Search Results (71)

Accurate estimation of forest carbon storage is fundamental to climate change mitigation. However, the highly heterogeneous site conditions, together with the biological limitations of conventional linear and polynomial models, have posed considerable challenges for estimating carbon accumulation in voluntary carbon reduction projects. Based on data from 1190 permanent sample plots across Taiwan, this study developed a site-class-based carbon storage prediction model for coniferous and broad-leaved plantations in Taiwan by using the nonlinear Chapman–Richards growth function. The estimated model parameters indicated distinct carbon accumulation patterns for the two forest types. Coniferous plantations exhibited a higher model-estimated asymptotic carbon storage (1232.506 Mg CO₂ ha⁻¹), whereas broad-leaved plantations accumulated carbon more rapidly during the early stages of stand development. Because a single base model may produce substantial prediction errors when applied over large spatial scales, the stands were classified into high-, medium-, and low-site classes by using a reference age of 30 years. Site classification reduced the out-of-sample root mean square error by 56.8% for coniferous and 50.8% for broad-leaved plantations under nested cross-validation. The resulting site class models may provide useful quantitative tools for establishing baseline and project scenarios in voluntary forest carbon reduction projects. Full article

Photorespiration and drought-induced stomatal closure are two important physiological constraints that reduce carbon assimilation and productivity in C₃ forest trees under Mediterranean climatic conditions. Türkiye’s two dominant commercial pine species, Pinus brutia Ten. (Calabrian pine) and Pinus nigra J.F. Arnold subsp. pallasiana (Anatolian black pine), together cover approximately 8.15 million hectares. This study integrated published gas-exchange measurements, radiation-use efficiency estimates from MODIS, official forest inventory data, and dendrochronological growth records into a counterfactual accounting framework and propagated parameter uncertainty by Monte Carlo simulation (N = 40,000 draws). The two constraints jointly reduced weighted-mean net primary productivity (NPP) from a radiation-limited potential of 5.61 to an actual 3.46 Mg C ha⁻¹ yr⁻¹, a reduction of 37.9% (95% CI 32.2–43.4%). Decomposition shows that 47.7% of this loss is the obligate metabolic cost of C₃ carboxylation, which no silvicultural intervention can address, while 52.3%—20.1 of the 37.9 percentage points—is drought-attributable. Nationally, the deficit corresponds to 64.3 Mt CO₂ yr⁻¹ of forgone sequestration (47.8–81.2) and 34.4 Mm³ yr⁻¹ of forgone stemwood-volume equivalent (24.9–44.5), of which approximately 20.6 Mm³ would be merchantable, giving an annual economic deficit of USD 3.37 billion (2.40–6.48). Filtering the drought-attributable component for eligible area, recovery efficiency, additionality, leakage, and permanence yields approximately 1.0 Mt CO₂ yr⁻¹ of potentially issuable credits, fewer than two per cent of the headline figure. Eco-physiological suppression of this magnitude is currently invisible in national forest carbon accounting, and its recognition bears directly on dynamic baseline design and on the credibility of offsets generated from Mediterranean conifer forests. Full article

