Can timber provision from Amazonian production forests be sustainable?

Around 30 Mm3 of sawlogs are extracted annually by selective logging of natural production forests in Amazonia, Earth's most extensive tropical forest. Decisions concerning the management of these production forests will be of major importance for Amazonian forests' fate. To date, no regional assessment of selective logging sustainability supports decision-making. Based on data from 3500 ha of forest inventory plots, our modelling results show that the average periodic harvests of 20 m3 ha−1 will not recover by the end of a standard 30 year cutting cycle. Timber recovery within a cutting cycle is enhanced by commercial acceptance of more species and with the adoption of longer cutting cycles and lower logging intensities. Recovery rates are faster in Western Amazonia than on the Guiana Shield. Our simulations suggest that regardless of cutting cycle duration and logging intensities, selectively logged forests are unlikely to meet timber demands over the long term as timber stocks are predicted to steadily decline. There is thus an urgent need to develop an integrated forest resource management policy that combines active management of production forests with the restoration of degraded and secondary forests for timber production. Without better management, reduced timber harvests and continued timber production declines are unavoidable

“Carbon Cowboys” could inflate REDD+ payments through positive measurement bias

<p>The United Nations Framework Convention on Climate Change (UNFCCC) guidelines aim for a maximum 10% uncertainty in forest biomass inventories, after which penalties accrue. Identification of this magnitude of error requires recognition of discrepancies in carbon stock estimates between project proponents and by Measurement, Reporting, and Verification (MRV) auditors for REDD+ (Reduced Emissions from Deforestation and Forest Degradation, plus the role of conservation, forest management and enhancement of carbon stocks). Given that carbon stocks might be intentionally overestimated by profiteers who would thereby benefit financially, it is important to know how those estimates might most expeditiously be inflated by systematic or random positive biases in measurements of tree diameter, height and wood density. We explore the differences in magnitudes of forest biomass estimate inflation that result from a scenario in which positive bias is added to a random selection of 1–20% of all trees, and a systematic “Carbon Cowboy” scenario in which 1–20% is added to the measurements of the largest trees. As expected, biases under the random scenario must be both highly frequent (>20% of trees) and large (>10%) to breach the UNFCCC 10% uncertainty threshold. In contrast, for the Carbon Cowboy scenario, a measurement bias in tree diameter as small as 10% reaches the same limit if added to the largest 5% of trees. A 10% upward bias achieves the same result if applied to the diameter, height and wood density of the largest 1% of trees. These findings suggest that MRV auditors of REDD+ projects should be especially vigilant about systematic measurement biases that involve large trees.</p

Potential conservation gains from improved protected area management in the Brazilian Amazon

Protected areas (PAs) are important policy instruments for forest conservation, but it is unclear if improved management can increase PA effectiveness. In Brazil, formal management plans are required to be in place shortly after the creation of a PA. This requirement is rarely enforced and, as a result, several PAs undergo many years without approved plans. We take advantage of this variation among PAs to study the impact of management plans on deforestation. We provide estimates from two quasi-experimental evaluation approaches based on the generalization of the difference-in-differences estimator: (1) matching-based methods for time-series cross-sectional data analysis and (2) the generalized synthetic control (GSC) method. We find weak, yet generally consistent, evidence across these two methods suggesting that PAs with approved management plans protect forests more effectively over time. Significant impact estimates from the matching-based approach ranged more widely than the GSC method (0.01%–0.09% versus 0.04%–0.05% of avoided deforestation per year, respectively). The effect size of these impacts is relatively substantial given that the average annual forest loss from our PA sample was 0.07% (±0.40%). To the extent that PAs with approved management plans reflect actual differences in PA management quality, our findings suggest that investments in improving PA management could result in positive conservation gains over time

Potential conservation gains from improved protected area management in the Brazilian Amazon

Protected areas (PAs) are important policy instruments for forest conservation, but it is unclear if improved management can increase PA effectiveness. In Brazil, formal management plans are required to be in place shortly after the creation of a PA. This requirement is rarely enforced and, as a result, several PAs undergo many years without approved plans. We take advantage of this variation among PAs to study the impact of management plans on deforestation. We provide estimates from two quasi-experimental evaluation approaches based on the generalization of the difference-in-differences estimator: (1) matching-based methods for time-series cross-sectional data analysis and (2) the generalized synthetic control (GSC) method. We find weak, yet generally consistent, evidence across these two methods suggesting that PAs with approved management plans protect forests more effectively over time. Significant impact estimates from the matching-based approach ranged more widely than the GSC method (0.01%–0.09% versus 0.04%–0.05% of avoided deforestation per year, respectively). The effect size of these impacts is relatively substantial given that the average annual forest loss from our PA sample was 0.07% (±0.40%). To the extent that PAs with approved management plans reflect actual differences in PA management quality, our findings suggest that investments in improving PA management could result in positive conservation gains over time

Diversification of forestry portfolios for climate change and market risk mitigation

Investments in forestry are long-term and thus subject to numerous sources of risk. In addition to the volatility from markets, forestry investments are directly exposed to future impacts from climate change. We examined how diversification of forest management regimes can mitigate the expected risks associated with forestry activities in New Zealand based on an application of Modern Portfolio Theory. Uncertainties in the responses of Pinus radiata (D. Don) productivity to climate change, from 2050 to 2090, were simulated with 3-PG, a process-based forest growth model, based on future climate scenarios and Representative Concentration Pathways (RCPs). Future timber market scenarios were based on RCP-specific projections from the Global Timber Model and historical log grade prices. Outputs from 3-PG and the market scenarios were combined to compute annualized forestry returns for four P. radiata regimes for 2050–2090. This information was then used to construct optimal forestry portfolios that minimize investment risk for a given target return under different RCPs, forest productivity and market scenarios. While current P. radiata regimes in New Zealand are largely homogenous, our results suggest that regime diversification can mitigate future risks imposed by climate change and market uncertainty. Nevertheless, optimal portfolio compositions varied substantially across our range of scenarios and portfolio objectives. The application of this framework can help forest managers to better account for future risks in their management decisions