Search Results (538)

The Land Use and Land Cover (LULC) of many regional landscapes are changing due to natural effects and anthropogenic activities, impacting biodiversity and ecosystem services. LULC dynamics reflect the altered flow of energy, water, and greenhouse gases, influencing the pillars of sustainability: society, environment, and economy. Thus, assessing LULC changes is vital for understanding the relationship between nature and society. This study used multi-temporal remotely sensed imagery to examine LULC change between 1990 and 2019 in the context of Forest Transition Theory (FTT) across the Greater Shawnee National Forest (GSNF) area of southern Illinois, USA, using a random forest algorithm, and projecting change to 2050 with a Land Change Model integrated with IPCC temperature and precipitation scenarios. From 1990 to 2019, LULC analysis showed increases in deciduous forest (1.35%), mixed forest (26.40%), agriculture (2.15%), and built-up areas (6.70%), while hay/grass/pasture declined (16.0%). LULC change intensity was highest from 1990 to 2001 (2.35% annually), slowing to 0.23% (2001–2010) and 0.18% (2010–2019). The overall accuracy (OA) of LULC classification ranged from 0.9 to 0.95 at a 95% confidence interval (CI). Projections to 2050 showed consistent increases in built-up areas (17.12–42.61%), water (28.75–39.70%), and hay/grass/pasture (6.23–38.38%), while overall forest cover declined in all scenarios. Deciduous forests decreased by 3.11–19.87% and were replaced by mixed forests in some scenarios (12.45–23.63%), while evergreen forests showed mixed responses, ranging from a decline of up to 17.13% to an increase of 2.90%. The OA of projected LULC ranged from 0.71 to 0.83 (95% CI) across SSP-RCP-based temperature and precipitation scenarios. The results showed that the GSNF broadly follows the FTT framework: forest recovery since 2001 coincided with rural depopulation, slow agricultural expansion, and rising incomes. However, climate change is expected to disrupt this recovery, pushing transitions toward mixed and evergreen forests. Findings demonstrate the importance of integrating remote sensing-based LULC with socio-economic trends and climate adaptation strategies to sustain forests and ecosystem services under future environmental pressures. Full article

Older adults are particularly vulnerable to extreme heat, but evidence of the role of social factors in regional heat vulnerability remains limited. To assess the impacts of heat waves on cardiorespiratory hospitalizations and mortality, we developed a Climate Vulnerability Index by the Regional Health Department (RHD), including adults aged ≥ 60 years across 17 RHDs in São Paulo State, Brazil. Health data were obtained from national information systems, and heat wave exposure was derived from ERA5 reanalysis data, defined as periods of at least three consecutive days with daily mean temperature exceeding the seasonal climatological mean by ≥3 °C, for 2010–2019 and 2023–2024, excluding 2020–2022. Associations between heat waves and health outcomes were estimated using distributed lag non-linear models with lags of 0–15 days. Cumulative relative risks, along with sociodemographic, sanitation, and health system indicators, were integrated to construct the Index based on IPCC sensitivity and adaptive capacity domains. Heat waves were associated with increased risks of cardiorespiratory hospitalizations and mortality across all RHDs, with stronger effects observed for mortality and inland regions. Higher vulnerability was concentrated in RHDs characterized by larger older adult populations, greater heat-related risks, and weaker health system and sanitation indicators, whereas more developed regions showed lower vulnerability. Overall, the Index provides a practical tool to support territorial prioritization and targeted heat–health adaptation strategies in ageing populations. Full article

Greenhouse gas emissions from sanitation systems remain underquantified, particularly when considering the entire service chain. Previous studies have largely focused on emissions from containment, with limited attention to later stages such as collection, transport, treatment and disposal. To address this gap, this research comprehensively estimates greenhouse gas (GHG) emissions from sanitation systems in Lahan municipality, Nepal. We used an extended version of the IPCC-based Tier-1 approach. Data collection included a household survey and key informant interviews. In scenario A, the baseline total annual emissions are 8.7 Gg CO₂e, mostly from the digestion of faecal sludge in the containment (7.3 Gg CO₂e). In scenario B, when a projected faecal sludge treatment plant (FSTP) is built and in operation, annual emissions reach 10.0 Gg CO₂e, driven by methane emitted by the anaerobic digester in the plant. Scenario C considers climate mitigation strategies: increasing the share of households emptying their containments, increased emptying frequency and adding of methane capture in the FSTP. This can reduce annual emissions to 7.9 Gg CO₂e per year, which is 21% less than in scenario B. Our results suggest that methane capture in the FSTP is the most critical mitigation strategy. Full article