Research Highlights: This study quantifies aboveground biomass for Oreopanax oroyanus and Escallonia resinosa in an Andean relict forest and examines their conservation relevance related to the scale of emissions voluntarily reported by small corporate emitters. Background and Objectives: Andean relict forests face severe fragmentation and degradation. This research evaluates carbon stocks in the Bosque de Zárate Reserved Zone (Peru) and explores how these findings may inform climate mitigation and conservation initiatives by examining their potential alignment with emissions voluntarily reported by Peruvian firms participating in a carbon disclosure system. Materials and Methods: A total of 27 plots were evaluated between 3034 and 3200 m a.s.l., tree height and diameter (DBH ≥ 10 cm) were measured for key species, and biomass was estimated using a pantropical allometric equation. Landsat imagery (1985–2025) was analyzed to assess long-term vegetation conditions, while Dynamic World land cover and Sentinel-1 radar (2018–2025) were used to assess forest cover and canopy structure changes. Voluntarily reported emissions of Peruvian firms participating in the “Carbon Footprint Peru” system (2012–2024) were analyzed to contextualize the forest results in the potential corporate interest in climate mitigation in Peru. Results: Total aboveground carbon stock for the altitudinal belt in the study area was 919.4 Mg C (18.6 Mg C ha⁻¹), equivalent to 3374.2 Mg CO₂, with Escallonia resinosa accounting for approximately 71% of the estimated stock. Multi-decadal satellite observations indicated persistent forest cover within the evaluated belt, while analysis of voluntarily reported corporate emissions identified numerous service-sector firms with annual emissions below 100 Mg CO₂ eq, providing context for the potential scale of future conservation-financing initiatives. Conclusions: Relict forests offer relevant localized carbon storage linked to other ecosystem services. Providing field-based carbon data may support the development of locally relevant community-led initiatives meaningful to climate-financing initiatives. However, the existing carbon stock does not by itself represent a source of carbon credits, and carbon capture-specific studies would need to be implemented to fully assess the mitigation capacity of these ecosystems. Full article

This paper assesses the role of nature-based solutions as a way to scale sustainability goals, focusing on the use of carbon credits in voluntary corporate climate commitments. To accomplish this, we adapt the DICE23 model by incorporating a demand function for voluntary corporate carbon abatement and by including the costs of supplying nature-based and non-CO₂ credits to that market. Through scenario analysis, we examine how likely current and proposed new commitments are to meet 1.5 °C and 2 °C climate thresholds by 2030 and 2050 with and without the use of nature-based carbon credits. We find that the inclusion of nature-based credits would increase the probability of meeting a 2 °C threshold by 2030 by lowering costs and significantly increasing overall mitigation. A key result of this paper is that allowing companies to utilize nature-based credits to deliver on near-term mitigation targets can provide the same number of emission reductions as efforts to expand corporate commitments three-fold, but is limited to reductions in the energy sector alone. Overall, incorporating forests and other nature-based credits into corporate commitments could provide immediate and substantial climate benefits while also supporting people and nature impacts today, enabling companies to better achieve multiple social and sustainability goals simultaneously. Full article

Alternate Wetting and Drying (AWD) is a proven water-saving irrigation technique that reduces irrigation water use and methane emissions from rice cultivation. The emission reduction achievable through AWD irrigation practices represents a significant opportunity for credits generation, particularly for the major rice-producing countries. To capitalize on this opportunity, a scalable, reliable, and cost-effective information system for AWD irrigation monitoring, reporting, and verification (MRV) is urgently needed. However, most existing MRV systems depend on manual data collection or software systems driven by field-based observation. Satellite remote sensing, derived from different tools and techniques, has achieved considerable traction in agriculture monitoring. This study attempts to develop a remote sensing and Internet of Things (IoT)-based system for large-scale AWD irrigation detection and monitoring as a potential tool for the MRV system. IoT sensor-based water level measurement, L-band PALSAR-2 full polarimetric data, and intensive field survey data were integrated and analyzed. Three study sites in the Naogaon District of Bangladesh, one of the major rice-growing regions, were selected as the study area. The PALSAR-2 full-polarimetric data were collected, radiometrically and geometrically corrected, and converted into the backscattered coefficient (Sigma-naught) value. Using the full-polarimetric channel of VV, VH, HH, and HV, the Freeman–Durden three-component decomposition, surface scattering, double-bounce, and volume scattering were constructed to assess the irrigation water condition of the rice paddy field. IoT sensors data, field survey data, and three-component data on 8 different dates and a total of 704 fields during the rice growing period were subsequently analyzed and cross-calibrated. The results showed that surface scattering and double bounce are more sensitive to irrigation water status, while volume scattering primarily responds to plant height changes. By leveraging the backscatter characteristics of these three components, a Random Forest classifier was applied to classify AWD and non-AWD irrigated paddy fields. Classification accuracy achieve 94% in early crop growth stages and declined to 80% during dense canopy stages. These findings offer a reliable and scalable approach to documenting water regime management with direct applicability to carbon emissions reduction verification and carbon credits claims. Full article

