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Research on the impacts of climate change on crop yield is crucial for improving agricultural management practices and enhancing climate adaptability. Although previous studies have explored the effects of climate trends and fluctuations on wheat yield, their combined impacts under future climate scenarios in the North China Plain (NCP) remain insufficiently understood. This study employs the DSSAT model to analyze the impacts of future climate trends and fluctuations on winter wheat yield. The results indicate that in the 2030s, the benefits of increased precipitation outweighed the losses from rising temperatures, leading to a 1.5% increase in winter wheat yield in the NCP. However, by the 2080s, continuous temperature rise dominated yield reduction, resulting in a 13.4% decline, which exceeded the compensatory capacity of increased precipitation. Irrigated wheat was primarily influenced by temperature trends, while rainfed systems were more sensitive to precipitation fluctuations. Delaying the planting date and increasing field fertility could mitigate 6–7.5% of the potential losses caused by rising temperatures, whereas increasing irrigation had limited mitigation effects (only improving yield by 3%). This study quantifies the climate impact benefits on winter wheat in the NCP and highlights the need for prioritizing heat-tolerant varieties and optimizing sowing and fertilization practices over water-intensive adaptation strategies. The findings provide decision-making support for ensuring food security under a warming climate.

Urban land in floodplains (ULF) is a vital component of flood exposure and its variations can cause changes in flood risk. In the context of rapid urbanization, ULF is expanding rapidly in China and imperiling societal sustainability. However, a national-scale analysis of ULF patterns and dynamics has yet to be conducted. Therefore, this study aims to investigate the spatiotemporal changes in China's ULF at different spatial scales (the country, region, basin, and sub-basin scales) from 1992-2015. We found that ULF accounted for 44.41% of the total urban land in China in 2015, which was 3.68 times greater than the proportion of floodplains relative to the total land area in China (12.06%). From 1992-2015, the ULF area increased by 26.43 × 103 km2, or 542.21%. Moreover, the ULF area is expected to grow by 16.89 × 103 km2 (53.38%) between 2015 and 2050. ULF growth was strongly associated with the flood occurrence in China, and continued growth will pose a considerable challenge to urban sustainability, particularly in basins with poor flood defenses. Greater attention should thus be paid to ULF dynamics in China.

The coastal part of China and its surrounding regions are dominated by a highly dense population and highly developed economy. Extreme precipitation events (EPEs) cause a lot of damage and hence changes in these events and their causes have been drawing considerable attention. This study investigated EPEs resulting from western North Pacific (WNP) tropical cyclones (TCs) and their potential link to El Niño–Southern Oscillation (ENSO), using TC track data, daily precipitation data from 2313 stations for 1951–2014, and the NCAR–NCEP reanalysis dataset. Two types of EPEs were considered: EPEs within 500 km from the TC center, and those caused by mesoscale and synoptic systems, referred to as predecessor rain events (PREs), beyond 1000 km from the TC center. Results indicated significant impacts of TCs on EPEs along the coastal areas, and discernable effects in inland areas of China. However, the effect of TCs on EPEs tended to be modulated by ENSO. During neutral years, inland areas of China are more affected by TC-induced extreme precipitation than during El Niño or La Niña years, with the highest density of TC tracks and larger-than-average numbers of tropical storms, typhoons, and landfalling TCs. During the El Niño phase, the central and eastern equatorial Pacific was characterized by higher sea surface temperature (SST), greater low-level vorticity (1000 hPa) and upper-level divergence (250 hPa), and stronger prevailing westerlies, which combined to trigger the movement of mean genesis to the eastern and southeastern WNP, resulting in fewer TCs passing through the Chinese territory.

