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This study innovatively evaluated ecological civilization in China from the perspective of environment and health. A Composite Environmental Health Index (CEHI) was constructed based on the Driving force-Pressure-State-Impact-Response (DPSIR) and Coupling Coordination Degree (CCD) models. Results showed that significant and sustained improvements were observed in the ecological environment after ecological civilization, while economic development continued to progress at a steady pace. However, the advancement in population health (impact subsystem), exhibited comparatively modest progress, potentially linked to issues such as demographic aging and the enduring consequences of past exposure to environmental pollutants. At the provincial level, the regional development was uneven. The CEHI performance was highest in the eastern regions, followed by the central regions, with the western regions showing the least progress. Beijing, Guangdong, Jiangsu, Shanghai, and Zhejiang emerged as top performers with higher CEHI scores, which can be attributed to their favorable geographical positioning and the response subsystem. Conversely, northeastern regions (Heilongjiang, Jilin, and Liaoning) and northwestern regions (Shanxi, Gansu, Ningxia, and Qinghai) experienced limited advancements in post-ecological civilization implementation. For these underperforming regions, there is a pressing need to intensify efforts aimed at enhancing their response subsystems. In summary, China's pursuit of ecological civilization has yielded significant successes, potentially offering valuable insights for other nations striving for sustainable development. The ecological civilization model's integration of ecological environmental protection into economic, political, cultural, and social constructs may serve as a meaningful reference for the sustainable development of other countries.

Understanding how spring phenology of temperate trees responds to global change is essential for assessing the vegetation dynamics in the future. Besides chilling and forcing temperatures, the influences of other environmental and biotic factors on leaf unfolding date (LUD) are still unclear. Based on long-term records (1960–2015) of LUD for 6 typical temperate tree species at 4242 stations in Europe, we first used a classic process-based model (the Unified model) to describe the relationship between chilling and forcing requirements of LUD. Furthermore, we used 3 explainable machine learning (ML) algorithms (RF, EBM, and GAMI-Net) to quantify the influences of 45 biotic and environmental factors on the LUD. The root-mean-square error (RMSE) of the ML-based models averaged from all species (7.03 to 7.33 days) was lower than the Unified model (8.73 days). The ML algorithms detected 2 biotic (elevation and previous leaf senescence date) and 3 temperature-related variables (chilling accumulation, freezing days in March and annual temperature range) with high importance for most species. The trees adapted to a higher elevation or with later leaf senescence date in the previous year exhibited later LUD. The increase in chilling accumulation, freezing days in March and the annual temperature range could reduce the forcing requirement of LUD. Water-related and light-related variables were only important for one or two species. The increase in relative humidity in February could advance LUD, while soil moisture in March had the opposite effect. The increase in March day length and downward surface shortwave radiation exerted an advancing influence on the LUD of light-sensitive species. Our results suggest that incorporating multiple biotic and environmental factors into ML algorithms not only could effectively improve the accuracy of phenological prediction but also enhance our understanding of how spring phenology responds to each factor.

Abstract In the post-COVID-19 pandemic era, the world faces a choice between trade protection and cooperation. However, current literature provides very little information on the benefit or loss of trade friction or cooperation on global economy and climate mitigation. This study applied the Global Trade Analysis Project model to assess the impacts of trade friction on global economic recovery and climate change. The results indicated that international trade friction can both delay global economic recovery and affect CO2 emission reduction. The shocks of consumption reduction and production suspension have a higher marginal effect in developing and emerging economies, whereas trade friction has a higher effect on developed countries. Trade friction has more negative economic effects for developed countries, but developing countries cannot reduce CO2 emissions proportionally with the decrease in trade and related production. In the post-pandemic era, if the global trade barrier increases, the world may face a co-occurring economic decline and an increase in or low abatement of carbon emissions.

