Earths Future最新文献

Expanding public transportation and conserving biodiversity are two major components of the United Nations Sustainable Development Goals. However, these objectives often conflict, as habitat loss and fragmentation caused by transportation networks and subsequent development are regarded as significant threats to biodiversity. Quantifying the extent of these contradictions and identifying strategies for balancing these goals are essential. In this study, an index of natural habitat areas without roads (i.e., roadless areas) is employed to quantify the contradiction and analyze changes in China's roadless areas from 2000 to 2020, with projections for potential changes from 2020 to 2035. The findings indicate that China's roadless area decreased significantly from 4.94 million km² in 2000 to 4.31 million km² in 2020, reflecting a reduction rate of 12.7%. It is conservatively estimated that this loss will continue at a rate of 4.58% over the next 15 years. Currently, only 22.8% and 35.8% of the total roadless area in 2020 are situated within biodiversity conservation priority areas and protected areas, respectively, highlighting significant conservation gaps. To address the rapid decline and inadequate protection of roadless areas in China, this study proposes several recommendations, including optimizing transportation route planning to avoid unnecessary loss of roadless area, constructing wildlife crossing structures to enhance habitat connectivity, and integrating high ecological value roadless areas into the conservation framework. This study offers valuable insights into the effective protection of ecosystem integrity amidst the expansion of road networks and presents new solutions for ensuring timely compliance with international agreements, such as the Convention on Biological Diversity.

There is growing interest in how the climate would change under net zero carbon dioxide emissions pathways as many nations aim to reach net zero in coming decades. In today's rapidly warming world, many changes in the climate are detectable, even in the presence of internal variability, but whether climate changes under net zero are expected to be detectable is less well understood. Here, we use a set of 1000-year-long net zero carbon dioxide emissions simulations branching from different points in the 21st century to examine detectability of large-scale, regional and local climate changes as time passes under net zero emissions. We find that even after net zero, there are continued detectable changes to climate for centuries. While local changes and changes in extremes are more challenging to detect, Southern Hemisphere warming and Northern Hemisphere cooling become detectable at many locations within a few centuries under net zero emissions. We also study how detectable delays in achieving emissions cessation are across climate indices. We find that for global mean surface temperature and other large-scale indices, such as Antarctic and Arctic sea ice extent, the effects of an additional 5 years of high greenhouse gas emissions are detectable. Such delays in emissions cessation result in significantly different local temperatures for most of the planet, and most of the global population. The long simulations used here help with identifying local climate change signals. Multi-model frameworks will be useful to examine confidence in these changes and improve understanding of post-net zero climate changes.

Stratospheric aerosol injection (SAI) has been proposed as a complementary option to mitigate anthropogenic climate change risks. Using Community Earth System Model ensemble simulations, we assess the response of climate metrics relevant to a set of climate tipping elements in SAI scenarios targeting different temperature stabilization goals and for implementation at different latitudes. We analyze responses of tipping element metrics in simulations of a multi-objective SAI strategy that is designed to simultaneously stabilize global mean temperature (T0), interhemispheric temperature gradient (T1), and equator-to-pole temperature gradient (T2), as well as simulations of SAI strategies designed just to stabilize T0. We show that SAI strategies considered here would reduce the risks for many tipping elements, but may either increase or decrease the risk of Antarctic ice sheet collapse and Sahel greening, depending on the specifics of injection strategy. For the same 1.0°C temperature stabilization target, high-latitude injection would reduce the risk of northern cryosphere-related tipping elements more effectively, such as Greenland ice sheet, Barents winter sea ice, and boreal permafrost. Meanwhile, low-latitude injection would be more effective in stabilizing low-latitude biosphere-related tipping elements such as Amazon rainforest and coral reefs. The multi-objective SAI injection is more effective in reducing the risk of most high-latitude tipping elements than low-latitude injection, and is more effective in reducing the risk of most low-latitude tipping elements than high-latitude injection. Our study highlights the importance of careful consideration in the trade-offs between tipping element risk reduction and temperature pattern optimization in response to SAI strategies.

