Market-based mechanisms assist in the abatement of excessive greenhouse gas emissions. We had postulated earlier that carbon markets are evolving into a complex 'climate change supermarket'. India is at the forefront of carbon forestry; however, the range of projects developed so far lacks versatility in terms of carbon pools, greenhouse gases, and intervention types. Based on the existing literature and field observations from two registered AR-CDM projects in Kashi and Mahoba forest divisions in Uttar Pradesh (India), questions were formulated for an expert survey. Through this survey of 43 experts, we assessed India's potential to lead in forest-based carbon markets and examined how well-prepared the institutional framework in India is to adopt new market-based mechanisms in carbon forestry and identify areas where improvements are needed. The individual ranking of experts was converted to a scoring matrix using reverse scores. As per the sum of the reverse scores obtained, forest-dependent people were revealed to be the most crucial strength, followed by forest governance. About 42% of the experts did not consider the measurement, reporting, and verification arrangements to berobust. Around 35% of the experts thought that there is a probability of more than 50% that India can meet its forest-related NDC goal. Of the 43 respondents, 56% believed that this goal can be met if international support is available. Out of 43, a total of 25 experts (58%) believed that India is somewhat ready for a domestic carbon market, while 17 experts (40%) believed that India is not prepared. Of the total, 72% of the experts foresee India as a leading country in the event of international carbon markets revival as an implementation of the Paris Agreement progress.

In this study, fir (Abies nordmanniana) and poplar (Populus sp.) woods were modified with citric acid and five different polyols (glucose, sorbitol, glycerol, sucrose, and maltodextrin) at 10% concentration for 2 hours at 150℃. Equilibrium moisture content, water uptake, anti-swelling efficiency, compression strength parallel to the grain, anti-bacterial test, and decay test were applied to measure the changes in the modified wood. As a result, the equilibrium moisture content at 90% RH and the water uptake rates of the modified woods decreased, but the anti-swelling efficiency increased up to 57%. Compression strength parallel to the grain increased by 10% in fir wood and 21.3% in poplar wood. Anti-bacterial properties were observed withcitric acid in fir wood and citric acid, citric acid/sorbitol, citric acid/glycerol samples in poplar wood. As a result of the fungal decay test, the weight loss of the wood samples was between 4.46 and 7.65% in fir wood and between 6.51 and 12.70% in poplar wood. The modification conditions of wood samples were optimized with citric acid, which improved wood properties by blocking reactive groups by cross-linking on wood fibers. As a result, citric acid treatment improved some physical, mechanical, and biological properties of fir and poplar wood, and it is recommended as a promising, environmentally friendly, and cost-effective wood preservation method.

Traditional forest measurement methods are labour-intensive, costly, and prone to errors, hindering scalability and accessibility. While remote sensing offers valuable insights, it falls short in capturing crucial details beneath forest canopies, leading to inaccuracies in carbon stock calculations. This study introduces a citizen-science-based approach that leverages smartphone technology and artificial intelligence (AI) to democratize and enhance real-time forest analytics.

The methodology employs a mobile application that guides users through Point Sampling, as described by Bitterlich (1948), eliminating the need for specialized tools and expertise. Users capture geotagged photos at designated points within the forest, which are then analysed by a computer vision model to reproduce forestry equipment like a prism, counting tree trunks, identifying their species, and determining ground characteristics, paired with remote sensing inputs. By integrating smartphone capabilities with AI-driven analysis, the platform enables rapid estimation of forest parameters, including basal area, biomass, vegetation structure, and biodiversity insights.

The qualitative results highlight the efficacy of this approach in overcoming the limitations of traditional field forest inventory methods. The user-friendly interface of the mobile app empowers local communities to actively participate in data collection alongside experts, fostering inclusivity and environmental stewardship. This innovative approach not only reduces costs and time associated with forest assessments but also promotes community engagement and contributes to more sustainable forest management practices.

The need for innovative technologies in sustainable forest management is evident in the face of the escalating challenges posed by climate change and deforestation. This study introduces an approach combining artificial intelligence (AI) and remote sensing techniques to transform precision forestry management for enhanced climate resilience. By harnessing AI algorithms to scrutinize data sets sourced from satellite imagery, drones, and IoT sensors, stakeholders can make well-informed real-time decisions to optimize forest health, biodiversity preservation, and carbon sequestration potential. The methodology involves using convolutional neural networks (CNNs) to analyze satellite imagery for identifying forest cover changes with an accuracy rate of 92%. Drone-based LIDAR data are employed to assess canopy structure and biomass, providing detailed 3D models that have a margin of error as low as 5%. IoT sensors deliver ground-level data on soil moisture, temperature, and other critical parameters at an update frequency of every 15 minutes, ensuring timely data collection. These diverse data streams are seamlessly integrated through a machine learning platform that offers predictive analytics and visualization tools. This platform enables forest managers to monitor ecosystem health proactively, with predictive models achieving up to 87% accuracy in forecasting potential disturbances. Results from pilot implementations in diverse forest ecosystems, including temperate, tropical, and boreal forests, demonstrate the efficacy of this approach. In a year-long pilot in a tropical forest, illegal logging activities were reduced by 40%, while response times for the early detection of wildfires improved by 30%. Biodiversity indices showed a 15% improvement due to targeted conservation efforts guided by AI-driven insights. By synergizing advanced technologies, this inventive solution not only optimizes forest management practices but also lays the groundwork for a more sustainable and resilient future for our forests amidst environmental adversities. The integration of AI and remote sensing in precision forestry management represents a significant step towards achieving climate-resilient forests and safeguarding biodiversity for future generations.