Carbon emissions (CEs) are a primary driver of global climate change, particularly pronounced in China’s Yangtze River Delta (YRD) region, where rapid economic development and urbanization have led to a substantial increase in CEs. At fine spatial scales (e.g., county level) or in regions with limited statistical data, traditional methods for CE accounting are constrained by data gaps and inconsistencies, which hinders the accurate characterization of regional disparities. Therefore, this study proposes a CE spatial downscaling method based on nighttime light (NTL) data. By integrating remote sensing data with the IPCC emission inventory model, energy consumption-related carbon emissions (ECCEs) across the YRD region from 2000 to 2020 were quantified. Through global spatial autocorrelation analysis and standard deviation ellipse (SDE) analysis, the spatial distribution characteristics and temporal variation trends of ECCEs were revealed. Results indicate that total CEs increased significantly over the study period. CE hotspots were concentrated in the Hangzhou Bay area and the Shanghai–Nanjing corridor, while coldspots were identified in southwestern Anhui and Zhejiang. From 2010, the CE centroid shifted toward the southwest or northwest, and the regional CE distribution evolved from a point pattern to a band-shaped pattern. These findings offer a novel approach for CE monitoring and can provide scientific support for low-carbon development policies and precise emission reduction strategies in data-scarce regions of developing countries. Full article

Sea-level rise (SLR) and coastal flooding are among the most pressing climate-related challenges facing coastal regions worldwide, and their impacts are further intensified by rapid urbanization. These processes pose serious socioeconomic and environmental risks, including increased flood exposure, threats to public health, and damage to critical infrastructure. In Saudi Arabia, more than 3100 km² of coastal land lies at elevations of 1 m or lower; however, reliable assessments of future sea-level rise and its potential impacts remain limited, creating significant uncertainty for long-term planning. This study addresses this knowledge gap by identifying areas vulnerable to sea-level rise and coastal flooding through the development of inundation maps for the Dammam Metropolitan Area (DMA) as a case study, while also outlining potential adaptation measures. Using satellite imagery and geospatial datasets, changes in the DMA shoreline between 2014 and 2024 were analyzed, and sea-level rise scenarios were simulated based on projections from the Intergovernmental Panel on Climate Change (IPCC). The results indicate that under a 0.6 m sea-level rise scenario, flooding would be limited to a small area of approximately 0.2 km² in the Half-Moon residential district. In contrast, a 1.1 m sea-level rise scenario reveals a substantial increase in risk, with nearly 83 km² of the DMA potentially exposed to coastal flooding. Based on these findings, targeted disaster management and adaptation strategies are recommended for areas most vulnerable to sea-level rise. The study highlights the need for policies regulating coastal reclamation and other climate-sensitive developments to minimize future flood risks. It supports the United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action) by enhancing urban flood risk assessment and improving understanding of climate-driven sea-level rise impacts. Full article