Abstract Flash droughts pose large threats to crop yields and ecosystem services due to their sudden onset and rapid intensification, arousing wide public concern in a warming climate. Their long-term characteristics of change, underlying mechanisms, and especially potential impacts on agriculture, forests, and populations at a global scale, however, remain largely unknown. We used in situ observations, two observation-based global reanalysis data sets, and 22 Earth system models to determine that flash droughts are shifting toward more frequent, accelerated-onset, and longer duration. These changes increased the exposure of agricultural areas, forested areas, and populations to flash droughts by 20.3%, 17.1%, and 30.0%, respectively, during 2001–2020 compared to 1981–2000, with a disproportionate increase in integrated risks across the Amazon Basin and eastern and southern Asia. The increase in concurrent hot and dry climatic conditions driven by warming has been mostly responsible for enabling and intensifying flash droughts over large regions. State-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) models, however, failed to identify the acceleration of the onset time of flash droughts and widely underestimated the occurrence of flash droughts that are driven only by precipitation deficits or by heat waves, probably because they misrepresent the dependence between precipitation and temperature and underestimate the sensitivity of soil moisture to temperature and precipitation at short timescales (e.g., 5 days). These syntheses comprehensively advance our understanding of the characteristics and impacts of flash droughts but also highlight that the CMIP6 models need to be validated to represent the correct covariability between climatic variables at short timescales to provide more reliable projections of flash droughts.

The auto-generation of urban roads can greatly improve efficiency and productivity in urban planning and designing. However, it has also raised concerns amongst researchers over the past decade. In this paper, we present an image-based urban road network generation framework using conditional diffusion models. We first trained a diffusion model capable of generating road images with similar characteristics to the ground truth using four context factors. Then, we used the trained model as the generator to synthesize road images conditioned in a geospatial context. Finally, we converted the generated road images into road networks with several post-processes. The experiments conducted in five cities of the United States showed that our model can generate reasonable road networks, maintaining the layouts and styles of real examples. Moreover, our model has the ability to show the obstructive effect of geographic barriers on urban roads. By comparing models with different context factors as input, we find that the model that considers all four factors generally performs the best. The most important factor in guiding the shape of road networks is intersections, implying that the development of urban roads is not only restricted by the natural environment but is more strongly influenced by human design.

Abstract Hydroclimate reconstruction for the last millennium is essential to understand the differences in hydroclimate extremes and their causes under cold/warm conditions. In this study, the first gridded drought/flood grades (D/F grades) dataset in eastern China (EC) during the last millennium was generated. This D/F grades dataset mainly consisted of two components. The first section was created by interpolating drought/flood grades from 1500 to 2000 using the angular distance weight method. Sampling error estimates were employed to assess the effects of the interpolated dataset. The second section for the D/F grades dataset during 960–1500 was generated by constructing best subset regression models using selected tree-ring chronologies in the United States through atmospheric teleconnection. The validation parameters of the calibration equations were also derived, including the adjusted R 2 , predicted R 2 , RE, and CE. This dataset provides critical support for investigating the characteristics and causes of hydroclimate extremes in EC at various spatiotemporal scales, as well as the relationship with climate modes, such as El Niño-Southern Oscillation, Pacific Decadal Oscillation, and East Asia Summer Monsoon.

This paper shows that desertification combating practices decline incomes of farmers and herders, and China needs to adapt its ecological programmes to address the impacts of climate change and create positive synergies to combat desertification.

Human security is threatened by terrorism in the 21st century. A rapidly growing field of study aims to understand terrorist attack patterns for counter-terrorism policies. Existing research aimed at predicting terrorism from a single perspective, typically employing only background contextual information or past attacks of terrorist groups, has reached its limits. Here, we propose an integrated deep-learning framework that incorporates the background context of past attacked locations, social networks, and past actions of individual terrorist groups to discover the behavior patterns of terrorist groups. The results show that our framework outperforms the conventional base model at different spatio-temporal resolutions. Further, our model can project future targets of active terrorist groups to identify high-risk areas and offer other attack-related information in sequence for a specific terrorist group. Our findings highlight that the combination of a deep-learning approach and multi-scalar data can provide groundbreaking insights into terrorism and other organized violent crimes.

Objectives: Understand whether and how the COVID-19 pandemic affects the risk of different types of conflict worldwide in the context of climate change. Methodology: Based on the database of armed conflict, COVID-19, detailed climate, and non-climate data covering the period 2020–2021, we applied Structural Equation Modeling specifically to reorganize the links between climate, COVID-19, and conflict risk. Moreover, we used the Boosted Regression Tree method to simulate conflict risk under the influence of multiple factors. Findings: The transmission risk of COVID-19 seems to decrease as the temperature rises. Additionally, COVID-19 has a substantial worldwide impact on conflict risk, albeit regional and conflict risk variations exist. Moreover, when testing a one-month lagged effect, we find consistency across regions, indicating a positive influence of COVID-19 on demonstrations (protests and riots) and a negative relationship with non-state and violent conflict risk. Conclusion: COVID-19 has a complex effect on conflict risk worldwide under climate change. Implications: Laying the theoretical foundation of how COVID-19 affects conflict risk and providing some inspiration for the implementation of relevant policies.