Addressing water scarcity requires significant attention to reducing water footprint (WF) related to food consumption. Since individuals' dietary behavior is largely influenced by their demographic and anthropometric attributes, it is crucial to identify individuals who have a high dietary WF and prioritize them as the focus of policies. Several studies analyzing the driving factors behind dietary WF exist but have multiple limitations. These include the statistical models with rather modest performances, lack of rigorous sensitivity analysis/feature importance (FI) analysis, and lack of generalization ability. Here, we developed a novel ML-based framework for analyzing the driving forces behind dietary WF. The framework incorporated three machine learning (ML) models (Extra-Trees (ET), Histogram-based Gradient Boosting (HGB), and eXtreme Gradient Boosting (XGB)) and an ML explanation approach Shapley Additive exPlanations (SHAP). This framework was applied to a case study on Chinese inhabitants. The derived results validated the proposed framework and demonstrated ML's superiority over conventional statistical methods. XGB was identified as the optimal model as it effectively captured the variability in the data and showed good generalization performance. The FI analysis for XGB revealed the most influential features on dietary WF, with income level, urbanization level, education level, and gender emerging as the top four features in descending order. Through the subsequent SHAP dependence analysis, the priority groups for dietary WF reduction interventions were identified as high-income residents, urban residents, highly educated residents, and male residents. In light of these findings and their underlying causes, the paper concluded with a set of policy recommendations.

The lack of seasonal observations of CO₂ in lakes at altitude gradients over the Qinghai-Tibet Plateau (QTP) limits the accurate assessment of CO₂ fluxes in lakes and the understanding of their variation mechanisms. We carried out seasonal observation of CO₂ fluxes in lakes at different elevation gradients over the QTP. We found that low-altitude (<3,000 m) lakes can release a large amount of CO₂ (1.03 Tg C a⁻¹), medium-altitude (3,000–4,500 m) lakes can reduce CO₂ release (0.04 Tg C a⁻¹), high-altitude (>4,500 m) lakes were capable of absorbing large amounts of CO₂ (−0.65 Tg C a⁻¹) from the atmosphere. The CO₂ fluxes of lakes on the QTP showed an inverse elevation effect. The multi-source data analysis showed that the high-altitude lakes of the QTP could also absorb a large amount of CO₂ (−0.25 Tg C a⁻¹) from the atmosphere from 2000s to 2020s, which was equivalent to 4.5% of the CO₂ absorbed by the terrestrial vegetation of the QTP.

Nitrogen cycles control the structure, function, and composition of ecosystems globally. Despite their importance, our understanding of long-term changes in global nitrogen cycles remains limited. The foliar nitrogen stable isotope ratio (δ¹⁵N) serves as a valuable metric for assessing changes in nitrogen cycling and potentially in plant nitrogen availability. However, existing observations of δ¹⁵N suffer from spatial bias and temporal discontinuity with contradictory findings across biomes, hindering our ability to detect and attribute drivers of change. Leveraging ground-based observations as our calibration source, we derived annual maps of foliar δ¹⁵N spanning from 1984 to 2022 globally from Landsat spectra. We found that the Landsat-derived δ¹⁵N effectively captured the observations, with an R² of 0.77 and a normalized root mean square error of 0.15. Globally, we found widespread temporal changes in δ¹⁵N with significant decreases for 44% and increases for 16% of vegetated ecosystems. Foliar δ¹⁵N mostly declined in forest ecosystems but increased in non-forest land cover types. Gross primary productivity and its trend consistently explained spatiotemporal variation of δ¹⁵N globally, indicating increasing plant demand could lead to decreasing δ¹⁵N. Our study presents an innovative approach to effectively monitor and track potential changes in global nitrogen cycles over the past four decades, setting the stage for more impactful management and conservation strategies.

