‘Yes-in-my-backyard’: Spatial differences in the valuation of forest services and local co-benefits for carbon markets in México

Forests provide many and large benefits, including cost-efficient climate change mitigation. However international carbon markets have not stimulated the demand for forestry offsets. Domestic market-mechanisms are emerging inmany countries and forests could be highly valued through these policies asmost of the benefits produced by forests are enjoyed locally. Here, a choice experiment explores drivers of valuation and willingness to pay for forest carbon services in voluntary markets in Mexico by comparing the valuation of citizens from four regions to test geographical preference for projects (n = 645). Findings from multinomial-logit models show valuation of forest carbon services is transferable and citizens would pay more for offsets from projects closer to their homes. Proximate forests provide a range of co-benefits to local users, including environmental services and opportunities for recreation. Factors related to valuation include sense of responsibility, previous knowledge of carbon emissions, previous visits to the sites, regional identification and the valuation of local environmental services (e.g. improvements in local air quality). Knowledge of spatial heterogeneity in valuation of the use of forest services can help to design market-based instruments by identifying highly valued areas for environmental services programs and carbon markets

Indonesian natural gas policy reform

Abstract: This paper investigates the challenge to improve Indonesia’s domestic gas market and the remedy to alleviate the problems. Gifted with abundant natural resources, primary oil, and natural gas, Indonesia became a leading world liquid natural gas (LNG) producer. However, since 2001 there has been a shifting paradigm in gas policy from export – oriented to domestic market development. The implication is that Indonesia needs to find the balance between producer and consumer interests and at the same time has to deal with the geographical challenge of the country’s archipelago. The new paradigm also requires that natural resources should promote economic growth instead of state revenue, therefore Indonesia must reform its gas policy to fulfil the multi objective of gas utilisation. This paper suggests ways to overcome these dilemmas

Economic implications of domestic natural gas allocation in Indonesia

Purpose: The purpose of the paper is to provide to a better scientific understanding of Indonesia’s domestic gas allocation policy and its effects on the national economy and to answer the question of what best priorities can be set in allocating the natural gas for the domestic market to maximize the benefits for the national economy. Design/methodology/approach: The authors apply a Computabled General Equilibrium (CGE). The Social Accounting Matrix 2008 is used to calibrate the CGE Model. There are two scenarios proposed, each is simulated with certain percentage of gas supply curtailment (50 MMSCFD, Scenario A), (100 MMSCFD, Scenario B). Findings: It is confirmed that government’s current policy to give priority to oil production is not the optimum way to maximize added value of natural gas to Indonesian economy. While oil production generates state revenue, it is industry and petrochemical sector that induces high economic impacts because of strong backward and forward linkages. Research limitations/implications: Due to the limited data availability, it is assumed that the data on the SAM 2008 are valid for describing the structure of Indonesian economy. Practical implications: The paper provides recommendation to the government to revise gas allocation policy by changing the rank of consumers’ priority. Originality/value: This paper provides instruments to measure the impact of Indonesia’s domestic gas allocation policy. Finding the best hierarchy of consumer priorities is essential for maximizing added value of natural gas for the national economy.</p

Predicting tree distributions in an East African biodiversity hotspot : model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix. (C) 2008 Elsevier B.V. All rights reserved

Tropical moist forests

No abstract available

Using standardized indicators to analyse dry/wet conditions and their application for monitoring drought/floods: A study in the Logone catchment, Lake Chad basin

The standardized precipitation index (SPI) and standardized streamflow index (SSI) were used to analyse dry/wet conditions in the Logone catchment over a 50-year period (1951–2000). The SPI analysis at different time scales showed several meteorological drought events ranging from moderate to extreme; and SSI analysis showed that wetter conditions prevailed in the catchment from 1950 to 1970 interspersed with a few hydrological drought events. Overall, the results indicate that both the Sudano and Sahelian zones are equally prone to droughts and floods. However, the Sudano zone is more sensitive to drier conditions, while the Sahelian zone is sensitive to wetter conditions. Correlation analysis between SPI and SSI at multiple time scales revealed that the catchment has a low response to rainfall at short time scales, though this progressively changed as the time scale increased, with strong correlations (≥0.70) observed after 12 months. Analysis using individual monthly series showed that the response time reduced to 3 months in October