Transparent wood has garnered significant attention in recent years due to its high optical transmittance, safety, lightweight nature, excellent mechanical robustness, and low thermal conductivity, all of which make it an energy-efficient building material. To turn it transparent, wood needs to be chemically bleached, a process by which lignin and extractives are removed. Then, a polymer with a refraction index similar to that of delignified wood is integrated into its structure to reach high optical transparency. In this preliminary work, two common wood species in Uruguay were compared to determine which is more suitable for the process, as their characteristics are very different. Pinus taeda is a coniferous species of low-density and low extractive content; meanwhile, Eucalyptus bosistoana is a hardwood of high density and high extractive content. The behaviour of two wood species was compared. Delignification was carried out with NaCl₂; the effects of temperature and NaCl₂ concentration were studied; and the delignified wood was finally impregnated with epoxy resin. The FTIR spectra were analysed, and the anatomy of the wood at each stage was analysed with SEM. The results suggest both wood species grown in Uruguay are suitable for the development of transparent wood; the delignification conditions turned out to be the most consequential factor. The best delignification conditions were a reaction time of 300 minutes, a NaClO₂ concentration of 3.5% at a constant temperature of 80°C, and a second round of bleaching with H₂O₂ for one hour at 80°C.

Terpenes serve important functions in enhancing plant thermotolerance, but the thermotolerance mechanism is still unknown. Cinnamomum camphora releases an abundance of monoterpenes to tolerate high temperature, and is subdivided into five chemotypes, such as camphor chemotype, eucalyptol chemotype, linalool chemotype, borneol chemotype, and iso-nerolidol chemotype. To uncover the thermotolerance mechanism of the uppermost monoterpenes in C. camphora and promote their development as anti-high temperature agents, the thermotolerance functions of eucalyptol in the corresponding chemotype of C. camphora were investigated. In contrast to normal temperature (28^oC), reactive oxygen species (ROS), thiobarbituric acid reactive substance (TBARS) levels, and antioxidant enzyme activities increased under 38^oC, and further increased in the treatment with fosmidomycin (Fos), inhibiting monoterpene synthesis at 38^oC (Fos+38^oC) due to alterations in the expression of the genes related to non-enzymatic and enzymatic antioxidant formations. Compared with Fos+38^oC treatment, Fos+38^oC treatment with eucalyptol fumigation (Fos+38^oC+eucalyptol) lowered ROS levels and antioxidant enzyme activities for increased non-enzymatic antioxidant gene expression and decreased enzymatic antioxidant gene expression. High temperature at 38^oC reduced the chlorophyll and carotenoid content as well as photosynthetic abilities by reducing the expression of the genes associated with photosynthetic pigment biosynthesis, light reaction, and carbon fixation. Fos+38^oC treatment aggravated the reduction. In contrast to Fos+38^oC treatment, Fos+38^oC+eucalyptol treatment increased photosynthetic pigment content and improved photosynthetic abilities by up-regulating related gene expression. Therefore, the findings first uncover that the uppermost monoterpenes should serve important signaling functions in enhancing C. camphora thermotolerance, which provides a new thought for uncovering the thermotolerance mechanism of terpenes.

The inter-relationships between populations, anthropogenic components of landscapes, the environment, and abiotic--biotic components of landscapes have a fundamental role both in the configuration and dynamics of landscapes. This aspect is of vital importance in landscapes subject to human influence, where social, economic, and ecological interactions, as well as feedback instruments, manage biodiversity and ecosystem services. Although it may seem unusual to us, biodiversity in cultural landscapes is sometimes greater than in natural landscapes, depending on the interaction between nature and human actions. In this frame of reference, traditional land-use practices in the northwest Iberian mountains have created a well-structured system controlled by seasonal cycles and human activity patterns. However, in recent decades, socioeconomic globalization has prompted complex changes in rural areas. Land abandonment has reduced open spaces and has brought about an increase in forest land or/and forestry systems, affecting both ecosystem services and biodiversity. Therefore, landscape homogenization results in a decrease in open habitats, an additional problem for the conservation of agropastoral activities, and an increased risk of destructive forest fires due to less fragmentation of forest areas. Their sustainability depends on traditional conservation: extensive grazing and suitable forest management. The environmental, cultural, and economic integration of agropastoral and forestry activities is vital to ensure the heterogeneity of landscapes, together with biodiversity and ecosystem services. Extensive grazing allowing a suitable livestock concentration in small areas avoids soil erosion and vegetation deterioration, increases mosaic diversity, and maintains open habitats. Selective felling is an economically viable and environmentally integrated silvicultural treatment that reduces forest fires by promoting landscape fragmentation.