A central goal of carbon (C) management and a critical outcome of sustainable land stewardship is reducing greenhouse gas (GHG) emissions from agriculture, forestry, and other land uses. Integrating GHG considerations into management can take many forms, but C credit markets are increasingly providing sources of private capital to offset the often high costs of stewardship. In Hawaiʻi, participation in voluntary C credit markets and the establishment of jurisdictional compliance C markets are constrained by a lack of institutional capacity, successful demonstrations, and high-quality data, making private capital for C market-based approaches in Hawaiʻi difficult to access. The State of Carbon in Hawaiʻi Hui (hui translates to partnership in ʻŌlelo Hawaiʻi, the Hawaiian language) convened landowners, researchers, federal and state government professionals, and for-profit and not-for-profit organization staff to better understand limitations to implementing C management in Hawaiʻi. This paper describes why the State of Carbon in Hawaiʻi Hui was formed, how we planned for, hosted, and assessed the success of a C-focused summit, and what outcomes resulted from this process. A Pathway Forward document, a decision support tool, and this article are outcomes. These products will serve as resources for those considering Hawaiʻi-based forest C projects, as well as contributing towards the legislated goal of reducing greenhouse gas emissions in Hawaiʻi. Our knowledge exchange process is readily replicable and can support a variety of efforts in environmental conservation and beyond. Full article

Mangrove ecosystems along Taiwan’s southwest coast have been increasingly stressed by climate change, subsidence, and sea level rise. Between 1897 and 2024, the mean annual temperature rose by 2.0 °C, and rainfall declined by 56.5 mm. Severe subsidence occurred in Taixi Township, Yunlin County (−283.0 cm, 1975–2023), where the gray/white mangrove (Avicennia marina) exhibited reduced growth and mortality. Long-term mangrove area (MA) was reconstructed using quadratic polynomials: Tougang Ditch, MA_TG(t) = −0.0084(t − 21.0)² + 2.8 peaking in 1995 (R² = 0.7274), and Budai Lagoon, MA_BD(t) = −0.0468(t − 12.3)² + 26.1 peaking in 1986 (R² = 0.782). Both sites yielded moderate fits indicating partial but less reliable reconstruction. In contrast, Jishui Estuary subsites displayed distinct maxima with stronger fits (R² > 0.85): JS-C, MA_JS-C(t) = −0.0201(t − 14.3)² + 7.0 peaking in 1996; JS-D, MA_JS-D(t) = −0.0093(t − 15.8)² + 2.2 peaking in 1998; and JS-G, and MA_JS-G(t) = −0.0077(t − 11.6)² + 4.3 peaking in 1994. SPOT-6 satellite imagery (22 February 2025) identified 281.9 ha of mangrove and windbreak forests in Chiayi County and 896.3 ha in Tainan City. By integrating climate records, subsidence data, sea level rise, polynomial modeling, and satellite observations, this study provides a robust framework for anticipating mangrove trajectories, assessing carbon sink potential, and refining carbon credit estimates in vulnerable coastal landscapes. Full article

This study evaluated airborne laser scanning (ALS) as a large-scale tool for forest carbon quantification by comparing ALS-derived estimates with traditional field sampling across multiple forest strata. Above-ground biomass was estimated using two different, commonly used equations, while below-ground biomass was derived from peer-reviewed root-to-shoot ratios. ALS and field estimates differed across forest strata and carbon pools: ALS detected higher live tree carbon in harvested areas—capturing residual trees often missed in traditional cruises—but underestimated dead wood carbon, relative to field-based methods. Consistent differences were also observed between biomass equations, with Woodall estimates being 12.8% and 16.7% lower than Jenkins estimates for ALS and field methods, respectively. The study further incorporated soil organic carbon (SOC) and carbon dating data, providing additional insight into subsurface carbon stocks and the temporal dynamics of forest carbon pools. Overall, ALS proved to be an efficient, repeatable, and scalable method for carbon assessment, offering clear advantages in monitoring carbon flux over time when integrated with forest management protocols. Although further research is needed to refine biomass equations and explore emerging technologies such as Geiger Mode LiDAR, ALS has strong potential to enhance forest carbon crediting processes and support climate change mitigation goals. 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