Ensuring food security is crucial in the context of climate change and increased extreme weather events. Crop production depends not only on yield but also on the harvestable fraction (HF), the ratio of harvested areas to planted areas. While the impacts of climate fluctuations on crop yields are well-document, the role of HF-a critical yet underexplored factor-remains poorly understood. This study introduces HF as a key metric for assessing how temperature, drought, and precipitation affect the production of major staple crops (winter wheat, spring wheat, and maize) in the United States at the county level. Our findings indicate that Killing Degree Days (KDD) and Drought Days (DD) are key drivers of production declines, reducing both yield and HF. From 1982 to 2020, changes in KDD and DD led to more significant reductions in crop production in the Midwest compared to other regions. Projections for 2021–2100 under different Shared Socioeconomic Pathways (SSPs) indicate even steeper declines in yield, HF and production, especially under the high-emission scenario (SSP5-8.5), with anticipated net production decreases of 6.08%, 8.2%, and 7.57% for the three crops. Although HF may increase in colder regions, this does not offset losses in warmer areas, leading to net HF decreases of 0.36%, 2.09%, and 5.82% for the three crops. This study finds that drought and extreme heat considerably reduce food production by simultaneously lowering yields and HF and underscores the need for adaptive strategies that address both yield and HF to enhance food security in a changing climate.

Forest disturbances pose an increasing threat to the ecosystem services provided by tropical forests in the Congo Basin, yet their distribution remains poorly understood. To address this, we create a high-confidence forest disturbance data set for the Congo Basin from 2000 to 2022 by harmonizing the Global Forest Change (GFC) and Tropical Moist Forest (TMF) data sets. Though the Democratic Republic of Congo (DRC) accounted for the most disturbance observed (61,174 km²), we identify Cameroon as an emerging deforestation frontier. Among all six Congo Basin countries, only Cameroon saw a significant increasing annual contribution to the total forest disturbance observed in the Congo Basin over the past 20 years (slope = 0.49% yr⁻¹, p = 0.004) and the second-highest extent of forest disturbance (7,013 km²). Across the Congo Basin, disturbances mainly occurred near roads (26,737 km²) and outside formal land allocations (19,217 km²). In Cameroon, the extent of forest disturbance in community forests (5 ± 1%) was higher than in agro-industrial plantations (3 ± 2%) and logging concessions (3 ± 1%). We observe a basin-wide increase in the extent and frequency of forest disturbances, suggesting a shift toward commercial land-use practices associated with larger clearing. Our findings reveal changes in forest disturbance patterns in the Congo Basin over the past 20 years that warrant continued monitoring and improved understanding of their socioeconomic drivers to prevent large-scale deforestation as observed in the Amazon and Southeast Asia.

Recent advancements in observational data and experimental research have significantly enhanced our understanding of the mechanisms governing the magnitude, distribution, and dynamics of soil organic carbon (SOC) patterns. However, few studies have systematically explored the hierarchical drivers of SOC. This study addresses this gap by integrating multiple independent datasets—covering productivity, carbon allocation, carbon turnover, and carbon fractions—to construct a comprehensive framework for the hierarchical drivers shaping global SOC patterns. Using this framework, we examine the impact pathways and contributions of primary drivers. Our findings show that climate, as the fundamental primary driver, mainly influences SOC through carbon input pathways, while soil properties, as a secondary driver, predominantly affect SOC via carbon output pathways. Further analysis reveals that carbon input plays a key role in shaping topsoil SOC distribution, while carbon output is more influential in regulating subsoil SOC. In both cases, these drivers exert their effects primarily through stable organic carbon fractions, particularly mineral-associated organic carbon (MAOC). The influence of these drivers on particulate organic carbon (POC), however, is in the opposite direction. This study provides the first quantification of the impact pathways and relative strengths of the hierarchical drivers shaping global SOC. It also underscores the need to consider the hierarchical structure of these drivers when assessing the magnitude, distribution, and dynamics of SOC, particularly in relation to its fractions.