Abstract Given that armed conflict has been seriously impeding sustainable development, reducing the frequency and intensity of armed conflicts has become an explicit goal and a common theme of the 2030 Sustainable Development Goals. Determining the factors shaping armed conflict risks in different regions could support formulating region-specific strategies to prevent armed conflicts. A machine learning approach was applied to reveal the drivers of, and especially the impact of climatic conditions on, armed conflict in Sub-Saharan Africa, the Middle East, and South Asia and characterizes their changes over time. The analyses show a rising impact of climatic conditions on armed conflict risk over the past decades, although the influences vary regionally. The overall percentage increases in the contribution of climatic conditions to conflict risks over the last 30 years in Sub-Saharan Africa, the Middle East, and South Asia are 4.25, 4.76, and 10.65 percentage points, respectively. Furthermore, it is found that the Climatic–Social–Geographical (“C–S–G”) patterns that characterize armed conflict risks vary across the three studied regions, while each regional pattern remains relatively stable over time. These findings indicate that when devising defenses against conflicts, it is required to adapt to specific situations in each region to more effectively mitigate the risk of armed conflict and pursue Sustainability Development Goals.

Measurement(s) irrigation area and distribution Technology Type(s) statistics and satellite remote sensing Factor Type(s) agricultural irrigation Sample Characteristic - Organism agricultural irrigation Sample Characteristic - Environment agricultural field Sample Characteristic - Location China

Using machine learning, the authors reveal that stable background conditions explain most variation in armed conflict risk worldwide. Positive temperature deviations and precipitation extremes also increase the risk of conflict onset and incidence.

Agricultural irrigation is an important vehicle for increasing crop yield, but large-scale irrigation has posed great challenges to global and regional water availability and climate change via altering land–atmosphere interactions. The knowledge of irrigation distribution is essential to understand regional water cycles and guide agricultural management decision-making, but such information is scarce in China. We developed a remote sensing-dominated framework to map irrigated croplands in China at 500 m resolution using a synergetic training sample generating method, machine learning classifier, and a cloud computing platform (Google Earth Engine, GEE). To overcome the challenges of lacking nationwide training samples, we first produced two provisional irrigation maps by fusing statistics and MODIS-derived annual peak greenness indices. The two provisional irrigation maps were then spatially filtered with an existing irrigation product (GRIPC) to construct the initial training sample pool. Next, to enhance the robustness and cover more irrigated candidates, we screened and introduced the irrigated croplands in three land use/cover maps (CCI-LC, GLC_FCS, and NLCD) to supplement the training data pool. Afterward, we utilized locally adaptive random forest classifiers and data cubes (MODIS-derived spectral indices, climatic and topographic variables) to generate irrigation maps in each province of China on GEE. The resulting map outperformed other current irrigation maps with an overall accuracy of 79.2% . The map also showed a reasonable consistency with statistical data at the province and prefecture levels, with the determination coefficient (R2) of 0.89 and 0.77, respectively. In total, we identified 87.04 million hectares of irrigated croplands in mainland China in 2015. Using the resulting map and water use statistics, we found a high correlation between irrigation area and agricultural water use in Northwest, Northeast, and South China, and a low correlation in North China Plain. This map is expected to serve national water resource management and assist decision-making in improving agricultural adaption to climate change.

Abstract Global food security is directly linked to human health and sustainable development. It is critical to comprehensively twig future changes in global food demand and progress in implementing the United Nations Sustainable Development Goals (SDGs), considering the transition of dietary patterns. We presented a global‐scale analysis quantifying the status of national food consumption and SDG performance in 2017, and the food demands and SDG index scores in 2030 were predicted under three healthy dietary patterns using 11 SDG indicators. High‐income nations scored well on most SDG indicators but poorly on food waste and the environment. Total global food demand was projected to be the highest under the national dietary guidelines pattern (12.69–13.37 million ton/d) and the lowest under the WHO healthy diet pattern (10.55–11.18 million ton/d). However, food demand under different dietary patterns varied among regions. Transitioning from current diets to healthy diets was projected to gain SDG index score for most countries. Strategies for shifts in dietary patterns to determine the tradeoff between global food security and sustainable development should be tailored to local conditions. The EAT‐Lancet dietary pattern is an optimal choice for determining the tradeoff between food security and sustainable development on a global scale. Given the current status of food access and nutrition, the National and the WHO dietary patterns are recommended for Asia and Africa, respectively.