Allometric equations are essential tools for estimating sustainable biomass and carbon dynamics in riparian tree species. This study derived and validated log–log transformation regression equations that relate diameter at breast height (DBH) to the dry weight, stem volume, and total biomass of Salix chaenomeloides Kimura across five river systems in Korea (Byeongcheon, Andong, Boseong, Topyeong, and Yeongdong). DBH was significantly correlated with biomass components and whole-tree biomass, with explanatory power ranging from 0.47 (Byeongcheon-root) to 0.99 (Topyeong-stem) (R²). Model evaluation metrics (RMSE, MAE, MPE) indicated high predictive accuracy across sites. Using the derived allometric equations, net primary productivity (NPP) of individual was 9.40 kg·tree⁻¹·yr⁻¹ and 2.45 ton C·ha⁻¹·yr⁻¹ at the stand level, with site-specific variability reflecting environmental differences. Biomass conversion coefficients, expansion factors, and root-to-aboveground biomass ratios were also obtained, with mean values of 0.29 (branches/stem), 0.10 (leaves/stem), and 0.25 (roots/AGB), a wood density of 0.63 g·cm⁻³, and a biomass expansion factor of 1.37. Independently derived NPP estimates based on stem analysis were comparable (9.02 kg tree⁻¹ yr⁻¹ and 2.43 t C ha⁻¹ yr⁻¹ at individual and stand levels, respectively), supporting the robustness of the approach. These findings provide robust, site-calibrated allometric models for S. chaenomeloides, supporting accurate biomass estimation, carbon accounting, and the evaluation of riparian ecosystems in climate change mitigation and restoration contexts. From a sustainability perspective, these results highlight the development of tools for evaluating the carbon budget of riparian vegetation, which are not yet incorporated into the Korean national IPCC report. They also demonstrate progress in carbon budget assessment by integrating both allometry and stem analysis. Full article

Mangrove ecosystems are recognized as highly efficient blue-carbon reservoirs, yet their monitoring requires scalable, transparent methods suitable for climate-finance and greenhouse-gas accounting applications. This study quantifies interannual carbon-stock dynamics and derives a carbon-market valuation indicator for Ecuador’s Churute Mangrove Ecological Reserve (2015–2021) using publicly available remote-sensing land-cover products. Annual activity data were derived from Copernicus Global Land Service LC100 (100 m, 2015–2019) and ESA WorldCover (10 m, 2020–2021), harmonized to a common reporting scheme, and combined with IPCC Tier 1 default coefficients for biomass and soil organic carbon in tropical wetlands. Total carbon stocks averaged 1.67 million t C across the period, remaining stable within the internally consistent LC100 phase (2015–2019), with trend statistics treated as descriptive given the short annual series, while a pronounced drop in 2020 primarily reflected methodological discontinuities between products rather than ecological change. Converted to CO₂e equivalents (mean 6.1 million t CO₂e), illustrative market values fluctuated between USD 18 and 123 million annually, driven predominantly by carbon-price variability. This remote-sensing-based, MRV-aligned approach provides a conservative baseline for protected-area blue-carbon accounting, highlighting the need for homogeneous high-resolution time series to distinguish real dynamics from classification artifacts in future assessments. Full article

Renewable energy systems are increasingly critical for achieving decarbonization and long-term energy security, particularly in rural regions with abundant local resources. While solar and wind technologies have become cost-competitive, their intermittency limits reliability when deployed independently. Biomass, by contrast, offers dispatchable renewable power but faces economic challenges related to feedstock logistics. This study evaluates a biomass-led hybrid renewable energy system (HRES) for Grenada County, Mississippi, integrating biomass, solar photovoltaic (PV), and wind resources to enhance system reliability and reduce environmental impacts. System performance and optimization were assessed using the System Advisor Model (SAM) and the Hybrid Optimization of Multiple Energy Resources (HOMER). The proposed configuration comprises approximately 80% biomass, 10% solar PV, and the remaining share from wind, producing a total annual electricity output of about 423 GWh, sufficient to meet regional demand. The subsystem-level levelized cost of energy (LCOE) was estimated at 12.10 cents/kWh for biomass, 4.07 cents/kWh for solar PV, and 8.62 cents/kWh for wind, with the overall hybrid cost influenced primarily by biomass feedstock transportation and storage. Environmental impact assessment based on U.S. EPA eGRID and IPCC factors indicates that the hybrid system achieves a weighted emission intensity of approximately 28.4 kg CO₂-eq/MWh, representing a reduction of over 94% compared to the regional grid. When scaled to annual generation, this corresponds to roughly 197,000 metric tons of avoided CO₂-equivalent emissions per year, alongside 80–95% reductions in acidification and eutrophication impacts. The results demonstrate that biomass-anchored hybrid systems can provide a reliable, low-carbon pathway for rural energy development, with further cost reductions achievable through targeted policy incentives and financing support. Full article