Metro systems have become high-risk entities due to the increased frequency and severity of urban flooding. Therefore, understanding the flood risk of metro systems is a prerequisite for mega-cities’ flood protection and risk management. This study proposes a method for accurately assessing the flood risk of metro systems based on an improved trapezoidal fuzzy analytic hierarchy process (AHP). We applied this method to assess the flood risk of 14 lines and 268 stations of the Guangzhou Metro. The risk results validation showed that the accuracy of the improved trapezoidal fuzzy AHP (90% match) outperformed the traditional trapezoidal AHP (70% match). The distribution of different flood risk levels in Guangzhou metro lines exhibited a polarization signature. About 69% (155 km2) of very high and high risk zones were concentrated in central urban areas (Yuexiu, Liwan, Tianhe, and Haizhu); the three metro lines with the highest overall risk level were lines 3, 6, and 5; and the metro stations at very high risk were mainly located on metro lines 6, 3, 5, 1, and 2. Based on fieldwork, we suggest raising exits, installing watertight doors, and using early warning strategies to resist metro floods. This study can provide scientific data for decision-makers to reasonably allocate flood prevention resources, which is significant in reducing flood losses and promoting Guangzhou’s sustainable development.

The IPCC's global mean sea-level rise scenarios do not necessarily provide the right information for coastal decision-making and risk management.

Land subsidence is impacting large populations in coastal Asia via relative sea-level rise (RSLR). Here we assesses these risks and possible response strategies for China, including estimates of present rates of RSLR, flood exposure and risk to 2050. In 2015, each Chinese coastal resident experienced on average RSLR of 11 to 20 mm/yr. This is 3 to 5 times higher than climate-induced SLR, reflecting that people are concentrated in subsiding locations. In 2050, assuming these subsidence rates continue, land area, population and assets exposed to the 100-year coastal flood event is 20%-39%, 17%-37% and 18%-39% higher than assuming climate change alone, respectively. Realistic subsidence control measures can avoid up to two thirds of this additional growth in exposure, with adaptation required to address the residual. This analysis emphasizes subsidence as a RSLR hazard in China that requires a broad-scale policy response, utilizing subsidence control combined with coastal adaptation. Chinese coastal populations are concentrated in subsiding locations, and also subject to sea-level rise. Here the authors find that more areas, population and assets are exposed to coastal flooding by 2050 but realistic subsidence control measures can avoid additional risks.

Temperature sensitivity of soil respiration (Q10) is an important parameter in modeling effects of global warming on ecosystem carbon release. Experimental studies of soil respiration have ubiquitously indicated that Q10 has high spatial heterogeneity. However, most biogeochemical models still use a globally constant Q10 in projecting future climate change, partly because no spatial pattern of Q10 values has been derived. In this study, we conducted an inverse analysis to retrieve a global pattern of spatially heterogeneous Q10 values by assimilating data of soil organic carbon into a process‐based terrestrial carbon model (Carnegie‐Ames‐Stanford Approach model) at spatial resolution of 1° by 1°. The estimated Q10 values were, in turn, incorporated into soil respiration models to evaluate their impacts on global respiratory carbon release from soil (i.e., total soil respiration is equal to microbial and root respiration) and from microbial decomposition (i.e., heterotrophic respiration). Our results show that the optimized Q10 values are spatially heterogeneous and vary with environmental factors. In general, Q10 value tends to be high in the high‐latitudinal regions. The mean Q10 values for different biomes range from 1.43 to 2.03, with the highest value in tundra and the lowest value in deserts. When spatially heterogeneous Q10 values were incorporated into global soil respiration models, simulated soil respiration has a feedback intensity of 3.21 Pg C °C−1 to climate warming, which is approximately 40% higher than that with a globally invariant Q10 value. The modeled heterotrophic respiration has a feedback intensity of 2.26 Pg C °C−1, about 25% higher than that derived from a globally invariant Q10 value. Overall, the feedback intensity of soil carbon release to climate warming depends not only on the magnitude of a global mean of Q10 values but also their spatial variability.