Wildfires comprise one of the most destructive and lethal natural hazards, where various physical factors, such as low soil moisture, and human factors, such as the absence of preventive measures during wildfire season, can contribute to the generation of megafires. Although human activities such as arson make up the most frequent factor for a fire event, there are several natural hazards, such as lightning, heatwaves as an outcome of extreme temperature, and drought, that can also trigger or increase the probability of wildfires. On the other hand, subsequent secondary hazards such as floods and landslides can be effectuated in the long term as a result of wildfires. It is worth mentioning that the frequency of events of the aforesaid natural hazards has been increased intensively since 1980, according to the United Nations Office for Disaster Risk Reduction (UNDRR). The prediction of natural hazards is of outstanding importance, with the aim of disaster mitigation; thus, the utilization of Artificial Intelligence and Data Science is necessary. Various Machine Learning (ML) algorithms have been used in the literature to forecast wildfires and the aforementioned disaster chain, each one providing divergent predictions, in combination with Earth Observation (EO) indices, geospatial, and socio-economic datasets. The objective of this research is to expound the methods used in the literature to model and predict wildfires and their interconnected hazards by performing a review process.

Dalbergia odorifera T. Chen, endemic to China, is the sole plant source for the valuable Chinese medicine Dalbergiae Odoriferae Lignum. Its heartwood, naturally forming over 40 years, is rare due to low formation rates and a scarcity of mature trees. Artificial induction of heartwood formation using ethylene shows promise, though its mechanisms remain unclear. This study investigates the regulation of heartwood biosynthesis by screening related signal factors, constructing and analyzing the transcriptome, and validating findings through suspension systems. Key results include: Ethylene is an effective endogenous hormone involved in regulating heartwood-like substances in D. odorifera. Ethephon (ETH) treatment enhanced heartwood characteristics, increasing ethylene synthesis and secondary metabolite production. After 14 days of ETH treatment, branches maintained healthy growth, and heartwood color matched natural heartwood. ETH and H2O2 promoted endogenous ethylene synthesis, enhancing overall metabolism, tree resistance, and enzyme activities. Inhibiting ethylene synthesis prevented heartwood color change and secondary metabolite accumulation. The ethylene signaling pathway is crucial for heartwood substance synthesis. Transcriptome analysis revealed that differentially expressed genes (DEGs) were enriched in pathways related to cysteine and methionine metabolism, phytohormone signaling, and secondary metabolite synthesis. Significant DEGs in the ethylene signaling pathway included upregulated ACO, EIN2, EIN3, and ERF1/2 in transition regions. qRT-PCR validation showed upregulated secondary metabolite synthesis genes (DoNES1, DoCHS1) and consistent expression patterns of ethylene signaling genes (DoACO1, DoEIN3-1). This research enhances understanding of heartwood formation mechanisms in D. odorifera and supports the development of artificial heartwood cultivation techniques.

Over the years, the active movement of the population in Portugal has contributed to increased pressure on urban--rural interface areas. The frequent abandonment of rural areas and increase in the aging rate, boosted by the rural exodus of younger age groups, have contribute to the lack of planning, the abandonment of agro-pastoral activities, an increase in the fuel load, and the growth of fuel use near population centres. The combination of these factors contributes to increasing the fire risk susceptibility in urban--rural interface areas.

This paper presents a case study that took place in the municipality of Vila Real, Portugal in 2022, the Samardã Wildfire fire. It was based on the use of geographic information system tools, statistical procedures, and historical fire analysis.

It was possible to verify that the area affected by the 2022 fire has a high recurrence rate, evidenced by the fires that occurred in 2005 and 2017, which consumed a large percentage of the same area as the 2022 fire.

Analysing the data providing by the map of the use of soil in 2021 (COS 2021), it was found that 85.5% of the burnt area was occupied by scrubland, sparse herbaceous vegetation, and areas without vegetation; 12.4% was occupied by various settlements; and only 1.6% was occupied by agricultural areas. The rest was occupied by artificialized areas and water. In 1990, this area was 80% occupied by scrubland and rocky areas, 14.9% by diverse settlements, 5% by agricultural areas, and only 0.1% by artificialized areas.

The results show that when rural fires hit areas at the urban--rural interface, the means of protection and rescue are primarily allocated to protecting people and property, to the detriment of extinguishing the fire front. This prioritization of actions has contributed to an increase in the area burned, since the fire front burns more freely.