The global carbon cycle has become increasingly unbalanced over the past century as anthropogenic fluxes into the atmosphere far exceed the sequestration capacity of land and ocean systems. Data from 2025 show estimated annual anthropogenic emissions of ≈11.2 gigatonnes of carbon (GtC), while only ≈5.6 GtC are sequestered by land and ocean sinks mainly provided by photosynthetic CO₂ fixation. The resulting surplus of carbon emissions has led to a doubling of atmospheric CO₂ concentrations above pre-industrial values to ≈430 ppm, which is a major driver of increasingly erratic climatic phenomena. Recent data indicate that fossil fuel use will continue rising up to and beyond 2050, largely negating the drive to cut CO₂ emissions as recommended by the IPCC and other reputable transnational bodies. Hence, there is an urgent need to reduce atmospheric CO₂ levels via carbon sequestration. This review focuses on the proven capacity of biological mechanisms to sequester CO₂ at a global scale with an annual capacity in the range of gigatonnes of carbon. New measures such as re- and a-forestation, plus improved and more sustainable management of tropical tree crops, can further increase the carbon sequestration potential of these plants. By implementing these and other nature-based solutions, the highly productive tropical vegetation belt could contribute an additional 1–2 Gt of carbon sequestration via natural forests and perennial tree crops. In order to expedite this process, we examine the use of new modalities of transparent carbon trading systems that include selected tropical crops. As highlighted at COP30 in Brazil and elsewhere, this would enable tropical countries to derive benefit for costs incurred in land management changes such as reforestation, regenerative farming, and intercropping to benefit smallholders and other rural communities. In particular, carbon finance is emerging as a critical driver, with appropriately regulated and transparent carbon credit schemes offering fungible monetary compensation for climate-positive land management. Full article

This study examines whether corporate climate performance is reflected in firm valuation by synthesising recent empirical evidence, using Tobin’s Q as a forward-looking indicator of market expectations. Employing a random-effects meta-analysis of 30 peer-reviewed studies published between 2020 and 2025 across multiple industries and regions, the findings reveal a modest yet statistically significant positive association between stronger climate performance and higher market valuations, suggesting that investors increasingly incorporate climate-related information into firm pricing. Contrary to prevailing assumptions in the literature, proactive climate strategies, such as emissions-reduction initiatives, do not systematically generate greater valuation benefits than disclosure-oriented approaches; both exhibit comparable positive effects. Similarly, valuation outcomes do not differ materially between self-reported and externally verified climate data. Meta-regression analysis identifies data source as the only statistically significant moderator, although its influence remains nuanced. Overall, the results indicate that climate performance enhances firm valuation in a context-dependent manner, challenging the view that only proactive strategies or externally verified data are uniquely rewarded by financial markets. The study contributes to the sustainable and corporate finance literature by clarifying how capital markets price climate-related corporate behaviour under heterogeneous strategic responses. Full article

Greenhouse gases (GHG), accumulated in the atmosphere, are the main cause of climate change. In 2017, the increase in average temperature was about 1 °C (between 0.8 °C–1.2 °C) above pre-industrial levels. Global warming refers to the increase in air surface, sea surface, and soil surface temperature and according to IPCC (Intergovernmental Panel Climate Change), since the industrial revolution, C emissions are due to land use changes like deforestation, biomass burning, conversion of natural lands, drainage of wetlands, soil cultivation, and tillage. As the world population has increased, world food production has risen too with a subsequent increase in GHG emissions and agricultural production, which is worsened by climate change. Negative consequences are well known such as the loss in water availability and in soil fertility, and pest infestations which are climate change’s effects on agriculture activity. Climate change’s main aftermath is the frequency of extreme weather events influencing crop yields. As climate change exacerbates degradation processes, land management can mitigate its impact and aid adaptation strategies for climate change. About 21–37% of GHGs have been caused by the agriculture activity, so the application of Nature-based Solutions (NbS) like sustainable agriculture could be a way to reduce GHGs worldwide. The aim of this article is to review how NbS may mitigate GHG emissions from soil, with solutions defined as an integrated approach to tackle climate change and to sustainably restore and manage ecosystems, delivering multiple benefits. NbS is a low-cost tool working within and with nature, which holds many benefits for people and the environment. Full article