Spring warming could induce earlier leaf-out or flowering of temperate plant species, and decreased chilling in winter has a delaying effect on spring phenology. However, the relative contribution of the decreased chilling and increased forcing on spring phenological change is unclear. Here, we analyzed the experimental data for 14 temperate woody species in Beijing, China and quantified the forcing requirements (FR) of spring phenology and chilling sensitivity (the ratio of the FR at the low chilling condition to the FR at the high chilling condition) for each species. Furthermore, using species-specific functions between the amount of chilling and FR, we established a phenological model to simulate the annual onset dates of spring events during the past 69 years (1952–2020) and in the future (2021–2099) under RCP 4.5 and RCP 8.5 climate scenarios. We also developed a novel method to quantitatively split the predicted phenological change into the effects caused by changes in forcing and those caused by changes in chilling. The results show that the FR of spring events decreased with the increase in the amount of chilling, and this relationship could be described as an exponential decay function. The basic FR (the FR at the high chilling condition) and chilling sensitivity varied greatly among species. In the 1952–2020 period, the advancing effect of increased forcing was stronger than the effect of chilling, leading to earlier spring events with a mean trend of −1.96 days/decade. In future climate scenarios, the spring phenology of temperate species would continue to advance but will be limited by the decreased chilling. The species with lower chilling sensitivities showed stronger trends than those with high chilling sensitivities. Our results suggested that the delaying effect of declining chilling could only slow down the spring phenological advance to a certain extent in the future.

Climate warming is advancing spring leaf unfolding, but it is also reducing the cold periods that many trees require to break winter dormancy. Here, the authors show that 7 of 12 current chilling models fail to account for the correct relationship between chilling accumulation and heat requirement, leading to substantial overestimates of the advance of spring phenology under climate change.

Cement plays a dual role in the carbon cycle like a sponge. Here, the authors employ a dynamic model to quantify such sponge effect and concluded that deep decarbonization of the global cement cycle will require radical technology advancements and widespread deployment of material efficiency measures.

The Tibetan Plateau, recognized as Earth's third pole and among the most responsive regions to climate shifts, profoundly influences regional and even global hydrological processes. Here, we discerned a significant weakening in the influence of temperature on the initiation of surface freeze-thaw cycle (the Start of Thawing, SOT), which can be ascribed to a multitude of climatic variables, with radiation emerging as the most pivotal factor. Additionally, we showed that the diminishing impact of warming on SOT yields amplified soil moisture within the root zone. This, in turn, fosters a greening third pole with increased leaf area index and solar-induced chlorophyll fluorescence. We further showed that current Earth system models failed to reproduce the linkage between weakened sensitivity and productivity under various shared socioeconomic pathways. Our findings highlight the dynamic shifts characterizing the influence of climate warming on spring freeze-thaw process and underscore the profound ecological implications of these changes in the context of future climate scenarios. [ABSTRACT FROM AUTHOR]

Abstract Precipitation extremes are among the most serious consequences of climate change around the world. The observed and projected frequency and intensity of extreme precipitation in some regions will greatly influence the social economy. The frequency of extreme precipitation and the population and economic exposure were quantified for a base period (1986–2005) and future periods (2016–2035 and 2046–2065) based on bias corrected projections of daily precipitation from five global climatic models forced with three representative concentration pathways (RCPs) and projections of population and gross domestic product (GDP) in the shared socioeconomic pathways (SSPs). The RCP8.5‐SSP3 scenario produces the highest global population exposure for 2046–2065, with nearly 30% of the global population (2.97 × 109 persons) exposed to precipitation extremes >10 days/a. The RCP2.6‐SSP1 scenario produces the highest global GDP exposure for 2046–2065, with a 5.56‐fold increase relative to the base period, of up to (2.29 ± 0.20) × 1015 purchasing power parity $‐days. Socioeconomic effects are the primary contributor to the exposure changes at the global and continental scales. Population and GDP effects account for 64–77% and 78–91% of the total exposure change, respectively. The inequality of exposure indicates that more attention should be given to Asia and Africa due to their rapid increases in population and GDP. However, due to their dense populations and high GDPs, European countries, that is, Luxembourg, Belgium, and the Netherlands, should also commit to effective adaptation measures.

