Will passive protection save Congo forests?

Central Africa\u27s tropical forests are among the world\u27s largest carbon reserves. Historically, they have experienced low rates of deforestation. Pressures to clear land are increasing due to development of infrastructure and livelihoods, foreign investment in agriculture, and shifting land use management, particularly in the Democratic Republic of Congo (DRC). The DRC contains the greatest area of intact African forests. These store approximately 22 billion tons of carbon in aboveground live biomass, yet only 10% are protected. Can the status quo of passive protection - forest management that is low or nonexistent - ensure the preservation of this forest and its carbon? We have developed the SimCongo model to simulate changes in land cover and land use based on theorized policy scenarios from 2010 to 2050. Three scenarios were examined: the first (Historical Trends) assumes passive forest protection; the next (Conservation) posits active protection of forests and activation of the national REDD+ action plan, and the last (Agricultural Development) assumes increased agricultural activities in forested land with concomitant increased deforestation. SimCongo is a cellular automata model based on Bayesian statistical methods tailored for the DRC, built with the Dinamica-EGO platform. The model is parameterized and validated with deforestation observations from the past and runs the scenarios from 2010 through 2050 with a yearly time step. We estimate the Historical Trends trajectory will result in average emissions of 139 million t CO2 year-1 by the 2040s, a 15% increase over current emissions. The Conservation scenario would result in 58% less clearing than Historical Trends and would conserve carbon-dense forest and woodland savanna areas. The Agricultural Development scenario leads to emissions of 212 million t CO2 year-1 by the 2040s. These scenarios are heuristic examples of policy\u27s influence on forest conservation and carbon storage. Our results suggest that 1) passive protection of the DRC\u27s forest and woodland savanna is insufficient to reduce deforestation; and 2): enactment of a REDD+ plan or similar conservation measure is needed to actively protect Congo forests, their unique ecology, and their important role in the global carbon cycle

Mining drives extensive deforestation in the Brazilian Amazon

Mining poses significant and potentially underestimated risks to tropical forests worldwide. In Brazil\u27s Amazon, mining drives deforestation far beyond operational lease boundaries, yet the full extent of these impacts is unknown and thus neglected in environmental licensing. Here we quantify mining-induced deforestation and investigate the aspects of mining operations, which most likely contribute. We find mining significantly increased Amazon forest loss up to 70 km beyond mining lease boundaries, causing 11,670 km2 of deforestation between 2005 and 2015. This extent represents 9% of all Amazon forest loss during this time and 12 times more deforestation than occurred within mining leases alone. Pathways leading to such impacts include mining infrastructure establishment, urban expansion to support a growing workforce, and development of mineral commodity supply chains. Mining-induced deforestation is not unique to Brazil; to mitigate adverse impacts of mining and conserve tropical forests globally, environmental assessments and licensing must considered both on- and off-lease sources of deforestation

Criteria for effective zero-deforestation commitments

Zero-deforestation commitments are a type of voluntary sustainability initiative that companies adopt to signal their intention to reduce or eliminate deforestation associated with commodities that they produce, trade, and/or sell. Because each company defines its own zero-deforestation commitment goals and implementation mechanisms, commitment content varies widely. This creates challenges for the assessment of commitment implementation or effectiveness. Here, we develop criteria to assess the potential effectiveness of zero-deforestation commitments at reducing deforestation within a company supply chain, regionally, and globally. We apply these criteria to evaluate 52 zero-deforestation commitments made by companies identified by Forest 500 as having high deforestation risk. While our assessment indicates that existing commitments converge with several criteria for effectiveness, they fall short in a few key ways. First, they cover just a small share of the global market for deforestation-risk commodities, which means that their global impact is likely to be small. Second, biome-wide implementation is only achieved in the Brazilian Amazon. Outside this region, implementation occurs mainly through certification programs, which are not adopted by all producers and lack third-party near-real time deforestation monitoring. Additionally, around half of all commitments include zero-net deforestation targets and future implementation deadlines, both of which are design elements that may reduce effectiveness. Zero-net targets allow promises of future reforestation to compensate for current forest loss, while future implementation deadlines allow for preemptive clearing. To increase the likelihood that commitments will lead to reduced deforestation across all scales, more companies should adopt zero-gross deforestation targets with immediate implementation deadlines and clear sanction-based implementation mechanisms in biomes with high risk of forest to commodity conversion.ISSN:0959-3780ISSN:1872-949

Local conditions and policy design determine whether ecological compensation can achieve No Net Loss goals.

