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Insect pollination improves the yield of most crop species and contributes to one-third of global crop production. The importance of this ecosystem service in improving agricultural production has largely been overlooked, however, in favour of practices that improve soil conditions such as fertiliser use and supplementary irrigation. Using economic modelling, this study estimates the value of insect pollination under different land-use types in Kenya. Our model assumes that a combination of land-use type and the foraging distance of insect pollinators influences the intensity of pollination and the value of agricultural output. To demonstrate the hypothesised relationships, areas under different land-use types, e.g. forest, grassland and cropland, and their distances from households were used as proxies for insect pollination. Concentric buffer zones representing foraging distances of pollinators from the land-use types were drawn at 250 m, 500 m, 1 000 m, 2 000 m and 3 000 m from the farms, and areas under each land use in the buffer zones were estimated for the years 2004, 2007 and 2010. Using the random-effects model and an output distance-function stochastic frontier model, the land-use areas, other factors of production and climate variables were regressed on the value of agricultural output in each buffer zone to determine their contribution to agricultural output resulting from insect pollination. The results indicate higher crop productivity on farms bordering forests and grasslands. This implies that insect pollinators are important for crop production, and increasing the number of pollinator habitats closer to the farms will increase food production in the tropics.

The Amazon rainforest, the world's largest tropical rainforest and an important constituent of the global biosphere, continues degrading by rapid deforestation, which is expected to continue despite policies to prevent it. Current international funding to protect the Amazon rainforest focuses on benefits from reduced carbon emissions. This paper examines an additional rationale for Amazon protection: the valuation of its biodiversity and forests as natural heritage to the international community. To measure the economic value of this benefit, the paper examines U.S. and Canadian households' willingness to pay to help finance Amazon rainforest protection. The analysis finds that mean willingness to pay to avoid forest losses projected to occur by 2050 despite current protective policies is $92 per household per year. Aggregating across all households and considering the area protected, the analysis finds that preserving the Amazon rainforest is worth $3,168 per hectare (95-percent confidence interval $1,580-$4,756), on average, to households in the United States and Canada. Considering households in other developed countries would generate yet larger estimates of aggregate value, likely comparable to the carbon benefits from rainforest protection. The results reveal high values of the Amazon rainforest to people geographically distanced from it, lending support to international efforts to reduce deforestation in the Amazon.

This study investigates the convergent validity of discrete choice contingent valuation (CV) and contingent rating/ranking (CR) methods using flexible econometric methods. Our results suggest that CV and CR can produce consistent data (achieve convergent validity) when respondent’s preferred choices and the same changes in environmental quality are considered. We also find that CR models that go beyond modeling the preferred choice and include additional ranks cannot be pooled with the CV models. Accounting for preference heterogeneity via random coefficient models and their flexible structure does not make rejection of the hypothesis of convergent validity less likely, but instead rejects the hypothesis to about the same degree or perhaps more frequently than fixed parameter models commonly used in the literature.

The animal husbandry industry is a major emitter of methane, which is an important greenhouse gas. The industry is also a major emitter of ammonia, which is a precursor of fine particulate matter, arguably the number-one environment-related public health threat facing the nation. We present an integrated process model of the engineering economics of technologies to reduce methane and ammonia emissions at dairy operations in California. Three policy options are explored: greenhouse gas offset credits for methane control, particulate matter offset credits for ammonia control, and expanded net metering policies to provide revenue for the sale of electricity generated from captured methane gas. Individually, any of these policies appears to be sufficient to provide the economic incentive for farm operators to reduce emissions. We report on initial steps to fully develop the integrated process model that will provide guidance for policymakers.

This paper considers the role of incentive payment programs in eliciting, estimating, and predicting landowners' conservation enrollments. Using both program participation and the amount of land enrolled, we develop two econometric approaches for predicting enrollments. The first is a multivariate censored regression model that handles zero enrollments and heterogeneity in the opportunity cost of enrollments by combining an inverse hyperbolic sine transformation of enrollments with alternative-specific correlation and random parameters. The second is a beta-binomial model, which recognizes that in practice elicited enrollments are essentially integer valued. We apply these approaches to Finland, where the protection of private nonindustrial forests is an important environmental policy problem. We compare both econometric approaches via cross-validation and find that the beta-binomial model predicts as well as the multivariate censored model yet has fewer parameters. The beta-binomial model also facilitates policy predictions and simulations, which we use to illustrate the framework.

Contingent valuation and contingent rating and ranking methods for measuring willingness-to-pay for non-market goods are compared by using random coefficient models and data pooling methods. Pooled models on CV data and CR data on the preferred choice accept pooling if scale differences between the model estimates of CV and CR methods are allowed for. More detailed response models, such as pooled CV model and rank-ordered models for two or three ranks, reject pooling of the data.

The concept of ecosystem services has become increasingly influential in conservation policy, uniting natural and social scientists in efforts to develop values for environmental benefits consistent with underlying ecological and social processes. Understanding the consequences for ecosystem services is crucial for crafting well-designed environmental policies and management practices. For example, in the United States, new air pollution policies are driving a reduction in atmospheric emissions of sulfur dioxide and reducing acid precipitation in the Southern Appalachians, which, in turn, is expected to contribute to the ecological recovery of the region. Linking detailed ecosystem modeling to stated preference methods, we illustrate how multidimensional ecosystems can be evaluated to identify policy priorities. We also report estimates of willingness to pay for these policies that can be used in benefit-cost analysis. We estimate these benefits to be $15.67 per year per household in the region to be achieved by 2060.