The effects of winter chilling, spring forcing temperature, and photoperiod on spring phenology are well known for many European and North American species, but the environmental cues that regulate the spring phenology of East Asian species have not yet been thoroughly investigated. Here, we conducted a growth chamber experiment to test the effects of chilling (controlled by different lengths of exposure to natural chilling conditions), forcing temperature (12, 15, or 18°C) and photoperiod (14 or 10 h) on first flowering date (FFD) of six woody species (three shrubs and three trees) native to East Asia. The three-way analysis of variance (ANOVA) separately for each species showed that the effects of chilling and forcing temperature were significant for almost all species (P < 0.05). Averaged over all chilling and photoperiod treatments, the number of days until FFD decreased by 2.3–36.1 days when the forcing temperature increased by 3°C. More chilling days reduced the time to FFD by 0.7–26 days, when averaged over forcing and photoperiod treatments. A longer photoperiod could advance the FFD by 1.0–5.6 days, on average, but its effect was only significant for two species (including one tree and one shrub). The effects of forcing temperature and photoperiod interacted with chilling for half of the studied species, being stronger in the low chilling than high chilling treatment. These results could be explained by the theory and model of growing degree-days (GDD). Increased exposure to chilling coupled to a longer photoperiod reduced the GDD requirement for FFD, especially when plants grew under low chilling conditions. However, shrubs (except Viburnum dilatatum) had lower chilling and heat requirements than trees, suggesting that, by leafing out sooner, they engage in a more opportunistic life strategy to maximize their growing season, especially before canopy closure from trees' foliage. Our results confirmed the varying effects of these three cues on the flowering phenology of woody species native to East Asia. In future climate change scenarios, spring warming is likely to advance the spring phenology of those woody species, although the reduced chilling and shorter photoperiod may partly offset this spring warming effect.

Climate change could affect botanical tourism by altering the plant phenology (e.g., flowering and leaf coloring date) and the physical comfort of tourists. To date, few studies have simultaneously considered the influence of plant phenology and physical comfort on the travel suitability of botanical tourism. Taking Beijing as an example, this study used phenological data of 73 species from 1963 to 2017 to construct a phenological ornamental index (POI) according to the flowering and leaf coloring date of ornamental plant. The climate comfort index (CCI) of tourism was calculated by using meteorological data of the corresponding periods. Finally, the travel suitability index (TSI) was constructed by integrating the two indices (POI and CCI). The POI showed that the best period for spring flower viewing was from April 4 to May 10, while the best period for autumn leaves viewing was from October 11 to November 6 on average. According to the variation of the CCI within the year, the most comfortable period for spring tourism was matched with the best period for spring flower viewing (April 4 to June 1), but the most comfortable period for autumn tourism (September 4 to October 19) was earlier than the best period for autumn leaves viewing. The TSI indicated that the best periods for spring and autumn botanical tourism were April 7 to May 10 and October 10 to November 7, respectively. Based on the climate data under different scenarios (representative concentration pathways 4.5 and 8.5), we simulated the climate and phenological suitability for botanical tourism in the next thirty years. The results showed that the best period for spring botanical tourism during 2040–2050 was earlier and the period for autumn botanical tourism was later than that in the past 55 years. Meanwhile, the duration would shorten by 2–7 days for both seasons. This study provided a reference for assessing the impact of global climate change on the best season of botanical tourism.

