• Energy Research
• Restricted close
• Open Source close
• 13. Climate action close
• 6. Clean water close
• CN close
• BD close

This study highlights how Chinese economic development detrimentally impacted water quality in recent decades and how this has been improved by enormous investment in environmental remediation funded by the Chinese government. To our knowledge, this study is the first to describe the variability of surface water quality in inland waters in China, the affecting drivers behind the changes, and how the government-financed conservation actions have impacted water quality. Water quality was found to be poorest in the North and the Northeast China Plain where there is greater coverage of developed land (cities + cropland), a higher gross domestic product (GDP), and higher population density. There are significant positive relationships between the concentration of the annual mean chemical oxygen demand (COD) and the percentage of developed land use (cities + cropland), GDP, and population density in the individual watersheds (p < 0.001). During the past decade, following Chinese government-financed investments in environmental restoration and reforestation, the water quality of Chinese inland waters has improved markedly, which is particularly evident from the significant and exponentially decreasing GDP-normalized COD and ammonium (NH4+-N) concentrations. It is evident that the increasing GDP in China over the past decade did not occur at the continued expense of its inland water ecosystems. This offers hope for the future, also for other industrializing countries, that with appropriate environmental investments a high GDP can be reached and maintained, while simultaneously preserving inland aquatic ecosystems, particularly through management of sewage discharge.

AbstractAlthough canopy height has long been a focus of interest in ecology, it has remained difficult to study at large spatial scales. Recently, satellite‐borne LiDAR equipment produced the first systematic high resolution maps of vegetation height worldwide. Here we show that this new resource reveals three marked modes in tropical canopy height ~40, ~12, and ~2 m corresponding to forest, savanna, and treeless landscapes. The distribution of these modes is consistent with the often hypothesized forest‐savanna bistability and suggests that both states can be stable in areas with a mean annual precipitation between ~1,500 and ~2,000 mm. Although the canopy height states correspond largely to the much discussed tree cover states, there are differences, too. For instance, there are places with savanna‐like sparse tree cover that have a forest‐like high canopy, suggesting that rather than true savanna, those are thinned relicts of forest. This illustrates how complementary sets of remotely sensed indicators may provide increasingly sophisticated ways to study ecological phenomena at a global scale.

The potential of renewable energy should be fully exploited in the transportation sector to achieve a cleaner production. Therefore, this paper proposes an on-grid hybrid hydrogen refueling and battery swapping station powered by wind energy. This novel concept can promote the development of low-carbon emission vehicles including hydrogen-based vehicle and battery electric vehicle. During the daily operation of the station, the multiple uncertainties may lead to a higher operational cost. To address this problem, a hybrid stochastic/distributionally robust optimization method is proposed to handle different uncertainties for the energy management problem. The first type of uncertainties can be depicted by a certain distribution, i.e. electricity price and wind power, which is processed by a stochastic optimization method. The second type of uncertainties is associated with human behaviors and is difficult to find its probability distribution, i.e. the hydrogen demand of hydrogen-based vehicles, so the second type is processed by a distributionally robust optimization method. The overall objective is to minimize the total operational cost of the station, which also considers the battery swapping station overstock punishment. Because a reasonable battery swapping scheduling can reduce the waiting time of users and operational cost of the station. The results indicate that the proposed method can effectively address the conservatism of solutions as its total operational cost is 4.4% lower than that of the hybrid stochastic/robust optimization method under a high confidence level.

