Generating carbon finance through avoided deforestation and its potential to create climatic, conservation and human development benefits

Recent proposals to compensate developing countries for reducing emissions from deforestation (RED) under forthcoming climate change mitigation regimes are receiving increasing attention. Here we demonstrate that if RED credits were traded on international carbon markets, even moderate decreases in deforestation rates could generate billions of Euros annually for tropical forest conservation. We also discuss the main challenges for a RED mechanism that delivers real climatic benefits. These include providing sufficient incentives while only rewarding deforestation reductions beyond business-as-usual scenarios, addressing risks arising from forest degradation and international leakage, and ensuring permanence of emission reductions. Governance may become a formidable challenge for RED because some countries with the highest RED potentials score poorly on governance indices. In addition to climate mitigation, RED funds could help achieve substantial co-benefits for biodiversity conservation and human development. However, this will probably require targeted additional support because the highest biodiversity threats and human development needs may exist in countries that have limited income potentials from RED. In conclusion, how successfully a market-based RED mechanism can contribute to climate change mitigation, conservation and development will strongly depend on accompanying measures and carefully designed incentive structures involving governments, business, as well as the conservation and development communities

Land system governance shapes tick-related public and animal health risks

Land cover and land use have established effects on hazard and exposure to vector-borne diseases. While our understanding of the proximate and distant causes and consequences of land use decisions has evolved, the focus on the proximate effects of landscape on disease ecology remains dominant. We argue that land use governance, viewed through a land system lens, affects tick-borne disease risk. Governance affects land use trajectories and potentially shapes landscapes favourable to ticks or increases contact with ticks by structuring human-land interactions. We illustrate the role of land use legacies, trade-offs in land-use decisions, and social inequities in access to land resources, information and decision-making, with three cases: Kyasanur Forest disease in India, Lyme disease in the Outer Hebrides (Scotland), and tick acaricide resistance in cattle in Ecuador. Land use governance is key to managing the risk of tick-borne diseases, by affecting the hazard and exposure. We propose that land use governance should consider unintended consequences on infectious disease risk

Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling: a case study

The objective of this study is to compare the abilities of logistic, auto-logistic and artificial neural network (ANN) models for quantifying the relationships between land uses and their drivers. In addition, the application of the results obtained by the three techniques is tested in a dynamic land-use change model (CLUE-s) for the Paochiao watershed region in Taiwan. Relative operating characteristic curves (ROCs), kappa statistics, multiple resolution validation and landscape metrics were used to assess the ability of the three techniques in estimating the relationship between driving factors and land use and its subsequent application in land-use change models. The validation results illustrate that for this case study ANNs constitute a powerful alternative for the use of logistic regression in empirical modeling of spatial land-use change processes. ANNs provide in this case a better fit between driving factors and land-use pattern. In addition, auto-logistic regression performs better than logistic regression and nearly as well as ANNs. Auto-logistic regression and ANNs are considered especially useful when the performance of more conventional models is not satisfactory or the underlying data relationships are unknown. The results indicate that an evaluation of alternative techniques to specify relationships between driving factors and land use can improve the performance of land-use change models

The challenges of applying planetary boundaries as a basis for strategic decision-making in companies with global supply chains

The Planetary Boundaries (PB) framework represents a significant advance in specifying the ecological constraints on human development. However, to enable decision-makers in business and public policy to respect these constraints in strategic planning, the PB framework needs to be developed to generate practical tools. With this objective in mind, we analyse the recent literature and highlight three major scientific and technical challenges in operationalizing the PB approach in decision-making: first, identification of thresholds or boundaries with associated metrics for different geographical scales; second, the need to frame approaches to allocate fair shares in the 'safe operating space' bounded by the PBs across the value chain and; third, the need for international bodies to co-ordinate the implementation of the measures needed to respect the Planetary Boundaries. For the first two of these challenges, we consider how they might be addressed for four PBs: climate change, freshwater use, biosphere integrity and chemical pollution and other novel entities. Four key opportunities are identified: (1) development of a common system of metrics that can be applied consistently at and across different scales; (2) setting 'distance from boundary' measures that can be applied at different scales; (3) development of global, preferably open-source, databases and models; and (4) advancing understanding of the interactions between the different PBs. Addressing the scientific and technical challenges in operationalizing the planetary boundaries needs be complemented with progress in addressing the equity and ethical issues in allocating the safe operating space between companies and sectors

Using fuzzy cognitive maps to describe current system dynamics and develop land cover scenarios: a case study in the Brazilian Amazon

In this study we developed a methodology to identify and quantify the relationships among determinants of land cover change using a regional case study in the Brazilian Amazon. The method is based on the application of fuzzy cognitive maps (FCMs), a semi-quantitative tool that provides a structured assessment of key feedbacks in scenario analysis. Novel to the application of FCMs is the use of spatial data-sets as the main input to build a cognitive map. Identification of interactions between land cover determinants and strengths is based on an empirical analysis of spatially explicit data and literature review. Expert knowledge is adopted to identify the strengths and weaknesses of the method. Potential pitfalls, such as spatial autocorrelation and scale issues, identified are intrinsic to the empirical data analysis. The outputs of the resulting FCMs are compared to the outputs of spatially explicit models under similar scenarios of change. The proposed method is said to be robust and reproducible when compared with participatory approaches, and it can endorse the consistency between demand and allocation in scenario analysis to be used in spatially explicit models

Land-cover change trajectories in Southern Cameroon

The objective of this study is to better understand the complexity of deforestation processes in southern Cameroon by testing a multivariate, spatial model of land-cover change trajectories associated with deforestation. The spatial model integrates a spectrum of independent variables that characterise land rent on a spatially explicit basis. The use of a time series of high-spatial resolution remote sensing images (Landsat MSS and SPOT XS), spanning two decades, allows a thorough validation of spatial projections of future deforestation. Remote sensing observations reveal a continuous trend of forest clearing and forest degradation in southern regions of Cameroon, but with a highly fluctuating rate. A significant proportion of the areas subject to a land-cover conversion experienced other changes in the following years. The study also demonstrates that modelling land-cover change trajectories over several observation years allows a better projection of areas with a high probability of change in land-cover than projecting such areas on the basis of observations from the previous time period alone. Statistical results suggest that, in our southern Cameroon study area, roads mostly increased the accessibility of the forest for migrants rather than providing incentives for a transformation of local subsistence agriculture into market-oriented farming systems. The spatial model developed in this study allows simulations of likely impacts of human actions, leading to a transformation of the landscape (e.g., road projects) on key landscape attributes (e.g., biodiversity). Currently, several road projects or major logging concessions exist in southern Cameroon