‘Belt and Road Initiative’ (B&R) countries play critical roles in mitigating global carbon emission under the Paris agreement, but their driving factors and feasibility to reduce carbon emissions remain unclear. This paper aims to identify the main driving factors (MDFs) behind carbon emissions and predict the future emissions trajectories of the B&R countries under different social-economic pathways based on the extended STIRPAT (stochastic impacts by regression on population, affluence, and technology) model. The empirical results indicate that GDP per capita and energy consumption structure are the MDFs that promote carbon emission, while energy intensity improvement is the MDF that inhibits carbon emission. Population, as another MDF, has a dual impact across countries. The carbon emissions in all B&R countries are predicted to increase from SSP1 to SSP3, but emissions trajectories vary across countries. Under the SSP1 scenario, carbon emissions in over 60% of B&R countries can peak or decline, and the aggregated peak emissions will amount to 21.97 Gt in 2030. Under the SSP2 scenario, about half of the countries can peak or decline, while their peak emissions and peak time are both higher and later than SSP1, the highest emission of 25.35 Gt is observed in 2050. Conversely, over 65% of B&R countries are incapable of either peaking or declining under the SSP3 scenario, with the highest aggregated emission of 33.10 Gt in 2050. It is further suggested that decline of carbon emission occurs when the inhibiting effects of energy intensity exceed the positive impacts of other MDFs in most B&R countries.

As the largest emitter in the world, China recently pledged to reach a carbon peak before 2030 and carbon neutrality before 2060, which could accelerate the progress of mitigating negative climate change effects. In this study, we used the Minimum Complexity Earth Simulator and a semi-empirical statistical model to quantify the global mean temperature and sea-level rise (SLR) response under a suite of emission pathways that are constructed to cover various carbon peak and carbon neutrality years in China. The results show that China will require a carbon emission reduction rate of no less than 6%/year and a growth rate of more than 10%/year for carbon capture capacity to achieve carbon neutrality by 2060. Carbon peak years and peak emissions contribute significantly to mitigating climate change in the near term, while carbon neutrality years are more influential in the long term. Mitigation due to recent China’s pledge alone will contribute a 0.16 °C–0.21 °C avoided warming at 2100 and also lessen the cumulative warming above 1.5 °C level. When accompanied by coordinated international efforts to reach global carbon neutrality before 2070, the 2 °C target can be achieved. However, the 1.5 °C target requires additional efforts, such as global scale adoption of negative emission technology for CO _2 , as well as a deep cut in non-CO _2 GHG emissions. Collectively, the efforts of adopting negative emission technolgy and curbing all greenhouse gas emissions will reduce global warming by 0.9 °C −1.2 °C at 2100, and also reduce SLR by 49–59 cm in 2200, compared to a baseline mitigation pathway already aiming at 2 °C. Our findings suggest that while China’s ambitious carbon-neutral pledge contributes to Paris Agreement’s targets, additional major efforts will be needed, such as reaching an earlier and lower CO _2 emission peak, developing negative emission technology for CO _2 , and cutting other non-CO _2 GHGs such as N _2 O, CH _4 , O _3 , and HFCs.

Climate change effects on global food security are not only limited to its effects on the yield of cereals but also their nutritional quality. However, climate change effects on crop nutritional quality, particularly grain protein concentration (PC) on a large geographical scale have not yet been quantified in China. For this purpose, we assessed the effects of three key climatic factors (temperature, precipitation, and solar radiation) on wheat PC in ten wheat-growing areas of China using a series of statistical models on a county-level PC dataset. The results demonstrated that the average PC in China from 2006 to 2018 ranged from 12.01% to 14.50% across the ten areas, with an obvious spatial difference pattern showing an increase in PC from south to north and from west to east. The sensitivity analysis indicated that PC showed a positive response to variation in the increasing temperature, and the PC of wheat grown in the Huanghuai area was less affected than the PC of wheat grown in other areas. Conversely, solar radiation posed negative effects on the PC in the southwestern area, whereas precipitation had intricate effects on the PC in all areas. Besides, the highest explanation of climate variability during five growth periods contributed 26.0%–47.6% of the PC variability in the northeastern area, whereas the lowest explanation of climate variability during five growth periods only accounted for 2.5%–3.7% of PC variability in the Yangtze River area. Our study further demonstrated that the effects of climate change on wheat grain PC in China were spatially heterogeneous with higher effects on PC in spring wheat-growing areas as compared to winter wheat-growing areas. We suggested that the northern and the northeastern area in China could be developed as alternative areas to produce wheat with high grain PC in the face of climate warming.