Blue economy aims to bring prosperity to coastal communities whilst also protecting natural ocean resources for future generations. But how can this vision be put into practice, especially in communities in which dependence on natural resources is high, and food and livelihood security are key concerns? This paper examines two cases of community-led nature-based enterprise in Kenya in a search for solutions to this challenge: fisheries reform through market access and gear sustainability; mangrove forest conservation and community development using carbon credit revenues. I use a ‘diverse economies framework’ for the first time in blue economy contexts to delve into the heterogeneous relations at work and in search of insights that can be applied in multiple contexts. Analysed through key informant interviews and field observation, the cases reveal a complex assemblage of institutions, knowledges, technologies, and practices within which enterprises operate. Whilst the enterprises featured are still relatively new and developing, they suggest a direction of travel for a community-led sustainable blue economy that both supports and benefits from nature recovery. The insights gained from this diverse economies analysis lead us to appreciate a sustainable blue economy as a rediscovered and reinvigorated relationship of reciprocity between society and nature—one that nurtures place-based nature-based livelihoods and nature recovery together, and which embodies a set of values and ethics shared by government, communities, and business. Full article

In the context of climate change and greenhouse gas emissions, the forestry sector holds significant potential to contribute to global mitigation efforts. One of the primary drivers of deforestation is land expansion for livestock production. However, both sectors are closely linked to issues of food security and food sovereignty, with the livestock sector playing a crucial role in ensuring food availability. Integrating these two sectors through silvopastoral systems offers a promising solution that supports forest conservation while simultaneously addressing the global food crisis. Among the leading initiatives in forest conservation is REDD+, a mechanism under the UNFCCC that has proven effective in reducing deforestation and forest degradation, as well as in enhancing carbon stock conservation. Following the ratification of Article 6 of the Paris Agreement in 2024, REDD+ has gained recognition as a viable approach for generating international carbon credits. Given the intersection of the livestock and forestry sectors, and the potential of carbon credits to advance the goals of the Paris Agreement, silvopastoral systems could be considered for inclusion in REDD+ strategies under the framework of Article 6. Full article

Forests are crucial for achieving carbon neutrality and the Sustainable Development Goals (SDGs). This study contributes to SDG 13 (Climate Action) and SDG 15 (Life on Land) by constructing a comprehensive evaluation system for high-quality forestry development (HQDF), integrating economic efficiency, ecological functions, and social benefits. Using provincial panel data for China from 2010 to 2023 and applying two-way fixed effects, panel quantile regression, and instrumental-variable methods, we examine the catalytic role of green finance. The results show that green finance significantly promotes HQDF and displays an inverted U-shaped effect over the development cycle. Regional heterogeneity is marked: the strongest effects appear in western and southern China, moderate effects in central regions, and negative effects in some eastern and northern provinces. Among specific instruments, green investment and green bonds exert the largest positive impacts, followed by green insurance and fiscal funds, while green credit plays an important role at particular stages. These findings provide evidence from a major emerging economy and offer practical guidance for optimizing forestry-related green finance strategies worldwide. Full article

