Charting the future of high forest low deforestation jurisdictions.

High forest low deforestation jurisdictions (HFLDs) contain many of the world's last intact forests with historically low deforestation. Since carbon financing typically uses historical deforestation rates as baselines, HFLDs facing the prospect of future threats may receive insufficient incentives to be protected. We found that from 2002 to 2020, HFLDs (n = 310) experienced 44% higher deforestation rates than their historical baselines, and 60 HFLDs underwent periods of high deforestation (deforestation rate > 0.501%) at 0.983 ± 0.649% (mean ± SD)-a rate 7.5 times higher than the 10-y historical baseline of all HFLDs. For HFLDs to receive sufficient carbon finance requires baselines that can better reflect future deforestation trajectories of HFLDs. Using an empirical multifactorial model, we show that most contemporary HFLDs are expected to undergo higher deforestation from 2020 to 2038 than their historical baselines, with 72 HFLDs likely (>66% probability) to undergo high deforestation. Over the next 18 y, HFLDs are expected to lose 2.16 Mha y-1 of forests corresponding to 585 ± 74 MtCO2e y-1 (mean ± SE) of emissions. Efforts to protect HFLD forests from future threats will be crucial. In particular, improving baselining methods is key to ensuring that sufficient financing can flow to HFLDs to prevent deforestation.

Uncertainties in deforestation emission baseline methodologies and implications for carbon markets.

Carbon credits generated through jurisdictional-scale avoided deforestation projects require accurate estimates of deforestation emission baselines, but there are serious challenges to their robustness. We assessed the variability, accuracy, and uncertainty of baselining methods by applying sensitivity and variable importance analysis on a range of typically-used methods and parameters for 2,794 jurisdictions worldwide. The median jurisdiction's deforestation emission baseline varied by 171% (90% range: 87%-440%) of its mean, with a median forecast error of 0.778 times (90% range: 0.548-3.56) the actual deforestation rate. Moreover, variable importance analysis emphasised the strong influence of the deforestation projection approach. For the median jurisdiction, 68.0% of possible methods (90% range: 61.1%-85.6%) exceeded 15% uncertainty. Tropical and polar biomes exhibited larger uncertainties in carbon estimations. The use of sensitivity analyses, multi-model, and multi-source ensemble approaches could reduce variabilities and biases. These findings provide a roadmap for improving baseline estimations to enhance carbon market integrity and trust.

Future land-use competition constrains natural climate solutions.

Natural climate solutions (NCS) are an essential complement to climate mitigation and have been increasingly incorporated into international mitigation strategies. Yet, with the ongoing population growth, allocating natural areas for NCS may compete with other socioeconomic priorities, especially urban development and food security. Here, we projected the impacts of land-use competition incurred by cropland and urban expansion on the climate mitigation potential of NCS. We mapped the areas available for implementing 9 key NCS strategies and estimated their climate change mitigation potential. Then, we overlaid these areas with future cropland and urban expansion maps projected under three Shared Socioeconomic Pathway (SSP) scenarios (2020-2100) and calculated the resulting mitigation potential loss of each selected NCS strategy. Our results estimate a substantial reduction, 0.3-2.8 GtCO2 yr-1 or 4-39 %, in NCS mitigation potential, of which cropland expansion for fulfilling future food demand is the primary cause. This impact is particularly severe in the tropics where NCS hold the most abundant mitigation potential. Our findings highlight immediate actions prioritized to tropical areas are important to best realize NCS and are key to developing realistic and sustainable climate policies.

Carbon prospecting in tropical forests for climate change mitigation.

Carbon finance projects that protect tropical forests could support both nature conservation and climate change mitigation goals. Global demand for nature-based carbon credits is outpacing their supply, due partly to gaps in knowledge needed to inform and prioritize investment decisions. Here, we show that at current carbon market prices the protection of tropical forests can generate investible carbon amounting to 1.8 (±1.1) GtCO2e yr-1 globally. We further show that financially viable carbon projects could generate return-on-investment amounting to $46.0b y-1 in net present value (Asia-Pacific: $24.6b y-1; Americas: $19.1b y-1; Africa: $2.4b y-1). However, we also find that ~80% (1.24 billion ha) of forest carbon sites would be financially unviable for failing to break even over the project lifetime. From a conservation perspective, unless carbon prices increase in the future, it is imperative to implement other conservation interventions, in addition to carbon finance, to safeguard carbon stocks and biodiversity in vulnerable forests.

Global potential and limits of mangrove blue carbon for climate change mitigation.

Despite the outsized role of mangrove forests in sustaining biodiversity, ecosystem function, and local livelihoods, the protection of these vital habitats through blue carbon financing has been limited.1,2 Here, we quantify the extent of this missed conservation and financial opportunity, showing that the protection of ∼20% of the world's mangrove forests (2.6 Mha) can be funded through carbon financing. Of these investible areas, 1.1-1.3 Mha can be financially sustainable over a 30-year time frame based on carbon prices of US$5-9.4 t-1CO2e. This contributes up to 29.8 MtCO2e year-1 and yields a return on investment of ∼US$3.7 billion per year. Our results point toward a disproportionately large potential of blue carbon finance that can be leveraged to meet national-level climate mitigation goals, particularly if combined with other conservation interventions that further safeguard carbon stocks and biodiversity in these irreplaceable forests. Robust information on return on investment highlights the potential for currently underutilized tropical coastal carbon credit projects.

Rapid identification of shallow inundation for mosquito disease mitigation using drone-derived multispectral imagery.

Mosquito breeding habitat identification often relies on slow, labour-intensive and expensive ground surveys. With advances in remote sensing and autonomous flight technologies, we endeavoured to accelerate this detection by assessing the effectiveness of a drone multispectral imaging system to determine areas of shallow inundation in an intertidal saltmarsh in South Australia. Through laboratory experiments, we characterised Near-Infrared (NIR) reflectance responses to water depth and vegetation cover, and established a reflectance threshold for mapping water sufficiently deep for potential mosquito breeding. We then applied this threshold to field-acquired drone imagery and used simultaneous in-situ observations to assess its mapping accuracy. A NIR reflectance threshold of 0.2 combined with a vegetation mask derived from Normalised Difference Vegetation Index (NDVI) resulted in a mapping accuracy of 80.3% with a Cohen's Kappa of 0.5, with confusion between vegetation and shallow water depths (< 10 cm) appearing to be major causes of error. This high degree of mapping accuracy was achieved with affordable drone equipment, and commercially available sensors and Geographic Information Systems (GIS) software, demonstrating the efficiency of such an approach to identify shallow inundation likely to be suitable for mosquito breeding.