China has committed to ambitious targets to reduce its carbon emissions in the next decades, in order to combat climate change and improve the environment. The realization of the targets depends on the fair and effective mitigation plans of all provinces. However, with varying ecological and environmental conditions and social-economic development, it is a critical issue to quantify the provinces’ efforts equally. This paper proposed a comprehensive fE index in coordinating ecology, equity and economy, by accounting for carbon emissions and sinks to characterize provincial carbon emission status in China, from 2000 to 2017, which shows a spatial pattern of “boundary high, central low”. The provinces with higher fE value (>1.5) in boundary areas can be seen as “relative equality” provinces with good ecology circulation, equity and economic efficiency. The provinces with lower fE value (2 than their equity share by occupying the carbon emission space of other provinces in recent decades. These results could provide a reference for a provincial guide for carbon reduction and sustainable development of the low-carbon economy.

Research Highlights: This study looks at poplar canker caused by Cytospora chrysosperma as a geographical phenomenon, and it applies spatial statistics to reveal the pattern and aggregation effects of the disease on a large scale in time and space. The incidence area of poplar canker in Northeast China has spatial (spatiotemporal) aggregation effects, which emphasize the importance of coordinated prevention. The results of spatial and spatiotemporal clusters can guide specific regional prevention and indicate the possible predisposing factors, respectively. Background and Objectives: Poplar canker, a harmful forest biological disease that is widespread throughout Northeast China, brings enormous ecological and economic losses. The limited cognition of its spatiotemporal pattern and aggregation effects restricts the decision-making for regional prevention and the identification of disease-inducing conditions. This study aims to explore the spatiotemporal pattern and to detect the aggregation effects of the disease, trying to provide references for prevention. Materials and Methods: According to the incidence data of poplar canker reported by each county in Northeast China from 2002 to 2015, we mapped the distribution of the incidence rate in ArcGIS and performed retrospective scan statistics in SaTScan to detect the spatial and spatiotemporal aggregation effects of the incidence area. Results: The spatiotemporal pattern of poplar canker’s incidence rate presents the characteristic of “outbreak-aggregation-spread-stability.” The incidence area of the disease when we performed spatial aggregation scan statistics showed the primary cluster covering Liaoning province (LLR = 86469.86, p < 0.001). The annual spatial scan statistics detected a total of 14 primary clusters and 37 secondary clusters, indicating three phases of aggregation. The incidence area of disease also shows spatiotemporal aggregation effects with the primary cluster located around Liaoning province, appearing from 2009 to 2015 (LLR = 64182.00, p < 0.001). Conclusions: The incidence area of poplar canker presents significant characteristics of spatial and spatiotemporal aggregation, and we suggest attaching importance to the clues provided by the aggregation effects in disease prevention and identification of predisposing factors.

Spring phenology is a sensitive indicator of climate change and has substantial impacts on the carbon cycle. The global N cycle has been greatly disturbed by anthropogenic activities resulting in altered atmospheric N deposition worldwide. Research has been focused on the changes in the spring phenology and its covariations with climatic factors. However, the influences of N deposition on spring phenology have not been well documented to date. Herein, we investigated the effects of N deposition on the start of growing season (SOS) in continental United States (CONUS) during the years 1986–2015 using the normalized difference vegetation index (NDVI) datasets derived from both the third generation NDVI dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS). We observed that N deposition could only explain approximately 5% of temporal variation in SOS in CONUS. However, the sensitivities of SOS in response to unit change in both temperature (S _T ) and precipitation (S _P ) showed clear decreasing spatial patterns with increasing N deposition. The S _T generally decreased from −6 d/°C in low N deposition regions (4 kg ha ^−1 . Furthermore, the positive S _P also showed a continuously decreasing pattern with the increase in N deposition, but the negative S _P was gradually weakened when N deposition was >1.0 kg ha ^−1 . The results have important implications as it reveals the role of N deposition on spring plant phenology, and strongly suggest the consideration of N deposition effects when analyzing or predicting spring phenology in response to future climate change.

Volcanic eruptions, climate changes and their influences on crop harvests and social development are of increasing concern in science communities. Using a dataset of crop harvest scores of southwest China from 1730 to 1910, which was derived from the memorials to the emperors in the Qing Dynasty of China, reconstructed climate proxies and the chronology of large volcanic eruptions occurring between 10°S and 15°N, we analysed possible relationships between crop harvests, climate changes and volcanic eruptions. In addition, some archives of policies and measures related to crops and social development extracted from the chronicles were used to analyse social resilience when faced with poor harvests. The results show that crop harvests in the study area generally increased with fluctuations when there were less low-latitude large volcanic eruptions from 1730 to 1810. However, from 1811 to 1910, volcanoes at low latitudes erupted more frequently, which contributed to concurrent low temperature and drought. Meanwhile, the crop harvests showed a step-down decrease during the following periods of 1810s, 1850s, 1870s and 1890s. Though, the local social system was certainly resilient in facing of such climate and agriculture disasters, i.e. the local society remained stable without significant famine, large-scale migration or social unrest until 1911. The strong resilience of local social systems owed largely to various relieving measures, such as, building barns, exempting or reducing local taxes, allocating farmland to immigrants, and central government dominated grain purchasing and distribution to alleviate disasters.