Mangrove forests are critical coastal ecosystems that store carbon, support marine life, and serve as natural barriers, protecting shorelines from erosion and reducing the impact of storms by absorbing wave energy. However, the rise of human activities and sea levels has led to their destruction over the past decades. It is important to know how the areas of mangrove forests change and adapt every year to plan for their restoration and protection and to support future trends like using carbon credits to help developing countries generate income. This study aims to map and monitor mangrove forest area changes over four decades in the Mekong region, comprising Myanmar, Thailand, Cambodia, and Vietnam, from 1984 to 2023 using a time series of Landsat data together with random forest (RF) classification. This analysis implemented multiple approaches, including creating stabilized Landsat imagery composites from the LandTrendr algorithm, Otsu edge detection, Minimum Mapping Unit (MMU), and RF classifier. The study found the map accuracy based on the RF model classifier achieved an overall accuracy between 86.2% and 88.8%, providing reliable data for analysis. Country-level analysis revealed increasing mangrove forest cover in Thailand (12.9%) and Vietnam (28.4%) since 1984. Conversely, mangrove areas in Cambodia and Myanmar have decreased significantly from 1984 to 2023 by about 14.6% and 22.7%, respectively. These findings have significant implications for resource allocation, investment strategies, and the development of carbon credits to support mangrove conservation efforts. This comprehensive dataset offers valuable insights for stakeholders involved in mangrove management and restoration in the Mekong region. By understanding the spatial-temporal distribution patterns of mangrove forest change, decision-makers can make informed decisions to safeguard these critical ecosystems for future generations. Full article

Climate change is driving the need for forest management strategies that simultaneously enhance ecosystem resilience and contribute to climate change mitigation. Voluntary carbon markets (VCMs), regulated in the European Union by the Carbon Removal Certification Framework (CRCF), offer potential financial incentives for such management, but eligibility criteria—particularly biodiversity requirements—limit the applicability of certain species. This study assessed the ecological and economic outcomes of six alternative management scenarios for a 4.7 ha, 99-year-old Scots pine (Pinus sylvestris) stand in western Hungary, comparing them against a business-as-usual (BAU) regeneration baseline. Using field inventory data, species-specific yield tables, and the Forest Industry Carbon Model, we modelled living and dead biomass carbon stocks for 2025–2050 and calculated potential CO₂ credit generation. Economic evaluation employed total discounted contribution margin (TDCM) analyses under varying carbon credit prices (€0–150/tCO₂). Results showed that an extended rotation yielded the highest carbon sequestration (958 tCO₂ above BAU) and TDCM but was deemed operationally unfeasible due to declining stand health. Black locust (Robinia pseudoacacia) regeneration provided high mitigation potential (690 tCO₂) but was ineligible under CRCF rules. Grey poplar (Populus × canescens) regeneration emerged as the most viable option, balancing biodiversity compliance, climate adaptability, and economic return (TDCM = EUR 22,900 at €50/tCO₂). The findings underscore the importance of integrating ecological suitability, market regulations, and economic performance in planning carbon farming projects, and highlight that regulatory biodiversity safeguards can significantly shape feasible mitigation pathways. Full article

Accurate forecasting of carbon credit prices is increasingly vital for the effective functioning of forest carbon markets, which play a growing role in global climate mitigation strategies. Against this backdrop, the present study conducts a systematic literature review to evaluate the state of carbon price forecasting methodologies, with particular emphasis on their applicability to forest-based carbon credits. The review highlights the predominance of machine learning (ML) and hybrid modeling approaches, which demonstrate enhanced predictive capabilities relative to conventional econometric techniques, particularly in capturing nonlinear dynamics and integrating heterogeneous data sources. However, their predictive power is limited by data scarcity, market opacity, and regulatory volatility. These issues are particularly severe in voluntary forest credit markets. The review identifies a critical research gap. Few studies explicitly model the behavior of forest credit prices. The findings suggest that future research should prioritize the development of policy-sensitive, scenario-based models that incorporate ecological, economic, and regulatory dimensions. While the majority of studies concentrate on compliance carbon markets, the methodological insights and forecasting approaches reviewed are highly relevant for the evolving forest carbon sector, nature-based mitigation strategies, and climate solutions. It also offers guidance for creating more transparent and robust forecasting tools in the forest carbon sector. Full article