruits (flowers), bark and aerial roots. The establishment and sharing of this dataset mainly provides data support for the biomass production research of Chinese fir plantation under the background of global change. It is of great significance to deeply understand the structural and functional characteristics of Chinese fir plantation ecosystem and formulate reasonable management measures of Chinese fir plantation. The area and volume of Chinese fir plantation is the largest in China. Based on a long-time biomass production observation on the Chinese fir plantation comprehensive observation field by Huitong National Forest Ecosystem Research Station, Hunan (Huitong Station). The dataset integrated annual biomass production of the Chinese fir plantation in Huitong Station in the period of 2007–2020, which both comprised the dry weight of trunk, branches, leaves, fruits (flowers), bark and aerial roots. The establishment and sharing of this dataset mainly provides data support for the biomass production research of Chinese fir plantation under the background of global change. It is of great significance to deeply understand the structural and functional characteristics of Chinese fir plantation ecosystem and formulate reasonable management measures of Chinese fir plantation.

NDVI-based grassland biomass model has been widely used for evaluating the growth and productivity of grassland communities, since remote sensing technology is able to monitor wide area with strong timeliness. Based on regression relation between grassland biomass and NDVI from the literature during 2000 – 2018, we built the dataset of regression relation between grassland biomass and NDVI in China. The dataset contains 12 types of grassland regression relationships between biomass and NDVI, each relationship type contains 4 kinds of regression expression, i.e., unitary linear, power, exponential and logarithmic relationships. Besides, grassland regionalization, distribution area, suitable period, NDVI data resources, NDVI temporal resolution and NDVI spatial resolution were compiled. This dataset provides important data resources for evaluating Chinese grassland productivity, grassland ecosystem carrying capacity, carbon cycle, and ecological protection. NDVI-based grassland biomass model has been widely used for evaluating the growth and productivity of grassland communities, since remote sensing technology is able to monitor wide area with strong timeliness. Based on regression relation between grassland biomass and NDVI from the literature during 2000 – 2018, we built the dataset of regression relation between grassland biomass and NDVI in China. The dataset contains 12 types of grassland regression relationships between biomass and NDVI, each relationship type contains 4 kinds of regression expression, i.e., unitary linear, power, exponential and logarithmic relationships. Besides, grassland regionalization, distribution area, suitable period, NDVI data resources, NDVI temporal resolution and NDVI spatial resolution were compiled. This dataset provides important data resources for evaluating Chinese grassland productivity, grassland ecosystem carrying capacity, carbon cycle, and ecological protection.

This dataset is composed of two parts of data stored in two respective Excel files. They are: 1. Data on Main crops AG-biomass and leaf area index (2006-2015); 2. Data on Main crops Root biomass in cultivated layer of soil (2006-2015). This dataset is composed of two parts of data stored in two respective Excel files. They are: 1. Data on Main crops AG-biomass and leaf area index (2006-2015); 2. Data on Main crops Root biomass in cultivated layer of soil (2006-2015).

氣候變遷乃人類目前所面臨的一項全球性問題。歐盟針對氣候變遷如何回應，採取哪些政策與行動，其特徵與成效又如何？這是本文關心的一項重點，其他議題包括：歐盟氣候變遷政策如何形成，有何理念與目標、法源依據、組織架構、決策流程、政策措施、國際談判，以及有何意涵與影響。氣候變遷乃一跨部門與超國界的議題，歐盟超國家氣候變遷政策因此得提供其他國家、區域性與國際性組織，從事氣候變遷國際合作的比較參考，頗具意義。 Climate change is currently a global issue faced by human beings. Combating climate change is a top priority for the EU policy. In 2007 the EU endorsed an integrated approach to climate and energy policy and committed to transforming the EU into a highly energy-efficient, low Carbon economy. The EU has long been a driving force in international negotiations for larger global actions to structure an effective global climate regime. This paper focuses primarily on examining climate change law, strategy and practices in the EU. The following core issues are exploited: What is the concept and impacts of climate change on the EU? What is the legal framework, decision-making procedure, institutional structure of the EU’s climate change policy? To what extent the EU’s climate change policy is influenced by the European Council and the EU’s foreign policy under the Lisbon Treaty? What is the strategy adopted by EU at the international negotiations on climate change? And what’s the significance and impacts of the EU model of climate change policy on third countries and international organizations?

In order to improve the competitiveness of wind power in participating in the power market, promote low-carbon operation of the power system, and meet the new requirements for the completeness and flexibility of the production simulation model due to the uncertainty of wind power output,this paper analyzes the electricity cost composition from the perspective of low-carbon economy, and applies the stochastic programming theory to propose a short-term production simulation model of power system containing wind power. Considering the participation of the carbon trading market, this model aims to minimize the expected cost of electricity production in a short-term time scale, and coordinately optimize the day-ahead power output, real-time power regulation, power reserve capacity, wind curtailment, and load shedding. Taking the modified IEEE 39-bus system as an example, this paper quantitatively evaluates the impact of carbon trading mechanism, carbon trading price, and wind power installed capacity on electricity costs and their contributions to carbon emission reduction. The simulation results show that the proposed model can effectively analyze the short-term electricity cost, carbon emissions, and operational risks of the power system containing wind power under the carbon trading environment, thus has a promise application prospect.

A day-ahead optimal decision-making model is established for an integrated electricity-heat energy system to participate in both the electric energy market and the spinning reserve market, and the step-by-step carbon trading is introduced into the proposed model. The conditional value at risk method is used to manage the uncertainty risk of renewable energy and electrical load. With the objective to minimize the operation scheme cost and carbon emission cost, an operation plan is developed and the reserve resources are arranged for the integrated electricity-heat energy system. The results of a case study show that the proposed model improves the reliability, economy, and low-carbon level by taking the complementary advantages of the integrated energy system and reasonably arranging reserve resources to deal with the risks caused by uncertain factors.