Over the past four decades, large-scale ecological programs, including the ‘Great Green Wall Program’ (1978–present), ‘Grain for Green Program’ (1999–present), ‘Grassland Ecological Protection Program’ (2000–present), and ‘Beijing-Tianjin Sandstorm Source Control Project’ (2002–present), were launched to restore vegetation, to combat desertification, and to control dust storms in arid and semiarid China. The gross investments of these programs have exceeded 1700 billion RMB (∼260 billion USD, accounting for ∼1% GDP in the region) by now, however, the effects of these programs on vegetation restoration have not been settled. In this study, the vegetation indices, land-uses, and climatic factors were used to estimate the contribution of the above programs on vegetation restoration. The results showed that consistent vegetation restoration has occurred in about 45.3% of the vegetated areas in arid and semiarid China from 1982 to 2000, and the percentage decreased to 33.6% after 2000 despite more ecological programs have been launched. Changes in climatic factors (precipitation, temperature, solar radiation, and wind speed) and elevated atmospheric CO _2 concentration contributed more than 50% and 70% to vegetation restoration in periods of 1982–2000 and 2001–2015, respectively, however, the contribution rate of ecological programs kept stable at about 20%. Climate changes in the past forty years played a dominant role, although the ecological programs showed a noticeable effect on vegetation restoration. Further investment in ecological restoration practices might need to be critically evaluated on the cost-effectiveness.

Climate change, induced by human greenhouse gas emission, has already influenced the environment and society. To quantify the impact of human activity on climate change, scientists have developed numerical climate models to simulate the evolution of the climate system, which often contains many parameters. The choice of parameters is of great importance to the reliability of the simulation. Therefore, parameter sensitivity analysis is needed to optimize the parameters for the model so that the physical process of nature can be reasonably simulated. In this study, we analyzed the parameter sensitivity of a simple carbon-cycle energy balance climate model, called the Minimum Complexity Earth Simulator (MiCES), in different periods using a multi-parameter sensitivity analysis method and output measurement method. The results show that the seven parameters related to heat and carbon transferred are most sensitive among all 37 parameters. Then uncertainties of the above key parameters are further analyzed by changing the input emission and temperature, providing reference bounds of parameters with 95% confidence intervals. Furthermore, we found that ocean heat capacity will be more sensitive if the simulation time becomes longer, indicating that ocean influence on climate is stronger in the future.

To limit global warming to well below 2°C in accord with the Paris Agreement, countries throughout the world have submitted their Intended Nationally Determined Contributions (INDCs) outlining their greenhouse gas (GHG) mitigation actions in the next few decades. However, it remains unclear what the resulting climate change is in response to the proposed INDCs and subsequent emission reductions. In this study, the global and regional warming under the updated INDC scenarios was estimated from a range of comprehensive Earth system models (CMIP5) and a simpler carbon-climate model (MAGICC), based on the relationship of climate response to cumulative emissions. The global GHG emissions under the updated INDC pledges are estimated to reach 14.2∼15.0 GtC/year in 2030, resulting in a global mean temperature increase of 1.29∼1.55°C (median of 1.41°C) above the preindustrial level. By extending the INDC scenarios to 2100, global GHG emissions are estimated to be around 6.4∼9.0 GtC/year in 2100, resulting in a global mean temperature increase by 2.67∼3.74°C (median of 3.17°C). The Arctic warming is projected to be most profound, exceeding the global average by a factor of three by the end of this century. Thus, climate warming under INDC scenarios is projected to greatly exceed the long-term Paris Agreement goal of stabilizing the global mean temperature at to a low level of 1.5‐2.0°C above the pre-industrial. Our study suggests that the INDC emission commitments need to be adjusted and strengthened to bridge this warming gap.