To mitigate the climate change-induced water shortage and realize the sustainable development of agriculture, drip irrigation, a more efficient water-saving irrigation method, has been intensively implemented in most arid agricultural regions in the world. However, compared to traditional border irrigation, how drip irrigation affects the biophysical conditions in the cropland and how crops physiologically respond to changes in biophysical conditions in terms of water, heat and carbon exchange remain largely unknown. In view of the above situation, to reveal the mechanism of drip irrigation in improving spring wheat water productivity, paired field experiments based on drip irrigation and border irrigation were conducted to extensively monitor water and heat fluxes at a typical spring wheat field (Triticum aestivum L.) in Northwest China during 2017–2020. The results showed that drip irrigation improved yield by 10.3 % and crop water productivity (i.e., yield-to-evapotranspiration-ratio) by 15.6 %, but reduced LAI by 16.9 % in contrast with border irrigation. Under drip irrigation, the lateral development of spring wheat roots was promoted by higher soil temperature combined with frequent dry-wet alternation in the shallow soil layer (0–20 cm), which was the basis for efficient absorption of water and fertilizer, as well as efficient formation of photosynthate. Meanwhile, drip irrigation increased net radiation and decreased latent heat flux by inhibiting leaf growth, thereby increased sensible heat, causing a higher soil temperature (+1.10 ℃) and canopy temperature (+1.11 ℃). Further analysis proved that soil temperature was the key factor affecting yield formation. Based on the above conditions, the decrease in leaf distribution coefficient (−0.030) led to the decrease in evapotranspiration (−5.7 %) and the increase in ear distribution coefficient (+0.029). Therefore, drip irrigation emphasized the role of soil moisture in the soil-plant-atmosphere continuum, enhanced crop activity by increasing field temperature, especially soil temperature, and finally improved yield and water productivity via carbon reallocation. The study revealed the mechanism of drip irrigation for improving spring wheat yield, and would contribute to improving Earth system models in representing agricultural cropland ecosystems with drip irrigation and predicting the subsequent biophysical and biogeochemical feedbacks to climate change.

AbstractClimate change alters surface water availability (WA; precipitation minus evapotranspiration, P − ET) and consequently impacts agricultural production and societal water needs, leading to increasing concerns on the sustainability of water use. Although the direct effects of climate change on WA have long been recognized and assessed, indirect climate effects occurring through adjustments in terrestrial vegetation are more subtle and not yet fully quantified. To address this knowledge gap, here we investigate the interplay between climate‐induced changes in leaf area index (LAI) and ET and quantify its ultimate effect on WA during the period 1982–2016 at the global scale, using an ensemble of data‐driven products and land surface models. We show that ~44% of the global vegetated land has experienced a significant increase in growing season‐averaged LAI and climate change explains 33.5% of this greening signal. Such climate‐induced greening has enhanced ET of 0.051 ± 0.067 mm year−2 (mean ± SD), further amplifying the ongoing increase in ET directly driven by variations in climatic factors over 36.8% of the globe, and thus exacerbating the decline in WA prominently in drylands. These findings highlight the indirect impact of positive feedbacks in the land–climate system on the decline of WA, and call for an in‐depth evaluation of these phenomena in the design of local mitigation and adaptation plans.

In order to achieve the Paris Agreement goals of keeping the temperature rise well below 2 °C or even 1.5 °C, all countries would need to make fair and ambitious contributions to reducing emissions. A vast majority of countries have adopted reduction targets by 2030 in their Nationally Determined Contributions (NDCs). There are many alternative ways to analyze the fairness of national mitigation contributions. This article uses a model framework based on six equity principles of effort-sharing, to allocate countries’ reduction targets under global emissions scenarios consistent with meeting the Paris climate goals. It further compares these allocations with the NDCs. The analysis shows that most countries need to adopt more ambitious reduction targets by 2030 to meet 2 °C, and even more for 1.5 °C. In the context of 2 °C, the NDCs of the United States of America and the European Union lack ambition with respect to the approaches that emphasize responsibility; China's NDC projection falls short of satisfying any approach in 2030. In the context of 1.5 °C, only India, by implementing its most ambitious efforts by 2030, could be in line with most equity principles. For most countries, the NDCs would use most of their allowed emissions space for the entire 21 st century by 2030, posing a major challenge to transform to a pathway consistent with their fair contributions in the long-term.

Residential energy consumption in China increased dramatically over the period of 2002-2010. In this paper, we undertake a decomposition analysis of changes in energy use by Chinese households for five energy-using activities: space heating/cooling, cooking, lighting and electric appliances. We investigate to what extent changes in energy use are due to changes from appliances and to change in floor space, population and energy mix. Our decomposition analysis is based on the logarithmic mean Divisia index technique using data from the China statistical yearbook and China energy statistical yearbook in the period of 2002-2010. According to our results, the increase in energy-using appliances is the biggest contributor to the increase of residential energy consumption during 2002-2010 but the effect declines over time, due to energy efficiency improvements in those appliances. The second most important contributor is floor space per capita, which increased with 28%. Of the four factors, population is the most stable factor and energy mix is the least important factor. We predicted electricity use, with the help of regression-based predictions for ownership of appliances and the energy efficiency of appliances. We found that electricity use will continue to rise despite a gradual saturation of demand