Global warming (GW) contributions from feedbacks and feedback loops are projected to rise from ≈54% (loops: 29%) in 2024 to ≈71% (loops: 50%) under faltering RCP pathways without Solar Geoengineering (SG) by about 2100. A critical threshold, RCP_Critical, defined as the point at which feedback loops account for more than half of GW, is projected to occur between 2075 and 2125. Beyond this point, reversing warming becomes severely constrained, and climate tipping points become more likely. From these trends, an average mitigation difficulty and cost increase rate (MDCR) of ≈1.33–1.5% per year is estimated. By 2100, absent mitigation, the effort required to offset global warming would roughly double relative to today, approaching an unsustainable mitigation critical threshold. Current feedback levels may already be driving nonlinear warming behavior. These diagnostic estimates align with three key indicators: a minimum-feedback baseline from 1870, an equilibrium climate sensitivity (ECS) range of 3.1 °C–4.3 °C (potentially reached by ≈2082), and consistency with IPCC AR6 confidence bounds. In response, this study proposes Annual Solar Geoengineering-PLUS pathways (ASG+Ps) as supplemental measures. These include Earth Brightening, targeted Arctic Stratospheric Aerosol Injection (SAI), and feasible L1 Space Sunshade systems designed to reduce feedback amplification and extend mitigation timelines. The “PLUS” component refers to the use of increased mitigation levels with a focus on high-amplification regions, particularly the Arctic and the tropics, to help reverse local feedbacks and promote negative feedback loops. These moderate ASG+P pathways directly address AR6 concerns while avoiding many governance challenges of full-scale SG. ASG+Ps are less controversial and provide ≈14× stronger cooling potential per Wm⁻² than Carbon Dioxide Removal (CDR), while allowing variable regional targeting. Meanwhile, RCP2.6 has already been missed, placing RCP4.5 and RCP6 at risk. In 2024, atmospheric CO₂ rose by ≈23 Gt (≈3 ppm), while forest tree losses exceeded afforestation gains by 2×, yielding a 2 GtCO₂ sink loss, further diminishing CDR’s effectiveness. Declines in planetary albedo since 1998 continue to amplify warming. Urbanization accounts for roughly 13% of total surface GW, affecting 60% of the population, underscoring the mitigation potential of urban Earth Brightening. New results here also show major Space Sunshading area reductions, at ≈32× less than prior flawed estimates (detailed here) and ≈1600× less under the ASG+P method, substantially improving feasibility and the importance of space agencies’ needed mitigation role. A coordinated global ASG+P strategy, supported by IPCC working groups and space agencies like NASA/SpaceX, are needed to provide a critical supplemental pathway for climate stabilization. Given the shrinking intervention window, rising MDCR, and the escalating risks to civilization, prioritizing timely work in this area is essential; the investment is minor compared to the trillions in climate financial damages that could be avoided. Full article

One of the most significant impacts of climate change is sea-level rise, which is increasingly threatening to the coastal setting, infrastructure, and socioeconomic systems. Since a change at the sea level is spatially non-uniform and highly modulated by local oceanographic and climatic events, local or regional-scale measurements are necessary—especially in semi-enclosed basins. This paper examines the long-term variability of sea levels throughout the Persian Gulf and illustrates a strong spatial variance of the trends over the past and the future. Using three decades of satellite-derived observations, regional sea-level trends were estimated from monthly sea-level anomaly (SLA) data, which were also used to generate future projections to 2100. The analysis shows that the rate of sea-level rise along the UAE–Oman stretch is 3.88 mm year⁻¹ and that of the Strait of Hormuz is 5.23 mm year⁻¹, with a mean of 4.44 mm year⁻¹ in the basin. Statistical forecasts of sea-level change were projected by a statistical forecasting scheme with high predictive ability with the optimal configuration of an average of 0.0391 m, an RMSE of 0.0492 m, and an R² of 0.80 when independent validation was conducted. It is estimated that by 2100, the average rise of the sea level in the Persian Gulf is about 0.30–0.40 m, and the peak rise in sea level is at the Strait of Hormuz. Since these projections are based on statistical extrapolation rather than physics-based climate models, they are interpreted within the uncertainty envelope defined by IPCC AR6 scenarios. This study presents a unique, regionally resolved viewpoint on sea-level rise that is relevant to coastal risk management and adaptation planning in semi-enclosed marine basins by connecting robust statistical performance with physically interpretable regional patterns. Full article

