Earths Future最新文献

Climate projections are being used for decision-making related to climate mitigation and adaptation and as inputs for impacts modeling related to climate change. The plethora of available projections presents end users with the challenge of how to select climate projections, known as the “practitioner's dilemma.” In addition, if an end-user determines that existing projections cannot be used, then they face the additional challenge of producing climate projections for their region that are useful for their needs. We present a methodology with novel features to address the “practitioner's dilemma” for generating downscaled climate projections for specific applications. We use the Edwards Aquifer region (EAR) in south-central Texas to demonstrate a process to select a subset of global climate models from both the CMIP5 and CMIP6 ensembles, followed by downscaling and verification of the accuracy of downscaled data against historical data. The results show that average precipitation changes range from a decrease of 10.4 mm to an increase of 25.6 mm, average temperature increases from 2.0°C to 4.3°C, and the number of days exceeding 37.8°C (100°F) increase by 35–70 days annually by the end of century. The findings enhance our understanding of the potential impacts of climate change on the EAR, essential for developing effective regional management strategies. Additionally, the results provide valuable scenario-based projected data to be used for groundwater and spring flow modeling and present a clearly documented example addressing the “practitioner's dilemma” in the EAR.

Floods, droughts, and rainfall-induced landslides are hydro-hazards that affect millions of people every year. Anticipation, mitigation, and adaptation to these hazards is increasingly outpaced by their changing magnitude and frequency due to climate change. A key question for society is whether the research we pursue has the potential to address knowledge gaps and to reduce potential future hazard impacts where they will be most severe. We use natural language processing, based on a new climate hazard taxonomy, to review, identify, and geolocate out of 100 million abstracts those that deal with hydro-hazards. We find that the spatial distribution of study areas is mostly defined by human activity, national wealth, data availability, and population distribution. Hydro-hazard events that impact large numbers of people lead to increased research activity, but with a strong disparity between low- and high-income countries. We find that 100 times more people need to be affected by hazards before low-income countries reach comparable research activity to high-income countries. This “Wealth over Woe” bias needs to be addressed by enabling and targeting research on hydro-hazards in highly impacted and under-researched regions, or in those sufficiently socio-hydrologically similar. We urgently need to reduce knowledge base biases to mitigate and adapt to changing hydro-hazards if we want to achieve a sustainable and equitable future for all global citizens.

Biodiversity credits are increasingly recognized as a potential instrument to incentivize and bolster efforts in biodiversity conservation. Nevertheless, their efficacy is impeded by a dearth of research. To mitigate these constraints, this study introduces a comprehensive and integrated framework for appraising biodiversity credits. Drawing upon the Emergy Accounting methodology, the framework encompasses four key perspectives: Emergy-based Ecosystem Potential (EEP), Emergy-based Ecosystem Network (EEN), Emergy-based “Species' to Human” contributions (ESH), and Emergy-based Species' Significance. Furthermore, this study scrutinizes the trajectory of biodiversity credits across 31 provinces spanning from 2000 to 2050, considering 220 distinct scenarios. The findings reveal that China has attained the no net loss (NNL) objective concerning conventional area-based conservation targets, with forest cover encompassing 27% of the total land area. However, biodiversity credits at the ecosystem level exhibit an escalating trend, with growth rates ranging from 0.73% to 1.0%, while credits at the species level depict a decremental trend, with an approximate growth rate of −0.21%. Under a scenario of moderate growth, projections for the year 2030 indicate that the EEP credit is poised to accrue approximately 4.76E + 20 solar emjoules (sej), the EEN credit is forecasted to accumulate around 1.03E + 21 sej, and the ESH credit is anticipated to decline by 1.46E + 23 sej within the context of the NNL paradigm. These outcomes underscore the necessity of delineating differentiated biodiversity goals, and furnish insights into the dynamics of supply and demand pertaining to biodiversity credits within the ambit of offsetting schemes across the nation.

Adaptive decision-making allows decision-makers to plan long-term coastal infrastructure under uncertain sea level rise projections. To date, economic assessments of adaptive decision-making that take into account future learning about sea level rise uncertainty are rare and the existing ones have relied on simple quantification of future learning not validated against sea level science. To address this gap, we develop an economic adaptive decision-making framework that takes into account future learning about sea level rise uncertainty and apply it to a coastal case study in Lübeck, Germany, to answer the question of how adaptation to sea level rise can be improved through adaptive adaptation pathways as opposed to non-adaptive pathways. To address this question, we use a Markov decision process to formulate the stochastic optimization problem. We quantify future learning about sea level rise uncertainty through sea level rise learning scenarios based on and validated against the latest scenarios of the Intergovernmental Panel on Climate Change. Our case study results show that the city of Lübeck is currently under-protected against storm surges and that immediate adaptation actions are advisable in the face of future sea level rise. We find that adaptive adaptation pathways, in contrast to non-adaptive pathways, generate sea level rise thresholds for adaptation actions that are similar across climate change scenarios and can reduce expected costs up to 1.8%.