The United Nations’ Sustainable Development Goal (SDG) 3.9 calls for a substantial reduction in deaths attributable to PM 2.5 pollution (DAPP). However, DAPP projections vary greatly and the likelihood of meeting SDG3.9 depends on complex interactions among environmental, socio-economic, and healthcare parameters. We project potential future trends in global DAPP considering the joint effects of each driver (PM 2.5 concentration, death rate of diseases, population size, and age structure) and assess the likelihood of achieving SDG3.9 under the Shared Socioeconomic Pathways (SSPs) as quantified by the Scenario Model Intercomparison Project (ScenarioMIP) framework with simulated PM 2.5 concentrations from 11 models. We find that a substantial reduction in DAPP would not be achieved under all but the most optimistic scenario settings. Even the development aligned with the Sustainability scenario (SSP1-2.6), in which DAPP was reduced by 19%, still falls just short of achieving a substantial (≥20%) reduction by 2030. Meeting SDG3.9 calls for additional efforts in air pollution control and healthcare to more aggressively reduce DAPP. Reducing PM 2.5 air pollution from biomass burning, transport, energy, and manufacturing, in combination with improvements in healthcare, especially in emerging economies like India and China, will be crucial to meeting SDG3.9

Climate change affects the spatial and temporal distribution of crop yields, which can critically impair food security across scales. A number of previous studies have assessed the impact of climate change on mean crop yield and future food availability, but much less is known about potential future changes in interannual yield variability. Here, we evaluate future changes in relative interannual global wheat yield variability (the coefficient of variation (CV)) at 0.25° spatial resolution for two representative concentration pathways (RCP4.5 and RCP8.5). A multi-model ensemble of crop model emulators based on global process-based models is used to evaluate responses to changes in temperature, precipitation, and CO2. The results indicate that over 60% of harvested areas could experience significant changes in interannual yield variability under a high-emission scenario by the end of the 21st century (2066–2095). About 31% and 44% of harvested areas are projected to undergo significant reductions of relative yield variability under RCP4.5 and RCP8.5, respectively. In turn, wheat yield is projected to become more unstable across 23% (RCP4.5) and 18% (RCP8.5) of global harvested areas—mostly in hot or low fertilizer input regions, including some of the major breadbasket countries. The major driver of increasing yield CV change is the increase in yield standard deviation, whereas declining yield CV is mostly caused by stronger increases in mean yield than in the standard deviation. Changes in temperature are the dominant cause of change in wheat yield CVs, having a greater influence than changes in precipitation in 53% and 72% of global harvested areas by the end of the century under RCP4.5 and RCP8.5, respectively. This research highlights the potential challenges posed by increased yield variability and the need for tailored regional adaptation strategies.

Non-communicable diseases have replaced infectious diseases as the leading cause of death among urban residents; the percentage of years of life lost because of such diseases as a fraction of all-cause years of life lost increased from 50·0% (95% CI 48·5–53·0) in 1990 to 77·3% (76·5–78·1) in 2015.4 Health inequality also increased in urban areas.5 China has acted to address urban health challenges by passing strict environmental regulations and investing heavily in urban infrastructure.In traditional chinese medicine, human health is seen as the consequence of harmonisation between human beings and their environments and between the various parts of the human body and the focus is on disease prevention rather than treatment.16 Concepts from traditional Chinese medicine, such as the maintenance of regular daily activities (rest, diet, and exercise) and avoidance of negative health effects from environmental factors (eg, Fengshui) have become essential parts of Chinese culture.In 2007 alone, particulate matter with a diameter of less than 2·5 μm (PM2·5) affected the productivity of about 72 million workers in 30 Chinese provinces, causing an estimated economic loss of ¥346·3 billion (US$44·4 billion, about 1·1% of the national gross domestic product [GDP]).19 Total economic losses attributed to the public health effects of pollution from particulate matter with a diameter of less than 10 μm (PM10) and sulphur dioxide (SO2) pollution in 74 cities were estimated to be as high as ¥439·8 billion ($70·9 billion, about 2·3% of these cities' GDP) in the first half of 2015.20 China will also have a massive future burden of non-communicable diseases—most of which will affect urban areas—that will strain future health systems and limit economic growth in the country.Total annual premature deaths from such diseases are expected to increase from 3·11 million in 2013 to 3·52 million in 2030.21 Between 2012 and 2030, economic losses attributable to five key non-communicable diseases–ischaemic heart disease, cerebrovascular disease, diabetes, breast cancer, and chronic obstructive pulmonary disease–will total $23·03 trillion USD,22 which is more than twice of China's total GDP in 2015 ($11·07 trillion).