Funder: Science for Nature and People Partnership Australian Research Council Discovery Early Career Research Award (DE170100684) Australian Research Council Future Fellowship (FT140100516) The Australian Government’s National Environmental Science Program through the Threatened Species Recovery Hub Agence Française de Développement Fonds Français pour l'environnement Mondial Mava FoundationFunder: Science for Nature and People Partnership Australian Research Council Future Fellowship FT140100516 National Environmental Science Program's Threatened Species Recovery HubMany nations use ecological compensation policies to address negative impacts of development projects and achieve No Net Loss (NNL) of biodiversity and ecosystem services. Yet, failures are widely reported. We use spatial simulation models to quantify potential net impacts of alternative compensation policies on biodiversity (indicated by native vegetation) and two ecosystem services (carbon storage, sediment retention) across four case studies (in Australia, Brazil, Indonesia, Mozambique). No policy achieves NNL of biodiversity in any case study. Two factors limit their potential success: the land available for compensation (existing vegetation to protect or cleared land to restore), and expected counterfactual biodiversity losses (unregulated vegetation clearing). Compensation also fails to slow regional biodiversity declines because policies regulate only a subset of sectors, and expanding policy scope requires more land than is available for compensation activities. Avoidance of impacts remains essential in achieving NNL goals, particularly once opportunities for compensation are exhausted

Carbon emissions due to deforestation for the production of charcoal used in Brazil's steel industry

Steel produced using coal generates 7% of global anthropogenic CO2 emissions annually(1). Opportunities exist to substitute this coal with carbon-neutral charcoal sourced from plantation forests to mitigate project-scale emissions(2) and obtain certified emission reduction credits under the Kyoto Protocol's Clean Development Mechanism(3). This mitigation strategy has been implemented in Brazil(4,5) and is one mechanism among many used globally to reduce anthropogenic CO2 emissions(6); however, its potential adverse impacts have been overlooked to date. Here, we report that total CO2 emitted from Brazilian steel production doubled (91 to 182 MtCO(2)) and specific emissions increased (3.3 to 5.2 MtCO(2) per Mt steel) between 2000 and 2007, even though the proportion of coal used declined. Infrastructure upgrades and a national plantation shortage increased industry reliance on charcoal sourced from native forests, which emits up to nine times more CO2 per tonne of steel than coal. Preventing use of native forest charcoal could have avoided 79% of the CO2 emitted from steel production between 2000 and 2007; however, doing so by increasing plantation charcoal supply is limited by socio-economic costs and risks further indirect deforestation pressures and emissions. Effective climate change mitigation in Brazil's steel industry must therefore minimize all direct and indirect carbon emissions generated from steel manufacture

Average carbon loss over time shows trends towards increasing or decreasing carbon stocks depending on the location and scenario assumptions.

<p>Average carbon loss over time shows trends towards increasing or decreasing carbon stocks depending on the location and scenario assumptions.</p

Deforestation extent under the Historical Trends, Conservation (including development of new protected areas based on population density and carbon in aboveground biomass), and Agricultural Development scenarios (including deforestation for croplands and oil palm).

<p>Deforestation extent under the Historical Trends, Conservation (including development of new protected areas based on population density and carbon in aboveground biomass), and Agricultural Development scenarios (including deforestation for croplands and oil palm).</p

Spatial variables driving the model that have strong correlation to deforestation and low spatial autocorrelation.

<p>Spatial variables driving the model that have strong correlation to deforestation and low spatial autocorrelation.</p

Modeled scenarios of future land cover change with average rates of forest and savanna clearing, including secondary forest conversions.

<p>Modeled scenarios of future land cover change with average rates of forest and savanna clearing, including secondary forest conversions.</p

Favorability for protected areas estimated by rural population density and carbon biomass.

<p>Favorability for protected areas estimated by rural population density and carbon biomass.</p