Climate change-induced heat stress has significant effects on human health, and is influenced by a wide variety of factors. Most assessments of future heat-related risks however are based on coarse resolution projections of heat hazards and overlook the contribution of relevant factors other than climate change to the negative impacts on health. Research highlights sociodemographic disparities related to heat stress vulnerability, especially among older adults, women and individuals with low socioeconomic status, leading to higher morbidity and mortality rates. There is thus an urgent need for detailed, local information on demographic characteristics underlying vulnerability with refined spatial resolution. This study aims to address the research gaps by presenting a new population projection exercise at high-resolution based on the Bayesian modeling framework for the case study of Madrid, using demographic data under the scenarios compatible with the Shared Socioeconomic Pathways. We examine the spatial and temporal distribution of population subgroups at the intra-urban level within Madrid. Our findings reveal a concentration of vulnerable populations, as measured by their age, sex and educational attainment level in some of the city's most disadvantaged neighborhoods. These vulnerable clusters are projected to widen in the future unless a sustainable trajectory is realized, driving vulnerability dynamics toward a more uniform and resilient change. These results can guide local adaptation efforts and support climate justice initiatives to protect vulnerable communities in urban environments.

As the world's largest carbon emitter, China has been confronting the dual challenge of climate change and air pollution. China's quest for reducing carbon emissions will promisingly benefit the air quality, yet its impact on carbon sinks remains unclear. Here, we assess the effect of China's clean air actions and carbon neutrality policy on air quality and its associated co-benefits for terrestrial carbon sinks by integrating multiple observations and numerical modeling. We find a quadratic response of plant photosynthesis to aerosol loading due to trade-offs between diffuse fertilization effect and light limitations. The estimations show that China's air pollution suppresses terrestrial carbon uptake through aerosol-induced light limitations, leading to a 7.3% decrease in plant productivity in the 2010s. In the context of carbon neutrality pledge, the associated aerosol reductions tend to alleviate the suppression and produce an additional CO₂ removal of 0.39 GtCO₂ year⁻¹. Our results uncover the enhanced terrestrial carbon sinks by aerosol mitigation, highlighting the synergy between carbon neutrality and clean air.

Coastal change and evolution are the product of physical drivers (e.g., waves) tightly coupled with human behavior. As climate change impacts intensify, demand is increasing for information on where, when, and how coastal areas may change in the future. Although considerable research investments have been made in understanding the physical drivers and processes that modify and shape coastal environments, many do not account for human behavior, compromising the accuracy of comprehensive future change predictions. We outline four social science approaches—historic case studies, simulations, longitudinal studies, and longitudinal studies supported by experimental data—that can be coupled with physical change information to support transdisciplinary understanding of future change. A fundamental need for each approach is more and better empirical data to better gauge human behavior. In addition, foundational investments in transdisciplinary collaboration help research teams support the integration of these approaches.

Permafrost, widely distributed in the Northern Hemisphere, plays a vital role in regulating heat and moisture cycles within ecosystems. In the last four decades, due to global warming, permafrost degradation has accelerated significantly in high latitudes and altitudes. However, the impact of permafrost degradation on vegetation remains poorly understood to date. Based on active layer thickness (ALT) monitoring data, meteorological data and normalized difference vegetation index (NDVI) data, we found that most ALT-monitored sites in the Northern Hemisphere show an increasing trend in NDVI and ALT. This suggests an overall increase in NDVI from 1980 to 2021 while permafrost degradation has been occurring. Permafrost degradation positively influences NDVI growth, with the intensity of the effects varying across land cover types and permafrost regions. Furthermore, based on Mann-Kendall trend test, we detected abrupt changes in NDVI and environmental factors, further confirming that there is a strong consistency between the abrupt changes of ALT and NDVI, and the consistency between the abrupt change events of ALT and NDVI is stronger than that of air temperature and precipitation. These findings work toward a better comprehending of permafrost effects on vegetation growth in the context of climate change.

Biomass plays a crucial role in the low-carbon energy transition, with a projected contribution of 18.7% to the global energy supply by 2050. However, biofuel combustion has been a notable source of toxic mercury emissions, yet the future trends and distribution of the emissions remain inadequately understood. Here, we projected biofuel combustion under various Shared Socioeconomic Pathways (SSPs) using the Global Change Assessment Model and assessed associated mercury emissions in cooking, heating, and power generation over 2020–2050, aligning with the carbon neutrality target. Our analysis reveals that global biofuel mercury emissions are projected to be 9.90–18.40 tons by 2050, compared to the annual emissions of 13.89 tons in 2020. Notably, a substantial increase in emissions from power generation is expected, escalating from 0.57 tons in 2020 to 4.69–8.27 tons by 2050, with China and Southeast Asia emerging as primary contributors. Conversely, mercury emissions from cooking and heating are expected to decrease from 13.32 tons in 2020 to 4.40–11.53 tons by 2050, except in Africa under SSP2, where the emissions may increase from 5.91 to 6.69 tons. Our findings provide a scientific basis for policies aimed at achieving carbon neutrality targets while adhering to the Minamata Convention on Mercury.