The rapid spread of climate change misinformation across digital platforms undermines scientific literacy, public trust, and evidence-based policy action. Advances in Natural Language Processing (NLP) and Large Language Models (LLMs) create new opportunities for automating the detection and correction of misleading climate-related narratives. This study presents a multi-stage system that employs state-of-the-art large language models such as Generative Pre-trained Transformer 4 (GPT-4), Large Language Model Meta AI (LLaMA) version 3 (LLaMA-3), and RoBERTa-large (Robustly optimized BERT pretraining approach large) to identify, classify, and generate scientifically grounded corrections for climate misinformation. The system integrates several complementary techniques, including transformer-based text classification, semantic similarity scoring using Sentence-BERT, stance detection, and retrieval-augmented generation (RAG) for evidence-grounded debunking. Misinformation instances are detected through a fine-tuned RoBERTa–Multi-Genre Natural Language Inference (MNLI) classifier (RoBERTa-MNLI), grouped using BERTopic, and verified against curated climate-science knowledge sources using BM25 and dense retrieval via FAISS (Facebook AI Similarity Search). The debunking component employs RAG-enhanced GPT-4 to produce accurate and persuasive counter-messages aligned with authoritative scientific reports such as those from the Intergovernmental Panel on Climate Change (IPCC). A diverse dataset of climate misinformation categories covering denialism, cherry-picking of data, false causation narratives, and misleading comparisons is compiled for evaluation. Benchmarking experiments demonstrate that LLM-based models substantially outperform traditional machine-learning baselines such as Support Vector Machines, Logistic Regression, and Random Forests in precision, contextual understanding, and robustness to linguistic variation. Expert assessment further shows that generated debunking messages exhibit higher clarity, scientific accuracy, and persuasive effectiveness compared to conventional fact-checking text. These results highlight the potential of advanced LLM-driven pipelines to provide scalable, real-time mitigation of climate misinformation while offering guidelines for responsible deployment of AI-assisted debunking systems. Full article

This study aims to develop and demonstrate a low-cost LiDAR-based 3D mapping approach for estimating tree carbon stock in university campuses. Unlike conventional field-based measurements, which are labor-intensive and error-prone, the proposed system integrates a 2D LiDAR sensor with a servo motor and odometry data to generate three-dimensional point clouds of trees. From these data, key biometric parameters such as diameter at breast height (DBH) and total height are automatically extracted and incorporated into species-specific and generalized allometric equations, in line with IPCC 2006/2019 guidelines, to estimate above-ground biomass, below-ground biomass, and total carbon storage. The experimental study is conducted over approximately 70,000 m² of green space at Gazi University, Ankara, where six dominant species have been identified, including Cedrus libani, Pinus nigra, Platanus orientalis, and Ailanthus altissima. Results revealed a total carbon stock of 16.82 t C, corresponding to 61.66 t CO₂eq. Among species, Cedrus libani (29,468.86 kg C) and Ailanthus altissima (13,544.83 kg C) showed the highest contributions, while Picea orientalis accounted for the lowest. The findings confirm that the proposed system offers a reliable, portable, cost-effective alternative to professional LiDAR scanners. This approach supports sustainable campus management and highlights the broader applicability of low-cost LiDAR technologies for urban carbon accounting and climate change mitigation strategies. Full article